CN117251770B - Method for identifying low-utility land - Google Patents

Method for identifying low-utility land Download PDF

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CN117251770B
CN117251770B CN202311538837.7A CN202311538837A CN117251770B CN 117251770 B CN117251770 B CN 117251770B CN 202311538837 A CN202311538837 A CN 202311538837A CN 117251770 B CN117251770 B CN 117251770B
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王健
赵海峰
吴凯
石晓
王晓宇
赖照峰
安红蕾
伊文超
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Abstract

The invention relates to the technical field of land planning, in particular to a method for identifying a low-utility land, which comprises the following steps of S1, calculating a short-time social activity type semantic sequence; s2, calculating social activity semantic tags; step S3, calculating the same social activity type difference of the short-time social activity type semantic sequence; s4, calculating different social activity type differences of the short-time social activity type semantic sequence; s5, calculating the social activity type difference of the short-time social activity type semantic sequence; step S6, clustering short-time social activity type semantic sequences; and S7, judging in a low-utility manner according to the short-time social activity type semantic sequence clustering result. The method judges the low-utility land according to the social activities, is used for overcoming the defect that the traditional object factors are only considered in the existing method, and identifies the low-utility land by clustering the social activities of people.

Description

Method for identifying low-utility land
Technical Field
The invention relates to the technical field of land planning, in particular to a method for identifying a low-utility land.
Background
Land resources are the most basic material foundation on which human society depends, and are in the core of irreplaceable other resources in population, resource, environment and regional development relations. With the increase of production level, the expansion and aggravation of human activity range, the utilization of land resources is under tremendous pressure. By evaluating and analyzing the bearing capacity of the land resources, the supporting degree of the land resources on population growth, town construction, agriculture and animal husbandry production, ecological balance and the like and the land development and utilization potential can be mastered, and an important basis is provided for coordinating regional environment systems and regional socioeconomic development.
In the prior art, CN107766415B is based on a town low-efficiency industrial land rapid identification method based on electricity consumption data, CN112419124B is a rapid identification method and device of a low-efficiency industrial land, a storage medium thereof, a system and a method for identifying the town low-efficiency land by CN114639027B according to land utilization classification data, CN115456372B is a rural low-efficiency construction land identification standard system construction method, and methods such as a CN116129202B stock land analysis method, a device and a storage medium thereof are used for identifying the low-efficiency land based on electricity consumption, water consumption and house conditions.
All the above methods only consider the traditional factors, and have a plurality of difficulties in acquiring data information. However, the consideration of the low-utility land is more related to the social activity behavior of the person, and if the social condition of the person can be accurately judged, the low-utility land can be more accurately identified.
Disclosure of Invention
Therefore, the invention provides a method for identifying the low-utility land, which explores a space-time behavior semantic expression and extraction method of social activities under an unsupervised condition, judges the low-utility land according to the social activities, is used for overcoming the defect that the traditional object factors are only considered in the existing method, measures the influence of the same and different activity sequence types on the behavior difference of the whole time window according to the distance of the semantic sequences, and can accurately cluster the social activities of people so as to identify the low-utility land.
To achieve the above object, the present invention provides a method for identifying a low utility land, comprising the steps of:
step S1, calculating a short-time social activity type semantic sequence;
s2, calculating social activity semantic tags;
step S3, calculating the same social activity type difference of the short-time social activity type semantic sequence;
s4, calculating different social activity type differences of the short-time social activity type semantic sequence;
s5, calculating the social activity type difference of the short-time social activity type semantic sequence;
step S6, clustering short-time social activity type semantic sequences;
and S7, judging in a low-utility manner according to the short-time social activity type semantic sequence clustering result.
Further, in the step S1, the specific step of calculating the short-time social activity type semantic sequence includes:
the identification of the low utility land is based on social activity, particularly the social activity which lasts for a period of time in a certain area, the social activity is processed according to time frames to obtain y window sequences with equal length, which are called short-time social activity type semantic sequences, and the short-time social activity type semantic sequences are expressed as follows:
wherein SP is i And (3) the social activity semantic label of the ith window, wherein y is the number of windows subjected to framing processing.
