CN113465609A

CN113465609A - Time sequence matching method and device for target object

Info

Publication number: CN113465609A
Application number: CN202010236579.7A
Authority: CN
Inventors: 陈纪凯; 苗振伟; 王振华
Original assignee: Alibaba Group Holding Ltd
Current assignee: Wuzhou Online E Commerce Beijing Co ltd
Priority date: 2020-03-30
Filing date: 2020-03-30
Publication date: 2021-10-01

Abstract

The application discloses a time sequence matching method for a target object, which comprises the following steps: acquiring first original detection data corresponding to a target object at a first moment, and acquiring second original detection data corresponding to the target object at a second moment; obtaining first hash characteristic information and second hash characteristic information corresponding to the target object according to the first original detection data and the second original detection data; and establishing a position corresponding relation of the target object at the first moment and the second moment according to the first hash characteristic information and the second hash characteristic information. According to the method, the obtained characteristic information and the position information of the target object at different moments are subjected to Hash quantization expression, and the corresponding relation of the position information of the target object at different moments is established according to the information after Hash quantization, so that the position corresponding relation of the target object on a time sequence can be quickly and accurately obtained.

Description

Time sequence matching method and device for target object

Technical Field

The application relates to the field of navigation, in particular to a time sequence matching method and device for a target object, electronic equipment and storage equipment. The application also relates to a method and a device for obtaining the target hash model, electronic equipment and storage equipment. The application also relates to a time sequence matching method and device for the detected vehicle, electronic equipment and storage equipment. The application also relates to a navigation method, a navigation device, electronic equipment and storage equipment. The application also relates to a time sequence matching method and device for the target base station, electronic equipment and storage equipment. The application also relates to a base station drive test method, a base station drive test device, electronic equipment and storage equipment.

Background

In recent years, with the development of computer technology, a navigation object, such as a navigation vehicle, a sweeping robot, an aircraft, or the like, is automatically driven even when the navigation object is automatically driven in an unmanned state by sensing the surrounding environment of the navigation object by mounting various sensors on the navigation object.

Currently, when a navigation object is automatically driven, an image sensor, such as a radar sensor and a depth camera, is generally mounted on the navigation object, so as to realize automatic perception of a target object by a navigation vehicle, wherein the target object includes a vehicle around the navigation object, a wall, a tree and other obstacle objects which may hinder driving of the navigation object. It should be noted that, at present, a method for implementing automatic perception of a target object by a navigation object through an image sensor generally includes: the target object in the surrounding environment detected by the image sensor is quickly matched to the corresponding target object of the previous frame in time sequence, so that the navigation object can quickly model the target object, the motion state, the speed, the position and other information of the target object can be estimated, and the corresponding navigation information can be obtained.

Therefore, if the position corresponding relation of the target object on the time sequence can be quickly and accurately obtained, the safety of the navigation object in automatic driving can be greatly improved.

Disclosure of Invention

The embodiment of the application provides a time sequence matching method for a target object, and aims to solve the problem that the position corresponding relation of the target object on a time sequence cannot be quickly and accurately obtained in the prior art.

The embodiment of the application provides a time sequence matching method for a target object, which comprises the following steps: acquiring first original detection data corresponding to a target object at a first moment, and acquiring second original detection data corresponding to the target object at a second moment; obtaining first hash feature information and second hash feature information corresponding to the target object according to the first original detection data and the second original detection data, wherein the first hash feature information is a hash quantization representation of feature information and position information of the target object at the first time, and the second hash feature information is a hash quantization representation of feature information and position information of the target object at the second time; and establishing a position corresponding relation of the target object at the first moment and the second moment according to the first hash characteristic information and the second hash characteristic information.

Optionally, the obtaining, according to the first original detection data and the second original detection data, first hash feature information and second hash feature information corresponding to the target object includes: according to the first original detection data and the second original detection data, acquiring first characteristic information and first position information of the target object at the first moment, and acquiring second characteristic information and second position information of the target object at the second moment; performing region feature aggregation processing on the first feature information and the first position information to acquire first aggregated feature information, and performing region feature aggregation processing on the second feature information and the second position information to acquire second aggregated feature information; and obtaining the first hash feature information and the second hash feature information according to the first aggregation feature information and the first aggregation feature information.

Optionally, the obtaining, according to the first original detection data and the second original detection data, first feature information and first position information of the target object at the first time, and obtaining second feature information and second position information of the target object at the second time includes: and inputting the first original detection data and the second original detection data into a target object detection model respectively to acquire the first characteristic information, the first position information, the second characteristic information and the second position information.

Optionally, the method includes: performing convolution processing on the first original detection data by using at least one intermediate convolution layer of the target object detection model, and acquiring first feature information according to feature information of at least one dimension, which is generated by the at least one intermediate convolution layer and corresponds to the target object; and performing convolution processing on the second original detection data by using at least one intermediate convolution layer of the target object detection model, and acquiring second characteristic information according to the characteristic information of at least one dimension, which is generated by the at least one intermediate convolution layer and corresponds to the target object.

Optionally, the performing the regional feature aggregation processing on the first feature information and the first position information to obtain first aggregated feature information, and performing the regional feature aggregation processing on the second feature information and the second position information to obtain second aggregated feature information includes: inputting the first feature information and the first position information into a first sub-model of a target hash model to obtain first aggregated feature information; inputting the second feature information and the second position information into a first sub-model of the target hash model to obtain the second aggregation feature information; the first sub-model is used for carrying out regional characteristic aggregation processing on the characteristic information and the position information corresponding to the target object.

Optionally, the obtaining, according to the first aggregation feature information and the first aggregation feature information, the first hash feature information and the second hash feature information corresponding to the target object includes: inputting the first aggregation characteristic information into a second submodel of the target hash model to obtain the first hash characteristic information; inputting the second aggregation characteristic information into a second sub-model of the target hash model to obtain second hash characteristic information; the second submodel is used for performing Hash quantization representation on the region aggregation characteristic information corresponding to the target object.

Optionally, the establishing, according to the first hash feature information and the second hash feature information, a position corresponding relationship of the target object at the first time and the second time includes: and if the first hash characteristic information and the second hash characteristic information meet a preset similarity condition, establishing a position corresponding relation of the target object at the first moment and the second moment.

Optionally, the method further includes: performing exclusive nor operation on the first hash characteristic information and the second hash characteristic information to obtain an operation result; and if the number of preset numerical values in the operation result is not less than a preset threshold value, judging that the first Hash characteristic information and the second Hash characteristic information meet the preset similarity condition.

Optionally, the target hash model is obtained by the following method: acquiring historical detection data of at least two moments corresponding to an original sample object, wherein the original sample object corresponds to the target object; obtaining at least two pieces of sample characteristic information corresponding to the original sample object in the historical detection data of the at least two moments and at least two pieces of sample position information corresponding to the two pieces of sample characteristic information; and training to obtain the target hash model by taking the at least two sample characteristic information and the at least two sample position information corresponding to the two sample characteristic information as sample data.

Optionally, the training, with the at least two sample feature information and the at least two sample position information corresponding to the two sample feature information as sample data, to obtain the target hash model includes: generating triple sample data according to the at least two sample characteristic information and the at least two sample position information corresponding to the two sample characteristic information; and training the target hash model by using the triple sample data, and adjusting parameters of the target hash model by using a loss function corresponding to the triple sample data in the training process to enable the target hash model to reach a preset convergence condition.

Optionally, the first time is a time that is not later than a current time, the second time is a time that is not later than the first time, and when the position corresponding relationship between the target object at the first time and the second time is established, the method further includes: acquiring third original detection data corresponding to the target object at the current moment; acquiring third hash characteristic information corresponding to the target object according to the third original detection data; and establishing a position corresponding relation between the current time and the first time of the target object according to the first hash characteristic information and the third hash characteristic information.

Optionally, the method is applied to a computing device for providing the most proximal service through edge computing

The embodiment of the present application further provides a method for obtaining a target hash model, including: acquiring historical detection data of at least two moments corresponding to an original sample object; obtaining at least two pieces of sample characteristic information corresponding to the original sample object in the historical detection data of the at least two moments and at least two pieces of sample position information corresponding to the two pieces of sample characteristic information; and training to obtain the target hash model by taking the at least two sample characteristic information and the at least two sample position information corresponding to the two sample characteristic information as sample data, wherein the target hash model is used for performing hash quantization representation on the characteristic information and the position information corresponding to a target object, and the original sample object corresponds to the target object.

The embodiment of the present application further provides a time sequence matching method for a detected vehicle, including: acquiring first point cloud data corresponding to a detected vehicle at a first moment, and acquiring second point cloud data corresponding to the detected vehicle at a second moment; obtaining first hash feature information and second hash feature information corresponding to the detected vehicle according to the first point cloud data and the second point cloud data, wherein the first hash feature information is hash quantitative representation of sparse feature information and position information of the detected vehicle at the first moment, and the second hash feature information is hash quantitative representation of sparse feature information and position information of the detected vehicle at the second moment; and establishing the position corresponding relation of the detected vehicle at the first moment and the second moment according to the first Hash characteristic information and the second Hash characteristic information.

The embodiment of the present application further provides a navigation method, including: acquiring the position corresponding relation of the detected vehicle on a time sequence; and providing navigation information for a navigation vehicle according to the position corresponding relation, wherein the position corresponding relation is obtained by using the time sequence matching method aiming at the detected vehicle, and the detected vehicle and the navigation vehicle are in the same environment.

