Material demand management and control early warning method based on data analysis technology

Abstract

The invention discloses a material demand management and control early warning method based on a data analysis technology, and particularly relates to the technical field of material demand data analysis. Comprising the steps of S01, steel-making production process time sequence division, S02, steel-making production material demand acquisition, S03, steel-making production raw material price fluctuation early warning, S04, steel-making production cost monitoring, S05, steel-making production material supply economic management and control, S06, steel-making production material supply economic management and control evaluation, and S07, steel-making production material demand prediction. The steelmaking production demand data of each monitoring sub-time sequence is obtained, the raw material price fluctuation early warning index and the steelmaking production cost risk control index are calculated, the steelmaking material demand economic management and control coefficient is further analyzed, and the steelmaking material demand prediction coefficient is calculated based on historical data. The influence degree of various factors on the material demand economic risk is analyzed, the management and control indexes are quantified, and dynamic management is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of material demand data analysis, and more specifically, to a material demand management and control early warning method based on data analysis technology. Background Art

[0002] Data analysis technology is the process of collecting, processing and analyzing data to reveal hidden patterns, trends and associations behind the data, especially through the use of statistics, mathematical models and algorithms to predict future events and assist decision making.

[0003] With the advent of the big data era, the application of data analysis technology in the material demand management of steel mills is becoming increasingly important. By analyzing historical data and real-time data, steel mills can more accurately manage consumption in production, thereby achieving dynamic management of material inventory, effectively identifying and correcting outliers and errors in the data, and thus improving data reliability.

[0004] However, in actual use, it still has some shortcomings, such as the lack of time series-based early warning for material demand control in existing steel mills to fully capture the dynamic changes in material demand, the lack of effective data integration and analysis methods, and the long response time of the early warning mechanism; The existing material early warning methods for steel mills may be limited to single-dimensional analysis, and the data sources and processing methods are backward, resulting in insufficient accuracy of the early warning and failure to fully cover the variability of material demand, thus affecting the production efficiency and cost control of the steel mill. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a material demand management and early warning method based on data analysis technology, which is used to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above purpose, the present invention provides the following technical solution: a material demand control and early warning method based on data analysis technology, comprising: Step S01: Steelmaking production process time series division: used to divide the steelmaking production process into various monitoring sub-time series according to the time series, and number each monitoring sub-time series of the steelmaking production process.

[0007] Step S02: Acquisition of steelmaking production material demand: used to acquire steelmaking production demand data of each monitoring sub-time series of the steelmaking production process, wherein the steelmaking production material demand includes a raw material price data acquisition unit and a production cost data acquisition unit.

[0008] Step S03: Early warning of raw material price fluctuations in steelmaking production: used to calculate the raw material price fluctuation early warning index of each monitoring sub-time series in the steelmaking production process according to the raw material price data of the raw material price data acquisition unit, compare it with the preset raw material price fluctuation early warning index, and warn of abnormal fluctuations.

[0009] Step S04: Steelmaking production cost monitoring: used to obtain the production cost data of the unit according to the production cost data, calculate the steelmaking production cost risk control index of each monitoring sub-time series of the steelmaking production process, compare it with the preset steelmaking production cost risk control index, and manage the production cost risk.

[0010] Step S05: Economic control of steelmaking production material supply: used to calculate the economic control coefficient of steelmaking material demand for each monitoring sub-time series of the steelmaking production process based on the raw material price fluctuation warning index and the steelmaking production cost risk control index of each monitoring sub-time series of the steelmaking production process.

[0011] Step S06: Evaluation of economic control over the supply of steelmaking production materials: used to obtain the economic control coefficient of steelmaking material demand for each monitoring sub-time series of the steelmaking production process, compare it with the preset economic control coefficient of steelmaking material demand, and process it.

[0012] Step S07: Steelmaking production material demand forecast: used to obtain the economic control coefficient of steelmaking material demand for each monitoring sub-time series of the steelmaking production process, analyze and obtain the steelmaking material demand smoothing factor, and calculate the steelmaking material demand forecast coefficient of the steelmaking production process based on historical data and the steelmaking material demand forecast model.

[0013] Preferably, the steelmaking production process time series is specifically divided as follows: The steelmaking production process is divided into various monitoring sub-time series according to the time series, and each monitoring sub-time series of the steelmaking production process is numbered 1, 2, ...i, ...n in sequence.

