Urban agglomeration geographical environment knowledge base construction method and system of distributed architecture

Abstract

The invention provides an urban agglomeration geographical environment knowledge base construction method and system of a distributed architecture. The method comprises the steps: cutting an initial mother graph of each city into a plurality of initial sub-graphs at a server end, and distributing the initial sub-graphs to corresponding operation terminals; at each operation terminal, preprocessingthe initial sub-graph, segmenting a plurality of target areas in the initial sub-graph through gray scale operation, and carrying out geographic feature recognition on the target areas; and the operation terminal sends the geographic attribute information of each target area of the initial sub-graph completing geographic feature recognition to the server side, and creates a knowledge base according to the geographic attribute information of the server side and the geographic entity information, corresponding to the geographic attribute information, of the transportation side. The method improves the utilization rate of existing equipment, reduces the equipment cost and energy consumption, makes full use of the calculation power of a plurality of calculation terminals, improves the creation speed of a knowledge base, and overcomes the defect that the accuracy, the fineness and the processing speed are contradictory during large-scale geographic feature recognition in the prior art.

Description

technical field [0001] The present invention relates to the technical field of artificial intelligence and information processing, in particular, to a method and system for constructing a geographical environment knowledge base of a distributed urban agglomeration. Background technique [0002] Geographical environment refers to the geographical location of a certain society and the sum of various natural conditions related to it, including climate, land, rivers, lakes, mountains, mineral deposits, animal and plant resources, etc. The geographical environment of urban agglomeration is an important type of geographical environment, including buildings, transportation facilities, leisure green spaces, urban mountains, waterways, underground features, etc. in multiple cities and between cities. [0003] The geographical environment of urban agglomerations involves a large area, many types of geographical environments, and relatively trivial information. In the prior art, the c...

AI Technical Summary

Problems solved by technology

In the prior art, the construction of the knowledge base related to the urban geographical environment is usually completed by experts in the field or other people through manual editing, but this construction of the knowledge base by personnel requires a lot of time, energy and cost, and It cannot be refined, and it is not easy to finely grasp and manage the geographical environment of urban agglomerations

When updating the knowledge base in the future, the operator needs to have a full understanding of the contents of the knowledge base before updating. Therefore, the existing method of manually constructing the geographic environment of urban agglomerations is not conducive to the maintenance of the knowledge base.

[0004] With the development of technology, high-resolution satellite images and aerial images are used to acquire urban surface images. The images contain the geographical environment information of urban agglomerations, especially the geographical environment information of the surface. However, image recognition in the prior art is mostly Partial recognition, discrete recognition or artificial marking by non-professionals, high error rate, incomplete, lack of calculation and storage of entity features

Statistics, and it is difficult to conduct accurate and comprehensive query and display through attribute information or fields

In addition, due to the large amount of high-resolution satellite images of urban agglomerations, when performing operations such as image recognition, processing, calculation, and storage, the requirements for computer equipment are extremely high, and a large amount of equipment costs and energy consumption are required.

In addition, traditional image recognition technology is generally only for a certain type of information recognition. Due to the limitation of computing power and recognition methods, the fineness, accuracy and processing speed of the image processing are usually contradictory, and it is difficult to apply to the processing of urban The situation where there is a large amount of data and complex types in the geographical environment of the group

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