Raw material real-time price calculation and prediction system

Abstract

The invention discloses a raw material real-time price calculation and prediction system, which belongs to the field of data processing and analysis, and comprises a raw material price database for collecting and storing historical price data, production cost data, upstream and downstream industry chain information and market supply and demand reports of various raw materials; the data management module is used for classifying and identifying various raw materials in the raw material price database and integrating data related to the various raw materials; the data model building module is used for building a raw material price prediction model according to the historical data and the real-time analysis result; and the price prediction module calculates the current raw material price in real time by using a raw material price prediction model and predicts the price trend in a period of time in the future. According to the raw material real-time price calculation and prediction system, by comprehensively considering factors influencing the raw material price in multiple aspects and utilizing advanced data processing and modeling technologies, the forming mechanism and the change trend of the raw material price can be reflected more accurately, and thus the accuracy of price prediction is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing and analysis, and particularly relates to a raw material real-time price calculation and prediction system. Background Art

[0002] In multiple industries such as industrial production, agricultural planting, and food processing, the fluctuations in raw material prices directly affect an enterprise's cost control, production planning, and market competitiveness. Real-time price information enables enterprises to quickly respond to market changes, adjust procurement strategies and production plans, thereby effectively controlling costs and optimizing profits. When raw material prices decline, enterprises can appropriately increase the procurement volume to reduce production costs; conversely, when prices rise, they can reduce procurement or adjust the product mix to cope. By mastering raw material prices in real time, enterprises can more flexibly adjust product prices to deal with market competition. Therefore, the real-time prediction of raw material prices plays a significant role in an enterprise's operational efficiency and market competitiveness.

[0003] Traditionally, the prediction of raw material prices relies on manual analysis of market reports, historical data, etc. This method is not only time-consuming and laborious, unable to update data and analysis results in real time, unable to promptly reflect market changes, but also difficult to comprehensively consider all factors affecting prices, resulting in inaccurate prediction results. Summary of the Invention

[0004] The purpose of the present invention is to propose a raw material real-time price calculation and prediction system to solve the problem that the existing prediction of raw material prices relies on manual analysis, resulting in inaccurate prediction results.

[0005] To achieve the above purpose, the present invention adopts the following technology: A raw material real-time price calculation and prediction system, including:

[0006] A raw material price database, which collects and stores historical price data, production cost data, upstream and downstream industrial chain information, and market supply and demand reports of various raw materials;

[0007] A data management module, which is used to classify and identify various raw materials in the raw material price database and integrate data related to various raw materials;

[0008] A data model construction module, which, based on historical data and real-time analysis results and using big data analysis technology, deeply analyzes multi-dimensional factors such as production costs, supply and demand capabilities, upstream price trends, downstream application demands, macroeconomic indicators, and policy environments, and then establishes a raw material price prediction model;

[0009] A price prediction module, which uses the raw material price prediction model to calculate the current raw material price in real time and predict the price trend in a future period of time.

[0010] As a further description of the above technical solution: The raw material price database includes a data collection unit, which obtains information such as the latest raw material prices, supply and demand data, and policy changes through the data collection unit.

[0011] As a further description of the above technical solution: The data collection unit uses web crawler technology or API interface methods to obtain data from major market research institutions, exchanges, and industry websites.

[0012] As a further description of the above technical solution: The data management module includes a data cleaning unit, which is used to clean the obtained market raw material price data to remove invalid or incorrect data.

[0013] As a further description of the above technical solution: The data management module also includes an algorithm optimization unit, which can automatically or manually adjust relevant parameters in the prediction algorithm according to data characteristics to further improve the accuracy and stability of prediction.

[0014] As a further description of the above technical solution: The data model construction module constructs a data model using machine learning regression algorithms.

[0015] As a further description of the above technical solution: The price prediction module includes a user interaction unit to support users in querying historical prices, current prices, predicted prices, and impact factor analysis of specific raw materials.