[0014] Preferably, the steelmaking production material demand acquisition is specifically as follows: Raw material price data acquisition unit: obtain the raw material price, raw material purchase quantity, and raw material consumption of each monitoring sub-time series of the steelmaking production process through the steelmaking plant supply chain management channel, and mark them as , , , where i=1,2,...n, i represents the number of the i-th monitoring sub-time series; Production cost data acquisition unit: obtain the steel production cost, human resource input cost, and steel production efficiency of each monitoring sub-time series of the steelmaking production process through the steelmaking plant production management channel, and mark them as , , .

[0015] Preferably, the steel production raw material price fluctuation warning is specifically: Step S41: Obtain the raw material prices of each monitoring sub-time series in the steelmaking production process, and calculate the raw material price deviation rate of each monitoring sub-time series based on the highest price and the lowest price in the raw material market:

[0016] in, It is expressed as the raw material price deviation rate of the ith monitoring sub-time series, Expressed as the highest price in the raw material market, Represented as the raw material price of the ith monitoring sub-time series, Expressed as the lowest market price for raw materials; Step S42: Obtain the raw material purchase quantity and raw material consumption of each monitoring sub-time series in the steelmaking production process, and calculate the raw material utilization rate of each monitoring sub-time series:

[0017] in, Expressed as the raw material utilization rate of the ith monitoring sub-time series, It is represented as the raw material consumption of the ith monitoring sub-time series, It is represented as the raw material purchase quantity of the i-th monitoring sub-time series; Step S43: The calculation formula of the raw material price fluctuation warning index is:

[0018] in, It is represented as the raw material price fluctuation warning index of the ith monitoring sub-time series, It is expressed as the raw material price deviation rate of the ith monitoring sub-time series, Expressed as the minimum value of the raw material price deviation rate, Expressed as the maximum value of the raw material price deviation rate, Expressed as the raw material utilization rate of the ith monitoring sub-time series, It is expressed as the preset raw material utilization rate, and e is expressed as a natural constant; Step S44: Obtain the raw material price fluctuation warning index of each monitoring sub-time series in the steelmaking production process, and compare it with the preset raw material price fluctuation warning index. If the raw material price fluctuation warning index of a certain monitoring sub-time series is less than the preset raw material price fluctuation warning index, it indicates that the higher the raw material price of the monitoring sub-time series, the smaller the deviation, resulting in a small amount of raw materials used, and the smaller the raw material price fluctuation warning index is, the warning should be issued for abnormal fluctuations in raw material prices; conversely, it indicates that the lower the raw material price of the monitoring sub-time series, the larger the deviation, resulting in a large amount of raw materials used, and the higher the raw material price fluctuation warning index is, and the management personnel should be notified to increase material demand.

[0019] Preferably, the steelmaking production cost monitoring is specifically as follows: Step S51: Obtain the total cost BZ of the steelmaking plant through the production management channel of the steelmaking plant; Step S52: Calculate the steel production input coverage rate of each monitoring sub-time series through the steel production cost and human resource input cost of each monitoring sub-time series in the steelmaking production process:

[0020] in, It is represented as the steel production input coverage rate of the ith monitoring sub-time series, It is expressed as the human resource input cost of the i-th monitoring sub-time series, denoted as the steel production cost of the ith monitoring sub-time series, and BZ denoted as the total cost of the steel plant; Step S53: The calculation formula of the steelmaking production cost risk control index is:

[0021] in, It is represented as the steelmaking production cost risk control index of the ith monitoring sub-time series, It is represented as the steel production input coverage rate of the i-1th monitoring sub-time series, It is represented as the steel production efficiency of the i-th monitoring sub-time series, It is represented as the steel production efficiency of the i-1th monitoring sub-time series, It is represented by the mean coverage rate of steel production input, e is represented by the natural constant, and n is represented by the number of monitoring sub-time series; Step S54: Obtain the steelmaking production cost risk control index of each monitoring sub-time series in the steelmaking production process, and compare it with the preset steelmaking production cost risk control index. If the steelmaking production cost risk control index of a monitoring sub-time series is greater than the preset steelmaking production cost risk control index, it indicates that the greater the steel production input and production efficiency input of the monitoring sub-time series, the greater the steelmaking production cost risk control index, and the management personnel should be notified to perform production cost risk management. Otherwise, it indicates that the steel production input and production efficiency input of the monitoring sub-time series are stable, and the production cost control is better.

[0022] Preferably, the calculation formula of the economic control coefficient of steelmaking material demand is:

[0023] in, It is expressed as the economic control coefficient of steelmaking material demand of the ith monitoring sub-time series, It is represented as the raw material price fluctuation warning index of the ith monitoring sub-time series, It is represented as the steelmaking production cost risk control index of the ith monitoring sub-time series, , They are respectively represented as the weight factor coefficients of the raw material price fluctuation early warning index and the steelmaking production cost risk control index, and + =1.