The embodiment of the present application further provides a timing matching method for a target base station, including: acquiring first wireless signal detection data corresponding to a target base station at a first moment, and acquiring second wireless signal detection data corresponding to a target object at a second moment; acquiring first hash feature information and second hash feature information corresponding to the target base station according to the first wireless signal and the second wireless signal detection data, wherein the first hash feature information is a hash quantization representation of feature information and position information of the target base station at the first time, and the second hash feature information is a hash quantization representation of feature information and position information of the target base station at the second time; and establishing a position corresponding relation of the target base station at the first time and the second time according to the first hash characteristic information and the second hash characteristic information.

The embodiment of the present application further provides a base station drive test method, including: acquiring a position corresponding relation of a target base station on a time sequence and wireless signal detection data, wherein the position corresponding relation is information acquired by using the time sequence matching method aiming at the target base station; and adjusting the wireless signal of the target base station according to the position corresponding relation and the wireless signal detection data.

The embodiment of the present application further provides a timing sequence matching apparatus for a target object, including: the device comprises an original data acquisition unit, a first detection unit and a second detection unit, wherein the original data acquisition unit is used for acquiring first original detection data corresponding to a target object at a first moment and acquiring second original detection data corresponding to the target object at a second moment; a hash feature information obtaining unit configured to obtain first hash feature information and second hash feature information corresponding to the target object, based on the first raw detection data and the second raw detection data, where the first hash feature information is a hash quantization representation of feature information and position information of the target object at the first time, and the second hash feature information is a hash quantization representation of feature information and position information of the target object at the second time; and the corresponding relation establishing unit is used for establishing the position corresponding relation of the target object at the first moment and the second moment according to the first hash characteristic information and the second hash characteristic information.

An embodiment of the present application further provides a first electronic device, including:

a processor;

a memory for storing a program of a timing matching method for a target object, the apparatus performing the following steps after being powered on and running the program of the timing matching method for the target object by the processor:

acquiring first original detection data corresponding to a target object at a first moment, and acquiring second original detection data corresponding to the target object at a second moment; obtaining first hash feature information and second hash feature information corresponding to the target object according to the first original detection data and the second original detection data, wherein the first hash feature information is a hash quantization representation of feature information and position information of the target object at the first time, and the second hash feature information is a hash quantization representation of feature information and position information of the target object at the second time; and establishing a position corresponding relation of the target object at the first moment and the second moment according to the first hash characteristic information and the second hash characteristic information.

The embodiment of the present application further provides a first storage device, in which a program of a timing matching method for a target object is stored, where the program is run by a processor and executes the following steps:

An embodiment of the present application further provides an obtaining apparatus of a target hash model, including:

the historical detection data acquisition unit is used for acquiring historical detection data of at least two moments corresponding to the original sample object; the sample information obtaining unit is used for obtaining at least two pieces of sample characteristic information corresponding to the original sample object in the historical detection data of the at least two moments and at least two pieces of sample position information corresponding to the two pieces of sample characteristic information; and the training unit is used for training the at least two pieces of sample characteristic information and the at least two pieces of sample position information corresponding to the two pieces of sample characteristic information to obtain the target hash model, wherein the target hash model is used for performing hash quantization representation on the characteristic information and the position information corresponding to a target object, and the original sample object corresponds to the target object.

An embodiment of the present application further provides a second electronic device, including:

a processor;

a memory for storing a program of an obtaining method of a target hash model, the apparatus performing the following steps after being powered on and running the program of the obtaining method of the target hash model by the processor:

acquiring historical detection data of at least two moments corresponding to an original sample object; obtaining at least two pieces of sample characteristic information corresponding to the original sample object in the historical detection data of the at least two moments and at least two pieces of sample position information corresponding to the two pieces of sample characteristic information; and training to obtain the target hash model by taking the at least two sample characteristic information and the at least two sample position information corresponding to the two sample characteristic information as sample data, wherein the target hash model is used for performing hash quantization representation on the characteristic information and the position information corresponding to a target object, and the original sample object corresponds to the target object.

The embodiment of the present application further provides a second storage device, in which a program of an obtaining method of a target hash model is stored, where the program is run by a processor and executes the following steps:

The embodiment of the present application further provides a time sequence matching device for a detected vehicle, including:

the system comprises a point cloud data acquisition unit, a point cloud data acquisition unit and a point cloud data acquisition unit, wherein the point cloud data acquisition unit is used for acquiring first point cloud data corresponding to a detected vehicle at a first moment and acquiring second point cloud data corresponding to the detected vehicle at a second moment; a hash feature information obtaining unit, configured to obtain first hash feature information and second hash feature information corresponding to the detected vehicle according to the first point cloud data and the second point cloud data, where the first hash feature information is a hash quantization representation of sparse feature information and location information of the detected vehicle at the first time, and the second hash feature information is a hash quantization representation of sparse feature information and location information of the detected vehicle at the second time; and the corresponding relation establishing unit is used for establishing the position corresponding relation of the detected vehicle at the first moment and the second moment according to the first Hash characteristic information and the second Hash characteristic information.

An embodiment of the present application further provides a third electronic device, including:

a processor;

a memory for storing a program of a timing matching method for a detected vehicle, the apparatus performing the following steps after being powered on and running the program of the timing matching method for the detected vehicle by the processor:

acquiring first point cloud data corresponding to a detected vehicle at a first moment, and acquiring second point cloud data corresponding to the detected vehicle at a second moment; obtaining first hash feature information and second hash feature information corresponding to the detected vehicle according to the first point cloud data and the second point cloud data, wherein the first hash feature information is hash quantitative representation of sparse feature information and position information of the detected vehicle at the first moment, and the second hash feature information is hash quantitative representation of sparse feature information and position information of the detected vehicle at the second moment; and establishing the position corresponding relation of the detected vehicle at the first moment and the second moment according to the first Hash characteristic information and the second Hash characteristic information.

The embodiment of the present application further provides a third storage device, in which a program of a timing matching method for a detected vehicle is stored, where the program is executed by a processor to perform the following steps:

An embodiment of the present application further provides a navigation device, including:

the position corresponding relation acquisition unit is used for acquiring the position corresponding relation of the detected vehicle on a time sequence;

a navigation information providing unit, configured to provide navigation information for a navigation vehicle according to the position correspondence, where the position correspondence is a position correspondence obtained by using the timing matching method for a detected vehicle according to claim 12, and the detected vehicle is in the same environment as the navigation vehicle.

An embodiment of the present application further provides a fourth electronic device, including:

a processor;

a memory for storing a program of a navigation method, the apparatus performing the following steps after being powered on and running the program of the navigation method by the processor:

acquiring the position corresponding relation of the detected vehicle on a time sequence; and providing navigation information for a navigation vehicle according to the position corresponding relation, wherein the position corresponding relation is obtained by using the time sequence matching method aiming at the detected vehicle, and the detected vehicle and the navigation vehicle are in the same environment.

The embodiment of the present application further provides a fourth storage device, in which a program of the navigation method is stored, where the program is run by the processor and executes the following steps:

The embodiment of the present application further provides a timing matching apparatus for a target base station, including: the wireless signal detection data acquisition unit is used for acquiring first wireless signal detection data corresponding to a target base station at a first moment and acquiring second wireless signal detection data corresponding to a target object at a second moment; a hash feature information obtaining unit, configured to obtain first hash feature information and second hash feature information corresponding to the target base station according to the first wireless signal and the second wireless signal detection data, where the first hash feature information is a hash quantization representation of feature information and location information of the target base station at the first time, and the second hash feature information is a hash quantization representation of feature information and location information of the target base station at the second time; and a location corresponding relationship establishing unit, configured to establish a location corresponding relationship between the target base station at the first time and the second time according to the first hash feature information and the second hash feature information.

An embodiment of the present application further provides a fifth electronic device, including:

a processor;

a memory for storing a program of a timing matching method for a target base station, the apparatus performing the following steps after being powered on and running the program of the timing matching method for the target base station by the processor:

acquiring first wireless signal detection data corresponding to a target base station at a first moment, and acquiring second wireless signal detection data corresponding to a target object at a second moment; acquiring first hash feature information and second hash feature information corresponding to the target base station according to the first wireless signal and the second wireless signal detection data, wherein the first hash feature information is a hash quantization representation of feature information and position information of the target base station at the first time, and the second hash feature information is a hash quantization representation of feature information and position information of the target base station at the second time; and establishing a position corresponding relation of the target base station at the first time and the second time according to the first hash characteristic information and the second hash characteristic information.

The embodiment of the present application further provides a fifth storage device, in which a program of a timing matching method for a target base station is stored, where the program is run by a processor and executes the following steps:

The embodiment of the present application further provides a base station drive test device, including: the data acquisition unit is used for acquiring a position corresponding relation of a target base station on a time sequence and wireless signal detection data, wherein the position corresponding relation is information acquired by using the time sequence matching method aiming at the target base station; and the signal adjusting unit is used for adjusting the wireless signal of the target base station according to the position corresponding relation and the wireless signal detection data.

An embodiment of the present application further provides a sixth electronic device, including:

a processor;

a memory for storing a program of a base station drive test method, the apparatus performing the following steps after being powered on and running the program of the base station drive test method through the processor:

acquiring a position corresponding relation of a target base station on a time sequence and wireless signal detection data, wherein the position corresponding relation is information acquired by using the time sequence matching method aiming at the target base station; and adjusting the wireless signal of the target base station according to the position corresponding relation and the wireless signal detection data.

