Copper industry data robust coordination and significant error detection method

Abstract

The invention provides a copper industry data robust coordination and significant error detection method, and relates to the technical field of information, and the method comprises the steps: employing real material inventory data, and roughly calculating the inventory data standard deviation of each data item according to the inventory data; According to the copper, sulfur, gold and silver metal amount recovery rate of a smelting plant of a copper production enterprise, in order to prevent significant errors in inventory data from interfering with other data items during data coordination, robust data coordination model is established; in order to realize rapid data coordination and significant error detection of all material attributes for inventory data of materials of a smelting plant, a Lagrange multiplier conversion method is adopted, and a high-dimensional variable space is represented by a low-dimensional constraint multiplier space. And in addition, a differential evolution algorithm is adopted to carry out optimization iterative solution. The result obtained by the method is more accurate in significant error detection, the coordinated data is more stable, the calculation efficiency meets the actual requirements, and the method can also be popularized and applied in copper production enterprises and other production enterprises.

Description

technical field [0001] The invention relates to the field of information technology, in particular to technologies such as robust calculation and meta-heuristic optimization, and is a method for robust coordination and significant detection of non-ferrous copper production data that combines a robust calculation method and a meta-heuristic method. Background technique [0002] As an important part of the entire non-ferrous copper production, the smelting plant's recovery of valuable metals such as copper, sulfur, gold, and silver directly affects the production control and profit accounting of the entire enterprise and plays a decisive role. The metal balance report of the month can be obtained through the measurement of the quality of each stock material and the grade of the valuable elements contained in it at the end of the month, and the production situation of the month can be measured by calculating the recovery rate and unnamed loss rate of each valuable element. Unde...

AI Technical Summary

Problems solved by technology

However, due to the high dimensionality of the coordination data items of the copper production enterprises and the constraint condition of the data coordination model is the bilinear form of the product of variable attributes, which is difficult to decouple, directly using the meta-heuristic method to solve the algorithm will lead to slow algorithm iteration. It is difficult to converge; secondly, because there may be multiple significant error terms at the same time, a loop detection method is required, which will cause the detection time to be too long

If a robust data coordination model is used, although multiple significant errors can be detected at the same time, the objective function becomes nonlinear, making it difficult to solve the model

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