A multi-dimensional data driving integrated portrait method of a transportation main body

Abstract

The invention discloses a multi-dimensional data driving integrated portrait method of a transportation main body. The method specifically comprises the following steps: collecting the original operation data of the integrated transportation main body; According to the original operation data of the main body of transportation, the core portrait elements are extracted, and the core portrait elements are classified. Analyzing the multi-dimensional operation characteristics of the integrated portrait elements of the transportation subject; Combined with the characteristics of portrait elements in transportation industry, the comprehensive portrait attribute label database of transportation subject is established, and the portrait elements are evaluated comprehensively. Screening the attribute labels of the portrait of the main body of transportation, and generating the comprehensive portrait based on the main body of transportation; By drawing a comprehensive portrait of the main body oftransportation, to achieve the characterization and description of the operating characteristics of integrated transport enterprises, to strengthen the overall control of the status of integrated transport enterprises operating services.

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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