The embodiment of the present application further provides a sixth storage device, in which a program of the base station drive test method is stored, where the program is run by the processor, and executes the following steps:

Compared with the prior art, the method has the following advantages:

the embodiment of the application provides a time sequence matching method for a target object, which comprises the following steps: acquiring first original detection data corresponding to a target object at a first moment, and acquiring second original detection data corresponding to the target object at a second moment; obtaining first hash feature information and second hash feature information corresponding to the target object according to the first original detection data and the second original detection data, wherein the first hash feature information is a hash quantization representation of feature information and position information of the target object at the first time, and the second hash feature information is a hash quantization representation of feature information and position information of the target object at the second time; and establishing a position corresponding relation of the target object at the first moment and the second moment according to the first hash characteristic information and the second hash characteristic information. The method carries out Hash quantization representation on the characteristic information and the position information corresponding to the target object at different moments, can obtain the Hash characteristic information of the target object at different moments, which is rich in information and relatively small in data volume, establishes the position corresponding relation of the target object in time sequence according to the Hash characteristic information, and can reduce the calculation amount, increase the calculation speed and increase the accuracy of the calculation result.

The embodiment of the application provides a method for obtaining a target hash model, which comprises the following steps: acquiring historical detection data of at least two moments corresponding to an original sample object; obtaining at least two pieces of sample characteristic information corresponding to the original sample object in the historical detection data of the at least two moments and at least two pieces of sample position information corresponding to the two pieces of sample characteristic information; and training to obtain the target hash model by taking the at least two sample characteristic information and the at least two sample position information corresponding to the two sample characteristic information as sample data, wherein the target hash model is used for performing hash quantization representation on the characteristic information and the position information corresponding to a target object, and the original sample object corresponds to the target object. According to the method, historical detection data corresponding to an original sample object are obtained, at least two pieces of sample characteristic information and corresponding sample position information which are obtained from the historical detection data and correspond to the original sample object are used as sample data, a target Hash model which expresses the characteristic information and the position information corresponding to a target object in a Hash quantization mode can be trained, the position corresponding relation of the target object in a time sequence is established through the target Hash model, and therefore the calculation amount can be reduced, the calculation speed can be increased, and the accuracy of a calculation result can be improved.

The embodiment of the application provides a time sequence matching method for a detected vehicle, which comprises the following steps: acquiring first point cloud data corresponding to a detected vehicle at a first moment, and acquiring second point cloud data corresponding to the detected vehicle at a second moment; obtaining first hash feature information and second hash feature information corresponding to the detected vehicle according to the first point cloud data and the second point cloud data, wherein the first hash feature information is hash quantitative representation of sparse feature information and position information of the detected vehicle at the first moment, and the second hash feature information is hash quantitative representation of sparse feature information and position information of the detected vehicle at the second moment; and establishing the position corresponding relation of the detected vehicle at the first moment and the second moment according to the first Hash characteristic information and the second Hash characteristic information. By establishing the position corresponding relation of the detected vehicle on the time sequence through the method, the calculation amount can be reduced, the calculation speed can be increased, and the accuracy of the calculation result can be increased.

The embodiment of the application provides a navigation method, which comprises the following steps: acquiring the position corresponding relation of the detected vehicle on a time sequence; and providing navigation information for a navigation vehicle according to the position corresponding relation, wherein the position corresponding relation is obtained by using a time sequence matching method aiming at a detected vehicle, and the detected vehicle and the navigation vehicle are in the same environment. The method can quickly and accurately acquire the position corresponding relation of the detected vehicle in the same environment with the navigation vehicle on the time sequence, provides navigation information for the navigation vehicle through the position corresponding relation, and can increase the safety of the navigation vehicle in automatic driving.

The embodiment of the application provides a time sequence matching method for a target base station, which comprises the following steps: acquiring first wireless signal detection data corresponding to a target base station at a first moment, and acquiring second wireless signal detection data corresponding to a target object at a second moment; acquiring first hash feature information and second hash feature information corresponding to the target base station according to the first wireless signal and the second wireless signal detection data, wherein the first hash feature information is a hash quantization representation of feature information and position information of the target base station at the first time, and the second hash feature information is a hash quantization representation of feature information and position information of the target base station at the second time; and establishing a position corresponding relation of the target base station at the first time and the second time according to the first hash characteristic information and the second hash characteristic information. By establishing the position corresponding relation of the target base station on the time sequence through the method, the calculation amount can be reduced, the calculation speed can be increased, and the accuracy of the calculation result can be increased.

The embodiment of the application provides a base station drive test method, which comprises the following steps: acquiring a position corresponding relation of a target base station on a time sequence and wireless signal detection data, wherein the position corresponding relation is information acquired by using a time sequence matching method aiming at the target base station; and adjusting the wireless signal of the target base station according to the position corresponding relation and the wireless signal detection data. The method can quickly and accurately acquire the position corresponding relation of the target base station on the time sequence and the corresponding wireless signal detection data, and can adjust and optimize the wireless signal of the target base station through the position corresponding relation and the wireless signal detection data, thereby improving the accuracy of wireless signal adjustment.

Drawings

Fig. 1-a is a schematic view of a first application scenario of a timing matching method for a target object according to a first embodiment of the present application.

Fig. 1-B is a schematic diagram of a second application scenario of a timing matching method for a target object according to a first embodiment of the present application.

Fig. 2 is a flowchart of a timing matching method for a target object according to a first embodiment of the present application.

Fig. 3 is a schematic processing procedure diagram of a timing matching method for a target object according to a first embodiment of the present application.

Fig. 4 is a flowchart of a method for obtaining a target hash model according to a second embodiment of the present application.

Fig. 5 is a flowchart of a timing matching method for a detected vehicle according to a third embodiment of the present application.

Fig. 6 is a flowchart of a navigation method according to a fourth embodiment of the present application.

Fig. 7 is a schematic diagram of a timing matching apparatus for a target object according to a fifth embodiment of the present application.

Fig. 8 is a schematic diagram of an electronic device according to a sixth embodiment of the present application.

Fig. 9 is a schematic diagram of an apparatus for obtaining a target hash model according to an eighth embodiment of the present application.

Fig. 10 is a schematic diagram of a timing matching apparatus for a detected vehicle according to an eleventh embodiment of the present application.

Fig. 11 is a schematic view of a navigation device according to a fourteenth embodiment of the present application.

Fig. 12 is a flowchart of a timing matching method for a target base station according to a seventeenth embodiment of the present application.

Fig. 13 is a flowchart of a base station drive test method according to an eighteenth embodiment of the present application.

Fig. 14 is a schematic diagram of a timing matching apparatus for a target base station according to a nineteenth embodiment of the present application.

Fig. 15 is a schematic diagram of a base station drive test apparatus according to a twenty-second embodiment of the present application.

Detailed Description

In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. This application is capable of implementation in many different ways than those herein set forth and of similar import by those skilled in the art without departing from the spirit of this application and is therefore not limited to the specific implementations disclosed below.

In order to make those skilled in the art better understand the scheme of the present application, a detailed description is given below to a specific application scenario of an embodiment of the present application based on the target object specific timing matching method provided in the embodiment of the present application. Fig. 1-a and fig. 1-B are a schematic view of a first application scenario and a schematic view of a second application scenario of a timing matching method for a target object according to a first embodiment of the present application, respectively.

As shown in fig. 1-a, in order to increase the safety of the navigation object in the automatic driving process, the time sequence matching method for the target object may be used to quickly and accurately establish the position corresponding relationship in time sequence with the target object in the same environment as the navigation object, so that the navigation object may quickly sense the surrounding target object and plan a safe and reliable driving route when navigating and driving. Specifically, the navigation object acquires, by a computing device, first original detection data corresponding to a target object at a first time and second original detection data corresponding to the target object at a second time during driving; then, obtaining first hash feature information and second hash feature information corresponding to a target object according to the first original detection data and the second original detection data, wherein the first hash feature information is a hash quantization representation of feature information and position information of the target object at the first time, and the second hash feature information is a hash quantization representation of feature information and position information of the target object at the second time; then, according to the first hash characteristic information and the second hash characteristic information, establishing a position corresponding relation of the target object at the first time and the second time, so as to obtain a position corresponding relation of the target object in a time sequence; and then, the computing equipment can provide navigation information for the navigation object according to the acquired position corresponding relation, such as planning a safe and reliable driving route and increasing the safety of the navigation object in the driving process.

It should be noted that the computing device may be a client computing device, or may also be a server computing device, where the client computing device may be a mobile terminal device, such as a mobile phone, a tablet computer, and the like, carried on a navigation object, or may be a navigation device carried on the navigation object; the server-side computing device generally refers to a server corresponding to a client-side computing device carried or carried on a navigation object, for example, an arithmetic server corresponding to a navigation device; the navigation object can be an object such as a navigation vehicle, a sweeping robot, an aircraft and the like; the target object may be a physical object corresponding to the navigation object and located in the same environment as the navigation object, and the target object may block a driving path of the navigation object and affect driving of the navigation object.

