CN106874288B - Map information processing method and device - Google Patents
Map information processing method and device Download PDFInfo
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- CN106874288B CN106874288B CN201510920869.2A CN201510920869A CN106874288B CN 106874288 B CN106874288 B CN 106874288B CN 201510920869 A CN201510920869 A CN 201510920869A CN 106874288 B CN106874288 B CN 106874288B
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Abstract
The embodiment of the invention provides a method and a device for processing map information, wherein the processing method comprises the following steps: acquiring characteristic information of a plurality of map characteristic objects to be analyzed; acquiring word frequency information of each word in the feature information of a plurality of map feature objects to be analyzed; according to the word frequency information of each word, the name and the range of the business circle are determined.
Description
Technical Field
The present invention relates to the field of map technologies, and in particular, to a method and an apparatus for processing map information.
Background
Currently, the work of determining the business circles on the map is mainly done manually by workers. Specifically, the staff can draw the range of the business circles on the map according to the known names of the business circles, other description information and the knowledge of the individuals on the actual situation. However, the method for determining the quotient circle has the following defects:
(1) because no obvious range judgment reference object exists in the map, the error exists between the manually sketched range and the actual range only by depending on personal experience and descriptive language of network sources;
(2) and the uncalibrated business circles cannot be mined, and only can be sketched according to the provided names. For example: the staff only demarcate the range according to the provided trade circle list (such as five mouths and middle guancun), and can not automatically extract the real existing 'Wanquan' trade circle.
In summary, the method for determining business circles cannot accurately determine the names and the ranges of the business circles in the map.
Disclosure of Invention
An object of the embodiments of the present invention is to provide a method and an apparatus for processing map information, which can simply and accurately determine names and ranges of business circles on a map.
In order to achieve the above object, an embodiment of the present invention provides a method for processing map information, including:
acquiring characteristic information of a plurality of map characteristic objects to be analyzed;
acquiring word frequency information of each word in the feature information of a plurality of map feature objects to be analyzed;
and determining the name and the range of the business circle according to the word frequency information of each word.
The step of obtaining the word frequency information of each word in the feature information of the map feature objects to be analyzed comprises the following steps:
performing word segmentation processing on the feature information of a plurality of map feature objects to be analyzed, and extracting words in the feature information of the plurality of map feature objects to be analyzed;
and acquiring the extracted word frequency information of each word, wherein the word frequency information is the frequency of occurrence of each word in the feature information of the map feature objects to be analyzed.
The characteristic information of the map characteristic object to be analyzed at least comprises the following steps: the name, spatial location and attribute information of the map feature object to be analyzed,
determining the name and the range of the business circle according to the word frequency information of each word, wherein the step comprises the following steps:
judging whether the frequency of the words indicated by the word frequency information of the words in the feature information of the map feature objects to be analyzed is greater than a preset value or not;
when the times are greater than a preset value, performing cluster analysis on each spatial position of the word;
judging whether the word can be used as the name of a business circle or not according to the clustering analysis result;
when the word can be used as the name of the business circle, the word is used as the name of the business circle, and the clustering boundary in the clustering analysis result is used as the range of the business circle.
Wherein, according to the cluster analysis result, judge whether this word can be as the step of the name of business circle, include:
judging whether the first degree of affinity and sparseness of each spatial position in the clustering analysis result is greater than the preset degree of affinity and sparseness;
when the first degree of affinity and sparseness of each spatial position is larger than the preset degree of affinity and sparseness, determining that the word can be used as the name of a business circle;
and when the first degree of affinity and sparseness of each spatial position is smaller than the preset degree of affinity and sparseness, determining that the word cannot be used as the name of the business circle.
An embodiment of the present invention further provides a device for processing map information, including:
the first acquisition module is used for acquiring the characteristic information of a plurality of map characteristic objects to be analyzed;
the second acquisition module is used for acquiring the word frequency information of each word in the characteristic information of the plurality of map characteristic objects to be analyzed;
and the determining module is used for determining the name and the range of the business circle according to the word frequency information of each word.
Wherein, the second acquisition module includes:
the word segmentation unit is used for performing word segmentation processing on the feature information of the map feature objects to be analyzed and extracting words in the feature information of the map feature objects to be analyzed;
and the acquisition unit is used for acquiring the extracted word frequency information of each word, wherein the word frequency information is the frequency of occurrence of each word in the feature information of the map feature objects to be analyzed.
