CN111737374A - Position coordinate determination method and device, electronic equipment and storage medium - Google Patents
Position coordinate determination method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The embodiment of the application provides a position coordinate determination method and device, electronic equipment and a storage medium, and relates to the technical field of computers. The method comprises the following steps: the method comprises the steps of obtaining POI data of at least two data sources of a target entity, wherein the POI data have corresponding position coordinates; clustering position coordinates of POI data of at least two data sources to obtain at least two clustering clusters; each cluster respectively comprises at least one POI data; and respectively scoring at least two clustering clusters, and determining the position coordinate of the target entity according to the position coordinate of POI data in each clustering cluster corresponding to the scoring result. According to the method and the device, the position coordinates of the target entity are determined by clustering and grading the position coordinates of the POI data of different data sources, so that the accuracy of the position coordinates of the POI data is improved.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a method and an apparatus for determining position coordinates, an electronic device, and a storage medium.
Background
With the development of computer technology and mobile communication technology, electronic maps are increasingly applied to terminal devices, and users navigate through the electronic maps to reach desired target positions. Point of Interest (POI) data is data for location services in an electronic map. In a geographic information system, a POI is a location point in the real world, with attributes such as name, address, coordinates, etc. The coordinates of a POI are a description of the real location, expressed in latitude and longitude. The coordinates of the POI need to be consistent with the real physical position of the POI, and if the coordinates are inconsistent, the coordinates are regarded as wrong coordinates, and the wrong coordinates may cause misleading to the user.
In the prior art, the coordinates of POI data are determined based on all attributes of the POI data. However, the correctness of the coordinates and the correctness of attributes such as the name and the address are relatively independent, the source level of a piece of POI data is low, the address is not standard, and the name is incorrect, but the coordinates may be correct, so that the correctness of the coordinates determined by the coordinate determination method in the prior art is not high, and the accuracy of the electronic map position navigation is influenced.
Disclosure of Invention
The application provides a position coordinate determination method, a position coordinate determination device, electronic equipment and a computer readable storage medium, which can solve the problem that the accuracy of electronic map position navigation is influenced because the correctness of coordinates determined by a coordinate determination method in the prior art is not high.
The embodiment of the application provides the following specific technical scheme:
in a first aspect, an embodiment of the present application provides a method for determining position coordinates, where the method includes:
the method comprises the steps of obtaining POI data of at least two data sources of a target entity, wherein the POI data have corresponding position coordinates;
clustering position coordinates of POI data of at least two data sources to obtain at least two clustering clusters; each cluster respectively comprises at least one POI data;
and respectively scoring at least two clustering clusters, and determining the position coordinate of the target entity according to the position coordinate of POI data in each clustering cluster corresponding to the scoring result.
In a possible implementation manner, scoring at least two clustering clusters respectively, and determining a position coordinate of a target entity according to a position coordinate of POI data in each clustering cluster corresponding to a scoring result specifically includes:
scoring each cluster according to the quantity of the data sources in the cluster corresponding to the POI data in each cluster and the cluster reliability related information of the data sources in the cluster;
and determining the position coordinate of the target entity according to the position coordinate of each POI data in the clustering cluster with the highest score value.
In a possible implementation manner, scoring at least two clustering clusters respectively, and determining a position coordinate of a target entity according to a position coordinate of POI data in each clustering cluster corresponding to a scoring result specifically includes:
according to the number of POI data meeting the preset communication matching condition in each cluster and the cluster reliability related information of the data source of the POI data meeting the preset communication matching condition, scoring each cluster respectively;
and determining the position coordinate of the target entity according to the position coordinate of each POI data in the clustering cluster with the highest score value.
In a possible implementation manner, scoring at least two clustering clusters respectively, and determining a position coordinate of a target entity according to a position coordinate of POI data in each clustering cluster corresponding to a scoring result specifically includes:
scoring each cluster according to the number of different data sources in each cluster of POI data and the cluster reliability related information of the different data sources in each cluster;
and determining the position coordinate of the target entity according to the position coordinate of each POI data in the clustering cluster with the highest score value.
In a possible implementation manner, scoring at least two clustering clusters respectively, and determining a position coordinate of a target entity according to a position coordinate of POI data in each clustering cluster corresponding to a scoring result specifically includes:
according to the quantity of the data sources in each cluster corresponding to the POI data in each cluster and the cluster reliability related information of the data sources in each cluster, scoring each cluster respectively to obtain a first scoring result;
according to the number of POI data meeting the preset communication matching condition in each cluster and the cluster reliability related information of the data source of the POI data meeting the preset communication matching condition, scoring each cluster respectively to obtain a second scoring result;
according to the number of different data sources in each cluster of POI data in each cluster and the cluster reliability related information of the different data sources in each cluster, respectively scoring each cluster to obtain a third scoring result;
acquiring weighted average values of at least two items in the first scoring result, the second scoring result and the third scoring result, and taking the weighted average values as score values corresponding to each cluster;
and determining the position coordinate of the target entity according to the position coordinate of each POI data in the clustering cluster with the highest score value.
In a possible implementation manner, determining the position coordinate of the target entity according to the position coordinate of each POI data in the cluster with the highest score value specifically includes:
and respectively obtaining the credibility of each POI data in the clustering cluster with the highest score value, and taking the position coordinate of the POI data with the highest credibility as the position coordinate of the target entity.
In a possible implementation manner, clustering position coordinates of POI data of at least two data sources to obtain at least two cluster clusters, specifically including:
and clustering the position coordinates of the POI data of at least two data sources by using a DBSCAN algorithm according to a preset clustering distance and a preset minimum clustering point number to obtain a plurality of clustering clusters.