As shown in fig. 1-B, to describe the timing matching method for a target object more specifically, taking a navigation object as a navigation vehicle and a target object as a detected vehicle in the same environment as the navigation object for more visual description, in a specific implementation, in order to increase the safety of the navigation vehicle in an automatic driving process, a computing device mounted or carried on the navigation vehicle obtains first point cloud data corresponding to the detected vehicle in the environment around the navigation vehicle at a first time through a radar sensor connected to the computing device, and obtains second point cloud data corresponding to the detected vehicle at a second time; obtaining first hash feature information and second hash feature information corresponding to the detected vehicle according to the first point cloud data and the second point cloud data, wherein the first hash feature information is hash quantitative representation of sparse feature information and position information of the detected vehicle at the first moment, and the second hash feature information is hash quantitative representation of sparse feature information and position information of the detected vehicle at the second moment; according to the first Hash characteristic information and the second Hash characteristic information, establishing a position corresponding relation of the detected vehicle at the first moment and the second moment, so that the position corresponding relation of the detected vehicle on a time sequence is obtained; and then, the computing equipment can provide navigation information for the navigation vehicle according to the acquired position corresponding relation, such as planning a safe and reliable driving route and increasing the safety of the navigation vehicle in the driving process.

It should also be noted that, when implemented, the method may be applied in an Edge Computing (Edge Computing) scenario, that is, the method is applied in a Computing device that provides the nearest service through Edge Computing. Specifically, the method may be applied to a computing device carried or carried by a navigation object, and the computing device may quickly and accurately establish a time-series position correspondence relationship with a target object in the same environment as the navigation object by the method to provide a nearest-end service to the navigation object or a user corresponding to the navigation object. Of course, with the increasing of the wireless communication speed, the method may also be applied to an interaction scenario between the client computing device and the server computing device, for example, the client computing device on the navigation object acquires original detection data corresponding to the target object at different times, and transmits the acquired original detection data to the corresponding cloud server through the wireless communication network, and the cloud server obtains a position corresponding relationship of the target object in a time sequence by using the time sequence matching method for the target object, calculates navigation information corresponding to the navigation object according to the position corresponding relationship, and provides the navigation information to the client computing device.

The above application scenarios are merely specific examples of the timing matching method for the target object provided in the first embodiment of the present application, and are provided for the purpose of facilitating understanding of the method, and are not intended to limit the method.

Fig. 2 is a flowchart of a timing matching method for a target object according to a first embodiment of the present application, which is described below with reference to fig. 2.

Step S201, acquiring first original detection data corresponding to a target object at a first time, and acquiring second original detection data corresponding to the target object at a second time.

The target object refers to an entity object which is in the same environment as the navigation object and has corresponding position information, the target object has a possibility of obstructing a driving path of the navigation object and forming obstruction to driving of the navigation object, wherein the navigation object can be an entity object which needs to be subjected to path planning to drive to a target position.

For example, the navigation object may be an object such as a navigation vehicle, a sweeping robot, an aircraft, etc.; the target object may be a physical object in the same environment as the navigation object, for example, the target object may be a wall, another vehicle, a table, a chair, a tree, or other physical objects. In the first embodiment of the present application, a navigation object is taken as a navigation vehicle, and a target object is taken as at least one detected vehicle corresponding to the navigation vehicle for example.

The acquiring of the first original detection data corresponding to the target object at the first time and the acquiring of the second original detection data corresponding to the target object at the second time refer to acquiring detection data of the target object at different times and including position information of the target object, and specifically may acquire the original detection data corresponding to the target object through an image sensor, such as a radar sensor, a depth camera, and the like, carried or carried on the navigation object. For example, three-dimensional Point Cloud (Point Cloud) data corresponding to a detected vehicle at different times may be obtained by a radar sensor mounted on a navigation vehicle, that is, the original detection data may be Point Cloud data. Here, the "first" and "second" are a general concept for describing original detection data at a plurality of different times corresponding to a target object.

Step S202, obtaining first hash feature information and second hash feature information corresponding to the target object according to the first raw detection data and the second raw detection data, where the first hash feature information is a hash quantization representation of feature information and position information of the target object at the first time, and the second hash feature information is a hash quantization representation of feature information and position information of the target object at the second time.

Fig. 3 is a schematic process diagram of a timing matching method for a target object according to a first embodiment of the present application. Specifically, after first original detection data and second original detection data corresponding to a target object are obtained, in order to quickly and accurately establish a positional correspondence relationship of the target object in time series, the first embodiment of the present application establishes the correspondence relationship by: according to the first original detection data and the second original detection data, acquiring first characteristic information and first position information of the target object at the first moment, and acquiring second characteristic information and second position information of the target object at the second moment; performing region feature aggregation processing on the first feature information and the first position information to acquire first aggregated feature information, and performing region feature aggregation processing on the second feature information and the second position information to acquire second aggregated feature information; and obtaining the first hash feature information and the second hash feature information according to the first aggregation feature information and the first aggregation feature information. The above steps will be described below.

Step S202-1, according to the first original detection data and the second original detection data, acquiring first feature information and first position information of the target object at the first time, and acquiring second feature information and second position information of the target object at the second time;

in the first embodiment of the present application, the feature information and the position information of the target object at different times may be obtained by performing detection processing on raw detection data corresponding to the target object at different times by using a target object detection model, where the target object detection model is a model obtained by training in advance.

As shown in fig. 3, in consideration of the characteristic that original detection data obtained by an image sensor, such as a radar sensor, such as point cloud data, has sparsity, in order to increase the resolution of obtained feature information, the target object detection model according to the first embodiment of the present application may specifically be a sparse three-dimensional convolution model based on Deep Learning (DL), a detection method used by the model may use a conventional One-Stage (One Stage) target object detection method or One-Stage (Two Stage) target object detection method, and a specific training and detection processing process thereof is described in detail in the prior art, and is not repeated herein.

After the target object detection model is obtained, first original detection data and second original detection data corresponding to the target object are respectively input into the target object detection model, and then first position information and second position information corresponding to the target object can be obtained. In addition, in the detection process, when different convolution layers in the target object detection model perform convolution processing on the input raw detection data, feature information of multiple dimensions corresponding to the target object, that is, feature maps (feature maps) of multiple dimensions, may also be acquired, and therefore the method for acquiring the first feature information includes: performing convolution processing on the first original detection data by using at least one intermediate convolution layer of the target object detection model, and acquiring first feature information according to feature information of at least one dimension, which is generated by the at least one intermediate convolution layer and corresponds to the target object; the method for obtaining the second characteristic information comprises the following steps: and performing convolution processing on the second original detection data by using at least one intermediate convolution layer of the target object detection model, and acquiring second characteristic information according to the characteristic information of at least one dimension, which is generated by the at least one intermediate convolution layer and corresponds to the target object.

As can be seen from the above description, the feature information corresponding to the target object, such as the first feature information and the second feature information, acquired in the first embodiment of the present application includes feature information with multiple dimensions. For example, if the target object detection model includes k intermediate convolution layers, where k is not less than 1, the feature information generated by the k intermediate convolution layers is used as the target object pair described in the first embodiment of the present applicationCorresponding characteristic information, the characteristic information of a plurality of dimensions corresponding to the target object at different time can be expressed as { F }₀，F₁，...，F_k}。

In the first embodiment of the present application, the position information, specifically, the coordinate data of the rectangular frame (bounding box) of the target object in the point cloud coordinate system, may be in the form of (x, y, x _ length, y _ length, Φ), where x, y respectively represent an x coordinate and a y coordinate of a central point of the rectangular frame of the position information, x _ length represents a length of the rectangular frame on an x axis, y _ length represents a length of the rectangular frame on a y axis, and Φ is a deflection angle of the rectangular frame. Of course, in specific implementation, other forms may be used to describe the location information of the target object, and are not described herein again.

As described above, how to obtain the feature information and the position information corresponding to the target object at different times is introduced, it is obvious that, because the dimensions of these pieces of information are not completely the same, when the corresponding relationship is established for the positions of the target object at different times according to these feature information and position information, the data volume is large, the corresponding calculation amount is also large, and the problems of slow speed and relatively low accuracy exist.

After step S202-1, performing step S202-2, performing region feature aggregation processing on the first feature information and the first position information to obtain first aggregated feature information, and performing region feature aggregation processing on the second feature information and the second position information to obtain second aggregated feature information;

the method includes the steps of inputting the characteristic information and the position information of the target object at different moments corresponding to the target object into a target Hash model obtained through pre-training respectively, and mapping the characteristic information and the position information of the target object at multiple dimensions at a single moment to a Hamming Space (Hamming Space) through a Hash mapping function obtained in advance, so that the obtained Hash characteristic information, namely a Hash Code (Hash Code), has the advantages of high storage efficiency, small occupied memory, relatively small calculation amount and relatively small calculation complexity, and the obtained Hash characteristic information also keeps the similarity between original characteristic information, and the target Hash model is introduced below.

As shown in fig. 3, the target hash model is configured to perform hash quantization on feature information and location information corresponding to a target object, and includes at least a first sub-model and a second sub-model, where the first sub-model is configured to perform regional feature aggregation processing on the feature information and the location information corresponding to the target object, the second branch sub-model is configured to perform hash quantization on regional aggregated feature information corresponding to the target object, and the second branch sub-model includes 1 convolution layer of 1 × 1 and a full connection layer.

In the first embodiment of the present application, the performing regional characteristic aggregation processing on the characteristic information and the location information corresponding to the target object specifically is to perform processing on the characteristic information and the location information corresponding to the target object through a RoiAlign layer in the first sub-model, so as to quantize the characteristic information and the location information respectively corresponding to the target object at different times into aggregated characteristic information with a fixed size, where details of the RoiAlign layer are introduced in the prior art, and are not described here again.

