Multi-person cooperative review method based on offline networking environment

Abstract

A multi-person collaborative review method based on an offline networking environment comprises the steps that firstly, performing data importing on external multi-batch data sources, completing dataintegration processing by analyzing data structures and associations of data sets, and fusing the multiple data sets into a complete structured data set; then, according to personalized attributes ofdifferent review experts, automatically analyzing content features of the to-be-reviewed data set, and performing association evaluation and automatic matching with expert attribute features to realize automatic division of review contents; in the evaluation process, after an evaluation expert audits and adjusts the data, automatically completing data integration and aggregation, data associationcalculation and hierarchical summary calculation; and finally, carrying out word segmentation and semantic analysis on review opinions filled by experts, extracting keywords and quantized data in thetext content, carrying out data summarization calculation and content integration processing on the multi-person review content on the basis of the identifiers of the content attributes, and automatically generating a review result report.

Description

technical field [0001] The invention relates to the technical field of multi-person collaborative review, in particular to a multi-person collaborative review method based on an offline networking environment. Background technique [0002] For confidential business environments such as the government, military, public security, and scientific research institutions, offline networking work scenarios constructed to meet the needs of mobile offices need to export the intranet environment data in batches according to relevant management requirements, and then import them into the offline office environment. So as to have the working conditions of the offline environment. For review tasks oriented to a large amount of data and reports, when the data collection structure of the review object is complex, involving many types of tables, and a large amount of data, it will become more difficult to effectively organize and carry out the review work. The data synchronization and versi...

AI Technical Summary

Problems solved by technology

For review tasks oriented to a large amount of data and reports, when the data collection structure of the review object is complex, involving many types of tables, and a large amount of data, it will become more difficult to effectively organize and carry out the review work. Environmental data synchronization and version control are also important obstacles restricting its working mechanism, which brings a very large workload and pressure to business development

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