CN-121996666-A - Knowledge-driven clustered aggregation space updating method and system

CN121996666ACN 121996666 ACN121996666 ACN 121996666ACN-121996666-A

Abstract

The invention discloses a clustered aggregation space updating method based on knowledge driving, which relates to the technical field of digital aided design of building and urban and rural planning, and comprises the steps of constructing a quantitative characterization index system of an aggregation space according to an aggregation space cognitive theory; the method comprises the steps of constructing a knowledge graph of a colony based on quantitative characterization indexes of the colony space, developing a colony space analysis function module based on the quantitative characterization indexes of the colony space and the knowledge graph of the colony, constructing a webpage front end of clustered colony form analysis, calling the colony space analysis function module through webpage front end interactive operation, identifying colony form problems, defining a clustered protection range and optimizing a reference case, and providing a colony space updating strategy. The invention also discloses a system of the clustered aggregation space updating method based on knowledge driving, and provides an efficient and interactive technical approach for the systematic management and clustered updating design of the aggregation space.

Inventors

ZHU XUERONG
Tang Pi
WANG XIAO
SONG ZHEHAO
WANG YUJIAO

Assignees

东南大学

Dates

Publication Date: 20260508
Application Date: 20251225

Claims (9)

1. A knowledge-driven clustered aggregation space updating method, comprising: s1, constructing a quantitative characterization index system of a aggregation space according to an aggregation space cognitive theory; s2, constructing a knowledge graph of the aggregation group based on indexes in a quantitative characterization index system of the aggregation space; s3, developing a aggregation space analysis functional module based on a quantitative characterization index system of the aggregation space and a knowledge graph of an aggregation group; Step S4, constructing the front end of a webpage for clustered aggregation and fall morphological analysis, and providing an interactive operation interface for an aggregation and fall space analysis functional module; And S5, calling a landing space analysis function module through web page front-end interactive operation, identifying landing morphological characteristics, defining a clustering protection range and screening reference cases, and providing a landing space updating strategy according to the identified landing morphological characteristics, the defined clustering protection range and the screened reference cases.
2. The knowledge-driven clustered aggregation space updating method according to claim 1, wherein step S1 constructs a quantitative characterization index system of the aggregation space, comprising: based on a landing space cognition theory, dividing the landing space into three space elements of a built environment, a natural environment and a social environment; constructing a quantitative characterization index system of the aggregation space according to the three types of space elements; acquiring multi-source space data, and extracting quantitative characterization indexes of the aggregation space according to a quantitative characterization index system; and establishing a aggregation base database, and standardizing storage and management of multi-source space data and quantitative characterization indexes.
3. The knowledge-driven clustered aggregation space updating method as claimed in claim 1, wherein the index in the aggregation space-based quantitative characterization index system comprises: Building foundation area, village area, building density, building space average value, boundary contour length-width ratio, boundary contour shape index, building orientation disorder degree, building distance disorder degree, building gathering disorder degree, village space fractal dimension value and village space continuity index of the built environment; altitude, climate, topography, three-level river distance and five-level river distance indexes of natural environment; Regional economy, population density, traffic accessibility, traffic network density index of the social environment.
4. The knowledge-driven clustered aggregation space updating method according to claim 1, wherein the knowledge graph of the aggregation group constructed in the step S2 is characterized in that attributes of aggregation nodes are constructed according to indexes in a quantitative characterization index system, and similarity relations of the aggregation nodes are established according to similarity of vectors constructed by multidimensional quantitative characterization indexes of an aggregation-built environment.
5. The method for updating a clustered aggregation space based on knowledge driving according to claim 1, wherein the aggregation space analysis function module developed in the step S3 comprises an information retrieval module, an association inference module, an information screening module and a data visualization module, The information retrieval module is used for receiving the landing address information input by a user, inquiring a landing basic database based on the landing address information, and acquiring multisource space data and quantitative characterization indexes for inquiring landing; the association inference module is used for receiving the landing address information and association inference options input by a user, inferring a landing node group with a similar relation to the query landing according to the knowledge graph of the landing group, querying a landing base database, and obtaining multi-source space data of the landing node group with the similar relation to the query landing; The information screening module is used for receiving attribute screening conditions input by a user, carrying out attribute screening on the aggregation node groups from the association inference module, screening out aggregation node subsets conforming to the attribute conditions, and inquiring an aggregation basic database to obtain multi-source space data of the aggregation node subgroups conforming to the attribute screening conditions; and the data visualization module is used for receiving the quantitative characterization indexes of the query aggregation from the information retrieval module and drawing a statistical chart based on the quantitative characterization indexes.
