CN113592463A - Special topic block generation method and device - Google Patents

Abstract

The application provides a thematic block generation method, which comprises the following steps: acquiring a service data set comprising a regional data set, a traffic route data set, a natural resource data set and a city detection data set; unifying the reference system of the service data set according to a thematic template, and dividing the area data set by the traffic route data set to form a divided block; inquiring a division block with an area larger than a preset threshold value in the division blocks to set as an alternative block, and screening the alternative block according to data of a city detection data set in the alternative block; and erasing the screened alternative blocks through a natural resource data set to obtain thematic blocks. And the block is generated through automatic operation of the data set, so that manual intervention is reduced, block generation time is shortened, and block generation cost is reduced. The application also provides a special topic block generating device.

Description

Special topic block generation method and device

Technical Field

The application provides a local topic matching technology, and particularly relates to a topic block generation method. The application also relates to a special topic block generation device.

Background

A block is a unit and a carrier for resource allocation, spatial analysis and display, and is usually a closed block object defined by natural features. Compared with administrative divisions, the granularity of the block is finer, access factors such as roads, rivers and railways and human factors are comprehensively considered, and the block has the characteristics of reasonable structure and clear boundary in the field of statistical analysis. The block generated based on the basic geographic elements is called a natural block, and on the basis of the natural block, thematic attributes and events are superposed to obtain a more targeted block called a thematic block, such as a demographic block, a market analysis block, a service block coverage block and the like.

Traditional block production needs to be equipped with professional geographic information technical personnel, purchases expensive commercial geographic information platform software, and each link coupling degree of production technology is high, can not in time deal with the adjustment and the change of demand, and the development cycle is long, and block granularity's suitability aassessment lags behind, and the border adjustment is too much to rely on artificial intervention, leads to the production chain extension, and efficiency reduces, and the cost increases, can't realize becoming more meticulous the delivery fast.

Disclosure of Invention

The application provides a special block generation method which can solve the problems of long block generation period and high cost. The application also provides a special topic block generating device.

The application provides a thematic block generation method, which comprises the following steps:

acquiring a service data set comprising a regional data set, a traffic route data set, a natural resource data set and a city detection data set;

unifying the reference system of the service data set according to a thematic template, and dividing the area data set by the traffic route data set to form a divided block;

inquiring a division block with an area larger than a preset threshold value in the division blocks to set as an alternative block, and screening the alternative block according to data of a city detection data set in the alternative block;

and erasing the screened alternative blocks through a natural resource data set to obtain thematic blocks.

Optionally, unifying the reference system for the service data set includes:

shearing the traffic route data set, the natural resource data set and the city detection data set according to the area data set;

and unifying a spatial reference system and a measurement unit for the cut service data set.

Optionally, the segmenting the area surface data set with the traffic route data set includes:

respectively setting cache areas with preset distances according to the traffic types in the traffic route data set;

and erasing the area surface by using the corresponding traffic route corresponding to the traffic type and the cache region.

Optionally, the method further includes: if no alternative block passes the screening, then:

recursively refining object granularity of the candidate blocks, the objects comprising: area number, population number and service point number;

and screening the alternative blocks after the recursive refinement according to the data of the city detection data set in the alternative blocks.

Optionally, the method further includes: if the alternative block without recursive refinement passes the screening, then:

performing secondary segmentation on the alternative block to form a secondary alternative block;

and screening the secondary alternative blocks according to the data of the city detection data set in the alternative blocks.

Optionally, the method further includes: if no secondary neighborhood passes the screening, then:

erasing the area data set by using a minimum level route in the traffic route data set and a buffer area of the route to obtain a new segmentation block;

and inquiring the division blocks with the area larger than a preset threshold value in the new division blocks to set as alternative blocks, and screening the alternative blocks according to the data of the city detection data set in the alternative blocks.

The present application further provides a topic block generation apparatus, including:

the acquisition module is used for acquiring a service data set comprising an area data set, a traffic route data set, a natural resource data set and a city detection data set;

the segmentation module is used for unifying the reference system of the business data set according to a thematic template and segmenting the area data set by the traffic route data set to form a segmentation block;

the query module is used for querying the division blocks with the area larger than a preset threshold value in the division blocks to set as alternative blocks and screening the alternative blocks according to the data of the city detection data set in the alternative blocks;

and the determining module is used for erasing the screened alternative blocks through the natural resource data set to obtain thematic blocks.

