An automatic statistical method for product structure analysis across multiple production lines using three dimensions (steel grade + thickness range + width range)

Abstract

This invention discloses an automatic statistical method for three-dimensional product structure analysis across multiple production lines based on stored procedures. It encapsulates statistical logic through database stored procedures and utilizes GROUP BY and CASE WHEN interval judgments to achieve automatic aggregation by steel type, thickness range, and width range. This method supports unified processing of data from 14 or more production lines and allows for flexible simplification of statistical dimensions. It solves the problems of complexity and inefficiency in traditional multi-production-line product structure analysis, and features high efficiency, stability, and ease of maintenance. This invention has been practically implemented in state-owned steel plants for many years.

Description

Technical Field

[0001] This invention relates to the field of metallurgical production data processing technology, and in particular to a statistical method based on database stored procedures for automatically classifying and summarizing products from multiple production lines in three dimensions: steel type, thickness range, and width range. Background Technology

[0002] Traditional multi-production-line product structure statistics have the following problems: With a large number of production lines (e.g., 14 production lines) and scattered data, manual data aggregation is extremely difficult. It requires statistical analysis based on a three-dimensional combination of steel type, thickness range, and width range, which is logically complex and prone to errors. There is no unified automated implementation method, and it relies on manual processing, which is extremely inefficient. Unable to quickly switch statistical dimensions according to management requirements. Summary of the Invention

[0003] This invention provides an automatic statistical method for three-dimensional product structure of multiple production lines based on stored procedures, which realizes automatic classification and summarization through database stored procedures.

[0004] 3.1 Data Collection Production performance data from 14 production lines were integrated into a unified database to form a unified data source.

[0005] 3.2 Technical Implementation Methods The overall logic is encapsulated through database stored procedures, enabling unified scheduling and execution of statistical processes. Within the stored procedure, groups and summaries are performed using GROUP BY steel type, thickness range, and width range. The actual thickness and width are automatically determined to fall within the preset range (such as 0.2~0.3, 0.3~0.4mm, etc.) by using CASE WHEN or range judgment logic. The system automatically sums and calculates the output, tonnage, and quantity for each group.

[0006] 3.3 Automated 3D Statistics The system automatically categorizes data according to the following dimensions: Grouped by steel type; Grouped by preset thickness range; Grouped by preset width range.

[0007] 3.4 Dimensional Flexibility and Simplification It supports dynamically simplifying statistical dimensions according to management needs. For example, it can perform statistics only by steel type and thickness range, without splitting the width range, so as to achieve flexible report adaptation.

Claims

1. An automatic statistical method for three-dimensional product structure across multiple production lines, characterized in that, Implemented through database stored procedures, including: (1) Collect production data from 14 or more production lines in a unified manner; (2) Grouping by steel type, thickness range, and width range during storage; (3) The thickness and width are automatically placed into the corresponding preset intervals by the interval judgment logic; (4) Automatically complete the summary calculation of production, tonnage and quantity.

2. The method according to claim 1, characterized in that, The interval determination is implemented through the CASE WHEN logic, which matches the actual product thickness and width to the corresponding interval.

3. The method according to claim 1, characterized in that, It supports dynamically simplifying statistical dimensions, allowing you to disable width ranges or other dimensions as needed, retaining only certain dimensions for aggregation.

4. The method according to claim 1, characterized in that, All statistical logic is encapsulated in the stored procedure, resulting in high execution efficiency and suitability for scenarios with large data volumes and multiple production lines.