[0016] As a further description of the above technical solution: The price prediction module also includes a data output unit, which can generate the user's query content in the form of charts, reports, etc.

[0017] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are as follows:

[0018] By comprehensively considering various factors affecting raw material prices and using advanced data processing and modeling technologies, it can more accurately reflect the formation mechanism and change trend of raw material prices, thereby improving the accuracy of price prediction. The real-time updated price information and prediction results help enterprises quickly respond to market changes, optimize procurement and production plans, reduce operating costs, automate the data collection and analysis process, reduce manual intervention, improve enterprise management efficiency, and promote the industry's transformation towards intelligence and digitization. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 Shows a flowchart according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0020] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0021] Referring to Figure 1 , a real-time raw material price calculation and prediction system provided in this embodiment includes:

[0022] A raw material price database that collects and stores historical price data, production cost data, upstream and downstream industrial chain information, and market supply and demand reports of various raw materials;

[0023] A data management module for classifying and identifying various raw materials in the raw material price database and integrating data related to various raw materials;

[0024] A data model construction module, based on historical data and real-time analysis results, and using big data analysis technology, deeply analyzes multi-dimensional factors such as production costs, supply and demand capabilities, upstream price trends, downstream application demands, macroeconomic indicators, and policy environments, and then establishes a raw material price prediction model. The data model construction module uses a machine learning regression algorithm to construct the data model;

[0025] A price prediction module that uses the raw material price prediction model to calculate the current raw material price in real time and predict the price trend in the next period of time.

[0026] This system can be widely applied to multiple industries such as chemical industry, energy, metallurgy, agriculture, and food processing, helping enterprises make more informed decisions in aspects such as procurement, production, and inventory management. It is especially suitable for enterprises sensitive to raw material price fluctuations, such as petrochemical, metal smelting, and agricultural product processing. The system can provide raw material price predictions for short-term (such as 1 - 7 days), medium-term (such as 1 - 3 months), and long-term (such as more than 6 months). The prediction results not only include price trends, but also can provide the probability distribution of price fluctuations, analysis of key influencing factors (such as supply and demand changes, policy changes, etc.), and risk warnings.

[0027] Its technical value lies in that by integrating multi-dimensional data, it can perform complex correlation analysis, identify key factors affecting raw material prices, use advanced machine learning regression algorithms to automatically learn and optimize the model, improve prediction accuracy, and obtain and process data in real time to ensure the timeliness of prediction results;

[0028] The economic value lies in that through accurate prediction, enterprises can purchase at low price points, reducing raw material costs. Based on price prediction, enterprises can reasonably arrange inventory, avoiding inventory backlogs or shortages caused by price fluctuations. By predicting price fluctuations and providing risk warnings, enterprises can take measures in advance to avoid market risks.

[0029] Specifically, the raw material price database includes a data collection unit. The data collection unit uses web crawler technology or API interface methods to obtain data from major market research institutions, exchanges, and industry websites, such as industry reports and market analyses provided by IHS Markit, Wood Mackenzie, etc., real-time trading data provided by the London Metal Exchange (LME), New York Mercantile Exchange (NYMEX), etc., industry dynamics and price information provided by chemical industry websites, metal industry websites, etc., as well as macroeconomic data and policy information released by the country. The latest raw material prices, supply and demand data, policy changes, etc. are obtained through the data collection unit.

[0030] Among them, a database with strong real-time performance can ensure that the prediction model is trained and predicted based on the latest market data, helping the model capture the latest dynamics and trends of raw material prices, thereby improving the accuracy of prediction. The real-time and up-to-date information ensures the real-time performance and accuracy of the database content.

[0031] Specifically, the data management module includes a data cleaning unit for cleaning the obtained market raw material price data to remove invalid or incorrect data. The data management module also includes an algorithm optimization unit, which can automatically or manually adjust relevant parameters in the prediction algorithm according to data characteristics to further improve the accuracy and stability of prediction.