The characteristic information of the map characteristic object to be analyzed at least comprises the following steps: the name, spatial location and attribute information of the map feature object to be analyzed,
the determining module comprises:
the first judgment unit is used for judging whether the times of the words indicated by the word frequency information of the words appearing in the feature information of the map feature objects to be analyzed are larger than a preset value or not, and triggering the clustering unit when the times are larger than the preset value;
the clustering unit is used for carrying out clustering analysis on each spatial position of the word according to the triggering of the first judging unit;
the second judgment unit is used for judging whether the word can be used as the name of the business circle according to the clustering analysis result and triggering the determination unit when the word can be used as the name of the business circle;
and the determining unit is used for taking the word as the name of the business circle according to the triggering of the second judging unit and taking the clustering boundary in the clustering analysis result as the range of the business circle.
Wherein the second judgment unit includes:
the judging subunit is used for judging whether the first degree of affinity and sparseness of each space position in the clustering analysis result is greater than the preset degree of affinity and sparseness, triggering the first determining subunit when the first degree of affinity and sparseness of each space position is greater than the preset degree of affinity and sparseness, and triggering the second determining subunit when the first degree of affinity and sparseness of each space position is less than the preset degree of affinity and sparseness;
the first determining subunit is used for determining that the word can be used as the name of the business circle according to the triggering of the judging subunit;
and the second determining subunit is used for determining that the word cannot be used as the name of the business circle according to the triggering of the judging subunit.
The scheme of the invention at least comprises the following beneficial effects:
in the embodiment of the invention, the name and the range of the business circle are determined according to the word frequency information of each word in the feature information of the map feature objects to be analyzed, so that the problem that the name and the range of each business circle in the map cannot be accurately determined is solved, and the effect of simply and accurately determining the name and the range of the business circle on the map is achieved.
Drawings
Fig. 1 is a flowchart of a map information processing method according to a first embodiment of the present invention;
FIG. 2 is a flowchart illustrating a detailed step of step S103 in FIG. 1 according to a first embodiment of the present invention;
fig. 3 is a schematic structural diagram of a map information processing apparatus according to a second embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
As shown in fig. 1, a first embodiment of the present invention provides a processing method of map information, the processing method including:
step S101, obtaining characteristic information of a plurality of map characteristic objects to be analyzed.
In the first embodiment of the present invention, the map feature object to be analyzed may be a point of Interest (POI), a road, an address, and the like. The interest points are a landmark and a sight spot on an electronic map, and are used for marking places such as government departments represented by the place, commercial institutions of various industries (such as gas stations, department stores, supermarkets, restaurants, hotels, convenience stores, hospitals and the like), tourist attractions (such as parks, public toilets and the like), historic sites, transportation facilities (such as various stations, parking lots, overspeed cameras, speed limit marks) and the like.
In the first embodiment of the present invention, the above-described feature information includes the name, spatial position, and attribute information of the map feature object to be analyzed. The space position is a specific position of the map feature object to be analyzed in the map, and the attribute information is category information (such as residence, shopping, and the like) of the map feature object to be analyzed.
Step S102, acquiring word frequency information of each word in the feature information of the map feature objects to be analyzed.
In a first embodiment of the present invention, the step S102 specifically includes the following steps:
the method comprises the steps of firstly, performing word segmentation processing on feature information of a plurality of map feature objects to be analyzed, and extracting words in the feature information of the plurality of map feature objects to be analyzed. The word segmentation process is common knowledge to those skilled in the art, and therefore will not be described herein.
And secondly, acquiring the extracted word frequency information of each word. The word frequency information is the frequency of occurrence of each word in the feature information of the map feature objects to be analyzed. Therefore, before the second step is performed, the number of times each extracted word appears in the feature information of the plurality of map feature objects to be analyzed needs to be counted. For example, the word frequency information of the word "Zhongguancun" is 213232 times, the word frequency information of the word "park" is 2122 times, and the word frequency information of the word "shang di" is 211333 times.
And S103, determining the name and the range of the business circle according to the word frequency information of each word.
Specifically, the purpose of step S103 is to determine the name and range of the business turn in a map (mainly an electronic map) based on the word frequency information of each word. The trade circle refers to a commercial area with concentrated activities such as catering, shopping and sports.
In a first embodiment of the present invention, as shown in fig. 2, the step S103 specifically includes the following steps:
step S201, determining whether the frequency of the word indicated by the word frequency information of the word in the feature information of the map feature objects to be analyzed is greater than a preset value. When the number of times is greater than the preset value, step S202 is executed, and when the number of times is less than the preset value, the word is directly discarded. It should be noted that the preset value may be set according to actual situations, for example, 10000 times.
And S202, when the times are larger than a preset value, performing cluster analysis on each spatial position of the word. The cluster analysis is common knowledge to those skilled in the art, and therefore will not be described herein. When performing cluster analysis on each spatial position of the term, the weight coefficient of the spatial position is related to the attribute information of the map feature object to be analyzed to which the spatial position belongs, for example, the weight coefficient of a shopping mall is much higher than that of a supermarket. In order to improve the accuracy of the cluster analysis result, the weight coefficient of each spatial position can be continuously adjusted according to the actual cluster analysis result executed for multiple times. Of course, when the cluster analysis is started, an initial weight coefficient may be set for each spatial position.