In a second aspect, there is provided a position coordinate determination apparatus, the apparatus comprising:
the system comprises an acquisition module, a processing module and a display module, wherein the acquisition module is used for acquiring POI data of at least two data sources of a target entity, and the POI data has corresponding position coordinates;
the clustering module is used for clustering the position coordinates of the POI data of the at least two data sources to obtain at least two clustering clusters; each cluster respectively comprises at least one POI data;
and the determining module is used for respectively scoring the at least two clustering clusters and determining the position coordinate of the target entity according to the position coordinate of the POI data in each clustering cluster corresponding to the scoring result.
In a possible implementation manner, the determining module is specifically configured to:
scoring each cluster according to the quantity of the data sources in the cluster corresponding to the POI data in each cluster and the cluster reliability related information of the data sources in the cluster;
and determining the position coordinate of the target entity according to the position coordinate of each POI data in the clustering cluster with the highest score value.
In a possible implementation manner, the determining module is specifically configured to:
according to the number of POI data meeting the preset communication matching condition in each cluster and the cluster reliability related information of the data source of the POI data meeting the preset communication matching condition, scoring each cluster respectively;
and determining the position coordinate of the target entity according to the position coordinate of each POI data in the clustering cluster with the highest score value.
In a possible implementation manner, the determining module is specifically configured to:
scoring each cluster according to the number of different data sources in each cluster of POI data and the cluster reliability related information of the different data sources in each cluster;
and determining the position coordinate of the target entity according to the position coordinate of each POI data in the clustering cluster with the highest score value.
In a possible implementation manner, the determining module is specifically configured to:
according to the quantity of the data sources in each cluster corresponding to the POI data in each cluster and the cluster reliability related information of the data sources in each cluster, scoring each cluster respectively to obtain a first scoring result;
according to the number of POI data meeting the preset communication matching condition in each cluster and the cluster reliability related information of the data source of the POI data meeting the preset communication matching condition, scoring each cluster respectively to obtain a second scoring result;
according to the number of different data sources in each cluster of POI data in each cluster and the cluster reliability related information of the different data sources in each cluster, respectively scoring each cluster to obtain a third scoring result;
acquiring weighted average values of at least two items in the first scoring result, the second scoring result and the third scoring result, and taking the weighted average values as score values corresponding to each cluster;
and determining the position coordinate of the target entity according to the position coordinate of each POI data in the clustering cluster with the highest score value.
In a possible implementation manner, when the determining module performs the step of determining the position coordinate of the target entity according to the position coordinate of each POI data in the cluster with the highest score value, the determining module is specifically configured to:
and respectively obtaining the credibility of each POI data in the clustering cluster with the highest score value, and taking the position coordinate of the POI data with the highest credibility as the position coordinate of the target entity.
In a possible implementation manner, the clustering module is specifically configured to:
and clustering the position coordinates of the POI data of at least two data sources by using a DBSCAN algorithm according to a preset clustering distance and a preset minimum clustering point number to obtain a plurality of clustering clusters.
In a third aspect, an electronic device is provided, which includes:
one or more processors;
a memory;
one or more application programs, wherein the one or more application programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs configured to: the method of determining position coordinates according to the first aspect as such or as shown in any of the possible implementations of the first aspect is performed.
In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored, where the computer program, when executed by a processor, implements the position coordinate determination method according to the first aspect of the present application or any one of the possible implementation manners of the first aspect.
The beneficial effect that technical scheme that this application provided brought is:
the application provides a position coordinate determination method, a position coordinate determination device, electronic equipment and a storage medium, wherein POI data of at least two data sources of a target entity are obtained, and the POI data have corresponding position coordinates; clustering position coordinates of POI data of at least two data sources to obtain at least two clustering clusters; each cluster respectively comprises at least one POI data; and respectively scoring at least two clustering clusters, and determining the position coordinate of the target entity according to the position coordinate of POI data in each clustering cluster corresponding to the scoring result. According to the method and the device, the position coordinates of the target entity are determined by clustering and grading the position coordinates of the POI data of different data sources, so that the accuracy of the position coordinates of the POI data is improved, and the position navigation accuracy of the electronic map is further improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings used in the description of the embodiments of the present application will be briefly described below.
Fig. 1 is a schematic flowchart of a method for determining position coordinates according to an embodiment of the present disclosure;
fig. 2 is a schematic diagram of clustering performed by using a DBSCAN algorithm according to an embodiment of the present application;
fig. 3 is a schematic diagram illustrating a clustering result displayed in an electronic map according to an embodiment of the present application;
fig. 4 is a schematic flow chart of a method for determining position coordinates according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of a position coordinate determination apparatus according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of an electronic device for determining position coordinates according to an embodiment of the present disclosure.
Detailed Description
Reference will now be made in detail to embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are exemplary only for the purpose of explaining the present application and are not to be construed as limiting the present application.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or wirelessly coupled. As used herein, the term "and/or" includes all or any element and all combinations of one or more of the associated listed items.
The execution subject of the technical scheme of the application is computer equipment, including but not limited to a server, a personal computer, a notebook computer, a tablet computer, a smart phone and the like. The computer equipment comprises user equipment and network equipment. Wherein, the user equipment includes but is not limited to computers, smart phones, PDAs, etc.; network devices include, but are not limited to, a single network server, a server group of multiple network servers, or a Cloud of numerous computers or network servers in Cloud Computing (Cloud Computing), wherein Cloud Computing is one type of distributed Computing, a super virtual computer consisting of a collection of loosely coupled computers. The computer equipment can run independently to realize the application, and can also be accessed to the network to realize the application through the interactive operation with other computer equipment in the network. The network in which the computer device is located includes, but is not limited to, the internet, a wide area network, a metropolitan area network, a local area network, a VPN network, etc.
To make the objects, technical solutions and advantages of the present application more clear, embodiments of the present application will be described in further detail below with reference to the accompanying drawings.