[0032] Among them, invalid or abnormal data points are usually caused by data input errors, equipment failures, transmission errors, etc. These data points will interfere with the normal operation of the system and the accuracy of prediction results, and will also affect the training effect of the prediction model, resulting in inaccurate prediction results of the model. After removing these data points through the data cleaning unit, the prediction model can more accurately learn the fluctuation rules and trends of raw material prices, thereby improving prediction performance. Using the algorithm optimization unit for parameter adjustment can reduce unnecessary calculations and resource waste.

[0033] Specifically, the price prediction module includes a user interaction unit to support users in querying the historical prices, current prices, predicted prices, and impact factor analyses of specific raw materials. The price prediction module also includes a data output unit, which can generate the user's query content in the form of charts, reports, etc.

[0034] Among them, users can query the historical prices, current prices, and predicted prices of specific raw materials in real time, ensuring that users can quickly grasp market dynamics and make timely and accurate decisions. It supports users to customize and generate reports according to actual needs, including price trend charts, factor analysis tables, etc., to meet the personalized needs of different users for information presentation methods.

[0035] It should be noted that the market data that the system can process and analyze includes:

[0036] Historical price data: the price fluctuations of raw materials in the past few years;

[0037] Production cost data: including raw material costs, energy costs, labor costs, etc.;

[0038] Supply and demand data: market supply, demand, inventory levels, etc.;

[0039] Macroeconomic indicators: such as GDP growth rate, inflation rate, exchange rate changes, etc.;

[0040] Policy environment: such as tariff policies, environmental protection regulations, industry subsidies, etc.;

[0041] Weather and natural disasters: weather and natural disaster data that have a significant impact on the prices of raw materials such as agricultural products and energy.

[0042] The above is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, makes equivalent substitutions or changes, and should be covered by the protection scope of the present invention.

Claims

1. A raw material real-time price calculation and prediction system, characterized in that: include: Raw material price database, which collects and stores historical price data, production cost data, upstream and downstream industry chain information, and market supply and demand reports of various raw materials; The data management module is used to classify and identify various raw materials in the raw material price database and integrate the data related to each type of raw material; The data model building module establishes a raw material price forecasting model based on historical data and real-time analysis results and big data analysis technology, after conducting in-depth analysis of multi-dimensional factors such as production costs, supply and demand capabilities, upstream price trends, downstream application needs, macroeconomic indicators, and policy environment; The price forecasting module uses the raw material price forecasting model to calculate the current raw material price in real time and predict the price trend in the future.

2. A raw material real-time price calculation and prediction system according to claim 1, characterized in that: The raw material price database includes a data collection unit, through which the latest raw material prices, supply and demand data, policy changes and other information are obtained.

3. A raw material real-time price calculation and prediction system according to claim 2, characterized in that: The data collection unit uses crawler technology or API interface to obtain data from major market research institutions, exchanges, and industry websites.

4. A raw material real-time price calculation and prediction system according to claim 1, characterized in that: The data management module includes a data cleaning unit for cleaning the acquired market raw material price data to remove invalid or erroneous data.

5. A raw material real-time price calculation and prediction system according to claim 4, characterized in that: The data management module also includes an algorithm optimization unit, which can automatically or manually adjust the relevant parameters in the prediction algorithm according to the data characteristics to further improve the accuracy and stability of the prediction.

6. A raw material real-time price calculation and prediction system according to claim 1, characterized in that: The data model building module uses machine learning regression algorithms to build data models.

7. A raw material real-time price calculation and prediction system according to claim 1, characterized in that: The price prediction module includes a user interaction unit to support users in querying the historical price, current price, predicted price and influencing factor analysis of specific raw materials.

8. A raw material real-time price calculation and prediction system according to claim 7, characterized in that: The price prediction module also includes a data output unit, which can generate user query content in the form of charts, reports, etc.

Images