A multi-dimensional data-driven comprehensive portrait method for transportation subjects

Abstract

The invention discloses a multi-dimensional data-driven comprehensive portrait method of transportation subjects. The method specifically includes: collecting original operation data of comprehensive transportation subjects; and extracting core portrait elements according to the original operation data of transportation subjects. , and grade the core portrait elements; carry out multi-dimensional operational feature analysis on the comprehensive portrait elements of the transportation subject; combine the characteristics of the portrait elements of various transportation industries to establish a comprehensive portrait attribute tag library of the transportation subject, and synthesize the portrait elements. Evaluate; filter the portrait attribute labels of transportation entities to generate a comprehensive portrait based on the transportation entity; by drawing a comprehensive portrait for the transportation entity, to achieve the characterization and description of the operation characteristics of integrated transportation enterprises, and strengthen the operation of integrated transportation enterprises. Overall control of service status.

Description

technical field [0001] The invention relates to a multi-dimensional data-driven comprehensive portrait technology of transportation subjects, which is used to analyze and evaluate the operating characteristics of comprehensive transportation enterprises, and belongs to the field of comprehensive transportation data mining applications and data visualization. Background technique [0002] Traditional transportation market players lack quantitative judgments on the management model, system intelligence, rationality of personnel allocation, and scientific decision-making of operating companies in various industries. They cannot obtain the overall evaluation results of each company, and it is difficult to predict Estimate the credit rating and enterprise quality of each enterprise. The current enterprise quality and reputation assessment is scattered, and cross-field comprehensive scoring has not been realized, making it difficult to scientifically and objectively evaluate enter...

AI Technical Summary

Problems solved by technology

[0002] Traditional transportation market players lack quantitative judgments on the management model, system intelligence, rationality of personnel allocation, and scientific decision-making of operating companies in various industries. They cannot obtain the overall evaluation results of each company, and it is difficult to predict Estimate the credit rating and enterprise quality of each enterprise

The current enterprise quality and reputation assessment is scattered, and cross-field comprehensive scoring has not been realized, making it difficult to scientifically and objectively evaluate enterprises covering multiple fields

Moreover, the dynamic and static data generated by its internal management and operation system have not been well mined, and each system is relatively separated, and the construction content is separated from each other. A large number of data information resources and application results are accumulated in the management departments at all levels of each enterprise, forming a Departmental structure; at the same time, the transportation industry lacks the integration technology of multi-source heterogeneous data and information resources, which restricts the extraction of comprehensive characteristics of the transportation market, the prediction of development, and the early warning and prediction of abnormal behavior; the lack of portraits of the transportation market technology, it is difficult to intuitively display the status quo, credit rating and development trend of each transportation market, which seriously affects the accuracy and timeliness of management decisions

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