[0024] Preferably, the economic management and control assessment of steel production material supply is specifically as follows: Obtain the economic control coefficient of steelmaking material demand for each monitoring sub-time series in the steelmaking production process, and compare it with the preset economic control coefficient of steelmaking material demand. If the economic control coefficient of steelmaking material demand of a certain monitoring sub-time series is greater than the preset economic control coefficient of steelmaking material demand, it indicates that the economic investment in material supply of the steelmaking plant in this monitoring sub-time series is too large, and an early warning signal should be issued and the management personnel should be notified to conduct economic risk assessment management. Otherwise, it indicates that there is no abnormal risk in the economic investment in material supply of the steelmaking plant in this monitoring sub-time series.

[0025] Preferably, the steelmaking production material demand forecast is specifically: Step S81: Calculate the steelmaking material demand smoothing factor through the steelmaking material demand economic control coefficient of each monitoring sub-time series in the steelmaking production process:

[0026] in, Expressed as the steelmaking material demand smoothing factor, It is expressed as the economic control coefficient of steelmaking material demand of the ith monitoring sub-time series, It is expressed as the preset economic control coefficient of steelmaking material demand. It is represented as the steelmaking production time of the ith monitoring sub-time series; Step S82: the historical data is the raw material price fluctuation warning index and the steelmaking production cost risk control index of each monitoring sub-time series in the steelmaking production process; Step S83: The steelmaking material demand forecasting model is:

[0027] in, Expressed as the steelmaking material demand forecast coefficient, It is represented as the raw material price fluctuation warning index of the i-1th monitoring sub-time series, It is represented as the steelmaking production cost risk control index of the i-1th monitoring sub-time series, It is expressed as a preset raw material price fluctuation warning index. Represented as a preset steelmaking production cost risk control index.

[0028] Technical effects and advantages of the present invention: The present invention provides a material demand control and early warning method based on data analysis technology, which obtains steelmaking production demand data of each monitoring sub-time series in the steelmaking production process, calculates the raw material price fluctuation early warning index of each monitoring sub-time series in the steelmaking production process according to the raw material price data of the raw material price data acquisition unit, compares it with the preset raw material price fluctuation early warning index, and warns of abnormal fluctuations. By real-time monitoring of the raw material price situation in the steelmaking production process, abnormal fluctuations can be discovered in time and early warning can be issued, so as to take measures to avoid production interruption or input-output imbalance. According to the production cost data of the production cost data acquisition unit, the early warning index of raw material price fluctuation is calculated. Calculate the steelmaking production cost risk control index of each monitoring sub-time series in the steelmaking production process, compare it with the preset steelmaking production cost risk control index, manage the production cost risk, and avoid market fluctuations and reduce the capital occupation of steel production by real-time monitoring of the cost situation in the steelmaking production process. Further calculate the steelmaking material demand economic control coefficient of each monitoring sub-time series in the steelmaking production process and compare it with the preset steelmaking material demand economic control coefficient. If the steelmaking material demand economic control coefficient of a certain monitoring sub-time series is greater than the preset steelmaking material demand economic control coefficient, it indicates that the steelmaking material demand of the monitoring sub-time series is insufficient. If the economic investment in supply is too large, an early warning signal should be issued and the management personnel should be notified to conduct economic risk assessment management. Otherwise, it indicates that there is no abnormal risk in the economic investment in the material supply of the steelmaking plant in the monitoring sub-time series. By issuing an early warning signal when the fluctuation of the economic control coefficient of steelmaking material demand exceeds the preset threshold, the procurement strategy and production plan can be adjusted in time to reduce the impact of excessive resource investment on the enterprise. The steelmaking plant can analyze the influence of various factors on the economic risk of material demand through the application of data analysis technology, quantify the economic control indicators of material demand, and realize dynamic management of material demand. The present invention provides a material demand based on data analysis technology. The control and early warning method uses the steelmaking production material demand forecast to obtain the economic control coefficient of steelmaking material demand for each monitoring sub-time series in the steelmaking production process, and analyzes to obtain the steelmaking material demand smoothing factor. Based on the historical raw material price fluctuation early warning index and the historical steelmaking production cost risk control index of each monitoring sub-time series in the steelmaking production process, the steelmaking material demand forecast coefficient of the steelmaking production process is calculated according to the steelmaking material demand forecast model. The historical data and current material demand situation are used to predict future material demand, thereby improving the steel plant's prediction accuracy and response speed to changes in material demand, while enhancing the ability to control cost risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 The present invention is a flow chart of a material demand control and early warning method based on data analysis technology.