Optionally, the segmentation module further includes:

the cutting unit is used for cutting the traffic route data set, the natural resource data set and the city detection data set according to the area data set;

and the segmentation module unifies a spatial reference system and a measurement unit on the cut service data set.

Optionally, the segmentation module further includes:

the setting unit is used for respectively setting cache areas with preset distances according to the traffic types in the traffic route data set;

and erasing the area surface by using the corresponding traffic route corresponding to the traffic type and the cache region.

Optionally, the queried module further performs the following steps:

recursively refining object granularity of the candidate blocks, the objects comprising: area number, population number and service point number;

and screening the alternative blocks after the recursive refinement according to the data of the city detection data set in the alternative blocks.

Optionally, the queried module further performs the following steps:

performing secondary segmentation on the alternative block to form a secondary alternative block;

and screening the secondary alternative blocks according to the data of the city detection data set in the alternative blocks.

Optionally, the queried module further performs the following steps:

erasing the area data set by using a minimum level route in the traffic route data set and a buffer area of the route to obtain a new segmentation block;

and inquiring the division blocks with the area larger than a preset threshold value in the new division blocks to set as alternative blocks, and screening the alternative blocks according to the data of the city detection data set in the alternative blocks.

The application has the advantages relative to the prior art:

the application provides a thematic block generation method, which comprises the following steps: acquiring a service data set comprising a regional data set, a traffic route data set, a natural resource data set and a city detection data set; unifying the reference system of the service data set according to a thematic template, and dividing the area data set by the traffic route data set to form a divided block; inquiring a division block with an area larger than a preset threshold value in the division blocks to set as an alternative block, and screening the alternative block according to data of a city detection data set in the alternative block; and erasing the screened alternative blocks through a natural resource data set to obtain thematic blocks. And the block is generated through automatic operation of the data set, so that manual intervention is reduced, block generation time is shortened, and block generation cost is reduced.

Drawings

FIG. 1 is a block generation flow diagram of the present application.

Fig. 2 is a flow chart of alternative neighborhood screening in the present application.

Fig. 3 is a schematic diagram of a block generation apparatus according to the present application.

Detailed Description

In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application, but the present application may be implemented in many ways other than those described herein, and it will be apparent to those of ordinary skill in the art that the present application is not limited to the specific implementations disclosed below without departing from the spirit of the present application.

The application provides a thematic block generation method, which comprises the following steps: acquiring a service data set comprising a regional data set, a traffic route data set, a natural resource data set and a city detection data set; unifying the reference system of the service data set according to a thematic template, and dividing the area data set by the traffic route data set to form a divided block; inquiring a division block with an area larger than a preset threshold value in the division blocks to set as an alternative block, and screening the alternative block according to data of a city detection data set in the alternative block; and erasing the screened alternative blocks through a natural resource data set to obtain thematic blocks. The invention realizes the purposes of shortening the generation time of the block and reducing the generation cost of the block by adopting automation and a block generation process of manual intervention.

FIG. 1 is a block generation flow diagram of the present application.

Referring to fig. 1, in S101, a service data set including a regional data set, a traffic route data set, a natural resource data set, and a city detection data set is obtained.

The business data set is stored in a database, and the database comprises a thematic template, a customized interface and an evaluation system.

The database stores a large amount of data of natural resources, geography, economy and the like, and service data sets including a regional data set, a traffic route data set, a natural resource data set and a city detection data set can be obtained from the database.

As shown in fig. 1, S102 unifies the reference system of the business data set according to a thematic template, and segments the area data set with the traffic route data set to form a segmented block.

Each type of data in the service data set is data of different formats and types, so that data needs to be unified first, specifically, the method includes: unification of the reference systems is performed. The reference system is a spatial reference system, and spatial positions of data in each data set are unified into the same spatial reference system; the format reference system unifies the formats of the data in the data sets, and the formats comprise: xlsx,. csv,. shp,. geojson,. json.

Specifically, the traffic route data set, the natural resource data set and the city detection data set are cut according to the area data set, and the cut business data set is unified by a spatial reference system and a measurement unit.