For example, the feature information of multiple dimensions corresponding to the target object at different time points can be expressed as { F }₀，F₁，...，F_kThen, feature information of the multiple dimensions at a single time and position information of the target object at the time are input into a first branch sub-model of the target hash model, and region aggregation processing is performed on the feature information of the multiple dimensions through the RoiAlign layer and the position information, so that aggregated feature information { f quantized to a fixed size can be obtained₀，f₁，...，f_k}。

In addition, the target hash model can be obtained by training through the following method: acquiring historical detection data of at least two moments corresponding to an original sample object, wherein the original sample object corresponds to the target object; obtaining at least two pieces of sample characteristic information corresponding to the original sample object in the historical detection data of the at least two moments and at least two pieces of sample position information corresponding to the two pieces of sample characteristic information; and training to obtain the target hash model by taking the at least two sample characteristic information and the at least two sample position information corresponding to the two sample characteristic information as sample data.

For example, the same vehicle to be detected at different times is used as an original sample object, and the corresponding relation of the actual positions of the vehicle to be detected at different times can be obtained from historical navigation data as sample supervision information; obtaining sample characteristic information of different moments corresponding to the vehicle to be detected from historical point cloud data of different moments corresponding to the vehicle to be detected, and obtaining sample position information of different moments obtained through a target object detection model corresponding to the sample characteristic information; sample data can be constructed and obtained through the information, the target Hash model is trained by using the sample data, and the error of the corresponding relation between the predicted position and the actual position of the vehicle to be detected at different moments, which is calculated according to the Hash characteristic information of the model at different moments, is within a preset range.

In specific implementation, the feature information of the same original sample object can be acquired as a positive sample, and the feature information of other original sample objects can be acquired as a negative sample, so that triple sample data is constructed, and the target hash model is trained by using a triple loss function. The method specifically comprises the following steps: generating triple sample data according to the at least two sample characteristic information and the at least two sample position information corresponding to the two sample characteristic information; and training the target hash model by using the triple sample data, and adjusting parameters of the target hash model by using a loss function corresponding to the triple sample data in the training process to enable the target hash model to reach a preset convergence condition.

For example, the definition of a triple may be expressed as: (f)_i，f_j，f_k):sim(f_i，f_j)>sim(f_i，f_k) I.e. characteristic f_i、f_jThe similarity between the two is more than f_i、f_kA similarity therebetween, wherein f_i、f_jRepresenting the characteristics of the same original sample object, e.g. the sample characteristic information and corresponding sample position information of the same vehicle to be detected at different times after being processed by the first branch sub-model, f_kAnd may be any feature unrelated to the original sample object, such as features corresponding to other vehicles to be detected, and even trees or buildings. In addition, if the feature f_i、f_j f_kThe hash characteristic information obtained after the processing of the second branch submodel is h respectively_i、h_j、h_kThen the triplet loss function can be expressed as: l (h)_i，h_j，h_k)＝max(0,ε-||h_i-h_j||+||h_j-h_k| |) where ε is a hyperparameter.

The above description is made on a structure and an obtaining method of a target hash network, and after obtaining a target hash model, the target hash model may be used to perform hash quantization on feature information and location information of a target object at different times, specifically, a first branch sub-model in the target hash model is used to perform region aggregation processing on the obtained feature information and location information of the target object at different times, including: inputting the first feature information and the first position information into a first sub-model of a target hash model to obtain first aggregated feature information; inputting the second feature information and the second position information into a first sub-model of the target hash model to obtain the second aggregation feature information; the first sub-model is used for carrying out regional characteristic aggregation processing on the characteristic information and the position information corresponding to the target object.

After step S202-2, step S202-3 is executed to obtain the first hash feature information and the second hash feature information according to the first aggregation feature information and the first aggregation feature information.

After regional feature aggregation processing is performed on feature information and position information at different times corresponding to a target object through a first branch model of a target hash model, the obtained aggregated feature information is input into a second branch submodel of the target hash model, all features in the aggregated feature information are connected (configured) together, and hash mapping is performed, so that the characteristic information of western China corresponding to the target object can be obtained. The method specifically comprises the following steps: inputting the first aggregation characteristic information into a second submodel of the target hash model to obtain the first hash characteristic information; inputting the second aggregation characteristic information into a second sub-model of the target hash model to obtain second hash characteristic information; the second submodel is used for performing Hash quantization representation on the region aggregation characteristic information corresponding to the target object.

It should be noted that, the hash feature information corresponding to the target object, for example, the first hash feature information and the second hash feature information are hash-quantized information, that is, binary feature information having compact characteristics and containing original multi-dimensional feature information, and the corresponding relationship of the positions of the target object at different times is established by the hash feature information corresponding to the target object, so that the calculation amount can be reduced and the calculation speed can be increased.

In the above, how to obtain the hash feature information corresponding to the target object at different times in step S202 is described in detail, and after obtaining the hash feature information, the time-series position corresponding relationship of the target object can be established according to the hash feature information corresponding to the target object at different times.

After step S202, step S203 is executed to establish a corresponding relationship between positions of the target object at the first time and the second time according to the first hash feature information and the second hash feature information.

After the hash feature information at different times corresponding to the target object is obtained, by judging whether the hash feature information at different times and corresponding to different target objects meets a preset similarity condition, a time-series position corresponding relationship can be established for the same target object in different target objects, which specifically includes: and if the first hash characteristic information and the second hash characteristic information meet a preset similarity condition, establishing a position corresponding relation of the target object at the first moment and the second moment.

For example, performing exclusive nor (XNOR) operation processing on the first hash feature information and the second hash feature information to obtain an operation result; if the number of preset values in the operation result is not less than a preset threshold, it is determined that the first hash feature information and the second hash feature information satisfy the preset similarity condition, where the preset value is "1" in the first embodiment of the present application, and of course, the preset threshold may also be set according to actual needs in specific implementation.

That is, after the hash feature information of the target object at different times is obtained, the hash feature information of the target object at different times is subjected to exclusive nor operation between two hash feature information of the target object at different times, and the number of "1" included in the operation result is obtained, and if the number is greater than a preset threshold, the two target objects seen at different times can be regarded as the same target object, and then the position corresponding relationship of the target object at different times can be established.

The method provided in the first embodiment of the present application is described in detail above by taking a vehicle navigation scenario, that is, a navigation object is a navigation vehicle, and a target object is at least one detected vehicle corresponding to the navigation vehicle as an example, it should be noted that, when the method is specifically implemented, the method may also be applied to other scenarios, for example, the method may be used in a Drive Test scenario, that is, the navigation object is a computing device for performing a Drive Test, such as a laptop, a Test collection device, a GPS receiver, and the like; the target object is a Base Station (Base Station) in a road section involved in the drive test; the original detection data is wireless signal detection data obtained by the computing device from each base station, such as data of user throughput, Frame Error Rate (FER), sch (synchronization channel) Rate distribution, and the like; and the computing equipment establishes the position corresponding relation of each base station in the road section involved in the drive test on the time sequence through the method, and adjusts the wireless signal of each base station according to the position corresponding relation so as to optimize the communication quality of the wireless signal of each base station.

In addition, in the above description, the original detection data corresponding to the target object at each moment is collected first, and then the position corresponding relationship of the target object in the time sequence is established according to the original detection data. However, it should be noted that, in the implementation, the acquisition of the original detection data and the establishment of the position correspondence of the target object in the time sequence may be performed synchronously. Specifically, when the first time in the method is a time not later than the current time and the second time is a time not later than the first time, the method may be used to establish a position correspondence relationship between the first time and the second time of the target object, and at the same time, may further obtain third raw detection data corresponding to the current time of the target object, obtain third hash feature information corresponding to the target object according to the third raw detection data, and establish a position correspondence relationship between the current time and the first time of the target object according to the first hash feature information and the third hash feature information.

For example, at time Tn, the positional correspondence relationship between the target objects at Tn-1 and Tn-2 can be established, and the raw detection data at time Tn can also be acquired, and the positional correspondence relationship between the target objects at time Tn-1 and Tn can also be established. Of course, in specific implementation, in order to increase the accuracy of the calculation result, the setting may also be performed as needed, for example, only the corresponding relationship between the first time and the second time that is earlier than the current time is established at the current time, and details are not described here.

In summary, the timing matching method for a target object provided in the first embodiment of the present application includes: acquiring first original detection data corresponding to a target object at a first moment, and acquiring second original detection data corresponding to the target object at a second moment; obtaining first hash feature information and second hash feature information corresponding to the target object according to the first original detection data and the second original detection data, wherein the first hash feature information is a hash quantization representation of feature information and position information of the target object at the first time, and the second hash feature information is a hash quantization representation of feature information and position information of the target object at the second time; and establishing a position corresponding relation of the target object at the first moment and the second moment according to the first hash characteristic information and the second hash characteristic information. The method carries out Hash quantization representation on the characteristic information and the position information corresponding to the target object at different moments, can obtain the Hash characteristic information which is rich in information and relatively small in data volume of the target object at different moments, establishes the position corresponding relation of the target object in time sequence according to the Hash characteristic information, and can reduce the calculation amount, increase the calculation speed and increase the accuracy of the calculation result.

Corresponding to the time sequence matching method for the target object provided in the first embodiment of the present application, the second embodiment of the present application further provides a method for obtaining the target hash model, please refer to fig. 4, which is a flowchart of the method for obtaining the target hash model provided in the second embodiment of the present application, wherein some steps have been described in detail in the first embodiment, so that the description herein is relatively simple, and for the relevant points, reference may be made to some descriptions in the first embodiment of the present application, and the processing procedure described below is only exemplary.