Further, in the step S2, the specific step of calculating the social activity semantic tag includes:
calculating social activity semantic tags of an ith window, expressed as:
wherein SC is provided with i Indicates the social activity type, SD i Representing the social activityRegion semantic tags with highest region distribution proportion in dynamic types, and classifying the region semantic tags according to region functions, ST i Duration segment with highest duration distribution proportion in activity type of the social activity, SU i Representing the duration of time with the highest distribution ratio in the social activity type, SE i The most highly distributed social activities are represented.
Further, in the step S3, the specific step of calculating the same social activity type differences of the short-time social activity type semantic sequence includes:
s31, calculating single same social activity type differences, wherein the single same social activity type differences are calculated according to the ratio of the corresponding time length differences to the whole window duration, and are expressed as:
TD i and TD (time division) k The ith short-time social activity type semantic sequence and the kth short-time social activity type semantic sequence are respectively SC i,j Type tag, SC, representing the jth social activity in the ith short-term social activity type semantic sequence k,l Type label for representing the first social activity in the kth short message social activity type semantic sequence, |TD i I represents TD i The window length of the time window in which it is located,representing TD i And TD (time division) k Social activity type is poor and ST is the social type s i,j Representing the duration time segment with highest duration time distribution proportion in the jth social activity in the ith short message social activity type semantic sequence, ST k,l Representing the duration time period with the highest duration time distribution proportion in the first social activity in the kth short message social activity type semantic sequence;
s32, if a plurality of same social activity types exist in the short-time social activity type semantic sequence, adding the differences of each social activity type, and representing the differences of the same social activity types of the short-time social activity type semantic sequence as follows:
wherein the method comprises the steps ofSemantic sequence TD for short-term social activity type i And TD (time division) k Is different in social activity type, n is TD i And TD (time division) k There are different social activity types s.
Further, in the step S4, the specific step of calculating the different social activity type differences of the short-time social activity type semantic sequence includes:
s41, calculating single different social activity type differences, wherein the single different social activity type differences are calculated according to the ratio of the corresponding time length differences to the whole window duration, and are expressed as:
representing TD i Contains but TD k The ratio of the time length of the social activity node without the social activity type s to the total time length of the window;
s42, adding the single different social activity type differences to obtain different social activity type differences, wherein the different social activity type differences of the short-time social activity type semantic sequence are expressed as follows:
wherein the method comprises the steps ofSemantic sequence TD for short-term social activity type i And TD (time division) k Is different in social activity type, n is TD i And TD (time division) k There are different social activity types s.
Further, in the step S5, the specific step of calculating the social activity type difference of the short-time social activity type semantic sequence includes:
the social activity type differences of the short-term social activity type semantic sequence are averaged from the same social activity type differences of the short-term social activity type semantic sequence and different social activity type differences of the short-term social activity type semantic sequence, expressed as:
wherein the method comprises the steps ofSmaller values represent a social activity type semantic sequence TD i And TD (time division) k The higher the similarity of (c).
Further, in the step S6, the specific step of clustering the short-term social activity type semantic sequence includes:
setting a social activity type difference threshold of the short-time social activity type semantic sequence, wherein the social activity type difference threshold is not greater than the social activity type difference threshold, and considering the short-time social activity type semantic sequence as the same category, so that the short-time social activity type semantic sequence is clustered, g categories are obtained through clustering processing of the short-time social activity type semantic sequence, and the g categories are recorded as M 1 ，M 2 ，M 3 ，…，M g 。
Further, in the step S7, the specific step of performing low-utility judgment according to the short-time social activity type semantic sequence clustering result includes:
for class M 1 ，M 2 ，M 3 ，…，M g Categories with a number less than the set social activity number threshold are considered to be of low utility.