For example, after unifying the reference systems of the data sets, it is desirable to define the range of the data, including cutting the traffic route data set, the natural resource data set, and the city detection data set according to the area-plane data set, so that each data set is in the same space as the area-plane data set. The respective data sets are then stored and archived.

Next, a calculation is performed on the traffic data set, including segmentation of the area-plane data set by the traffic route data set.

The traffic route data set mainly includes: railway networks, highways. The highway further comprises: the highway comprises a first-level highway, a second-level highway, a third-level highway, a fourth-level highway and a highway below the fourth level.

And respectively setting buffer areas with preset distances according to the traffic types in the traffic route data set aiming at the traffic route data set, and erasing the area surface by using the traffic routes corresponding to the traffic types and the buffer areas. Specifically, the cache area of the railway network is set to be 20 meters; the buffer areas of the highways are respectively set to be 20 meters, 10 meters, 5 meters and 3 meters, and respectively correspond to a first-level road, a second-level road, a third-level road, a fourth-level road and a road below the fourth level, wherein the buffer areas are arranged on two sides of the traffic route.

The cache region erases the area data set, so that the traffic route data set is divided into the area data set to form a division block, and the formula is as follows:

BK1=AD-RWBF

BK2=BK1-RDBF1

BK3=BK2-RDBF2

the AD is a regional plane dataset, the RWB is a buffer of a road network, the RDBF1 is a primary road buffer, and the RDBF2 is a secondary road buffer. The present application preferably operates only on the rail network, first level roads and second level roads, but may further operate on specific applications, such as third level roads and fourth level roads.

By the above formula, the division of the area-plane data set is formed to form a plurality of divided blocks.

As shown in fig. 1, S103 queries a division block with an area larger than a preset threshold value in the division blocks as an alternative block, and filters the alternative block according to data of the city detection data set in the alternative block.

The BK3 is the final segment block, and the first round of screening is performed on the segment block, including screening the area of the segment block. Preferably, a partitioned block having an area greater than 10000 square kilometers is used as the candidate block. The newly-built enumeration field Type is used for storing the block Type of the block, and the newly-built Boolean field Flag is used for storing whether the evaluation result passes or not.

Screening alternative blocks according to data of a city detection data set in the alternative blocks, wherein the city detection data set comprises: service points per square kilometer and population in the block. Specifically, whether the service points per square kilometer in the alternative block and the population number in the block meet preset requirements or not is detected, preferably, the service points per square kilometer do not exceed 5, and the population number in the block cannot be greater than 5000. Further, the screening may be performed by an aggregation function, the aggregation function including: SUM, COUNT, STDEVP, VAR.

As shown in fig. 2, S201 segments the evaluation of the neighborhood.

Calculating a region surface data set according to the block area to obtain a default evaluation condition, wherein the default evaluation condition is as follows:

COUNT(BKTP)<=BKArea*(TPCount/CityArea)

wherein, the count (bktp) is the number of service points in the block, BKArea is the block area, the number of service points recorded by the TPCount service data set, and the area of the citylarea area.

As shown in fig. 2, S202 evaluates after refining the neighborhood for the first time.

Judging the screening passing result of each division block according to the evaluation conditions, and if the screening passing result does not pass the evaluation conditions, performing the next step, including:

if no alternative block passes the screening, then: recursively refining object granularity of the candidate blocks, the objects comprising: area count, population count, and service points.

Specifically, the block can be further segmented by the third-level road to obtain a segmented block with a finer granularity, and the formula is as follows:

BK4=BK3-RDBF3

wherein the RDBF3 is a three-level highway buffer.

And screening the alternative blocks after the recursive refinement according to the data of the city detection data set in the alternative blocks.

As shown in fig. 2, S203 evaluates after refining the neighborhood for the second time.

Further, if the alternative block without recursive refinement passes the screening, then: and carrying out secondary segmentation on the alternative block to form a secondary alternative block.

Specifically, the block may be further segmented by the four-level highway to obtain a segmented block with a finer granularity, where the formula is as follows:

BK5=BK4-RDBF4

wherein the RDBF4 is a four-level highway buffer.

And screening the secondary alternative blocks according to the data of the city detection data set in the alternative blocks.

As shown in fig. 2, S203 evaluates after refining the neighborhood for the third time.