And step S203, judging whether the word can be used as the name of the business circle or not according to the clustering analysis result.
In the first embodiment of the present invention, whether the word can be used as the name of the business turn can be determined by determining whether the first degree of affinity of each spatial position in the cluster analysis result is greater than the preset degree of affinity. Specifically, when the first degree of affinity of each spatial position is greater than the preset degree of affinity, it is determined that the word may be used as the name of the business district, and then step S204 is performed, and when the first degree of affinity of each spatial position is less than the preset degree of affinity, it is determined that the word may not be used as the name of the business district, and the word is directly discarded. The preset degree of affinity and hydrophobicity may be set according to actual conditions.
And step S204, when the word can be used as the name of the business circle, the word is used as the name of the business circle, and the clustering boundary in the clustering analysis result is used as the range of the business circle.
In the first embodiment of the invention, the name and the range of the business district are determined according to the word frequency information of each word in the feature information of the map feature objects to be analyzed, so that the problem that the name and the range of each business district in the map cannot be accurately determined is solved, and the effect of simply and accurately determining the name and the range of the business district on the map is achieved.
Second embodiment
As shown in fig. 3, a second embodiment of the present invention provides a map information processing apparatus, including:
a first obtaining module 301, configured to obtain feature information of a plurality of map feature objects to be analyzed;
a second obtaining module 302, configured to obtain word frequency information of each word in feature information of a plurality of map feature objects to be analyzed;
the determining module 303 is configured to determine the name and range of the business turn according to the word frequency information of each word.
Wherein, the second obtaining module 302 includes:
the word segmentation unit is used for performing word segmentation processing on the feature information of the map feature objects to be analyzed and extracting words in the feature information of the map feature objects to be analyzed;
and the acquisition unit is used for acquiring the extracted word frequency information of each word, wherein the word frequency information is the frequency of occurrence of each word in the feature information of the map feature objects to be analyzed.
The characteristic information of the map characteristic object to be analyzed at least comprises the following steps: the name, spatial location and attribute information of the map feature object to be analyzed,
the determination module 303 includes:
the first judgment unit is used for judging whether the times of the words indicated by the word frequency information of the words appearing in the feature information of the map feature objects to be analyzed are larger than a preset value or not, and triggering the clustering unit when the times are larger than the preset value;
the clustering unit is used for carrying out clustering analysis on each spatial position of the word according to the triggering of the first judging unit;
the second judgment unit is used for judging whether the word can be used as the name of the business circle according to the clustering analysis result and triggering the determination unit when the word can be used as the name of the business circle;
and the determining unit is used for taking the word as the name of the business circle according to the triggering of the second judging unit and taking the clustering boundary in the clustering analysis result as the range of the business circle.
Wherein the second judgment unit includes:
the judging subunit is used for judging whether the first degree of affinity and sparseness of each space position in the clustering analysis result is greater than the preset degree of affinity and sparseness, triggering the first determining subunit when the first degree of affinity and sparseness of each space position is greater than the preset degree of affinity and sparseness, and triggering the second determining subunit when the first degree of affinity and sparseness of each space position is less than the preset degree of affinity and sparseness;
the first determining subunit is used for determining that the word can be used as the name of the business circle according to the triggering of the judging subunit;
and the second determining subunit is used for determining that the word cannot be used as the name of the business circle according to the triggering of the judging subunit.
In the second embodiment of the invention, the name and the range of the business district are determined according to the word frequency information of each word in the feature information of the map feature objects to be analyzed, so that the problem that the name and the range of each business district in the map cannot be accurately determined is solved, and the effect of simply and accurately determining the name and the range of the business district on the map is achieved.