The following describes the technical solutions of the present application and how to solve the above technical problems with specific embodiments. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments. Embodiments of the present application will be described below with reference to the accompanying drawings.
An embodiment of the present application provides a method for determining position coordinates, as shown in fig. 1, the method includes:
step S101, point of interest (POI) data of at least two data sources of a target entity are obtained, and the POI data have corresponding position coordinates;
step S102, clustering position coordinates of POI data of at least two data sources to obtain at least two clustering clusters; each cluster respectively comprises at least one POI data;
and step S103, scoring at least two clustering clusters respectively, and determining the position coordinates of the target entity according to the position coordinates of the POI data in each clustering cluster corresponding to the scoring result.
According to the position coordinate determining method provided by the embodiment of the application, POI data of at least two data sources of a target entity are obtained, and the POI data have corresponding position coordinates; clustering position coordinates of POI data of at least two data sources to obtain at least two clustering clusters; each cluster respectively comprises at least one POI data; and respectively scoring at least two clustering clusters, and determining the position coordinate of the target entity according to the position coordinate of POI data in each clustering cluster corresponding to the scoring result. According to the method and the device, the position coordinates of the target entity are determined by clustering and grading the position coordinates of the POI data of different data sources, so that the accuracy of the position coordinates of the POI data is improved, and the position navigation accuracy of the electronic map is further improved.
The following provides a detailed description of the above-described aspects of the embodiments of the present application.
Step S101, point of interest (POI) data of at least two data sources of a target entity are obtained, and the POI data have corresponding position coordinates;
the target entity may be a specific target object, such as a school, a business, a building, etc. The target entity corresponds to at least one POI data, the data source is the data source of the POI data corresponding to the target entity, the data source comprises the POI data acquired in a crowdsourcing mode, and the crowdsourcing mode specifically refers to the POI data provided by a user using an electronic map and a related application program; the data source also comprises POI data acquired by manually acquiring the POI data in the field; the data source can also be manually operated POI data, and various attributes of the POI data in the data source are verified and have higher accuracy. The data source may also include other sources that can occur to those skilled in the art, and the present embodiment is not limited thereto.
It should be noted that different levels of credibility may be set in advance for each data source, and the credibility characterizes the credibility of the data source. The higher the confidence, the higher the trustworthiness of the data source.
The attribute of the POI data includes a name, an address, position coordinates, a category, communication information, and the like, and the position coordinates are represented by latitude and longitude. Categories of POI data may include several major categories, such as, for example, dining services, shopping services, science and education services, scenic spots, public facilities, corporate enterprises, transportation facility services, financial insurance services, business housing, living services, sports and leisure services, healthcare services, government agencies and social groups, lodging services, etc., each of which encompasses several categories and several minor categories.
The method comprises the steps that POI data in the embodiment are POI data of different data sources corresponding to the same target entity, position coordinates in the POI data of different sources are possibly accurate and possibly wrong, the POI data with accurate position coordinates are selected from the POI data of different data sources in a scoring mode, the position coordinates of the POI data with accurate position coordinates are used as the position coordinates of the target entity, the POI data with accurate position coordinates can become valuable information when the POI data is applied to an electronic map of a navigation system, and therefore accurate position navigation is achieved.
Step S102, clustering position coordinates of POI data of at least two data sources to obtain at least two clustering clusters;
each cluster comprises at least one POI data; the POI data of different data sources are clustered into different clustering clusters according to the position coordinates by utilizing a clustering algorithm, the adopted clustering algorithm comprises but is not limited to a clustering algorithm based on division (such as k-means), a clustering algorithm based on hierarchy (such as BIRCH), a clustering algorithm based on density (such as DBSCAN), a clustering algorithm based on grids (such as STING) and the like, and a suitable clustering algorithm can be adopted according to specific needs.
In a possible implementation manner, clustering position coordinates of POI data of at least two data sources to obtain at least two cluster clusters, specifically including:
and clustering the position coordinates of the POI data of at least two data sources by using a DBSCAN algorithm according to a preset clustering distance and a preset minimum clustering point number to obtain a plurality of clustering clusters.
The DBSCAN (Density-Based Spatial Clustering of Applications with noise) algorithm is a Density-Based Clustering algorithm, and defines a cluster as the maximum set of points connected by Density, divides a region with sufficiently high Density into clusters, and can find clusters of any shape in a Spatial database of noise. The clustering distance and the minimum clustering point number can be set according to specific needs.
In one example, the DBSCAN algorithm is used to cluster the position coordinates of the POI data of different data sources of the same entity into a plurality of cluster clusters, the cluster distance is 30 meters, the minimum cluster point number is 1, and optionally, starting with one position coordinate that is not visited, all nearby position coordinates within the cluster distance range (including the cluster distance) from the position coordinate are found. If the number of nearby location coordinates is greater than or equal to the minimum cluster point number, the current location coordinate and its nearby location coordinates form a cluster, and the departure location coordinate is marked as visited. And recursively processing all position coordinates which are not marked as visited in the clustering cluster in the same method, thereby expanding the clustering cluster. If the number of nearby position coordinates is less than the minimum cluster point number, the position coordinates are temporarily marked as noise points. If the cluster is sufficiently expanded, i.e., all location coordinates within the cluster are marked as visited, then the same algorithm is used to process the location coordinates that are not visited. As shown in fig. 2, each solid point and each hollow point in fig. 2 respectively represent position coordinates of POI data of different data sources of the same entity, after clustering, 6 solid points corresponding to a become one cluster, and hollow points corresponding to B, C, N are respectively clustered into other cluster.
And step S103, scoring at least two clustering clusters respectively, and determining the position coordinates of the target entity according to the position coordinates of the POI data in each clustering cluster corresponding to the scoring result.
Specifically, all cluster clusters obtained by clustering are scored by using a scoring model, and the position coordinates corresponding to the target entity are determined according to the scoring result corresponding to each cluster.