Further, if no secondary neighborhood passes the screening, then: and erasing the area data set by using the minimum level route in the traffic route data set and the buffer area of the route to obtain a new segmentation block.

Specifically, the block can be further segmented by the four-level road to obtain a segmented block with a finer granularity, and the formula is as follows:

BK6=BK5-RDBF5

wherein the RDBF5 is a four or lower level highway buffer.

And screening the secondary alternative blocks according to the data of the city detection data set in the alternative blocks.

And inquiring the division blocks with the area larger than a preset threshold value in the new division blocks to set as alternative blocks, and screening the alternative blocks according to the data of the city detection data set in the alternative blocks.

If no street passes the screening, repeating the evaluation, marking the street which still does not accord with the evaluation condition, newly building a field Flag in the street data, and marking the object which does not accord with the evaluation condition as false.

As shown in fig. 1, S104 erases the screened candidate blocks through the natural resource dataset to obtain thematic blocks.

In this application, the natural resource data set includes: water system data set, green land data set.

In the first step, a space update operation is performed on a street using a water system dataset, and a Type field value corresponding to the water system dataset is set to W, so that a result dataset BK7 is obtained.

Second, a spatial update operation is performed on BK7 using the greenfield dataset, and the value of the Type field corresponding to the green dataset is set to G, resulting in a result dataset BK 8.

The update operation refers to deleting the blocks by the water system data set and the green space data set.

And traversing the updated data set, and then evaluating the data set to obtain a final special street block. The evaluation comprises the above steps S201, S202, S203.

The present application further provides a topic block generation apparatus, including: the system comprises an acquisition module 301, a segmentation module 302, a query module 303 and a determination module 304.

Fig. 3 is a schematic diagram of a block generation apparatus according to the present application.

Referring to fig. 3, the obtaining module 301 is configured to obtain a service data set including a regional data set, a traffic route data set, a natural resource data set, and a city detection data set.

The business data set is stored in a database, and the database comprises a thematic template, a customized interface and an evaluation system.

The database stores a large amount of data of natural resources, geography, economy and the like, and service data sets including a regional data set, a traffic route data set, a natural resource data set and a city detection data set can be obtained from the database.

As shown in fig. 3, the dividing module 302 is configured to unify the reference systems of the service data sets according to a topic template, and divide the area data set by the traffic route data set to form a divided block.

Each type of data in the service data set is data of different formats and types, so that data needs to be unified first, specifically, the method includes: unification of the reference systems is performed. The reference system is a spatial reference system, and spatial positions of data in each data set are unified into the same spatial reference system; the format reference system unifies the formats of the data in the data sets, and the formats comprise: xlsx,. csv,. shp,. geojson,. json.

Specifically, the segmentation module further includes: and the cutting unit is used for cutting the traffic route data set, the natural resource data set and the city detection data set according to the area data set and unifying a space reference system and a measurement unit on the cut service data set.

For example, after unifying the reference systems of the data sets, it is desirable to define the range of the data, including cutting the traffic route data set, the natural resource data set, and the city detection data set according to the area-plane data set, so that each data set is in the same space as the area-plane data set. The respective data sets are then stored and archived.

Next, a calculation is performed on the traffic data set, including segmentation of the area-plane data set by the traffic route data set.

The traffic route data set mainly includes: railway networks, highways. The highway further comprises: the highway comprises a first-level highway, a second-level highway, a third-level highway, a fourth-level highway and a highway below the fourth level.

The segmentation module further comprises: the setting unit is used for respectively setting cache areas with preset distances according to the traffic types in the traffic route data set; and erasing the area surface by using the corresponding traffic route corresponding to the traffic type and the cache region.

Specifically, for the traffic route data set, cache regions with preset distances are respectively set according to traffic types in the traffic route data set, and the traffic routes corresponding to the traffic types and the cache regions are used for erasing the area surface. Specifically, the cache area of the railway network is set to be 20 meters; the buffer areas of the highways are respectively set to be 20 meters, 10 meters, 5 meters and 3 meters, and respectively correspond to a first-level road, a second-level road, a third-level road, a fourth-level road and a road below the fourth level, wherein the buffer areas are arranged on two sides of the traffic route.