[0030] Figure 2This is a structural diagram of step S02 of the present invention: obtaining steelmaking production material requirements. DETAILED DESCRIPTION

[0031] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0032] See also Figure 1 As shown, the present invention provides a material demand control and early warning method based on data analysis technology, including step S01: time series division of steelmaking production process, step S02: acquisition of steelmaking production material demand, step S03: early warning of price fluctuation of raw materials for steelmaking production, step S04: monitoring of steelmaking production cost, step S05: economic control of steelmaking production material supply, step S06: evaluation of economic control of steelmaking production material supply, step S07: forecasting of steelmaking production material demand.

[0033] The step S01: time series division of the steelmaking production process is connected with the step S02: acquisition of material demand for steelmaking production, the step S02: acquisition of material demand for steelmaking production is connected with the step S03: early warning of price fluctuations of raw materials for steelmaking production, the step S03: early warning of price fluctuations of raw materials for steelmaking production is connected with the step S04: monitoring of steelmaking production costs, the step S04: monitoring of steelmaking production costs is connected with the step S05: economic management and control of material supply for steelmaking production, the step S05: economic management and control of material supply for steelmaking production is connected with the step S06: economic management and control evaluation of material supply for steelmaking production, the step S06: economic management and control evaluation of material supply for steelmaking production is connected with the step S07: forecasting of material demand for steelmaking production.

[0034] The step S01: dividing the steelmaking production process time series is used to divide the steelmaking production process into various monitoring sub-time series according to the time series, and numbering each monitoring sub-time series of the steelmaking production process, which is conducive to more effective monitoring and management of the steel plant production process.

[0035] In a possible design, the time series of the steelmaking production process is specifically divided as follows: The steelmaking production process is divided into various monitoring sub-time series according to the time series, and each monitoring sub-time series of the steelmaking production process is numbered 1, 2, ...i, ...n in sequence.

[0036] See also Figure 2As shown, the step S02: steelmaking production material demand acquisition is used to obtain the steelmaking production demand data of each monitoring sub-time series of the steelmaking production process, the steelmaking production material demand includes a raw material price data acquisition unit and a production cost data acquisition unit, the steelmaking production demand data includes raw material price data and production cost data, and the use of automated data collection technology is conducive to reducing human errors and improving the efficiency of data collection.

[0037] In a possible design, the steelmaking production material demand acquisition is specifically as follows: Raw material price data acquisition unit: obtain the raw material price, raw material purchase quantity, and raw material consumption of each monitoring sub-time series of the steelmaking production process through the steelmaking plant supply chain management channel, and mark them as , , , where i=1,2,...n, i represents the number of the i-th monitoring sub-time series; Production cost data acquisition unit: obtain the steel production cost, human resource input cost, and steel production efficiency of each monitoring sub-time series of the steelmaking production process through the steelmaking plant production management channel, and mark them as , , .

[0038] The step S03: early warning of raw material price fluctuations in steelmaking production is used to calculate the raw material price fluctuation early warning index of each monitoring sub-time series in the steelmaking production process according to the raw material price data of the raw material price data acquisition unit, compare it with the preset raw material price fluctuation early warning index, and warn of abnormal fluctuations. By real-time monitoring of the raw material prices in the steelmaking production process, abnormal fluctuations can be discovered in time and early warning can be issued, so as to take measures to avoid production interruptions or input-output imbalances.

[0039] In a possible design, the steel production raw material price fluctuation warning is specifically: Step S41: Obtain the raw material prices of each monitoring sub-time series in the steelmaking production process, and calculate the raw material price deviation rate of each monitoring sub-time series based on the highest price and the lowest price in the raw material market:

[0040] in, It is expressed as the raw material price deviation rate of the ith monitoring sub-time series, Expressed as the highest price in the raw material market, Represented as the raw material price of the ith monitoring sub-time series, Expressed as the lowest market price for raw materials; Step S42: Obtain the raw material purchase quantity and raw material consumption of each monitoring sub-time series in the steelmaking production process, and calculate the raw material utilization rate of each monitoring sub-time series:

[0041] in, Expressed as the raw material utilization rate of the ith monitoring sub-time series, It is represented as the raw material consumption of the ith monitoring sub-time series, It is represented as the raw material purchase quantity of the i-th monitoring sub-time series; Step S43: The calculation formula of the raw material price fluctuation warning index is:

[0042] in, It is represented as the raw material price fluctuation warning index of the ith monitoring sub-time series, It is expressed as the raw material price deviation rate of the ith monitoring sub-time series, Expressed as the minimum value of the raw material price deviation rate, Expressed as the maximum value of the raw material price deviation rate, Expressed as the raw material utilization rate of the ith monitoring sub-time series, It is expressed as the preset raw material utilization rate, and e is expressed as a natural constant; Step S44: Obtain the raw material price fluctuation warning index of each monitoring sub-time series in the steelmaking production process, and compare it with the preset raw material price fluctuation warning index. If the raw material price fluctuation warning index of a certain monitoring sub-time series is less than the preset raw material price fluctuation warning index, it indicates that the higher the raw material price of the monitoring sub-time series, the smaller the deviation, resulting in a small amount of raw materials used, and the smaller the raw material price fluctuation warning index is, the warning should be issued for abnormal fluctuations in raw material prices; conversely, it indicates that the lower the raw material price of the monitoring sub-time series, the larger the deviation, resulting in a large amount of raw materials used, and the higher the raw material price fluctuation warning index is, and the management personnel should be notified to increase material demand.

[0043] The step S04: steelmaking production cost monitoring is used to obtain the production cost data of the unit according to the production cost data, calculate the steelmaking production cost risk control index of each monitoring sub-time series of the steelmaking production process, compare it with the preset steelmaking production cost risk control index, and manage the production cost risk. By real-time monitoring of the cost situation in the steelmaking production process, it is helpful to avoid market fluctuations and reduce the capital occupation of steel production.

[0044] In a possible design, the steelmaking production cost monitoring is specifically as follows: Step S51: Obtain the total cost BZ of the steelmaking plant through the production management channel of the steelmaking plant; Step S52: Calculate the steel production input coverage rate of each monitoring sub-time series through the steel production cost and human resource input cost of each monitoring sub-time series in the steelmaking production process:

[0045] in, It is represented as the steel production input coverage rate of the ith monitoring sub-time series, It is expressed as the human resource input cost of the i-th monitoring sub-time series, denoted as the steel production cost of the ith monitoring sub-time series, and BZ denoted as the total cost of the steel plant; Step S53: The calculation formula of the steelmaking production cost risk control index is:

[0046] in, It is represented as the steelmaking production cost risk control index of the ith monitoring sub-time series, It is represented as the steel production input coverage rate of the i-1th monitoring sub-time series, It is represented as the steel production efficiency of the i-th monitoring sub-time series, It is represented as the steel production efficiency of the i-1th monitoring sub-time series, It is represented by the mean coverage rate of steel production input, e is represented by the natural constant, and n is represented by the number of monitoring sub-time series; Step S54: Obtain the steelmaking production cost risk control index of each monitoring sub-time series in the steelmaking production process, and compare it with the preset steelmaking production cost risk control index. If the steelmaking production cost risk control index of a monitoring sub-time series is greater than the preset steelmaking production cost risk control index, it indicates that the greater the steel production input and production efficiency input of the monitoring sub-time series, the greater the steelmaking production cost risk control index, and the management personnel should be notified to perform production cost risk management. Otherwise, it indicates that the steel production input and production efficiency input of the monitoring sub-time series are stable, and the production cost control is better.

[0047] The step S05: economic control of steel production material supply is used to calculate the economic control coefficient of steelmaking material demand for each monitoring sub-time series of the steelmaking production process according to the raw material price fluctuation warning index and the steelmaking production cost risk control index of each monitoring sub-time series of the steelmaking production process. The steel mill uses data analysis technology to analyze the impact of various factors on the economic risk of material demand, quantify the economic control indicators of material demand, and realize dynamic management of material demand.

[0048] In a possible design, the calculation formula of the economic control coefficient of steelmaking material demand is:

[0049] in, It is expressed as the economic control coefficient of steelmaking material demand of the ith monitoring sub-time series, It is represented as the raw material price fluctuation warning index of the ith monitoring sub-time series, It is represented as the steelmaking production cost risk control index of the ith monitoring sub-time series, , They are respectively represented as the weight factor coefficients of the raw material price fluctuation early warning index and the steelmaking production cost risk control index, and + =1.

[0050] The step S06: economic control assessment of steelmaking production material supply is used to obtain the economic control coefficient of steelmaking material demand for each monitoring sub-time series of the steelmaking production process, compare it with the preset economic control coefficient of steelmaking material demand, and process it, which is conducive to timely issuing an early warning signal when the economic control coefficient of steelmaking material demand fluctuates beyond the preset value.