The scoring model may be a bayesian statistics-based algorithm, and when a specific object in the database is scored according to the votes of the user, the scoring model is as shown in formula (1):
where v represents the number of votes, m represents the minimum number of votes required to rank a predetermined number before entry, R represents the average vote score for a particular object being scored, and C represents the average vote score for all particular objects in the database.
In a possible implementation manner, scoring at least two clustering clusters respectively, and determining a position coordinate of a target entity according to a position coordinate of POI data in each clustering cluster corresponding to a scoring result specifically includes:
scoring each cluster according to the quantity of the data sources in the cluster corresponding to the POI data in each cluster and the cluster reliability related information of the data sources in the cluster; and determining the position coordinate of the target entity according to the position coordinate of each POI data in the clustering cluster with the highest score value.
Specifically, the embodiment of the present application may be implemented by a scoring model shown in formula (2):
wherein v isppRepresenting the number of data sources in a cluster, m representing a parameter set to achieve smoothing, and a value of 1, RppThe cluster reliability related information representing the data sources in the cluster may be specifically an average value of the cluster reliability of the data sources of each POI data in the cluster, or other related information calculated according to the cluster reliability of each data source. The clustering reliability of the data sources in the clusters is preset according to specific needs.
It should be noted that, in this embodiment, the number of data sources in a cluster is not counted according to the difference. For example, any cluster contains 3 pieces of POI data: the data source of the POI data 1 is the data source 1, the data source of the POI data 2 is the data source 1, and the data source of the POI data 3 is the data source 2, and the number of the data sources in the cluster is 3.
In a possible implementation manner, scoring at least two clustering clusters respectively, and determining a position coordinate of a target entity according to a position coordinate of POI data in each clustering cluster corresponding to a scoring result specifically includes:
according to the number of POI data meeting the preset communication matching condition in each cluster and the cluster reliability related information of the data source of the POI data meeting the preset communication matching condition, scoring each cluster respectively; and determining the position coordinate of the target entity according to the position coordinate of each POI data in the clustering cluster with the highest score value.
Specifically, the embodiment of the present application may be implemented by a scoring model shown in formula (3):
wherein v isphoneDenotes the number of POI data satisfying a predetermined communication matching condition within a cluster, m denotes a parameter set for implementing a smoothing process, and may have a value of 1, RphoneThe related information indicating the cluster reliability of the data sources of the POI data satisfying the predetermined communication matching condition in the cluster, for example, the average value of the cluster reliability of the data sources, may be other related information calculated from the cluster reliability of each data source. The cluster credibility of the data sources of the POI data satisfying the predetermined communication matching condition in the cluster is preset according to specific needs. The predetermined communication matching condition is that the piece of POI data includes communication information and the communication information is matched with communication information in the communication information database, for example, one piece of POI data includes a contact phone and the contact phone is matched with a contact phone in the communication information database. The communication information database is a database established for the communication information of the verified POI data, in which the communication information is accurate.
In a possible implementation manner, scoring at least two clustering clusters respectively, and determining a position coordinate of a target entity according to a position coordinate of POI data in each clustering cluster corresponding to a scoring result specifically includes:
scoring each cluster according to the number of different data sources in each cluster of POI data and the cluster reliability related information of the different data sources in each cluster; and determining the position coordinate of the target entity according to the position coordinate of each POI data in the clustering cluster with the highest score value.
Specifically, the embodiment of the present application may be implemented by a scoring model shown in formula (4):
wherein v issourceRepresenting the number of different data sources within a cluster, m representing a parameter set to achieve smoothing, and the value may be 1, RsourceThe cluster reliability related information representing different data sources in the cluster may be specifically an average value of cluster reliability of different data sources of POI data in the cluster, or other related information calculated according to the cluster reliability of each data source. The clustering reliability of the data sources in the clusters is preset according to specific needs.
In a possible implementation manner, scoring at least two clustering clusters respectively, and determining a position coordinate of a target entity according to a position coordinate of POI data in each clustering cluster corresponding to a scoring result specifically includes:
according to the quantity of the data sources in each cluster corresponding to the POI data in each cluster and the cluster reliability related information of the data sources in each cluster, scoring each cluster respectively to obtain a first scoring result;
according to the number of POI data meeting the preset communication matching condition in each cluster and the cluster reliability related information of the data source of the POI data meeting the preset communication matching condition, scoring each cluster respectively to obtain a second scoring result;
according to the number of different data sources in each cluster of POI data in each cluster and the cluster reliability related information of the different data sources in each cluster, respectively scoring each cluster to obtain a third scoring result;
acquiring weighted average values of at least two items in the first scoring result, the second scoring result and the third scoring result, and taking the weighted average values as score values corresponding to each cluster;
and determining the position coordinate of the target entity according to the position coordinate of each POI data in the clustering cluster with the highest score value.
Specifically, each cluster is scored according to formulas (2), (3) and (4) respectively to obtain a scoring result WRpp、WRphone、WRsourceAnd performing weighted average calculation on at least two items in the three different scoring results to obtain formulas (5), (6), (7) and (8), wherein the specific contents are as follows:
calculating a score value corresponding to the clustering cluster according to any one of formulas (5), (6), (7) and (8), and determining the position coordinate of the target entity according to the position coordinate of POI data in the clustering cluster with the highest score value.
In a possible implementation manner, determining the position coordinates of the target entity according to each position coordinate in the cluster with the highest score value specifically includes:
and respectively obtaining the credibility of each POI data in the clustering cluster with the highest score value, and taking the position coordinate of the POI data with the highest credibility as the position coordinate of the target entity.