Step S401, obtaining historical detection data of at least two times corresponding to the original sample object.

Step S402, obtaining at least two sample feature information corresponding to the historical detection data of the original sample object at the at least two moments and at least two sample position information corresponding to the two sample feature information.

Step S403, taking the at least two sample feature information and the at least two sample position information corresponding to the two sample feature information as sample data, and training to obtain the target hash model, where the target hash model is used to perform hash quantization representation on the feature information and the position information corresponding to a target object, and the original sample object corresponds to the target object.

Wherein, the training to obtain the target hash model by using the at least two sample feature information and the at least two sample position information corresponding to the two sample feature information as sample data includes: generating triple sample data according to the at least two sample characteristic information and the at least two sample position information corresponding to the two sample characteristic information; and training the target hash model by using the triple sample data, and adjusting parameters of the target hash model by using a loss function corresponding to the triple sample data in the training process to enable the target hash model to reach a preset convergence condition.

Corresponding to the timing matching method for the target object provided in the first embodiment of the present application, a third embodiment of the present application further provides a timing matching method for a detected vehicle, which is a specific scenario application method corresponding to the method provided in the first embodiment of the present application, please refer to fig. 5, which is a flowchart of the timing matching method for the detected vehicle provided in the third embodiment of the present application, wherein some steps have been described in detail in the first embodiment, so that the description herein is relatively simple, and relevant points can be referred to some descriptions in the first embodiment of the present application, and the processing procedure described below is only schematic.

Step S501, first point cloud data corresponding to a detected vehicle at a first moment is obtained, and second point cloud data corresponding to the detected vehicle at a second moment is obtained.

Step S502, obtaining first Hash characteristic information and second Hash characteristic information corresponding to the detected vehicle according to the first point cloud data and the second point cloud data, wherein the first Hash characteristic information is Hash quantization representation of sparse characteristic information and position information of the detected vehicle at the first moment, and the second Hash characteristic information is Hash quantization representation of sparse characteristic information and position information of the detected vehicle at the second moment.

Step S503, establishing a corresponding relationship between the positions of the detected vehicle at the first time and the second time according to the first hash feature information and the second hash feature information.

Corresponding to the time sequence matching method for the target object provided in the third embodiment of the present application, a navigation method is further provided in the fourth embodiment of the present application, which is a further specific application method for the scenario corresponding to the methods provided in the first and third embodiments of the present application, please refer to fig. 6, which is a flowchart of a navigation method provided in the fourth embodiment of the present application, wherein some steps have been described in detail in the first embodiment, so that the description herein is relatively simple, and for the relevant points, reference may be made to some descriptions in the first embodiment of the present application, and the processing procedure described below is only schematic.

Step S601, a time-series position corresponding relationship of the detected vehicle is acquired.

Step S602, providing navigation information for a navigation vehicle according to the position corresponding relationship, where the position corresponding relationship is obtained by using the time sequence matching method for a detected vehicle provided in the third embodiment of the present application, and the detected vehicle and the navigation vehicle are in the same environment.

Corresponding to the method for matching a target object according to the first embodiment of the present application, a fifth embodiment of the present application further provides a device for matching a target object, please refer to fig. 7, which is a schematic diagram of the device for matching a target object according to the fifth embodiment of the present application. A timing matching apparatus for a target object according to a fifth embodiment of the present application includes:

an original data obtaining unit 701, configured to obtain first original detection data corresponding to a target object at a first time, and obtain second original detection data corresponding to the target object at a second time.

A hash feature information obtaining unit 702, configured to obtain, according to the first raw detection data and the second raw detection data, first hash feature information and second hash feature information corresponding to the target object, where the first hash feature information is a hash quantization representation of feature information and position information of the target object at the first time, and the second hash feature information is a hash quantization representation of feature information and position information of the target object at the second time.

A corresponding relationship establishing unit 703 is configured to establish a position corresponding relationship between the target object at the first time and the target object at the second time according to the first hash feature information and the second hash feature information.

Corresponding to the method for matching a target object according to the first embodiment of the present application, a sixth embodiment of the present application further provides an electronic device, please refer to fig. 8, which is a schematic diagram of the first electronic device according to the sixth embodiment of the present application. An electronic device provided in a sixth embodiment of the present application includes:

a processor 801;

a memory 802 for storing a program of a timing matching method for a target object, wherein after the apparatus is powered on and the program of the timing matching method for the target object is executed by the processor, the following steps are performed:

acquiring first original detection data corresponding to a target object at a first moment, and acquiring second original detection data corresponding to the target object at a second moment;

obtaining first hash feature information and second hash feature information corresponding to the target object according to the first original detection data and the second original detection data, wherein the first hash feature information is a hash quantization representation of feature information and position information of the target object at the first time, and the second hash feature information is a hash quantization representation of feature information and position information of the target object at the second time;

and establishing a position corresponding relation of the target object at the first moment and the second moment according to the first hash characteristic information and the second hash characteristic information.

Corresponding to the method for matching the time sequence of the target object provided by the first embodiment of the present application, the seventh embodiment of the present application further provides a storage device, since the embodiment of the storage device is substantially similar to the embodiment of the method, the description is relatively simple, and for relevant points, reference may be made to part of the description of the embodiment of the method, and the embodiment of the storage device described below is only illustrative. A storage device according to a seventh embodiment of the present application stores a program of a timing matching method for a target object, the program being executed by a processor to perform the steps of:

Corresponding to the method for obtaining a target hash model provided in the second embodiment of the present application, an eighth embodiment of the present application further provides a device for obtaining a target hash model, please refer to fig. 9, which is a schematic diagram of a timing matching device for a target object provided in the eighth embodiment of the present application. An eighth embodiment of the present application provides an apparatus for obtaining a target hash model, including:

a history detection data obtaining unit 901, configured to obtain history detection data of at least two time instants corresponding to an original sample object.

A sample information obtaining unit 902, configured to obtain at least two pieces of sample feature information corresponding to the original sample object in the historical detection data at the at least two time instants, and at least two pieces of sample position information corresponding to the two pieces of sample feature information.

A training unit 903, configured to train to obtain the target hash model by using the at least two sample feature information and the at least two sample position information corresponding to the two sample feature information as sample data, where the target hash model is used to perform hash quantization on feature information and position information corresponding to a target object, and the original sample object corresponds to the target object.

Corresponding to the method for obtaining the target hash model provided in the second embodiment of the present application, a ninth embodiment of the present application further provides an electronic device, since the embodiment of the electronic device is substantially similar to the embodiment of the method, the description is relatively simple, and the relevant points can be referred to the partial description of the embodiment of the method, and the embodiments of the electronic device described below are only schematic. An electronic device provided in a ninth embodiment of the present application includes:

a processor;

acquiring historical detection data of at least two moments corresponding to an original sample object;

obtaining at least two pieces of sample characteristic information corresponding to the original sample object in the historical detection data of the at least two moments and at least two pieces of sample position information corresponding to the two pieces of sample characteristic information;

and training to obtain the target hash model by taking the at least two sample characteristic information and the at least two sample position information corresponding to the two sample characteristic information as sample data, wherein the target hash model is used for performing hash quantization representation on the characteristic information and the position information corresponding to a target object, and the original sample object corresponds to the target object.

Corresponding to the method for obtaining the target hash model provided in the second embodiment of the present application, a tenth embodiment of the present application further provides a storage device, since the embodiment of the storage device is substantially similar to the embodiment of the method, the description is relatively simple, and the relevant points can be referred to the partial description of the embodiment of the method, and the embodiment of the storage device described below is only illustrative. A storage device according to a tenth embodiment of the present application stores a program of an obtaining method of a target hash model, the program being executed by a processor and executing the steps of:

Corresponding to a timing matching method for a detected vehicle provided by the third embodiment of the present application, an eleventh embodiment of the present application further provides a timing matching device for a detected vehicle, please refer to fig. 10, which is a schematic diagram of a timing matching device for a detected vehicle provided by the eleventh embodiment of the present application. An eleventh embodiment of the present application provides a timing matching apparatus for a detected vehicle, including:

the point cloud data acquiring unit 1001 is configured to acquire first point cloud data corresponding to a detected vehicle at a first time, and acquire second point cloud data corresponding to the detected vehicle at a second time.

A hash feature information obtaining unit 1002, configured to obtain first hash feature information and second hash feature information corresponding to the detected vehicle according to the first point cloud data and the second point cloud data, where the first hash feature information is a hash quantization representation of sparse feature information and location information of the detected vehicle at the first time, and the second hash feature information is a hash quantization representation of sparse feature information and location information of the detected vehicle at the second time.

A corresponding relationship establishing unit 1003, configured to establish a position corresponding relationship between the detected vehicle at the first time and the detected vehicle at the second time according to the first hash feature information and the second hash feature information.

Corresponding to the timing matching method for the detected vehicle provided by the third embodiment of the present application, the twelfth embodiment of the present application also provides an electronic device, since the embodiment of the electronic device is substantially similar to the embodiment of the method, the description is relatively simple, and the relevant points can be referred to the partial description of the embodiment of the method, and the embodiment of the electronic device described below is only illustrative. An electronic device provided by a twelfth embodiment of the present application includes:

a processor;

acquiring first point cloud data corresponding to a detected vehicle at a first moment, and acquiring second point cloud data corresponding to the detected vehicle at a second moment;

obtaining first hash feature information and second hash feature information corresponding to the detected vehicle according to the first point cloud data and the second point cloud data, wherein the first hash feature information is hash quantitative representation of sparse feature information and position information of the detected vehicle at the first moment, and the second hash feature information is hash quantitative representation of sparse feature information and position information of the detected vehicle at the second moment;

and establishing the position corresponding relation of the detected vehicle at the first moment and the second moment according to the first Hash characteristic information and the second Hash characteristic information.

