Algorithm for estimating consumption of raw materials of die-casting machine

Abstract

The invention discloses an algorithm for estimating consumption of raw materials of a die-casting machine. The algorithm comprises the following steps: data acquisition and preprocessing; extracting energy consumption features; analyzing the use efficiency of raw materials; energy consumption and raw material consumption correlation modeling; estimating raw material consumption; and optimizing and feeding back. According to the algorithm for estimating the consumption of the raw materials of the die-casting machine, energy consumption data of the die-casting machine are analyzed, and the consumption of the raw materials in production is estimated in combination with basic data of the die-casting machine and a raw material use efficiency model in the production process.

Description

Technical Field

[0001] The present invention relates to an algorithm for estimating the consumption of raw materials of a die-casting machine. Background Art

[0002] In modern industrial production, the consumption of raw materials is an important factor affecting production costs and efficiency. Traditional methods of raw material consumption management often rely on empirical estimates or simple proportional calculations, and it is difficult to accurately reflect the raw material consumption in the actual production process in real time.

[0003] With the development of industrial Internet of Things (IoT) technology, the ability to collect and analyze energy data in real time has been significantly improved. However, the existing technology has not fully explored the correlation between energy consumption and raw material consumption. Especially in complex processes such as die-casting, there is still a technical gap in how to accurately predict raw material demand through energy data. Summary of the Invention

[0004] The purpose of the present invention is to provide an algorithm for estimating the consumption of raw materials of a die-casting machine. By analyzing the energy consumption data of the die-casting machine and combining the basic data of the die-casting machine itself and the raw material usage efficiency model in the production process, the estimation of raw material consumption in production is realized.

[0005] To achieve the above purpose, the technical solution of the present invention is to design an algorithm for estimating the consumption of raw materials of a die-casting machine, including the following steps:

[0006] Data collection and preprocessing: Collect the equipment parameters of the die-casting machine, the energy consumption data and raw material usage data in the production process, and perform data preprocessing;

[0007] Energy consumption feature extraction: Extract key features from the preprocessed energy consumption data;

[0008] Raw material usage efficiency analysis: Analyze the raw material usage efficiency under different production conditions according to historical production data, and establish a raw material usage efficiency model;

[0009] Energy consumption and raw material consumption correlation modeling: Use machine learning or deep learning algorithms to combine the energy consumption features with the raw material usage efficiency model to establish a correlation model between energy consumption and raw material consumption;

[0010] Raw material consumption estimation: Estimate the raw material consumption situation according to the real-time or predicted energy consumption data using the correlation model;

[0011] Optimization and feedback: According to the estimation results, optimize the resource allocation in the production process to improve the raw material utilization efficiency; at the same time, collect the actual raw material consumption data to continuously feedback and optimize the algorithm.

[0012] Preferably, in the data collection and preprocessing step, data preprocessing includes steps such as data cleaning, outlier handling, and data standardization.

[0013] Preferably, in the energy consumption characteristic extraction step, the key characteristics include energy consumption, energy consumption rate, energy consumption pattern, etc.

[0014] The advantages and beneficial effects of the present invention are as follows:

[0015] 1. High accuracy: By correlating energy consumption with raw material consumption, accurate estimation of raw material consumption is achieved.

[0016] 2. Strong real-time performance: The estimation result of raw material consumption can be dynamically adjusted according to real-time energy consumption data.

[0017] 3. Strong scalability: The algorithm can be applied to different types of production processes and raw materials.

[0018] 4. Optimize resource allocation: By accurately estimating raw material consumption, resource allocation is optimized and production efficiency is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 The flowchart of an algorithm for estimating the consumption of raw materials of a die-casting machine provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION OF THE INVENTION

[0020] The following further describes the specific implementation manners of the present invention in conjunction with the drawings and embodiments. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and cannot be used to limit the protection scope of the present invention.

