Material supplier pushing method, system and equipment
A supplier and data technology, applied in the field of data processing, can solve problems such as potential safety hazards, engineering quality defects, and rising material transportation costs, and achieve the effects of improving prediction accuracy and accuracy, accurate and efficient push, and reducing cooperation risks
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Embodiment 1
[0024] refer to figure 1 As shown, an embodiment of the present invention provides a method for pushing material suppliers, comprising the following steps:
[0025] S1. Establish a material supplier database;
[0026] S2. The material supplier submits an application for storage;
[0027] S3. The first judgment step. The first judgment step is: review the material supplier's warehousing qualifications, collect the first supplier data when the review is passed, and disallow the material when the review fails. Supplier warehousing;
[0028] S4, preprocessing the first supplier data to form the second supplier data;
[0029] S5, evaluating and analyzing the second supplier data to obtain third supplier data;
[0030] S6. Receive the needs of the project unit;
[0031] S7. Screen out qualified material suppliers in the material supplier database according to the requirements of the project unit and the data of the third supplier;
[0032] S8. Sort the material suppliers accor...
Embodiment 2
[0036] refer to figure 2 As shown, based on the first embodiment of the present invention, the step S5 includes the following steps:
[0037] S51, vectorize multiple credit dimensions of the material supplier to obtain multiple variable parameters of the Logistic regression model;
[0038] S52, performing data cleaning on the second supplier data;
[0039] S53, calculate the evidence weight conversion value of each variable parameter;
[0040] S54, establish a logistic regression model;
[0041] S55, establish a priori scoring model, and train a Logistic regression model through the priori scoring model;
[0042] S56. Process the unbalanced data marked as untrustworthy data;
[0043] S57. Perform manual error correction on the prediction result trained by the Logistic regression model;
[0044] S58. Predict the comprehensive strength of the material supplier according to the Logistic regression model after manual error correction;
[0045] S59. Construct a labor service...
Embodiment 3
[0064] Based on the second embodiment of the present invention, the business scope of the material supplier is obtained by correlating the winning bid data and the competition analysis data of the material supplier, and the competition analysis data at least includes the following: The bidding items that the material supplier participated in, the data as the second winning candidate and the data as the third winning candidate.
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