It should be noted that the map information processing apparatus according to the second embodiment of the present invention is an apparatus to which the above map information processing method is applied, that is, all the embodiments of the above map information processing method are applicable to the map information processing apparatus, and can achieve the same or similar advantageous effects.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (4)
1. A method for processing map information, comprising:
acquiring characteristic information of a plurality of map characteristic objects to be analyzed;
acquiring word frequency information of each word in the feature information of the map feature objects to be analyzed;
determining the name and the range of a business circle according to the word frequency information of each word;
the step of obtaining the word frequency information of each word in the feature information of the map feature objects to be analyzed includes:
performing word segmentation processing on the feature information of the map feature objects to be analyzed, and extracting words in the feature information of the map feature objects to be analyzed;
acquiring extracted word frequency information of each word, wherein the word frequency information is the frequency of each word appearing in the feature information of the map feature objects to be analyzed;
the characteristic information of the map characteristic object to be analyzed at least comprises the following steps: name, spatial position and attribute information of the map feature object to be analyzed,
the step of determining the name and the range of the business circle according to the word frequency information of each word comprises the following steps:
judging whether the frequency of the words indicated by the word frequency information of the words in the feature information of the map feature objects to be analyzed is greater than a preset value or not;
when the times are greater than the preset value, performing cluster analysis on each spatial position of the word, wherein the weight coefficient of the spatial position is determined according to the attribute information of the map feature object to be analyzed to which the spatial position belongs, and the weight coefficient of each spatial position is continuously adjusted according to the cluster analysis result;
judging whether the word can be used as the name of a business circle or not according to the clustering analysis result;
when the word can be used as the name of the business circle, the word is used as the name of the business circle, and the clustering boundary in the clustering analysis result is used as the range of the business circle.
2. The processing method of claim 1, wherein the step of determining whether the word can be used as a name of a business turn according to the cluster analysis result comprises:
judging whether the first degree of affinity and sparseness of each spatial position in the clustering analysis result is greater than the preset degree of affinity and sparseness;
when the first degree of affinity and sparseness of each spatial position is larger than the preset degree of affinity and sparseness, determining that the word can be used as the name of a business circle;
and when the first degree of affinity and sparseness of each spatial position is smaller than the preset degree of affinity and sparseness, determining that the word cannot be used as the name of the business circle.
3. An apparatus for processing map information, comprising:
the first acquisition module is used for acquiring the characteristic information of a plurality of map characteristic objects to be analyzed;
the second acquisition module is used for acquiring the word frequency information of each word in the feature information of the map feature objects to be analyzed;
the determining module is used for determining the name and the range of the business circle according to the word frequency information of each word;
the word segmentation unit is used for performing word segmentation processing on the feature information of the map feature objects to be analyzed and extracting words in the feature information of the map feature objects to be analyzed;
the acquisition unit is used for acquiring the extracted word frequency information of each word, wherein the word frequency information is the frequency of each word appearing in the feature information of the map feature objects to be analyzed;
the characteristic information of the map characteristic object to be analyzed at least comprises the following steps: name, spatial position and attribute information of the map feature object to be analyzed,
the determining module comprises:
the first judgment unit is used for judging whether the frequency of the words indicated by the word frequency information of the words in the feature information of the map feature objects to be analyzed is greater than a preset value or not, and triggering the clustering unit when the frequency is greater than the preset value;
the clustering unit is used for performing clustering analysis on each spatial position of the word according to the triggering of the first judging unit, wherein the weight coefficient of the spatial position is determined according to the attribute information of the map feature object to be analyzed to which the spatial position belongs, and the weight coefficient of each spatial position is continuously adjusted according to the clustering analysis result;
the second judgment unit is used for judging whether the word can be used as the name of the business circle according to the clustering analysis result and triggering the determination unit when the word can be used as the name of the business circle;
and the determining unit is used for taking the word as the name of the business circle according to the triggering of the second judging unit and taking the clustering boundary in the clustering analysis result as the range of the business circle.
4. The processing apparatus according to claim 3, wherein the second determination unit includes:
the judging subunit is used for judging whether the first degree of affinity and sparseness of each space position in the clustering analysis result is greater than a preset degree of affinity and sparseness, triggering the first determining subunit when the first degree of affinity and sparseness of each space position is greater than the preset degree of affinity and sparseness, and triggering the second determining subunit when the first degree of affinity and sparseness of each space position is less than the preset degree of affinity and sparseness;
the first determining subunit is used for determining that the word can be used as the name of the business circle according to the triggering of the judging subunit;
and the second determining subunit is used for determining that the word can not be used as the name of the business circle according to the triggering of the judging subunit.
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| CN104965913A (en) * | 2015-07-03 | 2015-10-07 | 重庆邮电大学 | GPS (global positioning system) geographic position data mining based user classification method |
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| US9529823B2 (en) * | 2011-09-07 | 2016-12-27 | Microsoft Technology Licensing, Llc | Geo-ontology extraction from entities with spatial and non-spatial attributes |
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| CN101661461A (en) * | 2008-08-29 | 2010-03-03 | 阿里巴巴集团控股有限公司 | Method and system for determining core geographic information in document |
| CN103944932A (en) * | 2013-01-18 | 2014-07-23 | 阿里巴巴集团控股有限公司 | Method for searching and determining active zone, and server |
| CN104965913A (en) * | 2015-07-03 | 2015-10-07 | 重庆邮电大学 | GPS (global positioning system) geographic position data mining based user classification method |
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