Specifically, the POI data in the cluster with the highest score value may include at least one, and in the case of more than one POI data, the position coordinate of the POI data with the highest confidence is selected as the position coordinate corresponding to the target entity, so as to obtain the position coordinate of the target entity. The credibility of each piece of POI data may be determined according to factors such as the credibility of the data source of the POI data, and the consistency of each attribute of the POI data, where the consistency of the attribute is specifically the corresponding consistency of each attribute of the POI data and the target entity, for example, the target entity: XX city first school, POI data: name: XX City first middle school address: no. 101 coordinates of coastal roads in the X area: longitude a, latitude b; comparing the actual position with the actual information of the first middle school in XX city, wherein the actual position of the first middle school in XX city is the coastal road number 105 in the X area, and the coordinates are as follows: longitude c latitude d, the POI data do not have consistency.
In a particular embodiment, the target entity: the method comprises the steps that POI data of a plurality of data sources corresponding to the Xianglong hotel are obtained by the Xianglong hotel, 3 cluster clusters are obtained after position coordinate clustering, the cluster clusters are respectively cluster C1, cluster C2 and cluster C3, as shown in figure 3, after a scoring model of a formula (8) is adopted to score the 3 cluster clusters, the cluster C2 with the highest score is cluster C2, the cluster C2 contains 4 pieces of POI data, a mark 1, a mark 3, a mark 4 and a mark 6 respectively correspond to one piece of POI data, wherein the position coordinate with the highest reliability is the position coordinate corresponding to the position of the mark 4, the position coordinate of the mark 4 is used as the correct position coordinate of the Xianglong hotel, and the correct position of the Xianglong hotel is displayed to a user.
An embodiment of the present application provides a method for determining position coordinates, as shown in fig. 4, the method includes:
step S401, point of interest (POI) data of at least two data sources of a target entity are obtained, and the POI data have corresponding position coordinates;
the target entity may be a specific target object, such as a school, a business, a building, etc. The target entity corresponds to at least one POI data, the data source is the data source of the POI data corresponding to the target entity, the data source comprises the POI data acquired in a crowdsourcing mode, and the crowdsourcing mode specifically refers to the POI data provided by a user using an electronic map and a related application program; the data source also comprises POI data acquired by manually acquiring the POI data in the field; the data source can also be manually operated POI data, and various attributes of the POI data in the data source are verified and have higher accuracy. The data source may also include other sources that can occur to those skilled in the art, and the present embodiment is not limited thereto.
It should be noted that different levels of credibility may be set in advance for each data source, and the credibility characterizes the credibility of the data source. The higher the confidence, the higher the trustworthiness of the data source.
The attribute of the POI data includes a name, an address, position coordinates, a category, communication information, and the like, and the position coordinates are represented by latitude and longitude. Categories of POI data may include several major categories, such as, for example, dining services, shopping services, science and education services, scenic spots, public facilities, corporate enterprises, transportation facility services, financial insurance services, business housing, living services, sports and leisure services, healthcare services, government agencies and social groups, lodging services, etc., each of which encompasses several categories and several minor categories.
The method and the device aim to select the POI data with accurate position coordinates from the POI data of different data sources in a scoring mode, and the POI data with accurate position coordinates can be valuable information only by applying the position coordinates of the POI data with accurate position coordinates as the position coordinates of the target entity to an electronic map of a navigation system, so that accurate position navigation is realized.
Step S402, clustering position coordinates of POI data of at least two data sources by using a DBSCAN algorithm according to a preset clustering distance and a preset minimum clustering point number to obtain a plurality of clustering clusters.
The DBSCAN (Density-Based Spatial Clustering of Applications with noise) algorithm is a Density-Based Clustering algorithm, and defines a cluster as the maximum set of points connected by Density, divides a region with sufficiently high Density into clusters, and can find clusters of any shape in a Spatial database of noise. The clustering distance and the minimum clustering point number can be set according to specific needs.
In one example, the clustering distance is 30 meters, the minimum clustering point number is 1, and the position coordinates of the POI data of different data sources of the same entity are clustered into a plurality of clustering clusters by using the DBSCAN algorithm. As shown in fig. 2, each solid point and each hollow point in fig. 2 respectively represent position coordinates of POI data of different data sources of the same entity, after clustering, 6 solid points corresponding to a become one cluster, and hollow points corresponding to B, C, N are respectively clustered into other cluster.
Step S403, according to the quantity of the data sources in each cluster corresponding to the POI data in each cluster and the cluster reliability related information of the data sources in each cluster, scoring each cluster respectively to obtain a first scoring result;
specifically, the embodiment of the present application performs scoring according to a scoring model shown in formula (2):
wherein v isppRepresenting the number of data sources in a cluster, m representing a parameter set to achieve smoothing, and a value of 1, RppThe cluster reliability related information representing the data sources in the cluster may be specifically an average value of the cluster reliability of the data sources of the POI data in the cluster, or other related information calculated according to the cluster reliability of each data source. The clustering reliability of the data sources in the clusters is preset according to specific needs.
It should be noted that, in this embodiment, the number of data sources in a cluster is not counted according to the difference. For example, any cluster contains 3 pieces of POI data: the data source of the POI data 1 is the data source 1, the data source of the POI data 2 is the data source 1, and the data source of the POI data 3 is the data source 2, and the number of the data sources in the cluster is 3.
Step S404, according to the number of POI data meeting the preset communication matching condition in each cluster and the cluster reliability related information of the data source of the POI data meeting the preset communication matching condition, scoring each cluster respectively to obtain a second scoring result;
specifically, the embodiment of the present application performs scoring according to a scoring model shown in formula (3):
wherein v isphoneDenotes the number of POI data satisfying a predetermined communication matching condition within a cluster, m denotes a parameter set for implementing a smoothing process, and may have a value of 1, RphoneThe relevant information indicating the cluster reliability of the data sources of POI data satisfying a predetermined communication matching condition within the cluster, for example, an average value of the cluster reliability of the data sources. The cluster credibility of the data sources of the POI data satisfying the predetermined communication matching condition in the cluster is preset according to specific needs. The predetermined communication matching condition is that the piece of POI data includes communication information and the communication information is matched with communication information in the communication information database, for example, one piece of POI data includes a contact phone and the contact phone is matched with a contact phone in the communication information database. The communication information database is a database established for the communication information of the verified POI data, in which the communication information is accurate.
