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CN-121980935-A - Processing method and system for B-rep model data for Web browsing

CN121980935ACN 121980935 ACN121980935 ACN 121980935ACN-121980935-A

Abstract

The invention discloses a processing method and a system of B-rep model data for Web browsing, which relate to the technical field of data processing, and the method comprises the steps of obtaining a processing request aiming at a target Web environment; the method comprises the steps of combining network performance, utilizing a pre-trained neural network to identify a processing request so as to extract a plurality of characteristic items in a target Web environment, wherein each characteristic item corresponds to a characteristic processing scheme, introducing a browsing constraint rule, establishing a browsing constraint matrix aiming at the characteristic item, sequencing the characteristic processing schemes to obtain a first processing sequence, inputting a constraint matching engine, judging and verifying by using the browsing constraint matrix to obtain a second processing sequence, and then combining a current browsing navigation state to execute an analysis task in the target Web environment.

Inventors

  • ZHAO NAN

Assignees

  • 西安仁德智融信息技术有限公司

Dates

Publication Date
20260505
Application Date
20260122

Claims (10)

  1. 1. A processing method of B-rep model data for Web browsing is characterized by comprising the steps of acquiring a processing request aiming at a target Web environment; Combining network performance, utilizing a pre-trained neural network to identify and process the request so as to extract a plurality of characteristic items in a target Web environment, wherein each characteristic item corresponds to a characteristic processing scheme; Introducing a browsing constraint rule, establishing a browsing constraint matrix aiming at the characteristic item, sequencing and processing a characteristic processing scheme to obtain a first processing sequence, inputting the first processing sequence into a constraint matching engine, and judging, verifying and obtaining a second processing sequence by utilizing the browsing constraint matrix; And based on the second processing sequence, executing an analysis task in the target Web environment in combination with the current browsing navigation state.
  2. 2. A method of processing B-rep model data for Web browsing according to claim 1, wherein the processing request includes Web page data expressed in the B-rep model, and the Web page data is composed of model entities defined by the B-rep model and topology feature descriptors.
  3. 3. The processing method for B-rep model data for Web browsing according to claim 2, wherein extracting a plurality of feature items in a target Web environment comprises: analyzing the processing request, extracting a model entity and a topological feature descriptor, and establishing a topological association matrix; Acquiring current network performance, carrying out quantization processing, carrying out splicing processing with a topological association matrix, and identifying semantic categories of all manifold units in a model entity through a pre-trained neural network and dividing and aggregating a plurality of characteristic items in a target Web environment, wherein the manifold units comprise points, edges and faces; And identifying the semantic category of each characteristic item, and matching the corresponding characteristic processing scheme from a preset scheme library.
  4. 4. A method of processing B-rep model data for Web browsing according to claim 3, wherein identifying semantic categories of feature items comprises: a plurality of semantic category scheme descriptions are built in a preset scheme library, and semantic weight descriptions and analysis priority descriptions are configured for the scheme descriptions; The method comprises the steps of obtaining a first weight and a second weight of a certain feature item to form a target description set, matching the target description set with a scheme description one by one, and judging a semantic category corresponding to the feature item if the target description set accords with the semantic weight description The method comprises the steps of automatically activating analysis priority description, executing topology enhancement on high-priority to-be-processed feature items, and executing topology weakening on low-priority to-be-processed feature items.
  5. 5. The method for processing B-rep model data for Web browsing according to claim 1, wherein establishing a browsing constraint matrix for the feature item comprises: The browsing constraint rule comprises geometric constraint and webpage constraint, a first association relation between a topological hierarchy of the feature items and a first time tag is established according to a time sequence, and a second association relation between an interaction window of the feature items and a second time tag is established so as to determine a time sequence association model among the feature items; and constructing an initial constraint matrix, and calling a time sequence association model to dynamically assign values to the initial constraint matrix, wherein the dynamic values comprise forced assignment, combined assignment and concurrent assignment.
  6. 6. The processing method for B-rep model data for Web browsing according to claim 5, wherein the sorting process of the feature processing scheme comprises: According to the assigned initial constraint matrix, defining the position attribute of each feature processing scheme, and reordering the feature processing schemes, wherein the random bit is that the element corresponding to the row index and the element corresponding to the column index are all zero; first, the element corresponding to the row index is non-zero value and the element corresponding to the column index is all zero; Last bit, row index corresponding element is all zero and column index corresponding element is non-zero value; The middle bit is that the corresponding element of the row index and the corresponding element of the column index have non-zero values.
  7. 7. The method for processing B-rep model data for Web browsing according to claim 5, wherein the decision verification comprises: initializing state variables of all feature processing schemes to be 0, updating the state variables to be 1 after analyzing the feature processing schemes, calling an initial constraint matrix, and executing bidirectional analysis on a first processing sequence: The transverse analysis comprises the steps of searching a row index of a feature processing scheme in a first processing sequence corresponding to an initial constraint matrix, and counting the number of non-zero elements of the row index to determine the association density of each feature processing scheme; The longitudinal analysis comprises the steps of searching column indexes of a characteristic processing scheme corresponding to an initial constraint matrix in a first processing sequence, judging that verification passes if the column indexes are all zero, calculating the sum of the products of state variables and constraint intensities of all corresponding row indexes if the column indexes have non-zero values, and marking the sum as verification indexes; And comparing the verification index with a standard verification threshold, judging that verification passes if the verification index is larger than or equal to the standard verification threshold, and sorting according to the association density descending order, judging that verification fails if the verification index is smaller than the standard verification threshold, and keeping the feature processing scheme in a suspended state in the first processing sequence.
  8. 8. The method for processing B-rep model data for Web browsing according to claim 1, wherein the browsing navigation states include at least: forward, backward, and refresh.
  9. 9. The method for processing B-rep model data for Web browsing according to claim 8, wherein the performing of the parsing task in the target Web environment comprises: retrieving first webpage data from a first storage area, wherein the first webpage data comprises a topological structure of a B-rep model; Retrieving second web page data from the second storage area, the second web page data including a topology feature descriptor; And calling a Web control interface to generate and execute differential analysis in combination with the identified browsing navigation state: And if the current Web environment is identified as being in the forward or backward state, searching the locally stored topology snapshot, and identifying the missing characteristic items to execute incremental analysis.
  10. 10. A processing system for B-rep model data for Web browsing, the system comprising: The request acquisition module acquires a processing request aiming at a target Web environment; the model training module is used for combining network performance, utilizing a pre-trained neural network to identify and process the request so as to extract a plurality of characteristic items in the target Web environment, wherein each characteristic item corresponds to a characteristic processing scheme; The system comprises a browsing constraint module, a feature processing scheme sorting processing module, a constraint matching engine, a second processing sequence judging and verifying module, a feature processing scheme judging and verifying module and a feature processing module, wherein the browsing constraint module introduces a browsing constraint rule and establishes a browsing constraint matrix aiming at the feature item; and the analysis processing module is used for executing an analysis task in the target Web environment based on the second processing sequence and in combination with the current browsing navigation state.