[0051] In a possible design, the economic management and control evaluation of steel production material supply is specifically as follows: Obtain the economic control coefficient of steelmaking material demand for each monitoring sub-time series in the steelmaking production process, and compare it with the preset economic control coefficient of steelmaking material demand. If the economic control coefficient of steelmaking material demand of a certain monitoring sub-time series is greater than the preset economic control coefficient of steelmaking material demand, it indicates that the economic investment in material supply of the steelmaking plant in this monitoring sub-time series is too large, and an early warning signal should be issued and the management personnel should be notified to conduct economic risk assessment management. Otherwise, it indicates that there is no abnormal risk in the economic investment in material supply of the steelmaking plant in this monitoring sub-time series.

[0052] The step S07: steelmaking production material demand forecast is used to obtain the economic control coefficient of steelmaking material demand of each monitoring sub-time series in the steelmaking production process, analyze and obtain the steelmaking material demand smoothing factor, and calculate the steelmaking material demand forecast coefficient of the steelmaking production process based on historical data and the steelmaking material demand forecast model, which is conducive to using historical data and current material demand conditions to predict future material demand.

[0053] In a possible design, the steelmaking production material demand forecast is specifically as follows: Step S81: Calculate the steelmaking material demand smoothing factor through the steelmaking material demand economic control coefficient of each monitoring sub-time series in the steelmaking production process:

[0054] in, Expressed as the steelmaking material demand smoothing factor, It is expressed as the economic control coefficient of steelmaking material demand of the ith monitoring sub-time series, It is expressed as the preset economic control coefficient of steelmaking material demand. It is represented as the steelmaking production time of the ith monitoring sub-time series; Step S82: the historical data is the raw material price fluctuation warning index and the steelmaking production cost risk control index of each monitoring sub-time series in the steelmaking production process; Step S83: The steelmaking material demand forecasting model is:

[0055] in, Expressed as the steelmaking material demand forecast coefficient, It is represented as the raw material price fluctuation warning index of the i-1th monitoring sub-time series, It is represented as the steelmaking production cost risk control index of the i-1th monitoring sub-time series, It is expressed as a preset raw material price fluctuation warning index. Represented as a preset steelmaking production cost risk control index.

[0056] In the present embodiment, it is necessary to specifically explain that the present invention obtains the steelmaking production demand data of each monitoring sub-time series of the steelmaking production process, calculates the raw material price fluctuation warning index of each monitoring sub-time series of the steelmaking production process according to the raw material price data of the raw material price data acquisition unit, compares it with the preset raw material price fluctuation warning index, and warns of abnormal fluctuations. By real-time monitoring of the raw material price situation in the steelmaking production process, abnormal fluctuations can be discovered in time and early warnings can be issued, so as to take measures to avoid production interruption or input-output imbalance. According to the production cost data of the production cost data acquisition unit, the steelmaking production cost risk control index of each monitoring sub-time series of the steelmaking production process is calculated, and compared with the preset steelmaking production cost risk control index, production cost risks are managed, and by real-time monitoring of the cost situation in the steelmaking production process, market fluctuations can be avoided and capital occupation of steel production can be reduced. , calculate the economic control coefficient of steelmaking material demand of each monitoring sub-time series in the steelmaking production process, and compare it with the preset economic control coefficient of steelmaking material demand. If the economic control coefficient of steelmaking material demand of a certain monitoring sub-time series is greater than the preset economic control coefficient of steelmaking material demand, it indicates that the economic investment in material supply of the steelmaking plant in the monitoring sub-time series is too large, and an early warning signal should be issued and the management personnel should be notified to conduct economic risk assessment management. Otherwise, it indicates that there is no abnormal risk in the economic investment in material supply of the steelmaking plant in the monitoring sub-time series. By issuing an early warning signal when the fluctuation of the economic control coefficient of steelmaking material demand exceeds the preset threshold, the procurement strategy and production plan can be adjusted in time to reduce the impact of excessive resource investment on the enterprise. The steelmaking plant can analyze the influence of various factors on the economic risk of material demand, quantify the economic control indicators of material demand, and realize dynamic management of material demand through the application of data analysis technology.

[0057] The present invention utilizes steelmaking production material demand forecasting to obtain the steelmaking material demand economic control coefficient of each monitoring sub-time series in the steelmaking production process, analyzes to obtain the steelmaking material demand smoothing factor, and calculates the steelmaking material demand forecasting coefficient of the steelmaking production process according to the steelmaking material demand forecasting model based on the historical raw material price fluctuation early warning index and the historical steelmaking production cost risk control index of each monitoring sub-time series in the steelmaking production process. The future material demand is predicted using historical data and current material demand conditions, thereby improving the steel mill's prediction accuracy and response speed to material demand changes and enhancing the ability to control cost risks.