In correspondence with the timing matching method for the detected vehicle provided by the third embodiment of the present application, the thirteenth embodiment of the present application also provides a storage device, since the storage device embodiment is basically similar to the method embodiment, the description is relatively simple, and the relevant points can be referred to the partial description of the method embodiment, and the storage device embodiment described below is only illustrative. A storage device according to a thirteenth embodiment of the present application stores a program of a timing matching method for a detected vehicle, the program being executed by a processor to perform the steps of:

In a navigation device corresponding to the navigation method provided in the fourth embodiment of the present application, please refer to fig. 11, which is a schematic diagram of the navigation device provided in the fourteenth embodiment of the present application. A navigation device according to a fourteenth embodiment of the present application includes:

a position correspondence relationship acquisition unit 1101 configured to acquire a time-series position correspondence relationship of the detected vehicle.

The navigation information providing unit 1102 is configured to provide navigation information for a navigation vehicle according to the position corresponding relationship, where the position corresponding relationship is obtained by a time sequence matching method for a detected vehicle, and the detected vehicle and the navigation vehicle are in the same environment.

Corresponding to a navigation method provided by the fourth embodiment of the present application, the fifteenth embodiment of the present application further provides an electronic device, which is substantially similar to the method embodiment, so that the description is relatively simple, and for the relevant points, reference may be made to part of the description of the method embodiment, and the electronic device embodiment described below is only illustrative. A fifteenth embodiment of the present application provides an electronic device comprising:

a processor;

acquiring the position corresponding relation of the detected vehicle on a time sequence;

and providing navigation information for a navigation vehicle according to the position corresponding relation, wherein the position corresponding relation is obtained by using a time sequence matching method aiming at a detected vehicle, and the detected vehicle and the navigation vehicle are in the same environment.

In accordance with a navigation method provided by the fourth embodiment of the present application, a sixteenth embodiment of the present application further provides a storage device, since the storage device embodiment is substantially similar to the method embodiment, the description is relatively simple, and for the relevant points, reference may be made to part of the description of the method embodiment, and the storage device embodiment described below is only illustrative. A sixteenth embodiment of the present application provides a storage device, in which a program of a navigation method is stored, where the program is executed by a processor to perform the following steps:

Corresponding to the method for matching a target object in the first embodiment of the present application, a seventeenth embodiment of the present application further provides a method for matching a target base station, which is a method applied to a specific scenario corresponding to the method provided in the first embodiment of the present application, please refer to fig. 12, which is a flowchart of the method for matching a target base station in the seventeenth embodiment of the present application, wherein some steps have been described in detail in the first embodiment, so that the description herein is relatively simple, and relevant points can be referred to some descriptions in the first embodiment of the present application, and the processing procedure described below is only schematic.

Step S1201, acquiring first wireless signal detection data corresponding to a target base station at a first time, and acquiring second wireless signal detection data corresponding to the target object at a second time.

Step S1202, obtaining first hash feature information and second hash feature information corresponding to the target base station according to the first wireless signal and the second wireless signal detection data, where the first hash feature information is a hash quantization representation of the feature information and the location information of the target base station at the first time, and the second hash feature information is a hash quantization representation of the feature information and the location information of the target base station at the second time.

Step S1203, establishing a position corresponding relationship between the target base station at the first time and the second time according to the first hash feature information and the second hash feature information.

Corresponding to the timing matching method for the target base station provided in the seventeenth embodiment of the present application, the eighteenth embodiment of the present application further provides a base station drive test method, which is a further specific scenario application method corresponding to the methods provided in the first and seventeenth embodiments of the present application, please refer to fig. 13, which is a flowchart of the base station drive test method provided in the eighteenth embodiment of the present application, wherein some steps have been described in detail in the first embodiment, so that the description herein is relatively simple, and relevant points can be referred to some descriptions in the first embodiment of the present application, and the processing procedure described below is only schematic.

Step S1301, acquiring a position correspondence relationship of the target base station in the time sequence and the wireless signal detection data, where the position correspondence relationship is information acquired by using a time sequence matching method for the target base station.

Step S1302, adjusting the wireless signal of the target base station according to the position corresponding relationship and the wireless signal detection data.

Corresponding to the method for matching timing of a target base station provided in the seventeenth embodiment of the present application, please refer to fig. 14, which is a schematic diagram of the apparatus for matching timing of a target base station provided in the nineteenth embodiment of the present application, since the apparatus embodiment is substantially similar to the method embodiment, the description is relatively simple, and for the relevant points, reference may be made to part of the description of the method embodiment, and the apparatus embodiment described below is only exemplary. A timing matching apparatus for a target base station according to a nineteenth embodiment of the present application includes:

a wireless signal detection data obtaining unit 1401, configured to obtain first wireless signal detection data corresponding to a target base station at a first time, and obtain second wireless signal detection data corresponding to the target object at a second time.

A hash feature information obtaining unit 1402, configured to obtain first hash feature information and second hash feature information corresponding to the target base station according to the first wireless signal and the second wireless signal detection data, where the first hash feature information is a hash quantization representation of feature information and location information of the target base station at the first time, and the second hash feature information is a hash quantization representation of feature information and location information of the target base station at the second time.

A location correspondence relationship establishing unit 1403, configured to establish a location correspondence relationship between the target base station at the first time and the second time according to the first hash feature information and the second hash feature information.

Corresponding to the method for matching a timing sequence of a target base station provided in the seventeenth embodiment of the present application, the twentieth embodiment of the present application further provides an electronic device, which is substantially similar to the method embodiment, so that the description is relatively simple, and for relevant points, reference may be made to part of the description of the method embodiment, and the electronic device embodiment described below is only illustrative. A twentieth embodiment of the present application provides an electronic device including:

a processor;

acquiring first wireless signal detection data corresponding to a target base station at a first moment, and acquiring second wireless signal detection data corresponding to a target object at a second moment;

acquiring first hash feature information and second hash feature information corresponding to the target base station according to the first wireless signal and the second wireless signal detection data, wherein the first hash feature information is a hash quantization representation of feature information and position information of the target base station at the first time, and the second hash feature information is a hash quantization representation of feature information and position information of the target base station at the second time;

and establishing a position corresponding relation of the target base station at the first time and the second time according to the first hash characteristic information and the second hash characteristic information.

Corresponding to the method for matching a timing sequence of a target base station provided in the seventeenth embodiment of the present application, the twenty-first embodiment of the present application further provides a storage device, since the storage device embodiment is substantially similar to the method embodiment, the description is relatively simple, and for relevant points, reference may be made to part of the description of the method embodiment, and the storage device embodiment described below is only illustrative. A twenty-first embodiment of the present application provides a storage device, in which a program of a timing matching method for a target base station is stored, where the program is executed by a processor, and is configured to perform the following steps:

Corresponding to the method for testing a base station according to the eighteenth embodiment of the present application, a twenty-second embodiment of the present application further provides a device for testing a base station, please refer to fig. 15, which is a schematic diagram of the device for testing a base station according to the twenty-second embodiment of the present application. A twenty-second embodiment of the present application provides a base station drive test apparatus, including:

a data acquisition unit 1501, configured to acquire a time-series position correspondence relationship of a target base station and radio signal detection data, where the position correspondence relationship is information acquired by using the time-series matching method for the target base station according to claim 17.

A signal adjusting unit 1502, configured to adjust the wireless signal of the target base station according to the position correspondence and the wireless signal detection data.

Corresponding to the method for driving test of a base station provided in the eighteenth embodiment of the present application, the twenty-third embodiment of the present application further provides an electronic device, since the embodiment of the electronic device is substantially similar to the embodiment of the method, the description is relatively simple, and the relevant points can be referred to the partial description of the embodiment of the method, and the embodiments of the electronic device described below are only schematic. A twenty-third embodiment of the present application provides an electronic device including:

a processor;

acquiring a position corresponding relation of a target base station on a time sequence and wireless signal detection data, wherein the position corresponding relation is information acquired by using a time sequence matching method aiming at the target base station;

and adjusting the wireless signal of the target base station according to the position corresponding relation and the wireless signal detection data.

Corresponding to the method for driving test of a base station provided in the eighteenth embodiment of the present application, the twenty-fourth embodiment of the present application further provides a storage device, since the embodiment of the storage device is substantially similar to the embodiment of the method, the description is relatively simple, and the relevant points can be referred to the partial description of the embodiment of the method, and the embodiment of the storage device described below is only schematic. A twenty-fourth embodiment of the present application provides a storage device, in which a program of a base station drive test method is stored, where the program is executed by a processor to perform the following steps:

Although the present application has been described with reference to the preferred embodiments, it is not intended to limit the present application, and those skilled in the art can make variations and modifications without departing from the spirit and scope of the present application, therefore, the scope of the present application should be determined by the claims that follow.

In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.

The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.

Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include non-transitory computer readable media (transient media), such as modulated data signals and carrier waves.