Description

Processing method and system for B-rep model data for Web browsing Technical Field The invention relates to the technical field of data processing, in particular to a method and a system for processing B-rep model data for Web browsing. Background Along with the development of industrial digital transformation and cloud collaborative technology, high-precision industrial model data is urgently required by industry through direct browsing and interaction in the Web browsing process, at present, a B-rep model is mainly adopted for geometric modeling, manifold units and topological relations thereof are defined through an accurate mathematical equation, and the method has the advantages of high precision, support of engineering semantic recognition and the like; However, the processing of the traditional B-rep model in the Web environment has the following problems that on one hand, in the traditional B-rep model data processing, the B-rep model contains non-uniform rational B-splines, the characteristics of multiple control points, large data quantity and high topological continuity requirement are realized, on the other hand, the Web end cannot intelligently classify characteristic items represented by the B-rep model according to network performance by adopting general full-quantity transmission and simple geometric cutting, so that in the weak network environment, a browser can try to load all the characteristic items at the same time, so that the clamping, delay and even loading failure occur in the training process, the robustness of a system is greatly reduced, the suitability of the B-rep model is reduced, meanwhile, the topological continuity is damaged by simple geometric cutting, the characteristic items with high priority and low priority cannot be subjected to topological processing, the response is extremely slow, the semantic recognition accuracy is greatly reduced, on the other hand, in the traditional B-rep model data processing, the dependence among the topological elements in the B-rep model is usually realized in a mode, the situation that the characteristic items are disordered, the analysis relation among the characteristic items is not needed, the contour is easily arranged, the contour is not in a topological analysis sequence, and the problem is solved, and the accuracy is reduced, for example, the contour positioning accuracy is easily, and the problem is reduced due to the fact that the contour analysis is not to the contour positioning and the contour is difficult to be arranged in the position of a positioning condition. Disclosure of Invention (One) solving the technical problems Aiming at the defects of the prior art, the invention provides a processing method and a processing system for B-rep model data for Web browsing, which uses a pre-trained neural network to identify Web page data expressed by a B-rep model, establishes a browsing constraint matrix aiming at all characteristic items in the characteristic processing process, orders and processes all characteristic processing schemes of the Web page data according to a pre-stored scheme library to obtain a first processing sequence, inputs a constraint matching engine, obtains a second processing sequence through judging and verifying, realizes accurate conversion and automatic distribution of the Web page data, and solves the problems in the background technology. (II) technical scheme In order to achieve the above purpose, the invention is realized by the following technical scheme: in a first aspect, the present application provides a method for processing B-rep model data for Web browsing, the method comprising obtaining a processing request for a target Web environment; Combining network performance, utilizing a pre-trained neural network to identify and process the request so as to extract a plurality of characteristic items in a target Web environment, wherein each characteristic item corresponds to a characteristic processing scheme; Introducing a browsing constraint rule, establishing a browsing constraint matrix aiming at the characteristic item, sequencing and processing a characteristic processing scheme to obtain a first processing sequence, inputting the first processing sequence into a constraint matching engine, and judging, verifying and obtaining a second processing sequence by utilizing the browsing constraint matrix; And based on the second processing sequence, executing an analysis task in the target Web environment in combination with the current browsing navigation state. Further, the processing request includes web page data represented in a B-rep model, and the web page data is composed of model entities and topology feature descriptors defined by the B-rep model. Further, analyzing the processing request, extracting model entity and topology feature descriptor, and establishing topology association matrix; Acquiring current network performance, carrying out quantization processing, carrying out splic