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FIG. 1 is a flow chart of an embodiment of the present invention;
FIG. 2 is a step-by-step flowchart of step S3 of the present invention;
fig. 3 is a step-by-step flowchart of step S4 of the present invention.
Detailed Description
In order that the objects and advantages of the invention will become more apparent, the invention will be further described with reference to the following examples; it should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are merely for explaining the technical principles of the present invention, and are not intended to limit the scope of the present invention.
It should be noted that, in the description of the present invention, terms such as "upper," "lower," "left," "right," "inner," "outer," and the like indicate directions or positional relationships based on the directions or positional relationships shown in the drawings, which are merely for convenience of description, and do not indicate or imply that the apparatus or elements must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present invention.
Furthermore, it should be noted that, in the description of the present invention, unless explicitly specified and limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be either fixedly connected, detachably connected, or integrally connected, for example; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention can be understood by those skilled in the art according to the specific circumstances.
Referring to fig. 1, which is a flowchart illustrating an embodiment of the present invention, the present invention provides a method for identifying a low utility area, which includes:
step S1: calculating a short-time social activity type semantic sequence, identifying a low-utility land according to the short-time social activity, particularly referring to the social activity which lasts for a period of time in a certain area, and processing the social activity according to time frames to obtain y window sequences with equal length, namely the short-time social activity type semantic sequence, wherein the short-time social activity type semantic sequence is expressed as:
wherein SP is i And (3) the social activity semantic label of the ith window, wherein y is the number of windows subjected to framing processing.
Step S2: calculating social activity semantic tags of an ith window, expressed as:
wherein SC is provided with i Indicates the social activity type, SD i The regional semantic tags representing the highest proportion of regional distribution in the social activity type are classified according to regional functions, and can be set as business region, core region, finance region, remote urban region, industrial region, development region, and the like i Duration segment with highest duration distribution proportion in activity type of the social activity, SU i Representing the duration of time with the highest distribution ratio in the social activity type, SE i The most highly distributed social activities are represented.
Step S3: calculating the same social activity type differences of the short-time social activity type semantic sequence, specifically, referring to fig. 2, step S3 includes:
s31, calculating single same social activity type differences, wherein the single same social activity type differences are calculated according to the ratio of the corresponding time length differences to the whole window duration, and are expressed as:
TD i and TD (time division) k The ith short-time social activity type semantic sequence and the kth short-time social activity type semantic sequence are respectively SC i,j Type tag, SC, representing the jth social activity in the ith short-term social activity type semantic sequence k,l Represent the kth short message social activityType tag of the first social activity in the dynamic type semantic sequence, |TD i I represents TD i The window length of the time window in which it is located,representing TD i And TD (time division) k Social activity type is poor and ST is the social type s i,j Representing the duration time segment with highest duration time distribution proportion in the jth social activity in the ith short message social activity type semantic sequence, ST k,l Representing the duration time period with the highest duration time distribution proportion in the first social activity in the kth short message social activity type semantic sequence;
s32, if a plurality of same social activity types exist in the short-time social activity type semantic sequence, adding the differences of each social activity type, and representing the differences of the same social activity types of the short-time social activity type semantic sequence as follows:
wherein the method comprises the steps ofSemantic sequence TD for short-term social activity type i And TD (time division) k Is different in social activity type, n is TD i And TD (time division) k There are different social activity types s.
Step S4: calculating different social activity type differences of the short-time social activity type semantic sequence, specifically, referring to fig. 3, step S4 includes:
s41, calculating single different social activity type differences, wherein the single different social activity type differences are calculated according to the ratio of the corresponding time length differences to the whole window duration, and are expressed as:
representing TD i Contains but TD k The ratio of the time length of the social activity node without the social activity type s to the total time length of the window;
s42, adding the single different social activity type differences to obtain different social activity type differences, wherein the different social activity type differences of the short-time social activity type semantic sequence are expressed as follows:
wherein the method comprises the steps ofSemantic sequence TD for short-term social activity type i And TD (time division) k Is different in social activity type, n is TD i And TD (time division) k There are different social activity types s.