6. The method for updating the clustered aggregation space based on knowledge driving according to claim 5, wherein the front end of the clustered aggregation form analysis webpage constructed in the step S4 comprises a functional area, a knowledge graph interaction area, a data display area, an information display area and an image display area, wherein, The function area is used for receiving a landing address input by a user or a triggered function operation instruction and calling a corresponding landing space analysis function module according to the function operation instruction; The knowledge graph interaction area is used for receiving the data of the aggregation node group which is returned from the association inference module and has a similar relation with the query aggregation, and rendering the similar relation between the aggregation node group and the aggregation node; The data display area is used for receiving the statistical chart drawn by the data visualization module and rendering the statistical chart in the front-end interface; the information display area is used for receiving the quantitative characterization index of the query aggregation returned by the information retrieval module and displaying the quantitative characterization index in a structural form on the front-end interface; The image display area is used for receiving the multi-source space data returned by the aggregation space analysis functional module and displaying the multi-source space data on the front end interface, and the displayed image comprises the multi-source space data returned by the information retrieval module, the multi-source space data returned by the association inference and the multi-source space data returned by the information screening module.
7. The knowledge-driven clustered aggregation space updating method according to claim 6, wherein the functional area comprises an information retrieval frame, an association selection button and an attribute screening frame; the information retrieval frame receives the landing address information input by the user and sends the landing address information to the information retrieval module and the association inference module; The relevance selecting button is used for receiving a relevance inference option selected by a user, wherein the relevance inference option comprises a primary relevance inference and a secondary relevance inference, and sending the selected relevance inference option to the relevance inference module, wherein the primary relevance inference is a node inferred to have a similar relationship with a target node directly, and the secondary relevance inference is a node inferred to have a similar relationship with the target node indirectly; And the attribute screening frame is used for receiving one or more attribute screening conditions set by a user and sending the attribute screening conditions to the information screening module.
8. The knowledge-driven clustered aggregation space updating method according to claim 1, wherein step S5 comprises: Acquiring a aggregation basic database based on information query operation of the functional area, and assisting a user in recognizing the space current situation of aggregation so as to identify aggregation morphological characteristics existing in the aggregation; acquiring a knowledge map of the aggregation group based on the association inference operation of the functional area to realize cluster inference, and using the directly associated village group as a demarcation basis of a clustered protection range; setting retrieval conditions according to abnormal problems in the identified aggregation morphological characteristics based on attribute screening operation of the functional area, and retrieving cases meeting the retrieval conditions from an aggregation update case library as reference cases of aggregation update design; Setting an updating target according to the identified aggregation form characteristics, analyzing a reference case of aggregation updating design by taking the defined clustering protection range as a cooperative treatment unit, and providing an aggregation space updating strategy.
9. The system for knowledge-driven clustered aggregation space updating method of claim 1, wherein the system comprises an information layer, a function layer and a page layer, wherein, The information layer is used for constructing a quantitative characterization index system of the aggregation space according to the aggregation space cognitive theory; constructing a knowledge graph of the aggregation group based on indexes in the quantitative characterization index system of the aggregation space; the functional layer is used for developing a aggregation space analysis functional module based on the quantitative characterization index system of the aggregation space and the knowledge graph of the aggregation group; The page layer is used for building the front end of the webpage for clustered aggregation form analysis and providing an interactive operation interface for the aggregation space analysis functional module; Calling a landing space analysis function module through web page front-end interactive operation, identifying landing morphological characteristics, defining a clustering protection range and screening reference cases; and according to the identified aggregation morphological characteristics, the defined clustering protection range and the screened reference cases, a aggregation space updating strategy is proposed.