The cache region erases the area data set, so that the traffic route data set is divided into the area data set to form a division block, and the formula is as follows:

BK1=AD-RWBF

BK2=BK1-RDBF1

BK3=BK2-RDBF2

the AD is a regional plane dataset, the RWB is a buffer of a road network, the RDBF1 is a primary road buffer, and the RDBF2 is a secondary road buffer. The present application preferably operates only on the rail network, first level roads and second level roads, but may further operate on specific applications, such as third level roads and fourth level roads.

By the above formula, the division of the area-plane data set is formed to form a plurality of divided blocks.

As shown in fig. 3, the query module 303 is configured to query that a segmented block with an area larger than a preset threshold value in the segmented blocks is set as an alternative block, and filter the alternative block according to data of the city detection data set in the alternative block.

The BK3 is the final segment block, and the first round of screening is performed on the segment block, including screening the area of the segment block. Preferably, a partitioned block having an area greater than 10000 square kilometers is used as the candidate block. The newly-built enumeration field Type is used for storing the block Type of the block, and the newly-built Boolean field Flag is used for storing whether the evaluation result passes or not.

Screening alternative blocks according to data of a city detection data set in the alternative blocks, wherein the city detection data set comprises: service points per square kilometer and population in the block. Specifically, whether the service points per square kilometer in the alternative block and the population number in the block meet preset requirements or not is detected, preferably, the service points per square kilometer do not exceed 5, and the population number in the block cannot be greater than 5000. Further, the screening may be performed by an aggregation function, the aggregation function including: SUM, COUNT, STDEVP, VAR.

As shown in fig. 2, S201 segments the evaluation of the neighborhood.

Calculating a region surface data set according to the block area to obtain a default evaluation condition, wherein the default evaluation condition is as follows:

COUNT(BKTP)<=BKArea*(TPCount/CityArea)

wherein, the count (bktp) is the number of service points in the block, BKArea is the block area, the number of service points recorded by the TPCount service data set, and the area of the citylarea area.

As shown in fig. 2, S202 evaluates after refining the neighborhood for the first time.

Judging the screening passing result of each division block according to the evaluation conditions, and if the screening passing result does not pass the evaluation conditions, performing the next step, including:

if no alternative block passes the screening, then: recursively refining object granularity of the candidate blocks, the objects comprising: area count, population count, and service points.

Specifically, the block can be further segmented by the third-level road to obtain a segmented block with a finer granularity, and the formula is as follows:

BK4=BK3-RDBF3

wherein the RDBF3 is a three-level highway buffer.

And screening the alternative blocks after the recursive refinement according to the data of the city detection data set in the alternative blocks.

As shown in fig. 2, S203 evaluates after refining the neighborhood for the second time.

Further, if the alternative block without recursive refinement passes the screening, then: and carrying out secondary segmentation on the alternative block to form a secondary alternative block.

Specifically, the block may be further segmented by the four-level highway to obtain a segmented block with a finer granularity, where the formula is as follows:

BK5=BK4-RDBF4

wherein the RDBF4 is a four-level highway buffer.

And screening the secondary alternative blocks according to the data of the city detection data set in the alternative blocks.

As shown in fig. 2, S203 evaluates after refining the neighborhood for the third time.

Further, if no secondary neighborhood passes the screening, then: and erasing the area data set by using the minimum level route in the traffic route data set and the buffer area of the route to obtain a new segmentation block.

Specifically, the block can be further segmented by the four-level road to obtain a segmented block with a finer granularity, and the formula is as follows:

BK6=BK5-RDBF5

wherein the RDBF5 is a four or lower level highway buffer.

And screening the secondary alternative blocks according to the data of the city detection data set in the alternative blocks.

And inquiring the division blocks with the area larger than a preset threshold value in the new division blocks to set as alternative blocks, and screening the alternative blocks according to the data of the city detection data set in the alternative blocks.

If no street passes the screening, repeating the evaluation, marking the street which still does not accord with the evaluation condition, newly building a field Flag in the street data, and marking the object which does not accord with the evaluation condition as false.

As shown in fig. 3, the determining module 304 is configured to erase the filtered candidate blocks through the natural resource data set to obtain thematic blocks.

In this application, the natural resource data set includes: water system data set, green land data set.

In the first step, a space update operation is performed on a street using a water system dataset, and a Type field value corresponding to the water system dataset is set to W, so that a result dataset BK7 is obtained.