[0058] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A material demand control and early warning method based on data analysis technology, characterized in that: include: Step S01: Steelmaking production process time series division: used to divide the steelmaking production process into various monitoring sub-time series according to the time series, and number each monitoring sub-time series of the steelmaking production process; Step S02: steelmaking production material demand acquisition: used to acquire steelmaking production demand data of each monitoring sub-time series of the steelmaking production process, the steelmaking production material demand includes a raw material price data acquisition unit and a production cost data acquisition unit; Step S03: Early warning of raw material price fluctuations in steelmaking production: used to calculate the early warning index of raw material price fluctuations of each monitoring sub-time series in the steelmaking production process according to the raw material price data of the raw material price data acquisition unit, compare it with the preset early warning index of raw material price fluctuations, and warn of abnormal fluctuations; Step S04: steelmaking production cost monitoring: used to calculate the steelmaking production cost risk control index of each monitoring sub-time series of the steelmaking production process according to the production cost data of the production cost data acquisition unit, compare it with the preset steelmaking production cost risk control index, and manage the production cost risk; Step S05: Economic control of steelmaking production material supply: used to calculate the economic control coefficient of steelmaking material demand for each monitoring sub-time series of the steelmaking production process according to the raw material price fluctuation warning index and the steelmaking production cost risk control index of each monitoring sub-time series of the steelmaking production process; Step S06: Evaluation of economic control over the supply of steelmaking production materials: used to obtain the economic control coefficient of steelmaking material demand of each monitoring sub-time series in the steelmaking production process, compare it with the preset economic control coefficient of steelmaking material demand, and process it; Step S07: Steelmaking production material demand forecast: used to obtain the economic control coefficient of steelmaking material demand for each monitoring sub-time series of the steelmaking production process, analyze and obtain the steelmaking material demand smoothing factor, and calculate the steelmaking material demand forecast coefficient of the steelmaking production process based on historical data and the steelmaking material demand forecast model.

2. According to claim 1, a material demand control and early warning method based on data analysis technology is characterized by: The time series of the steelmaking production process is specifically divided into: The steelmaking production process is divided into various monitoring sub-time series according to the time series, and each monitoring sub-time series of the steelmaking production process is numbered 1, 2, ...i, ...n in sequence.

3. The material demand control and early warning method based on data analysis technology according to claim 1 is characterized by: The steelmaking production material demand acquisition is specifically as follows: Raw material price data acquisition unit: obtain the raw material price, raw material purchase quantity, and raw material consumption of each monitoring sub-time series of the steelmaking production process through the steelmaking plant supply chain management channel, and mark them as , , , where i=1,2,...n, i represents the number of the i-th monitoring sub-time series; Production cost data acquisition unit: obtain the steel production cost, human resource input cost, and steel production efficiency of each monitoring sub-time series of the steelmaking production process through the steelmaking plant production management channel, and mark them as , , .

4. The material demand control and early warning method based on data analysis technology according to claim 1 is characterized by: The specific warning of price fluctuation of raw materials for steel production is: Step S41: Obtain the raw material prices of each monitoring sub-time series in the steelmaking production process, and calculate the raw material price deviation rate of each monitoring sub-time series based on the highest price and the lowest price in the raw material market: in, It is expressed as the raw material price deviation rate of the ith monitoring sub-time series, Expressed as the highest price in the raw material market, Represented as the raw material price of the ith monitoring sub-time series, Expressed as the lowest market price for raw materials; Step S42: Obtain the raw material purchase quantity and raw material consumption of each monitoring sub-time series in the steelmaking production process, and calculate the raw material utilization rate of each monitoring sub-time series: in, Expressed as the raw material utilization rate of the ith monitoring sub-time series, It is represented as the raw material consumption of the ith monitoring sub-time series, It is represented as the raw material purchase quantity of the i-th monitoring sub-time series; Step S43: The calculation formula of the raw material price fluctuation warning index is: in, It is represented as the raw material price fluctuation warning index of the ith monitoring sub-time series, It is expressed as the raw material price deviation rate of the ith monitoring sub-time series, Expressed as the minimum value of the raw material price deviation rate, Expressed as the maximum value of the raw material price deviation rate, Expressed as the raw material utilization rate of the ith monitoring sub-time series, It is expressed as the preset raw material utilization rate, and e is expressed as a natural constant; Step S44: Obtain the raw material price fluctuation warning index of each monitoring sub-time series in the steelmaking production process, and compare it with the preset raw material price fluctuation warning index. If the raw material price fluctuation warning index of a certain monitoring sub-time series is less than the preset raw material price fluctuation warning index, it indicates that the higher the raw material price of the monitoring sub-time series, the smaller the deviation, resulting in a small amount of raw materials used, and the smaller the raw material price fluctuation warning index is, the warning should be issued for abnormal fluctuations in raw material prices; conversely, it indicates that the lower the raw material price of the monitoring sub-time series, the larger the deviation, resulting in a large amount of raw materials used, and the higher the raw material price fluctuation warning index is, and the management personnel should be notified to increase material demand.