Step S405, scoring each cluster according to the number of different data sources in each cluster of POI data and the cluster reliability related information of the different data sources in each cluster to obtain a third scoring result;
specifically, the embodiment of the present application performs scoring according to a scoring model shown in formula (4):
wherein v issourceRepresenting the number of different data sources within a cluster, m representing a parameter set to achieve smoothing, and the value may be 1, RsourceRepresenting cluster credibility of different data sources within a clusterThe related information may specifically be an average value of cluster credibility of different data sources of the POI data in the cluster. The clustering reliability of the data sources in the clusters is preset according to specific needs.
Step S406, obtaining a weighted average value in the first scoring result, the second scoring result and the third scoring result, and taking the weighted average value as a score value corresponding to each cluster;
specifically, the scoring results obtained according to the formulas (2), (3), and (4) are weighted and calculated, as shown in the formula (8):
and calculating the score value corresponding to each cluster according to a formula (8).
Step S407, determining the position coordinate of the target entity according to the position coordinate of each POI data in the clustering cluster with the highest score value.
Specifically, the credibility of each POI data in the clustering cluster with the highest score value is respectively obtained, and the position coordinate of the POI data with the highest credibility is used as the position coordinate of the target entity.
The POI data in the clustering cluster with the highest score value may include at least one POI data, and when the POI data is more than one POI data, the position coordinate of the POI data with the highest credibility is selected as the position coordinate corresponding to the target entity, so that the position coordinate of the target entity is obtained. The credibility of each piece of POI data may be determined according to factors such as the credibility of the data source of the POI data, and the consistency of each attribute of the POI data, where the consistency of the attribute is specifically the corresponding consistency of each attribute of the POI data and the target entity, for example, the target entity: XX city first school, POI data: name: XX City first middle school address: no. 101 coordinates of coastal roads in the X area: longitude a, latitude b; comparing the actual position with the actual information of the first middle school in XX city, wherein the actual position of the first middle school in XX city is the coastal road number 105 in the X area, and the coordinates are as follows: longitude c latitude d, the POI data do not have consistency.
According to the position coordinate determining method provided by the embodiment of the application, POI data of at least two data sources of a target entity are obtained, and the POI data have corresponding position coordinates; clustering position coordinates of POI data of at least two data sources to obtain at least two clustering clusters; each cluster respectively comprises at least one POI data; and respectively scoring at least two clustering clusters, and determining the position coordinate of the target entity according to the position coordinate of POI data in each clustering cluster corresponding to the scoring result. According to the method and the device, the position coordinates of the target entity are determined by clustering and grading the position coordinates of the POI data of different data sources, so that the accuracy of the position coordinates of the POI data is improved, and the position navigation accuracy of the electronic map is further improved.
Based on the same principle as the method shown in fig. 1, there is also provided in an embodiment of the present disclosure a position coordinate determination apparatus 50, as shown in fig. 5, the position coordinate determination apparatus 50 including:
an obtaining module 51, configured to obtain point of interest (POI) data of at least two data sources of a target entity, where the POI data have corresponding position coordinates;
the clustering module 52 is configured to cluster the position coordinates of the POI data of the at least two data sources to obtain at least two cluster clusters; each cluster respectively comprises at least one POI data;
the determining module 53 is configured to score at least two clustering clusters respectively, and determine a position coordinate of the target entity according to the position coordinate of the POI data in each clustering cluster corresponding to the scoring result.
In a possible implementation manner, the determining module 53 is specifically configured to:
scoring each cluster according to the quantity of the data sources in the cluster corresponding to the POI data in each cluster and the cluster reliability related information of the data sources in the cluster;
and determining the position coordinate of the target entity according to the position coordinate of each POI data in the clustering cluster with the highest score value.
In a possible implementation manner, the determining module 53 is specifically configured to:
according to the number of POI data meeting the preset communication matching condition in each cluster and the cluster reliability related information of the data source of the POI data meeting the preset communication matching condition, scoring each cluster respectively;
and determining the position coordinate of the target entity according to the position coordinate of each POI data in the clustering cluster with the highest score value.
In a possible implementation manner, the determining module 53 is specifically configured to:
scoring each cluster according to the number of different data sources in each cluster of POI data and the cluster reliability related information of the different data sources in each cluster;
and determining the position coordinate of the target entity according to the position coordinate of each POI data in the clustering cluster with the highest score value.
In a possible implementation manner, the determining module 53 is specifically configured to:
according to the quantity of the data sources in each cluster corresponding to the POI data in each cluster and the cluster reliability related information of the data sources in each cluster, scoring each cluster respectively to obtain a first scoring result;
according to the number of POI data meeting the preset communication matching condition in each cluster and the cluster reliability related information of the data source of the POI data meeting the preset communication matching condition, scoring each cluster respectively to obtain a second scoring result;
according to the number of different data sources in each cluster of POI data in each cluster and the cluster reliability related information of the different data sources in each cluster, respectively scoring each cluster to obtain a third scoring result;
acquiring weighted average values of at least two items in the first scoring result, the second scoring result and the third scoring result, and taking the weighted average values as score values corresponding to each cluster;
and determining the position coordinate of the target entity according to the position coordinate of each POI data in the clustering cluster with the highest score value.