Description

Knowledge-driven clustered aggregation space updating method and system Technical Field The invention relates to the technical field of digital aided design of building and urban and rural planning, in particular to a clustered aggregation space updating method and system based on knowledge driving. Background With urban and rural development entering into an inventory updating stage, a clustered aggregation space updating strategy increasingly becomes an important path for realizing regional collaboration and efficient management in face of a huge number of traditional aggregation updating demands. The mode emphasizes that under the framework of systematic management, through cross-regional aggregation comparison analysis, the commonality characteristics and the individual value are accurately identified, so that a scientific space development strategy is formulated. However, in the prior art, the digital foundation supporting clustered updates remains weak. Firstly, on the data level, various built environment information resources are scattered and different in standard, so that a large amount of aggregated space data are in an isolated and scattered state, and effective association comparison and integration analysis are difficult to carry out in a wide area range. Secondly, in the knowledge transformation and application level, although a space database or a knowledge map is initially constructed in a part of regions, the construction process is often insufficient to be combined with the specific requirements of design practice, and the problems of inconvenient knowledge calling, unsound analysis function and the like exist, so that the actual supporting capability of the knowledge in updating decisions is limited. Under the constraint of the conditions, the current aggregation update work still highly depends on individual experience and local information, and comprehensive analysis based on clustered knowledge is lacking. The mode has the advantages of limited visual field and low decision efficiency, and can easily cause the problems of homogeneous updating, repeated construction, resource waste and the like. Therefore, in order to break through the bottleneck of the traditional working mode, an intelligent space design auxiliary method capable of penetrating the whole flow of data integration, knowledge construction and design application is needed. Disclosure of Invention The invention aims to solve the technical problem of overcoming the defects of the prior art and providing a clustered aggregation space updating method and system based on knowledge driving. On one hand, the scattered aggregation space information is digitally managed, and an integral knowledge system is built. On the other hand, combining design analysis and planning decision requirement, constructing an interaction platform for knowledge calling. The invention aims to provide a systematic and intelligent analysis means for clustered aggregation space update through a knowledge-driven technical path. The method can promote the comparative analysis of regional aggregation to form a scientific planning decision, promote the space analysis efficiency and enhance the practical applicability of knowledge construction. The invention adopts the following technical scheme for solving the technical problems: The invention provides a knowledge-driven clustered aggregation space updating method, which comprises the following steps: s1, constructing a quantitative characterization index system of a aggregation space according to an aggregation space cognitive theory; s2, constructing a knowledge graph of the aggregation group based on indexes in a quantitative characterization index system of the aggregation space; s3, developing a aggregation space analysis functional module based on a quantitative characterization index system of the aggregation space and a knowledge graph of an aggregation group; Step S4, constructing the front end of a webpage for clustered aggregation and fall morphological analysis, and providing an interactive operation interface for an aggregation and fall space analysis functional module; And S5, calling a landing space analysis function module through web page front-end interactive operation, identifying landing morphological characteristics, defining a clustering protection range and screening reference cases, and providing a landing space updating strategy according to the identified landing morphological characteristics, the defined clustering protection range and the screened reference cases. As a further optimization scheme of the clustered aggregation space updating method based on knowledge driving, the step S1 constructs a quantitative characterization index system of the aggregation space, which comprises the following steps: based on a landing space cognition theory, dividing the landing space into three space elements of a built environment, a natural environment and a social environment; constructing