As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

Claims

1. A time sequence matching method for a target object is characterized by comprising the following steps:

2. The method for matching timing sequence of a target object according to claim 1, wherein the obtaining first hash feature information and second hash feature information corresponding to the target object according to the first original detection data and the second original detection data comprises:

according to the first original detection data and the second original detection data, acquiring first characteristic information and first position information of the target object at the first moment, and acquiring second characteristic information and second position information of the target object at the second moment;

performing region feature aggregation processing on the first feature information and the first position information to acquire first aggregated feature information, and performing region feature aggregation processing on the second feature information and the second position information to acquire second aggregated feature information;

and obtaining the first hash feature information and the second hash feature information according to the first aggregation feature information and the first aggregation feature information.

3. The method according to claim 2, wherein the obtaining first feature information and first position information of the target object at the first time and obtaining second feature information and second position information of the target object at the second time according to the first original detection data and the second original detection data comprises:

and inputting the first original detection data and the second original detection data into a target object detection model respectively to acquire the first characteristic information, the first position information, the second characteristic information and the second position information.

4. The timing matching method for the target object according to claim 3, comprising:

performing convolution processing on the first original detection data by using at least one intermediate convolution layer of the target object detection model, and acquiring first feature information according to feature information of at least one dimension, which is generated by the at least one intermediate convolution layer and corresponds to the target object;

and performing convolution processing on the second original detection data by using at least one intermediate convolution layer of the target object detection model, and acquiring second characteristic information according to the characteristic information of at least one dimension, which is generated by the at least one intermediate convolution layer and corresponds to the target object.

5. The method according to claim 2, wherein the performing a region feature aggregation process on the first feature information and the first location information to obtain first aggregated feature information, and performing a region feature aggregation process on the second feature information and the second location information to obtain second aggregated feature information includes:

inputting the first feature information and the first position information into a first sub-model of a target hash model to obtain first aggregated feature information;

inputting the second feature information and the second position information into a first sub-model of the target hash model to obtain the second aggregation feature information;

the first sub-model is used for carrying out regional characteristic aggregation processing on the characteristic information and the position information corresponding to the target object.

6. The method for matching timing for a target object according to claim 2, wherein the obtaining the first hash feature information and the second hash feature information corresponding to the target object according to the first aggregation feature information and the first aggregation feature information includes:

inputting the first aggregation characteristic information into a second submodel of the target hash model to obtain the first hash characteristic information;

inputting the second aggregation characteristic information into a second sub-model of the target hash model to obtain second hash characteristic information;

the second submodel is used for performing Hash quantization representation on the region aggregation characteristic information corresponding to the target object.

7. The method for matching a target object according to claim 1, wherein the establishing a correspondence relationship between positions of the target object at the first time and the second time according to the first hash feature information and the second hash feature information includes:

and if the first hash characteristic information and the second hash characteristic information meet a preset similarity condition, establishing a position corresponding relation of the target object at the first moment and the second moment.

8. The timing matching method for a target object according to claim 7, further comprising:

performing exclusive nor operation on the first hash characteristic information and the second hash characteristic information to obtain an operation result;

and if the number of preset numerical values in the operation result is not less than a preset threshold value, judging that the first Hash characteristic information and the second Hash characteristic information meet the preset similarity condition.

9. The timing matching method for the target object according to claim 5, wherein the target hash model is obtained by:

acquiring historical detection data of at least two moments corresponding to an original sample object, wherein the original sample object corresponds to the target object;

and training to obtain the target hash model by taking the at least two sample characteristic information and the at least two sample position information corresponding to the two sample characteristic information as sample data.

10. The method according to claim 9, wherein the training of the target hash model using the at least two sample feature information and the at least two sample position information corresponding to the two sample feature information as sample data includes:

generating triple sample data according to the at least two sample characteristic information and the at least two sample position information corresponding to the two sample characteristic information;

and training the target hash model by using the triple sample data, and adjusting parameters of the target hash model by using a loss function corresponding to the triple sample data in the training process to enable the target hash model to reach a preset convergence condition.

11. The timing matching method for a target object according to claim 1, wherein the first time is a time that is not later than a current time, the second time is a time that is not later than the first time, and the method further includes, while establishing a positional correspondence relationship between the target object at the first time and the second time:

acquiring third original detection data corresponding to the target object at the current moment;

acquiring third hash characteristic information corresponding to the target object according to the third original detection data;

and establishing a position corresponding relation between the current time and the first time of the target object according to the first hash characteristic information and the third hash characteristic information.

12. The timing matching method for target object according to claim 1, wherein the method is applied to a computing device providing the nearest service through edge computing.

13. A method for obtaining a target hash model, comprising:

14. A time sequence matching method for a detected vehicle is characterized by comprising the following steps:

15. A navigation method, comprising:

providing navigation information for a navigation vehicle according to the position correspondence, wherein the position correspondence is obtained by using the timing matching method for the detected vehicle according to claim 14, and the detected vehicle is in the same environment as the navigation vehicle.

16. A timing matching method for a target base station is characterized by comprising the following steps:

17. A method for base station drive test, comprising:

acquiring a position corresponding relation of a target base station on a time sequence and wireless signal detection data, wherein the position corresponding relation is information acquired by using the time sequence matching method for the target base station according to claim 16;

18. A timing matching apparatus for a target object, comprising:

the device comprises an original data acquisition unit, a first detection unit and a second detection unit, wherein the original data acquisition unit is used for acquiring first original detection data corresponding to a target object at a first moment and acquiring second original detection data corresponding to the target object at a second moment;

a hash feature information obtaining unit configured to obtain first hash feature information and second hash feature information corresponding to the target object, based on the first raw detection data and the second raw detection data, where the first hash feature information is a hash quantization representation of feature information and position information of the target object at the first time, and the second hash feature information is a hash quantization representation of feature information and position information of the target object at the second time;

and the corresponding relation establishing unit is used for establishing the position corresponding relation of the target object at the first moment and the second moment according to the first hash characteristic information and the second hash characteristic information.

19. An electronic device, comprising:

a processor;

20. A storage device, characterized by storing a program of a timing matching method for a target object, the program being executed by a processor to execute the steps of:

21. An apparatus for obtaining a target hash model, comprising:

the historical detection data acquisition unit is used for acquiring historical detection data of at least two moments corresponding to the original sample object;

the sample information obtaining unit is used for obtaining at least two pieces of sample characteristic information corresponding to the original sample object in the historical detection data of the at least two moments and at least two pieces of sample position information corresponding to the two pieces of sample characteristic information;

and the training unit is used for training the at least two pieces of sample characteristic information and the at least two pieces of sample position information corresponding to the two pieces of sample characteristic information to obtain the target hash model, wherein the target hash model is used for performing hash quantization representation on the characteristic information and the position information corresponding to a target object, and the original sample object corresponds to the target object.

22. An electronic device, comprising:

a processor;

23. A storage device storing a program of an acquisition method of a target hash model, the program being executed by a processor and performing the steps of:

24. A timing matching apparatus for a detected vehicle, comprising:

the system comprises a point cloud data acquisition unit, a point cloud data acquisition unit and a point cloud data acquisition unit, wherein the point cloud data acquisition unit is used for acquiring first point cloud data corresponding to a detected vehicle at a first moment and acquiring second point cloud data corresponding to the detected vehicle at a second moment;

a hash feature information obtaining unit, configured to obtain first hash feature information and second hash feature information corresponding to the detected vehicle according to the first point cloud data and the second point cloud data, where the first hash feature information is a hash quantization representation of sparse feature information and location information of the detected vehicle at the first time, and the second hash feature information is a hash quantization representation of sparse feature information and location information of the detected vehicle at the second time;

and the corresponding relation establishing unit is used for establishing the position corresponding relation of the detected vehicle at the first moment and the second moment according to the first Hash characteristic information and the second Hash characteristic information.

25. An electronic device, comprising:

a processor;

26. A storage device characterized by storing a program of a timing matching method for a detected vehicle, the program being executed by a processor to execute the steps of:

27. A navigation device, comprising:

a navigation information providing unit, configured to provide navigation information for a navigation vehicle according to the position correspondence, where the position correspondence is a position correspondence obtained by using the timing matching method for a detected vehicle according to claim 14, and the detected vehicle is in the same environment as the navigation vehicle.

28. An electronic device, comprising:

a processor;

29. A storage device storing a program of a navigation method, the program being executed by a processor to perform the steps of:

30. A timing matching apparatus for a target base station, comprising:

the wireless signal detection data acquisition unit is used for acquiring first wireless signal detection data corresponding to a target base station at a first moment and acquiring second wireless signal detection data corresponding to a target object at a second moment;

a hash feature information obtaining unit, configured to obtain first hash feature information and second hash feature information corresponding to the target base station according to the first wireless signal and the second wireless signal detection data, where the first hash feature information is a hash quantization representation of feature information and location information of the target base station at the first time, and the second hash feature information is a hash quantization representation of feature information and location information of the target base station at the second time;

and a location corresponding relationship establishing unit, configured to establish a location corresponding relationship between the target base station at the first time and the second time according to the first hash feature information and the second hash feature information.

31. An electronic device, comprising:

a processor;

32. A storage device, storing a program of a timing matching method for a target base station, the program being executed by a processor to perform the steps of:

33. A base station drive test apparatus, comprising:

a data obtaining unit, configured to obtain a position correspondence relationship of a target base station in a time sequence and wireless signal detection data, where the position correspondence relationship is information obtained by using the time sequence matching method for the target base station according to claim 16;

and the signal adjusting unit is used for adjusting the wireless signal of the target base station according to the position corresponding relation and the wireless signal detection data.

34. An electronic device, comprising:

a processor;

35. A storage device storing a program of a base station drive test method, the program being executed by a processor to perform the steps of: