CN113051470B

CN113051470B - Position accuracy evaluation method and device, electronic equipment and computer readable medium

Info

Publication number: CN113051470B
Application number: CN202110263934.4A
Authority: CN
Inventors: 汪爽
Original assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Current assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Priority date: 2021-02-26
Filing date: 2021-02-26
Publication date: 2024-03-26
Anticipated expiration: 2041-02-26
Also published as: CN113051470A

Abstract

The disclosure discloses a position accuracy assessment method and device, electronic equipment and a computer readable medium, relates to the technical field of data processing, and particularly relates to the technical field of intelligent searching and information flow. The specific implementation scheme is as follows: acquiring data to be verified and reference data; determining the data type of the data to be verified based on the identification degree of the interest point corresponding to the data to be verified, and determining the data type of the reference data based on the identification degree of the interest point corresponding to the reference data; acquiring a distance to be verified between an interest point corresponding to the data to be verified and a dotting position based on the data to be verified, and acquiring a reference distance between the interest point corresponding to the reference data and the dotting position based on the reference data; and evaluating the accuracy of the data to be verified according to the data type of the data to be verified, the data type of the reference data, the distance to be verified, the reference distance and a preset distance threshold. The method can improve the accuracy of position accuracy assessment.

Description

Position accuracy evaluation method and device, electronic equipment and computer readable medium

Technical Field

The present disclosure relates to the field of data processing technologies, and in particular, to the field of intelligent searching and information flow technologies, and in particular, to a method and apparatus for estimating position accuracy, an electronic device, and a computer readable medium.

Background

The location description service is mainly used for locating point description. The location description service is generally based on the data of points of interest (Point of Interest, simply referred to as poi), and obtains the returned data according to the ordering and coordinates of poi, so the quality of poi data determines the accuracy of the returned data.

In the real world, company data is continuously updated, so that the poi data is required to be continuously updated, and the accuracy of the updated poi data is required to be evaluated so as to improve the accuracy of returned data.

Disclosure of Invention

The disclosure provides a method and a device for evaluating position accuracy, electronic equipment and a computer readable medium.

According to a first aspect of the present disclosure, there is provided a position accuracy evaluation method including:

acquiring data to be verified and reference data; the data to be verified and the reference data are interest point data returned at the same dotting position, and the reference data and the data to be verified come from different map databases;

determining the data type of the data to be verified based on the identification degree of the interest point corresponding to the data to be verified, and determining the data type of the reference data based on the identification degree of the interest point corresponding to the reference data;

Acquiring a to-be-verified distance between an interest point corresponding to the to-be-verified data and the dotting position based on the to-be-verified data, and acquiring a reference distance between the interest point corresponding to the reference data and the dotting position based on the reference data;

and evaluating the accuracy of the data to be verified according to the data type of the data to be verified, the data type of the reference data, the distance to be verified, the reference distance and a preset distance threshold.

According to a second aspect of the present disclosure, there is provided a position accuracy evaluation apparatus including:

the first acquisition module is used for acquiring data to be verified and reference data; the data to be verified and the reference data are interest point data returned at the same dotting position, and the reference data and the data to be verified come from different map databases;

the data type determining module is used for determining the data type of the data to be verified based on the identification degree of the interest point corresponding to the data to be verified, and determining the data type of the reference data based on the identification degree of the interest point corresponding to the reference data;

the second acquisition module is used for acquiring a to-be-verified distance between the interest point corresponding to the to-be-verified data and the dotting position based on the to-be-verified data and acquiring a reference distance between the interest point corresponding to the reference data and the dotting position based on the reference data;

The evaluation module is used for evaluating the accuracy of the data to be verified according to the data type of the data to be verified, the data type of the reference data, the distance to be verified, the reference distance and a preset distance threshold value.

According to a third aspect of the present disclosure, there is provided an electronic device comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein,

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform any one of the methods of position accuracy assessment.

According to a fourth aspect of the present disclosure, there is provided a non-transitory computer-readable storage medium storing computer instructions for causing the computer to perform any one of the position accuracy evaluation methods.

According to a fifth aspect of the present disclosure, there is provided a computer program product comprising a computer program which, when executed by a processor, implements the method of any one of the above-mentioned position accuracy assessment methods.

It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the disclosure, nor is it intended to be used to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following specification.

Drawings

The drawings are for a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:

FIG. 1 is an exemplary system architecture diagram in which an embodiment of the present disclosure may be applied;

FIG. 2 is a flowchart of a position accuracy assessment method according to an embodiment of the present disclosure;

FIG. 3 is a schematic view of a position accuracy assessment method according to an embodiment of the present disclosure;

fig. 4 is a schematic diagram of a position accuracy evaluation device according to an embodiment of the present disclosure;

fig. 5 is a block diagram of an electronic device used to implement a position accuracy evaluation method of an embodiment of the present disclosure.

Detailed Description

Exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding, and should be considered as merely exemplary. Accordingly, one of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

The poi data includes retail store, restaurant, entertainment venue, residential district or university location data. Company location data often varies due to company management policies and the like. For example, in order to expand the business, a company has been moved, and the business address of the company has become another company. Therefore, the poi data needs to be updated, and the accuracy of the updated poi data needs to be evaluated before the line is brought on.

At present, return data of interest points are obtained in a dotting mode, and the accuracy of poi data is judged according to the return data. If the returned data exist, judging that the poi data are accurate; if the returned data is empty, the poi data is judged to be inaccurate. However, this judgment method can only judge the coverage of the poi data, and cannot judge the quality of the poi data. Therefore, it is difficult to ensure the quality of the poi data on line.

It should be noted that, without conflict, the embodiments of the present disclosure and features of the embodiments may be combined with each other. The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.

Fig. 1 illustrates an exemplary system architecture 100 to which embodiments of the position accuracy assessment method or position accuracy assessment apparatus of the present disclosure may be applied.

As shown in fig. 1, system architecture 100 may include a terminal device 101, a network 102, and a server 103. Network 102 is the medium used to provide communication links between terminal device 101 and server 103. Network 102 may include various connection types such as wired, wireless communication links, or fiber optic cables.

A user may interact with the server 103 via the network 102 using the terminal device 101 to receive or send messages or the like. For example, the user may determine the dotting position through the terminal device 101 and send the coordinates of the dotting position to the server 103 through the network 102. The terminal device 101 may have various communication client applications installed thereon, such as a map application, a navigation application, and the like.

The terminal device 101 may be hardware or software. When the terminal device 101 is hardware, it may be various electronic devices including, but not limited to, a smart phone, a tablet computer, an electronic book reader, a car computer, a laptop computer, a desktop computer, and the like.

When the terminal device 101 is software, it may be installed in the above-listed electronic device, which may be implemented as a plurality of software or software modules (for example, to provide distributed services), or may be implemented as a single software or software module. The present invention is not particularly limited herein.

The server 103 may be a server providing various services, such as a background server processing the dotting position transmitted by the terminal device 101. The background server obtains the interest point based on the received dotting position coordinates, and feeds back the return data related to the interest point to the terminal device 101.

The server 103 may be hardware or software. When the server 103 is hardware, it may be implemented as a distributed server cluster composed of a plurality of servers, or may be implemented as a single server. When the server 103 is software, it may be implemented as a plurality of software or software modules (for example, to provide distributed services), or may be implemented as a single software or software module. The present invention is not particularly limited herein.

It should be noted that, the method for evaluating position accuracy provided by the embodiment of the present disclosure may be performed by the terminal device 101 or may be performed by the server 103. Accordingly, the position accuracy evaluation means may be provided in the terminal device 101 or in the server 103.

It should be understood that the number of terminal devices, networks and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.

The embodiment of the disclosure provides a position accuracy assessment method, which judges the accuracy of poi data by returning data.

Fig. 2 is a flowchart of a position accuracy evaluation method according to an embodiment of the present disclosure. As shown in fig. 2, the position accuracy evaluation method provided by the embodiment of the present disclosure includes:

step S201, data to be verified and reference data are acquired.

Wherein the data to be verified is poi data obtained based on the dotting position. The dotting position refers to the position of a setpoint selected for testing the accuracy of poi data. In this embodiment, the dotting position may be selected according to a scene, for example, the dotting position of the data sample to be verified is selected according to a user log of an actual user of the map.

In this embodiment, the reference data and the data to be verified are acquired in the same manner, and the reference data and the data to be verified are poi data acquired at the same dotting position. Since the reference data and the data to be verified come from different map databases, the reference data and the data to be verified may be the same or different, so that the accuracy of the data to be verified can be verified by using the reference data.

For example, the data to be verified is from a database provided by the first service provider, and the reference data is from a database provided by the second service provider and the third service provider. The accuracy of the data to be verified provided by the first service provider can be verified through the reference data provided by the second service provider and the third service provider.

In the process of verifying the position accuracy, the comparison result of the data to be verified and the reference data has the following conditions that the data to be verified is the same as the reference data provided by the second service provider and the third service provider; or the data to be verified is different from the reference data provided by the second service provider and the third service provider, but the reference data provided by the second service provider and the third service provider are the same; or the data to be verified is the same as one of the reference data provided by the second service provider and the third service provider; or, the data to be verified is different from the reference data provided by the second service provider and the third service provider, and the accuracy of the data to be verified is evaluated in different ways according to different conditions.

It should be noted that, the reference data may be obtained through an application program interface (Application Programming Interface, API) provided by the second service provider and the third service provider, or may be obtained through other legal means, and the manner of obtaining the reference data is not limited in this embodiment.

Step S202, determining the data type of the data to be verified based on the identification degree of the interest point corresponding to the data to be verified, and determining the data type of the reference data based on the identification degree of the interest point corresponding to the reference data.

The identification degree refers to the resolvable degree of the interest point. For example, a office building and a hotel are usually independent buildings, and a residential area is a piece of area containing a plurality of buildings, so that the recognition degree of the office building and the hotel is higher than that of the residential area. For another example, hospitals are generally large in occupied area and concentrated in building, while residential areas are large in occupied area, the building is scattered, so that the identification degree of the hospitals is higher than that of the residential areas.

In this embodiment, after a user initiates a dotting request, the server obtains the point of interest based on the coordinate information of the dotting position, and sends the related information of the point of interest as return information to the user terminal. For example, the return information includes information such as the name of the point of interest, the distance between the point of interest and the point location, and the like.

In some embodiments, the points of interest in the obtained return data may or may not be the same type of point of interest using databases provided by different service providers at the same point of interest location. For example, dotting is performed at a dotting position of east longitude 116.2 and north latitude 40.5, the first service provider returns data of office building S and distance of 75 meters, the second service provider returns data of office building M and distance of 100 meters, and the third service provider returns data of office building M and distance of 54 meters. At this time, the data types returned by the first service provider, the second service provider and the third service provider are all office buildings.

For another example, dotting is performed at the dotting position of the east longitude 116.1 and the north latitude 41.3, the data returned by the first service provider is hotel N and the distance is 55 meters, the data returned by the second service provider is stretched noodle restaurant Y and the distance is 21 meters, and the data returned by the third service provider is stretched noodle restaurant Y and the distance is 9 meters. At this time, the data types returned by the first, second and third service providers are different.

In some embodiments, the data type is determined according to the degree of recognition of the point of interest, the higher the rank of the data type. For example, the data type includes three levels, i.e., a first data type, a second data type, and a third data type, and the first data type, the second data type, and the third data type are sequentially lowered.

In some embodiments, the first data type includes office buildings, hotels, hospitals, and industrial parks; the second data type includes residential areas, dormitory areas, and educational institutions for kindergartens, primary schools, middle schools, universities, and the like; the third data type includes roadside small stores such as restaurant, clothing store, barbershop, pharmacy, etc.

The data type of the reference data is the same as the data type of the data to be verified, and is not described herein.

In some embodiments, the data type of the data to be verified is determined based on the identification of the point of interest corresponding to the data to be verified, and the data type of the reference data is determined based on the identification of the point of interest corresponding to the reference data.

Step S203, obtaining a distance to be verified between the interest point corresponding to the data to be verified and the dotting position based on the data to be verified, and obtaining a reference distance between the interest point corresponding to the reference data and the dotting position based on the reference data.

In some embodiments, the data to be verified includes a tag of a point of interest corresponding to the data to be verified and a distance to be verified, where the distance to be verified is a distance between the dotting location and the point of interest corresponding to the data to be verified. And obtaining the identification degree and the distance to be verified of the interest point corresponding to the data to be verified according to the label of the interest point corresponding to the data to be verified.

In some embodiments, the reference data includes a tag of a point of interest corresponding to the reference data and a reference distance, wherein the reference distance is a distance between the dotting location and the point of interest corresponding to the reference data. And obtaining the identification degree of the interest point corresponding to the reference data according to the label of the interest point corresponding to the reference data.

Step S204, the accuracy of the data to be verified is evaluated according to the data type of the data to be verified, the data type of the reference data, the distance to be verified, the reference distance and a preset distance threshold.

In this embodiment, the accuracy of the data to be verified is evaluated according to the comparison condition of the data to be verified and the reference data, and the accuracy of the data to be verified is specifically evaluated according to the data type of the data to be verified, the data type of the reference data, the distance to be verified, the reference distance and a preset distance threshold according to the difference between the comparison results of the data to be verified and the reference data.

It should be noted that, the present embodiment is based on at least one of the following preconditions, the distance between the dotting position and the actual positioning position is not more than 100m, the macroscopic range is 150m, the building spacing of the residential area is not more than 40m, and the distance between small shops is not more than 30m. The building length is between 70m and 100m, a few of the buildings are above 100m, and the larger buildings are divided into sub-buildings, and the space between the buildings is about 100 m. The dotting position may have a deviation, but the deviation may not be as great as the length of the building. In addition, if a plurality of return data are obtained by dotting at the dotting position, that is, each database can obtain a plurality of return data, it is necessary to assume that the ordering of each interest point in the return data of each database is correct. Namely, taking the first-ordered return data as data to be verified from a plurality of return data obtained based on a database provided by a first service provider; and based on a plurality of returned data obtained by the databases provided by the second service provider and the third service provider, taking the returned data with the first order as reference data, and evaluating the data to be verified and the reference data with the first order.

According to the position accuracy evaluation method provided by the embodiment of the disclosure, the data type of the data to be verified, the distance to be verified between the interest point corresponding to the data to be verified and the dotting position are obtained according to the data to be verified, the type of the reference data and the reference distance between the interest point corresponding to the reference data and the dotting position are obtained according to the reference data, and then the accuracy of the data to be verified is evaluated according to the data type of the data to be verified, the data type of the reference data, the distance to be verified, the reference distance and the preset distance threshold value, so that the accuracy of the evaluation of the data to be verified can be improved, and a reliable data base is provided for subsequent poi data updating.

In some embodiments, the data to be verified includes a tag of a point of interest corresponding to the data to be verified, and the reference data includes a tag of a point of interest corresponding to the reference data.

The interest point labels are labels determined according to the identification degree of the interest points. The point of interest tag is typically a tag that the facilitator determines from the actual verification. For the same interest point, the interest point labels provided by different service providers are basically consistent. For example, different service providers give a single label to office buildings and hotels, and a label to residential areas is a patch. Therefore, the identification degree of the interest point can be determined according to the interest point label, and then the type of the data to be verified can be determined according to the identification degree of the interest point corresponding to the data to be verified.

In some embodiments, step S202, before determining the data type of the data to be verified based on the identification degree of the point of interest corresponding to the data to be verified, further includes:

determining the identification degree of the interest point corresponding to the data to be verified based on the label of the interest point corresponding to the data to be verified; and determining the identification degree of the interest point corresponding to the reference data based on the label of the interest point corresponding to the reference data.

Because the interest point tag is the data in the poi data provided by the service provider, the embodiment determines the identification degree of the interest point corresponding to the data to be verified according to the interest point tag, and can accurately obtain the identification degree of the interest point.

In some embodiments, the reference data includes at least two sets of data, and the reference data is obtained from different ones of the quality databases. The competition database is a database maintained by different service providers, the data sources are different, and the data maintenance modes are different, so that the accuracy of data evaluation can be improved.

It should be noted that, although the present embodiment exemplarily verifies the accuracy of the data to be verified provided by the first service provider through the reference data provided by the second service provider and the third service provider, this does not mean that the accuracy of the data to be verified provided by the first service provider can only be verified through the reference data provided by the two service providers. Indeed, the data to be verified in embodiments of the present disclosure may be verified by reference data provided by more service providers.

According to the embodiment, the data to be verified is verified through at least two groups of reference data from different competition databases, and as the competition databases are maintained by different servers, the data sources are different, the data maintenance modes are different, and the accuracy of the data to be verified can be improved by utilizing different competition databases.

In some embodiments, the reference data is from two competition databases, i.e., the reference data includes first reference data and second reference data, a first reference distance between the dotting position and the point of interest corresponding to the first reference data is obtained based on the first reference data, and a second reference distance between the dotting position and the point of interest corresponding to the second reference data is obtained based on the second reference data.

In some embodiments, the data to be verified and the reference data are determined according to the recognition degree of the interest point, and the data types are divided into a first data type, a second data type and a third data type.

In some embodiments, step S204 evaluates the accuracy of the data to be verified according to the data type of the data to be verified, the data type of the reference data, the distance to be verified, the reference distance, and the preset distance threshold, including the following three cases:

In the first case, the distance to be verified, the first reference distance and the second reference distance are the same.

Under the condition that the distance to be verified, the first reference distance and the second reference distance are the same, the accuracy of the data to be verified can be estimated to meet the preset requirement.

In the second case, the first reference distance and the second reference distance are the same, but the first reference distance and the second reference distance are different from the distance to be verified.

And under the condition that the first reference distance and the second reference distance are the same and are different from the to-be-verified distance, evaluating the accuracy of the to-be-verified data based on the data type of the to-be-verified data, the data type of the reference data, the first reference distance, the second reference distance and the distance threshold.

In the third case, the first reference distance, the second reference distance and the distance to be verified are all different.

Under the condition that the first reference distance, the second reference distance and the to-be-verified distance are all different, the accuracy of the to-be-verified data is evaluated based on the to-be-verified distance, the first reference distance, the second reference distance and a preset compensation value.

According to the embodiment, the two groups of reference data are utilized, and the accuracy of the data to be verified is evaluated in different modes according to the comparison result of the data to be verified and the reference data, so that a more accurate evaluation result can be obtained, the evaluation complexity is not increased, the operation amount is reduced, and the evaluation efficiency is improved.

Fig. 3 is a schematic diagram of a position accuracy evaluation method according to an embodiment of the present disclosure. In FIG. 3, S _A S is the distance to be verified obtained according to the data to be verified _B To obtain a first reference distance from reference data provided by a first service provider, S _C A second reference distance obtained from reference data provided by a second service provider.

As shown with reference to fig. 2 and 3, at a first reference distance S _B And a second reference distance S _C Identical, but first reference distance S _B And a second reference distance S _C Distance to be verifiedS _A Different, i.e. S _A ！＝(S _B ＝S _C ) In the case, the accuracy of the data to be verified is evaluated based on the data type of the data to be verified, the data type of the reference data, the first reference distance, the second reference distance and a preset distance threshold.

In some embodiments, a first reference distance S is calculated _B And a second reference distance S _C To obtain a reference mean value X, i.e. x= (S) _B +S _C ) 2; calculating a reference mean value X and a distance S to be verified _A To obtain a distance deviation Y, i.e. y=s _A -X; and evaluating the accuracy of the data to be verified based on the data type of the data to be verified, the data type of the reference data, the reference mean value, the distance deviation and the distance threshold value.

According to the method, the distance deviation is obtained through the distance to be verified, the first reference distance and the second reference distance, the accuracy of the data to be verified is estimated according to the distance deviation and the data type, and the accuracy of the data to be verified is improved due to the fact that the distance deviation is easy to obtain and small in operation amount; the accuracy of the data to be verified is evaluated by combining the distance deviation with the data type, so that the problem of inaccurate evaluation caused by the fact that interest points with different identification degrees adopt the same evaluation standard is avoided, and the accuracy of evaluation is improved.

In fig. 3, for convenience of description, the first data type is simply referred to as a first level, the second data type is simply referred to as a second level, and the third data type is simply referred to as a third level.

In some embodiments, when evaluating the accuracy of the data to be verified based on the data type of the data to be verified, the data type of the reference data, the reference mean, the distance deviation, and the distance threshold, there are several cases:

in this embodiment, the distance threshold is determined based on the length of the building and the visual range of the naked eye. For example, for different data types, the first distance threshold is set to 80 meters, the second distance threshold is set to 40 meters, the third distance threshold is set to 15 meters, and the fourth distance threshold is set to 100 meters. The distance threshold value may be set to other values.

In the first case, in the case that the data type of the data to be verified and the data type of the reference data are both first-level and the distance deviation Y is smaller than the first distance threshold value, it may be estimated that the accuracy of the data to be verified meets the preset requirement.

The data type of the data to be verified and the data type of the reference data are of one level, and the distance deviation Y is larger than a first distance threshold value, and the reference mean value X is larger than the distance S to be verified _A Under the condition of (1), the accuracy of the data to be verified can be evaluated to meet the preset requirement.

The data type of the data to be verified and the data type of the reference data are of one level, and the distance deviation Y is larger than a first distance threshold value, and the reference mean value X is smaller than the distance S to be verified _A Under the condition of (1), the accuracy of the data to be verified can be estimated to not meet the preset requirement.

In the second case, when the data type of the data to be verified is first-order, the data type of the reference data is second-order, and the distance deviation Y is smaller than the second distance threshold value, it can be estimated that the accuracy of the data to be verified meets the preset requirement.

When the data type of the data to be verified is first level, the data type of the reference data is second level, the distance deviation Y is larger than the second distance threshold value, and the reference mean value X is larger than the distance S to be verified _A Under the condition of (1), the accuracy of the data to be verified can be evaluated to meet the preset requirement.

When the data type of the data to be verified is the first level, the data type of the reference data is the second level, the distance deviation Y is larger than the second distance threshold value, and the reference mean value X is smaller than the distance S to be verified _A Under the condition of (1), the accuracy of the data to be verified can be estimated to not meet the preset requirement.

In the third case, when the data type of the data to be verified is one level, the data type of the reference data is three levels, and the distance deviation Y is smaller than the third distance threshold value, it can be estimated that the accuracy of the data to be verified meets the preset requirement.

When the data type of the data to be verified is one level, the data type of the reference data is three levels, and the distance deviation Y is larger than a third distance threshold value, and the reference mean value X is larger than the distance S to be verified _A Under the condition of (1), the accuracy of the data to be verified can be evaluated to meet the preset requirement.

When the data type of the data to be verified is one level, the data type of the reference data is three levels, and the distance deviation Y is larger than a third distance threshold value, and the reference mean value X is smaller than the distance S to be verified _A Under the condition of (1), the accuracy of the data to be verified can be estimated to not meet the preset requirement.

Fourth, in the case that the data type of the data to be verified is two-level, the data type of the reference data is one-level, and the distance deviation Y is smaller than the second distance threshold, the accuracy of the data to be verified is evaluated to not meet the preset requirement.

When the data type of the data to be verified is two-level, the data type of the reference data is one-level, the distance deviation Y is larger than the second distance threshold value, and the reference mean value X is larger than the distance S to be verified _A Under the condition of (1), the accuracy of the data to be verified is evaluated to meet the preset requirement.

When the data type of the data to be verified is two-level, the data type of the reference data is one-level, the distance deviation Y is larger than the second distance threshold value, and the reference mean value X is smaller than the distance S to be verified _A Under the condition of (1), the accuracy of the data to be verified is evaluated to not meet the preset requirement.

And in a fifth case, under the condition that the data type of the data to be verified is two-level, the data type of the reference data is two-level, and the distance deviation Y is smaller than a second distance threshold value, the accuracy of the data to be verified is evaluated to meet the preset requirement.

When the data type of the data to be verified is two-level, the data type of the reference data is two-level, the distance deviation Y is larger than the second distance threshold value, and the reference mean value X is larger than the distance S to be verified _A Under the condition of (1), the accuracy of the data to be verified is evaluated to meet the preset requirement.

When the data type of the data to be verified is two-level, the data type of the reference data is two-level, the distance deviation Y is larger than the second distance threshold value, and the reference mean value X is smaller than the distance S to be verified _A Under the condition of (1), the accuracy of the data to be verified is evaluated to not meet the preset requirement.

In a sixth case, when the data type of the data to be verified is two-level, the data type of the reference data is three-level, and the distance deviation Y is smaller than the fourth distance threshold value, the accuracy of the data to be verified is evaluated to meet the preset requirement.

When the data type of the data to be verified is two-level, the data type of the reference data is three-level, the distance deviation Y is larger than the fourth distance threshold value, and the reference mean value X is larger than the distance S to be verified _A Under the condition of (1), the accuracy of the data to be verified is evaluated to meet the preset requirement.

When the data type of the data to be verified is two-level, the data type of the reference data is three-level, the distance deviation Y is larger than the fourth distance threshold value, and the reference mean value X is smaller than the distance S to be verified _A Under the condition of (1), the accuracy of the data to be verified is evaluated to not meet the preset requirement.

In the seventh case, when the data type of the data to be verified is three-level, the data type of the reference data is one-level or two-level, the distance deviation Y is smaller than the fourth distance threshold, and the accuracy of the data to be verified is estimated to not meet the preset requirement.

When the data type of the data to be verified is three-level, the data type of the reference data is one-level or two-level, the distance deviation Y is larger than the fourth distance threshold value, and the reference mean value X is larger than the distance S to be verified _A Under the condition of (1), the accuracy of the data to be verified is evaluated to meet the preset requirement.

When the data type of the data to be verified is three-level, the data type of the reference data is one-level or two-level, the distance deviation Y is larger than the fourth distance threshold value, and the distance S to be verified _A And under the condition that the data is larger than the reference mean value X, evaluating the accuracy of the data to be verified does not meet the preset requirement.

Eighth, in the case that the data type of the data to be verified and the data type of the reference data are three-level and the distance deviation Y is smaller than the third distance threshold, the accuracy of the data to be verified is evaluated to meet the preset requirement.

The data type of the data to be verified and the data type of the reference data are three-level, and the distance deviation Y is larger than a third distance threshold value and the distance S to be verified _A Under the condition of being smaller than the reference mean value X, the accuracy of the data to be verified is evaluatedThe degree meets the preset requirement.

The data type of the data to be verified and the data type of the reference data are three-level, and the distance deviation Y is larger than a third distance threshold value and the distance S to be verified _A And under the condition that the data is larger than the reference mean value X, evaluating the accuracy of the data to be verified does not meet the preset requirement.

It should be noted that the preset requirements are determined according to actual needs, the accuracy requirements of the data to be verified are different, and the preset requirements are different. The present disclosure is not limited by the preset requirements.

In the embodiment, under the condition that the data type of the data to be verified is the same as the data type of the reference data, the accuracy of the data to be verified is evaluated through the reference mean value, the distance deviation and the distance threshold value, so that the data to be verified, which is the same as the data type of the reference data, can be accurately evaluated.

In some embodiments, the distance threshold is determined according to the recognition degree of the point of interest corresponding to the data to be verified. Under the condition that the data type of the data to be verified is the same as the data type of the reference data, the lower the identification degree of the interest point corresponding to the data to be verified is, the smaller the distance threshold value is. And under the condition that the data type of the data to be verified and the data type of the reference data are different, the distance threshold value is larger than the distance threshold value corresponding to the interest point with the highest identification degree.

In this embodiment, the distance threshold is determined according to the identification degree of the interest point corresponding to the data to be verified, and for the data to be verified with the same data type, the lower the identification degree of the interest point is, the smaller the distance threshold is, so that accurate evaluation of the data to be verified is facilitated.

In some embodiments, for to-be-tested data with different data types, the distance threshold is based on the distance threshold corresponding to the interest point with the highest identification degree and is larger than the distance threshold corresponding to the interest point with the highest identification degree, and comprehensive factors are considered to be beneficial to accurately evaluating the to-be-tested data.

In some embodiments, under the condition that the first reference distance, the second reference distance and the distance to be verified are all different, the compensation value is added on the basis of the distance to be verified, and the identification degree of the interest point corresponding to the data to be verified is different, and the compensation value is also different.

Under the condition that the first reference distance, the second reference distance and the to-be-verified distance are all different, evaluating the accuracy of the to-be-verified data based on the to-be-verified distance, the first reference distance, the second reference distance and a preset compensation value comprises the following steps:

calculating the sum of the distance to be verified and the compensation value to obtain a first numerical value; calculating the sum of the first reference distance and the compensation value to obtain a second numerical value; calculating the sum of the second reference distance and the compensation value to obtain a third numerical value; and under the condition that the first value is smaller than the second value and the third value, evaluating the accuracy of the data to be verified to meet the preset requirement.

According to the embodiment, under the condition that the distance to be verified, the first reference distance and the second reference distance are different, the accuracy of the data to be verified is evaluated under the condition that standard reference data are not available, and the flexibility of data verification is improved.

In some embodiments, the higher the identification degree of the interest point corresponding to the data to be verified, the lower the compensation value.

For example, the type of the data to be verified is determined to be one level according to the data to be verified obtained at the dotting position, the distance to be verified is 70 m, the type of the first reference data is determined to be two levels according to the reference data at the same dotting position, the type of the second reference data is three levels, the first reference distance is 60 m, and the second reference distance is 30 m.

Assume that a primary compensation value corresponding to a primary data type is 0 meters, a secondary compensation value corresponding to a secondary data type is 40 meters, and a tertiary compensation value corresponding to a tertiary data type is 100 meters. The first value is 70 m according to the distance to be verified and the first-level compensation value, the second value is 100 m according to the first reference distance and the second-level compensation value, and the third value is 130 m according to the second reference distance and the third-level compensation value.

According to the method and the device, the compensation value is set according to the identification degree of the interest point corresponding to the data to be verified and the reference data, so that the accuracy of the data to be verified can be evaluated more accurately.

The embodiment of the disclosure also provides a position accuracy evaluation device for evaluating the accuracy of poi data by returning data.

Fig. 4 is a schematic block diagram of a position accuracy evaluation apparatus according to an embodiment of the present disclosure. As shown in fig. 4, a position accuracy evaluation apparatus 400 provided in an embodiment of the present disclosure includes:

the first obtaining module 401 is configured to obtain data to be verified and reference data.

The data to be verified and the reference data are interest point data returned at the same dotting position, and the reference data and the data to be verified come from different map databases.

Wherein the data to be verified is poi data obtained based on the dotting position. The dotting position refers to the position of a setpoint selected for testing the accuracy of poi data. In this embodiment, the dotting position may be selected according to a scene, for example, the dotting position of the data to be verified is selected according to a user log of an actual user of the map.

In the process of verifying the position accuracy, the comparison result of the data to be verified and the reference data has the following conditions that the data to be verified is the same as the reference data provided by the second service provider and the third service provider; or the data to be verified is different from the reference data provided by the second service provider and the third service provider, but the reference data provided by the second service provider and the third service provider are the same; or the data to be verified is the same as one of the reference data provided by the second service provider and the third service provider, or the data to be verified is different from the reference data provided by the second service provider and the third service provider, and the accuracy of the data to be verified is evaluated in different ways according to different conditions.

The data type determining module 402 is configured to determine a data type of the data to be verified based on the identification degree of the point of interest corresponding to the data to be verified, and determine a data type of the reference data based on the identification degree of the point of interest corresponding to the reference data.

The second obtaining module 403 is configured to obtain, based on the data to be verified, a distance to be verified between the point of interest corresponding to the data to be verified and the dotting position, and obtain, based on the reference data, a reference distance between the point of interest corresponding to the reference data and the dotting position.

The evaluation module 404 is configured to evaluate the accuracy of the data to be verified according to the data type of the data to be verified, the data type of the reference data, the distance to be verified, the reference distance, and a preset distance threshold.

According to the position accuracy assessment device provided by the embodiment of the disclosure, the data type determining module obtains the data type of the data to be verified according to the data to be verified, the second obtaining module obtains the distance to be verified between the interest point corresponding to the data to be verified and the dotting position, the assessment module obtains the type of the reference data and the reference distance between the interest point corresponding to the reference data and the dotting position according to the reference data, and then assesses the accuracy of the data to be verified according to the data type of the data to be verified, the data type of the reference data, the distance to be verified, the reference distance and the preset distance threshold value, so that the accuracy of assessing the data to be verified can be improved, and a reliable data basis is provided for subsequent poi data updating.

In this embodiment, the data to be verified includes a tag of the point of interest corresponding to the data to be verified, and the reference data includes a tag of the point of interest corresponding to the reference data.

In some embodiments, the position accuracy evaluation device 400 further includes an identification determining module, configured to determine an identification degree of the point of interest corresponding to the data to be verified, according to the tag of the point of interest corresponding to the data to be verified; and determining the identification degree of the interest point corresponding to the reference data based on the label of the interest point corresponding to the reference data.

In some embodiments, the evaluation module evaluates the accuracy of the data to be verified according to the following several scenarios.

In the first case, under the condition that the distance to be verified, the first reference distance and the second reference distance are the same, the accuracy of the data to be verified can be evaluated to meet the preset requirement.

In the second case, the first reference distance and the second reference distance are the same, but the first reference distance and the second reference distance are the same and are different from the distance to be verified.

In some embodiments, the evaluation module comprises a calculation unit for calculating the reference mean and the distance deviation.

When the evaluation module evaluates the accuracy of the data to be verified based on the data type of the data to be verified, the data type of the reference data, the reference mean value, the distance deviation and the distance threshold value, eight situations exist, and detailed evaluation modes refer to fig. 3 and descriptions of method parts, which are not repeated herein.

According to embodiments of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

Fig. 5 illustrates a schematic block diagram of an example electronic device 500 that may be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the disclosure described and/or claimed herein.

As shown in fig. 5, the apparatus 500 includes a computing unit 501 that can perform various suitable actions and processes according to a computer program stored in a Read Only Memory (ROM) 502 or a computer program loaded from a storage unit 508 into a Random Access Memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the device 500 can also be stored. The computing unit 501, ROM 502, and RAM 503 are connected to each other by a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.

Various components in the device 500 are connected to the I/O interface 505, including: an input unit 506 such as a keyboard, a mouse, etc.; an output unit 507 such as various types of displays, speakers, and the like; a storage unit 508 such as a magnetic disk, an optical disk, or the like; and a communication unit 509 such as a network card, modem, wireless communication transceiver, etc. The communication unit 509 allows the device 500 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.

The computing unit 501 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of computing unit 501 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, etc. The calculation unit 501 performs the respective methods and processes described above, for example, a position accuracy evaluation method. For example, in some embodiments, the position accuracy assessment method may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as the storage unit 508. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 500 via the ROM 502 and/or the communication unit 509. When the computer program is loaded into the RAM 503 and executed by the computing unit 501, one or more steps of the position accuracy evaluation method described above may be performed. Alternatively, in other embodiments, the computing unit 501 may be configured to perform the position accuracy assessment method by any other suitable means (e.g. by means of firmware).

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.

Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the internet.

The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.

According to an embodiment of the present disclosure, the present disclosure also provides a computer program product comprising a computer program which, when executed by a processor, implements any one of the above-mentioned position accuracy assessment methods.

It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps recited in the present disclosure may be performed in parallel or sequentially or in a different order, provided that the desired results of the technical solutions of the present disclosure are achieved, and are not limited herein.

The above detailed description should not be taken as limiting the scope of the present disclosure. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present disclosure are intended to be included within the scope of the present disclosure.

Claims

1. A position accuracy evaluation method, characterized by comprising:

acquiring data to be verified and reference data; the data to be verified and the reference data are interest point data returned at the same dotting position, and the reference data and the data to be verified come from different map databases; the data to be verified and the reference data are location data;

2. The method of claim 1, wherein the data to be verified comprises tags of points of interest corresponding to the data to be verified, and the reference data comprises tags of points of interest corresponding to the reference data;

the determining the data type of the data to be verified based on the identification degree of the interest point corresponding to the data to be verified, and before determining the data type of the reference data based on the identification degree of the interest point corresponding to the reference data, further includes:

3. The method of claim 1, wherein the reference data comprises at least two sets of data, and wherein the reference data is obtained from different quality databases.

4. A method according to claim 3, wherein the reference data comprises first reference data and second reference data, the reference distance comprising a first reference distance corresponding to the first reference data and a second reference distance corresponding to the second reference data; the data type of the data to be verified and the data type of the reference data comprise three levels of data types;

the evaluating the accuracy of the data to be verified according to the data type of the data to be verified, the data type of the reference data, the distance to be verified, the reference distance and a preset distance threshold value comprises the following steps:

under the condition that the distance to be verified, the first reference distance and the second reference distance are the same, the accuracy of the data to be verified is evaluated to meet the preset requirement;

evaluating the accuracy of the data to be verified based on the data type of the data to be verified, the data type of the reference data, the first reference distance, the second reference distance and the distance threshold under the condition that the first reference distance and the second reference distance are the same and are different from the distance to be verified;

And under the condition that the first reference distance, the second reference distance and the to-be-verified distance are all different, evaluating the accuracy of the to-be-verified data based on the to-be-verified distance, the first reference distance, the second reference distance and a preset compensation value.

5. The method of claim 4, wherein the evaluating the accuracy of the data to be verified based on the data type of the data to be verified, the data type of the reference data, the first reference distance, the second reference distance, and the distance threshold value, if the first reference distance and the second reference distance are the same and different from the distance to be verified, comprises:

calculating the average value of the first reference distance and the second reference distance to obtain a reference average value;

calculating the difference value between the reference mean value and the distance to be verified to obtain a distance deviation;

and evaluating the accuracy of the data to be verified based on the data type of the data to be verified, the data type of the reference data, the reference mean value, the distance deviation and the distance threshold value.

6. The method of claim 5, wherein the evaluating the accuracy of the data to be verified based on the data type of the data to be verified, the data type of the reference data, the reference mean, the distance deviation, and the distance threshold comprises:

Under the condition that the data type of the data to be verified is the same as the data type of the reference data and the distance deviation is smaller than the distance threshold value, the accuracy of the data to be verified is evaluated to meet the preset requirement;

under the conditions that the data type of the data to be verified is the same as the data type of the reference data, the distance deviation is larger than the distance threshold value, and the distance to be verified is smaller than the reference mean value, the accuracy of the data to be verified is evaluated to meet the preset requirement;

and under the conditions that the data type of the data to be verified is the same as the data type of the reference data, the distance deviation is larger than the distance threshold value, and the distance to be verified is larger than the reference mean value, evaluating that the accuracy of the data to be verified does not meet the preset requirement.

7. The method of claim 5, wherein the distance threshold is determined according to a recognition degree of the point of interest corresponding to the data to be verified;

and under the condition that the data type of the data to be verified is the same as the data type of the reference data, the lower the identification degree of the interest point corresponding to the data to be verified is, the smaller the distance threshold value is.

8. The method of claim 4, wherein the points of interest corresponding to the data to be verified are identified differently, and the compensation values are different;

calculating the sum of the distance to be verified and the preset compensation value to obtain a first numerical value;

calculating the sum of the first reference distance and the preset compensation value to obtain a second numerical value;

calculating the sum of the second reference distance and the preset compensation value to obtain a third numerical value;

and under the condition that the first value is smaller than the second value and the third value, evaluating that the accuracy of the data to be verified meets the preset requirement.

9. The method of claim 8, wherein the higher the identification degree of the interest points corresponding to the data to be verified and the reference data, the lower the preset compensation value.

10. A position accuracy evaluation device, characterized by comprising:

The first acquisition module is used for acquiring data to be verified and reference data; the data to be verified and the reference data are interest point data returned at the same dotting position, and the reference data and the data to be verified come from different map databases; the data to be verified and the reference data are location data;

11. An electronic device, comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein,

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-9.

12. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method of any one of claims 1-9.