In a possible implementation manner, when the determining module 53 performs the step of determining the position coordinate of the target entity according to the position coordinate of each POI data in the cluster with the highest score value, specifically:
and respectively obtaining the credibility of each POI data in the clustering cluster with the highest score value, and taking the position coordinate of the POI data with the highest credibility as the position coordinate of the target entity.
In one possible implementation, clustering module 52 is specifically configured to:
and clustering the position coordinates of the POI data of at least two data sources by using a DBSCAN algorithm according to a preset clustering distance and a preset minimum clustering point number to obtain a plurality of clustering clusters.
The position coordinate determination apparatus of the embodiment of the present disclosure may execute the position coordinate determination apparatus method provided by the embodiment of the present disclosure, and the implementation principles thereof are similar, the actions executed by each module in the position coordinate determination apparatus in the embodiment of the present disclosure correspond to the steps in the position coordinate determination method in each embodiment of the present disclosure, and for the detailed functional description of each module of the position coordinate determination apparatus, reference may be specifically made to the description in the corresponding position coordinate determination method shown in the foregoing, and details are not repeated here.
The position coordinate determining device provided by the embodiment of the application acquires POI data of at least two data sources of a target entity, wherein the POI data have corresponding position coordinates; clustering position coordinates of POI data of at least two data sources to obtain at least two clustering clusters; each cluster respectively comprises at least one POI data; and respectively scoring at least two clustering clusters, and determining the position coordinate of the target entity according to the position coordinate of POI data in each clustering cluster corresponding to the scoring result. According to the method and the device, the position coordinates of the target entity are determined by clustering and grading the position coordinates of the POI data of different data sources, so that the accuracy of the position coordinates of the POI data is improved, and the position navigation accuracy of the electronic map is further improved.
The above embodiments describe the coordinate determination apparatus from the perspective of a virtual module, and the following describes an electronic device from the perspective of a physical module, specifically as follows:
an embodiment of the present application provides an electronic device, as shown in fig. 6, an electronic device 6000 shown in fig. 6 includes: a processor 6001 and a memory 6003. Processor 6001 and memory 6003 are coupled, such as via bus 6002. Optionally, the electronic device 6000 may also include a transceiver 6004. It should be noted that the transceiver 6004 is not limited to one in practical applications, and the structure of the electronic device 6000 is not limited to the embodiment of the present application.
The processor 6001 could be a CPU, general purpose processor, DSP, ASIC, FPGA or other programmable logic device, transistor logic device, hardware component, or any combination thereof. Which may implement or perform the various illustrative logical blocks, modules, and circuits described in connection with the disclosure. The processor 6001 might also be a combination that performs a computing function, such as a combination comprising one or more microprocessors, a combination of a DSP and a microprocessor, or the like.
The bus 6002 may include a path that conveys information between the aforementioned components. The bus 6002 may be a PCI bus, an EISA bus, or the like. The bus 6002 can be divided into an address bus, a data bus, a control bus, and so forth. For ease of illustration, only one thick line is shown in FIG. 6, but this is not intended to represent only one bus or type of bus.
The memory 6003 is used to store application code that implements aspects of the subject application, and execution is controlled by the processor 6001. Processor 6001 is configured to execute application program code stored in memory 6003 to implement the teachings of any of the foregoing method embodiments.
An embodiment of the present application provides an electronic device, where the electronic device includes: a memory and a processor; at least one program stored in the memory for execution by the processor, in comparison to the prior art: the electronic equipment provided by the embodiment of the application acquires POI data of at least two data sources of a target entity, wherein the POI data have corresponding position coordinates; clustering position coordinates of POI data of at least two data sources to obtain at least two clustering clusters; each cluster respectively comprises at least one POI data; and respectively scoring at least two clustering clusters, and determining the position coordinate of the target entity according to the position coordinate of POI data in each clustering cluster corresponding to the scoring result. According to the method and the device, the position coordinates of the target entity are determined by clustering and grading the position coordinates of the POI data of different data sources, so that the accuracy of the position coordinates of the POI data is improved, and the position navigation accuracy of the electronic map is further improved.
The present application provides a computer-readable storage medium, on which a computer program is stored, which, when running on a computer, enables the computer to execute the corresponding content in the foregoing method embodiments. Compared with the prior art, the position coordinate determination method provided by the embodiment of the application obtains the POI data of the interest points of at least two data sources of the target entity, wherein the POI data have corresponding position coordinates; clustering position coordinates of POI data of at least two data sources to obtain at least two clustering clusters; each cluster respectively comprises at least one POI data; and respectively scoring at least two clustering clusters, and determining the position coordinate of the target entity according to the position coordinate of POI data in each clustering cluster corresponding to the scoring result. According to the method and the device, the position coordinates of the target entity are determined by clustering and grading the position coordinates of the POI data of different data sources, so that the accuracy of the position coordinates of the POI data is improved, and the position navigation accuracy of the electronic map is further improved.
It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and may be performed in other orders unless explicitly stated herein. Moreover, at least a portion of the steps in the flow chart of the figure may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed alternately or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
The foregoing is only a partial embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (15)
1. A method of position coordinate determination, the method comprising:
the method comprises the steps of obtaining POI data of at least two data sources of a target entity, wherein the POI data have corresponding position coordinates;
clustering the position coordinates of the POI data of the at least two data sources to obtain at least two clustering clusters; each clustering cluster comprises at least one POI data;
and respectively scoring the at least two clustering clusters, and determining the position coordinate of the target entity according to the position coordinate of POI data in each clustering cluster corresponding to the scoring result.
2. The method according to claim 1, wherein the at least two clustering clusters are respectively scored, and the position coordinate of the target entity is determined according to the position coordinate of the POI data in each clustering cluster corresponding to the scoring result, specifically comprising:
scoring each cluster according to the quantity of the data sources in the cluster corresponding to the POI data in each cluster and the cluster reliability related information of the data sources in the cluster;
and determining the position coordinate of the target entity according to the position coordinate of each POI data in the clustering cluster with the highest score value.