[0021] The technical solution specifically implemented by the present invention is as follows:

[0022] Embodiment

[0023] As Figure 1 shown, the working process of the algorithm for estimating the consumption of raw materials of a die-casting machine provided by the present invention is as follows:

[0024] Step 1: Collect die-casting machine nameplate information: Obtain basic information such as die-casting machine model and rated power;

[0025] Step 2: Estimate production parameters based on the model: Use the die-casting machine model and historical production data or industry standards of similar models to estimate the range of defective product rates, the ratio of recyclable materials and non-recyclable materials for a single product;

[0026] Step 3: Collect IoT data: Record the actual energy consumption and the actual number of products produced per unit time;

[0027] Step 4: Determine the energy consumption of a single product: Calculate the average energy consumption of each product per unit time using IoT data.

[0028] Step 5: Estimate the raw material consumption of a single product: Estimate the amount of raw materials required to produce a single product based on the rated power and actual energy consumption;

[0029] Step 6. Calculate the impact of recyclable and non-recyclable materials: For each product, consider the ratio of recyclable and non-recyclable materials and calculate the actual amount of raw materials consumed;

[0030] Step 7. Consider the defective product rate: Based on the defective product rate range estimated by historical production data and industry experience, estimate the amount of additional raw materials consumed due to defective products;

[0031] Step 8. Calculate the total raw material consumption per unit time: multiply the raw material consumption of a single product by the number of products produced per unit time, and then consider the impact of defective products and recycled materials to obtain the total consumption;

[0032] Step 9. Output the estimated raw material consumption: Output the calculation result as the amount of raw material used by the die-casting machine per unit time.

[0033] The specific calculation formula example of the above embodiment is:

[0034] Average energy consumption of a single product

[0035] Estimate the raw material consumption W of a single product unit-预估 =f(P unt , efficiency coefficient )

[0036] Non-recyclable material ratio R non-reclaim and the recyclable material ratio R reclaim

[0037] Considering the consumption of raw materials after non-recyclable materials

[0038] W unit-adjusted =W unit-预估 ×(1+R non-reclaim )

[0039] Production quantity per unit time N products (obtained from IoT data)

[0040] Total raw material consumption per unit time W total =N products ×W unit-adjusted

[0041] Consider the defective rate D scrap Total raw material consumption after

[0042]

[0043] It should be noted that: in the example of the calculation formula, f is a function for determining the raw material consumption based on the energy consumption and the efficiency coefficient, and it may need to be customized according to the actual situation. In addition, this example of the calculation formula assumes a linear relationship, and the actual situation may need to be adjusted according to the specific production process and process parameters.

[0044] The above are only the preferred embodiments of the present invention. It should be pointed out that for those of ordinary skill in the art of this technology, without departing from the technical principle of the present invention, several improvements and refinements can still be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. An algorithm for estimating the consumption of raw materials of a die-casting machine, characterized in that, It includes the following steps: Data collection and preprocessing: Collect the equipment parameters of the die-casting machine, as well as the energy consumption data and raw material usage data during the production process, and perform data preprocessing; Energy consumption feature extraction: Extract key features from the preprocessed energy consumption data; Raw material usage efficiency analysis: Analyze the raw material usage efficiency under different production conditions based on historical production data, and establish a raw material usage efficiency model; Energy consumption and raw material consumption correlation modeling: Use machine learning or deep learning algorithms to combine the energy consumption features with the raw material usage efficiency model to establish an energy consumption and raw material consumption correlation model; Raw material consumption prediction: Predict the raw material consumption situation using the correlation model based on real-time or predicted energy consumption data; Optimization and feedback: According to the prediction results, optimize the resource allocation in the production process to improve the raw material utilization efficiency; at the same time, collect the actual raw material consumption data to continuously feedback and optimize the algorithm.

2. The algorithm for estimating the consumption of raw materials of the die-casting machine according to claim 1, characterized in that, In the data collection and preprocessing step, the data preprocessing includes steps such as data cleaning, outlier handling, and data standardization.

3. The algorithm for estimating the consumption of raw materials of the die-casting machine according to claim 1, wherein, In the energy consumption feature extraction step, the key features include energy consumption amount, energy consumption rate, energy consumption pattern, etc.

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