Second, a spatial update operation is performed on BK7 using the greenfield dataset, and the value of the Type field corresponding to the green dataset is set to G, resulting in a result dataset BK 8.

The update operation refers to deleting the blocks by the water system data set and the green space data set.

And traversing the updated data set, and then evaluating the data set to obtain a final special street block. The evaluation comprises the above steps S201, S202, S203.

Claims (10)

1. A method for generating a thematic block is characterized by comprising the following steps:

acquiring a service data set comprising a regional data set, a traffic route data set, a natural resource data set and a city detection data set;

unifying the reference system of the service data set according to a thematic template, and dividing the area data set by the traffic route data set to form a divided block;

inquiring a division block with an area larger than a preset threshold value in the division blocks to set as an alternative block, and screening the alternative block according to data of a city detection data set in the alternative block;

and erasing the screened alternative blocks through a natural resource data set to obtain thematic blocks.

2. The method of claim 1, wherein unifying the business data sets with reference to a system comprises:

shearing the traffic route data set, the natural resource data set and the city detection data set according to the area data set;

and unifying a spatial reference system and a measurement unit for the cut service data set.

3. The thematic block generation method as recited in claim 2, wherein the segmenting the area plane dataset with the traffic route dataset comprises:

respectively setting cache areas with preset distances according to the traffic types in the traffic route data set;

and erasing the area surface by using the traffic route corresponding to the traffic type and the cache region.

4. The method of generating a thematic block as claimed in claim 1, further comprising: if no alternative block passes the screening, then:

recursively refining object granularity of the candidate blocks, the objects comprising: area number, population number and service point number;

and screening the alternative blocks after the recursive refinement according to the data of the city detection data set in the alternative blocks.

5. The method of generating thematic blocks as claimed in claim 4, further comprising: if the alternative block without recursive refinement passes the screening, then:

performing secondary segmentation on the alternative block to form a secondary alternative block;

and screening the secondary alternative blocks according to the data of the city detection data set in the alternative blocks.

6. The method of generating a thematic block as claimed in claim 5, further comprising: if no secondary neighborhood passes the screening, then:

erasing the area data set by using a minimum level route in the traffic route data set and a buffer area of the route to obtain a new segmentation block;

and inquiring the division blocks with the area larger than a preset threshold value in the new division blocks to set as alternative blocks, and screening the alternative blocks according to the data of the city detection data set in the alternative blocks.

7. A topic block generation device, comprising:

the acquisition module is used for acquiring a service data set comprising an area data set, a traffic route data set, a natural resource data set and a city detection data set;

the segmentation module is used for unifying the reference system of the business data set according to a thematic template and segmenting the area data set by the traffic route data set to form a segmentation block;

the query module is used for querying the division blocks with the area larger than a preset threshold value in the division blocks to set as alternative blocks and screening the alternative blocks according to the data of the city detection data set in the alternative blocks;

and the determining module is used for erasing the screened alternative blocks through the natural resource data set to obtain thematic blocks.

8. The topic block generation apparatus according to claim 7, wherein the segmentation module further comprises:

the cutting unit is used for cutting the traffic route data set, the natural resource data set and the city detection data set according to the area data set;

and the segmentation module unifies a spatial reference system and a measurement unit on the cut service data set.

9. The topic block generation apparatus of claim 8, wherein the segmentation module further comprises:

the setting unit is used for respectively setting cache areas with preset distances according to the traffic types in the traffic route data set;

and erasing the area surface by using the corresponding traffic route corresponding to the traffic type and the cache region.

10. The topic block generation apparatus of claim 7, wherein the query module further performs the steps of:

recursively refining object granularity of the candidate blocks, the objects comprising: area number, population number and service point number;

screening the alternative blocks after the recursive refinement according to the data of the city detection data set in the alternative blocks;

performing secondary segmentation on the alternative block to form a secondary alternative block;

screening the secondary alternative blocks according to the data of the city detection data set in the alternative blocks;

erasing the area data set by using a minimum level route in the traffic route data set and a buffer area of the route to obtain a new segmentation block;

and inquiring the division blocks with the area larger than a preset threshold value in the new division blocks to set as alternative blocks, and screening the alternative blocks according to the data of the city detection data set in the alternative blocks.

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