3. The method according to claim 1, wherein the at least two clustering clusters are respectively scored, and the position coordinate of the target entity is determined according to the position coordinate of the POI data in each clustering cluster corresponding to the scoring result, specifically comprising:
according to the number of POI data meeting the preset communication matching condition in each cluster and the cluster reliability related information of the data source of the POI data meeting the preset communication matching condition, scoring each cluster respectively;
and determining the position coordinate of the target entity according to the position coordinate of each POI data in the clustering cluster with the highest score value.
4. The method according to claim 1, wherein the at least two clustering clusters are respectively scored, and the position coordinate of the target entity is determined according to the position coordinate of the POI data in each clustering cluster corresponding to the scoring result, specifically comprising:
scoring each cluster according to the number of different data sources in each cluster of POI data and the cluster reliability related information of the different data sources in each cluster;
and determining the position coordinate of the target entity according to the position coordinate of each POI data in the clustering cluster with the highest score value.
5. The method according to claim 1, wherein the at least two clustering clusters are respectively scored, and the position coordinate of the target entity is determined according to the position coordinate of the POI data in each clustering cluster corresponding to the scoring result, specifically comprising:
according to the quantity of the data sources in each cluster corresponding to the POI data in each cluster and the cluster reliability related information of the data sources in each cluster, scoring each cluster respectively to obtain a first scoring result;
according to the number of POI data meeting the preset communication matching condition in each cluster and the cluster reliability related information of the data source of the POI data meeting the preset communication matching condition, scoring each cluster respectively to obtain a second scoring result;
according to the number of different data sources in each cluster of POI data in each cluster and the cluster reliability related information of the different data sources in each cluster, respectively scoring each cluster to obtain a third scoring result;
obtaining a weighted average value of at least two items in the first scoring result, the second scoring result and the third scoring result, and taking the weighted average value as a score value corresponding to each cluster;
and determining the position coordinate of the target entity according to the position coordinate of each POI data in the clustering cluster with the highest score value.
6. The method according to any one of claims 2 to 5, wherein determining the position coordinates of the target entity according to the position coordinates of each POI data in the cluster with the highest score value specifically comprises:
and respectively acquiring the credibility of each POI data in the clustering cluster with the highest score value, and taking the position coordinate of the POI data with the highest credibility as the position coordinate of the target entity.
7. The method according to any one of claims 1 to 5, wherein clustering the position coordinates of the POI data of the at least two data sources to obtain at least two cluster clusters specifically comprises:
and clustering the position coordinates of the POI data of the at least two data sources by using a DBSCAN algorithm according to a preset clustering distance and a preset minimum clustering point number to obtain a plurality of clustering clusters.
8. A position coordinate determination apparatus, characterized in that the apparatus comprises:
the system comprises an acquisition module, a processing module and a display module, wherein the acquisition module is used for acquiring POI data of at least two data sources of a target entity, and the POI data has corresponding position coordinates;
the clustering module is used for clustering the position coordinates of the POI data of the at least two data sources to obtain at least two clustering clusters; each clustering cluster comprises at least one POI data;
and the determining module is used for respectively scoring the at least two clustering clusters and determining the position coordinate of the target entity according to the position coordinate of the POI data in each clustering cluster corresponding to the scoring result.
9. The apparatus of claim 8, wherein the determining module is specifically configured to:
scoring each cluster according to the quantity of the data sources in the cluster corresponding to the POI data in each cluster and the cluster reliability related information of the data sources in the cluster;
and determining the position coordinate of the target entity according to the position coordinate of each POI data in the clustering cluster with the highest score value.
10. The apparatus of claim 8, wherein the determining module is specifically configured to:
according to the number of POI data meeting the preset communication matching condition in each cluster and the cluster reliability related information of the data source of the POI data meeting the preset communication matching condition, scoring each cluster respectively;
and determining the position coordinate of the target entity according to the position coordinate of each POI data in the clustering cluster with the highest score value.
11. The apparatus of claim 8, wherein the determining module is specifically configured to:
scoring each cluster according to the number of different data sources in each cluster of POI data and the cluster reliability related information of the different data sources in each cluster;
and determining the position coordinate of the target entity according to the position coordinate of each POI data in the clustering cluster with the highest score value.
12. The apparatus of claim 8, wherein the determining module is specifically configured to:
according to the quantity of the data sources in each cluster corresponding to the POI data in each cluster and the cluster reliability related information of the data sources in each cluster, scoring each cluster respectively to obtain a first scoring result;
according to the number of POI data meeting the preset communication matching condition in each cluster and the cluster reliability related information of the data source of the POI data meeting the preset communication matching condition, scoring each cluster respectively to obtain a second scoring result;
according to the number of different data sources in each cluster of POI data in each cluster and the cluster reliability related information of the different data sources in each cluster, respectively scoring each cluster to obtain a third scoring result;
obtaining a weighted average value of at least two items in the first scoring result, the second scoring result and the third scoring result, and taking the weighted average value as a score value corresponding to each cluster;
and determining the position coordinate of the target entity according to the position coordinate of each POI data in the clustering cluster with the highest score value.
13. The apparatus according to any one of claims 9 to 12, wherein the determining module is specifically configured to:
and respectively acquiring the credibility of each POI data in the clustering cluster with the highest score value, and taking the position coordinate of the POI data with the highest credibility as the position coordinate of the target entity.
14. An electronic device, comprising:
one or more processors;
a memory;
one or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the one or more processors, the one or more programs configured to: performing the method of position coordinate determination according to any of claims 1-7.
15. An acquisition machine readable storage medium on which an acquisition machine program is stored, characterized in that the program, when executed by a processor, implements the position coordinate determination method of any one of claims 1 to 7.
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