5. The material demand control and early warning method based on data analysis technology according to claim 1 is characterized by: The steelmaking production cost monitoring is specifically as follows: Step S51: Obtain the total cost BZ of the steelmaking plant through the production management channel of the steelmaking plant; Step S52: Calculate the steel production input coverage rate of each monitoring sub-time series through the steel production cost and human resource input cost of each monitoring sub-time series in the steelmaking production process: in, It is represented as the steel production input coverage rate of the ith monitoring sub-time series, It is expressed as the human resource input cost of the i-th monitoring sub-time series, denoted as the steel production cost of the ith monitoring sub-time series, and BZ denoted as the total cost of the steel plant; Step S53: The calculation formula of the steelmaking production cost risk control index is: in, It is represented as the steelmaking production cost risk control index of the ith monitoring sub-time series, It is represented as the steel production input coverage rate of the i-1th monitoring sub-time series, It is represented as the steel production efficiency of the i-th monitoring sub-time series, It is represented as the steel production efficiency of the i-1th monitoring sub-time series, It is represented by the mean coverage rate of steel production input, e is represented by the natural constant, and n is represented by the number of monitoring sub-time series; Step S54: Obtain the steelmaking production cost risk control index of each monitoring sub-time series in the steelmaking production process, and compare it with the preset steelmaking production cost risk control index. If the steelmaking production cost risk control index of a monitoring sub-time series is greater than the preset steelmaking production cost risk control index, it indicates that the greater the steel production input and production efficiency input of the monitoring sub-time series, the greater the steelmaking production cost risk control index, and the management personnel should be notified to perform production cost risk management. Otherwise, it indicates that the steel production input and production efficiency input of the monitoring sub-time series are stable, and the production cost control is better.

6. The material demand control and early warning method based on data analysis technology according to claim 1 is characterized by: The calculation formula of the economic control coefficient of steelmaking material demand is: in, It is expressed as the economic control coefficient of steelmaking material demand of the ith monitoring sub-time series, It is represented as the raw material price fluctuation warning index of the ith monitoring sub-time series, It is represented as the steelmaking production cost risk control index of the ith monitoring sub-time series, , They are respectively represented as the weight factor coefficients of the raw material price fluctuation early warning index and the steelmaking production cost risk control index, and + =1.

7. The material demand control and early warning method based on data analysis technology according to claim 1 is characterized by: The economic management and control assessment of steel production material supply is specifically as follows: Obtain the economic control coefficient of steelmaking material demand for each monitoring sub-time series in the steelmaking production process, and compare it with the preset economic control coefficient of steelmaking material demand. If the economic control coefficient of steelmaking material demand of a certain monitoring sub-time series is greater than the preset economic control coefficient of steelmaking material demand, it indicates that the economic investment in material supply of the steelmaking plant in this monitoring sub-time series is too large, and an early warning signal should be issued and the management personnel should be notified to conduct economic risk assessment management. Otherwise, it indicates that there is no abnormal risk in the economic investment in material supply of the steelmaking plant in this monitoring sub-time series.

8. The material demand control and early warning method based on data analysis technology according to claim 1 is characterized by: The steel production material demand forecast is specifically as follows: Step S81: Calculate the steelmaking material demand smoothing factor through the steelmaking material demand economic control coefficient of each monitoring sub-time series in the steelmaking production process: in, Expressed as the steelmaking material demand smoothing factor, It is expressed as the economic control coefficient of steelmaking material demand of the ith monitoring sub-time series, It is expressed as the preset economic control coefficient of steelmaking material demand. It is represented as the steelmaking production time of the ith monitoring sub-time series; Step S82: the historical data is the raw material price fluctuation warning index and the steelmaking production cost risk control index of each monitoring sub-time series in the steelmaking production process; Step S83: The steelmaking material demand forecasting model is: in, Expressed as the steelmaking material demand forecast coefficient, It is represented as the raw material price fluctuation warning index of the i-1th monitoring sub-time series, It is represented as the steelmaking production cost risk control index of the i-1th monitoring sub-time series, It is expressed as a preset raw material price fluctuation warning index. Represented as a preset steelmaking production cost risk control index.

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