Step S5: calculating the social activity type difference of the short-time social activity type semantic sequence, wherein the social activity type difference of the short-time social activity type semantic sequence is obtained by averaging the same social activity type difference of the short-time social activity type semantic sequence and different social activity type differences of the short-time social activity type semantic sequence, and is expressed as follows:
wherein the method comprises the steps ofSmaller values represent a social activity type semantic sequence TD i And TD (time division) k The higher the similarity of (c).
Step S6: clustering short-term social activity type semantic sequences, setting a social activity type difference threshold value of the short-term social activity type semantic sequences, and considering that the short-term social activity type difference threshold value is not greater than the same category, so that the short-term social activity type semantic sequences are clustered, g categories are obtained through clustering the short-term social activity type semantic sequences, and recordingIs M 1 ，M 2 ，M 3 ，…，M g 。
Step S7: judging low utility according to the short-time social activity type semantic sequence clustering result, and classifying M 1 ，M 2 ，M 3 ，…，M g Categories with a number less than the set social activity number threshold are considered to be of low utility.
Thus far, the technical solution of the present invention has been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of protection of the present invention is not limited to these specific embodiments. Equivalent modifications and substitutions for related technical features may be made by those skilled in the art without departing from the principles of the present invention, and such modifications and substitutions will be within the scope of the present invention.
The foregoing description is only of the preferred embodiments of the invention and is not intended to limit the invention; various modifications and variations of the present invention will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (8)

1. A method of identifying a low utility location, comprising the steps of:
step S1, performing frame processing on social activities according to time to obtain window sequences with equal length, and obtaining short-time social activity type semantic sequences;
step S2, calculating social activity semantic tags of windows, wherein the social activity semantic tags comprise a social activity type, an area semantic tag with the highest area distribution proportion in the social activity type, a duration period with the highest duration distribution proportion in the activity type of the social activity, a duration period with the highest distribution proportion in the social activity type and a social activity number period with the highest distribution proportion;
step S3, firstly calculating single same social activity type differences, wherein the single same social activity type differences are calculated according to the ratio of the corresponding time length differences to the whole window duration, and if a plurality of same social activity types exist in the short-time social activity type semantic sequence, adding each social activity type difference to obtain the same social activity type differences of the short-time social activity type semantic sequence;
step S4, calculating single different social activity type differences according to the ratio of the corresponding time length difference to the whole window duration, and adding the single different social activity type differences to obtain different social activity type differences of the short-time social activity type semantic sequence;
step S5, calculating the social activity type difference of the short-time social activity type semantic sequence, wherein the social activity type difference of the short-time social activity type semantic sequence is obtained by averaging the same social activity type difference of the short-time social activity type semantic sequence and different social activity type differences of the short-time social activity type semantic sequence;
step S6, clustering short-time social activity type semantic sequences;
and S7, judging in a low-utility manner according to the short-time social activity type semantic sequence clustering result.
2. The method of claim 1, wherein in step S1, the step of framing the social activity in time to obtain a window sequence of equal length, and the step of obtaining a semantic sequence of short-time social activity type comprises:
the identification of the low utility land is based on social activity, particularly the social activity which lasts for a period of time in a certain area, the social activity is processed according to time frames to obtain y window sequences with equal length, which are called short-time social activity type semantic sequences, and the short-time social activity type semantic sequences are expressed as follows:
wherein SP is i And (3) the social activity semantic label of the ith window, wherein y is the number of windows subjected to framing processing.
3. The method according to claim 1, wherein in the step S2, the specific steps of calculating social activity semantic tags of windows, including social activity types, regional semantic tags with highest regional distribution ratio in the social activity types, duration segments with highest duration distribution ratio in the social activity types, duration segments with highest distribution ratio in the social activity types, and social activity number segments with highest distribution ratio, include:
calculating social activity semantic tags of an ith window, expressed as:
wherein SC is provided with i Indicates the social activity type, SD i The semantic tags of the regions with the highest region distribution ratio in the social activity type are classified according to the region functions, ST i Duration segment with highest duration distribution proportion in activity type of the social activity, SU i Representing the duration of time with the highest distribution ratio in the social activity type, SE i The most highly distributed social activities are represented.
4. The method of claim 1, wherein in the step S3, a single same social activity type difference is calculated first, the single same social activity type difference is calculated according to a ratio of a corresponding time length difference to the whole window duration, and if there are a plurality of same social activity types in the short-time social activity type semantic sequence, the specific step of adding each social activity type difference to obtain the same social activity type difference of the short-time social activity type semantic sequence includes:
s31, calculating single same social activity type differences, wherein the single same social activity type differences are calculated according to the ratio of the corresponding time length differences to the whole window duration, and are expressed as:
TD i and TD (time division) k The ith short-time social activity type semantic sequence and the kth short-time social activity type semantic sequence are respectively SC i,j Type tag, SC, representing the jth social activity in the ith short-term social activity type semantic sequence k,l Type label for representing the first social activity in the kth short message social activity type semantic sequence, |TD i I represents TD i The window length of the time window in which it is located,representing TD i And TD (time division) k Social activity type is poor and ST is the social type s i,j Representing the duration time segment with highest duration time distribution proportion in the jth social activity in the ith short message social activity type semantic sequence, ST k,l Representing the duration time period with the highest duration time distribution proportion in the first social activity in the kth short message social activity type semantic sequence;
s32, if a plurality of same social activity types exist in the short-time social activity type semantic sequence, adding the differences of each social activity type, and representing the differences of the same social activity types of the short-time social activity type semantic sequence as follows:
wherein the method comprises the steps ofSemantic sequence TD for short-term social activity type i And TD (time division) k Is of the same social activity type, n is TD i And TD (time division) k There are the same number of social activity types s.
5. The method of claim 1, wherein in step S4, the specific steps of calculating a single different social activity type difference, calculating the single different social activity type difference according to the ratio of the corresponding time length difference to the whole window duration, and adding the single different social activity type differences to obtain the different social activity type differences of the short-time social activity type semantic sequence include:
s41, calculating single different social activity type differences, wherein the single different social activity type differences are calculated according to the ratio of the corresponding time length differences to the whole window duration, and are expressed as:
representing TD i Contains but TD k The ratio of the time length of the social activity node without the social activity type s to the total time length of the window;
s42, adding the single different social activity type differences to obtain different social activity type differences, wherein the different social activity type differences of the short-time social activity type semantic sequence are expressed as follows:
wherein the method comprises the steps ofSemantic sequence TD for short-term social activity type i And TD (time division) k Is different in social activity type, n is TD i And TD (time division) k There are different social activity types s.
6. The method for identifying a low-utility place according to claim 1, wherein in the step S5, the specific step of calculating the social activity type differences of the short-term social activity type semantic sequence, the social activity type differences of the short-term social activity type semantic sequence being averaged from the same social activity type differences of the short-term social activity type semantic sequence and the different social activity type differences of the short-term social activity type semantic sequence, comprises:
the social activity type differences of the short-term social activity type semantic sequence are averaged from the same social activity type differences of the short-term social activity type semantic sequence and different social activity type differences of the short-term social activity type semantic sequence, expressed as:
wherein the method comprises the steps ofSmaller values represent a social activity type semantic sequence TD i And TD (time division) k The higher the similarity of (c).
7. A method of identifying a low utility land according to claim 1, wherein in said step S6, the specific step of clustering short-term social activity type semantic sequences comprises:
setting a social activity type difference threshold of the short-time social activity type semantic sequence, wherein the social activity type difference threshold is not greater than the social activity type difference threshold, and considering the short-time social activity type semantic sequence as the same category, so that the short-time social activity type semantic sequence is clustered, g categories are obtained through clustering processing of the short-time social activity type semantic sequence, and the g categories are recorded as M 1 ，M 2 ，M 3 ，…，M g 。
8. The method of claim 1, wherein in the step S7, the specific step of determining the low-utility land according to the short-term social activity type semantic sequence clustering result includes:
for class M 1 ，M 2 ，M 3 ，…，M g Social activities with the number less than the set numberThe category of the dynamic number threshold is considered to be a low utility ground.
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Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106502398A (en) * 2016-10-21 2017-03-15 浙江工业大学 A kind of semantization activity recognition method learnt based on acceleration transducer and Multi-view Integration
CN107766415A (en) * 2017-09-08 2018-03-06 华南理工大学 The poorly efficient industrial land method for quickly identifying in cities and towns based on electricity consumption data
CN110428146A (en) * 2019-07-11 2019-11-08 陕西华地勘察设计咨询有限公司 A kind of inefficient land used in cities and towns precisely identifies and small watersheds
CN112231381A (en) * 2020-10-20 2021-01-15 青岛腾远设计事务所有限公司 Spatial data information collaboration platform based on WEBGIS and construction method thereof
CN112419124A (en) * 2020-11-19 2021-02-26 广州市城市规划勘测设计研究院 Method and device for quickly identifying low-efficiency industrial land and storage medium thereof
CN114639027A (en) * 2022-05-20 2022-06-17 山东省地质科学研究院 System and method for identifying low-efficiency town land use according to land use classification data
CN115456372A (en) * 2022-08-26 2022-12-09 广东省城乡规划设计研究院有限责任公司 Construction method of rural inefficient construction land identification standard system
CN115858899A (en) * 2022-12-14 2023-03-28 中国人民解放军国防科技大学 Network event label popularity prediction method based on multi-label influence
CN116129202A (en) * 2023-04-20 2023-05-16 北京新兴科遥信息技术有限公司 Method, apparatus and storage medium for stock land analysis

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11815989B2 (en) * 2022-01-20 2023-11-14 Vmware, Inc. Automated methods and systems for identifying problems in data center objects

Patent Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106502398A (en) * 2016-10-21 2017-03-15 浙江工业大学 A kind of semantization activity recognition method learnt based on acceleration transducer and Multi-view Integration
CN107766415A (en) * 2017-09-08 2018-03-06 华南理工大学 The poorly efficient industrial land method for quickly identifying in cities and towns based on electricity consumption data
CN110428146A (en) * 2019-07-11 2019-11-08 陕西华地勘察设计咨询有限公司 A kind of inefficient land used in cities and towns precisely identifies and small watersheds
CN112231381A (en) * 2020-10-20 2021-01-15 青岛腾远设计事务所有限公司 Spatial data information collaboration platform based on WEBGIS and construction method thereof
CN112419124A (en) * 2020-11-19 2021-02-26 广州市城市规划勘测设计研究院 Method and device for quickly identifying low-efficiency industrial land and storage medium thereof
CN114639027A (en) * 2022-05-20 2022-06-17 山东省地质科学研究院 System and method for identifying low-efficiency town land use according to land use classification data
CN115456372A (en) * 2022-08-26 2022-12-09 广东省城乡规划设计研究院有限责任公司 Construction method of rural inefficient construction land identification standard system
CN115858899A (en) * 2022-12-14 2023-03-28 中国人民解放军国防科技大学 Network event label popularity prediction method based on multi-label influence
CN116129202A (en) * 2023-04-20 2023-05-16 北京新兴科遥信息技术有限公司 Method, apparatus and storage medium for stock land analysis

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
基于空间自相关模型的农村居民点时空演变格局与特征研究;任平;洪步庭;周介铭;;长江流域资源与环境(第12期);第1993-2002页 *

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