Production technical improvement project cost prediction method and system based on data mining

Abstract

The invention discloses a production technical improvement project cost prediction method and system based on data mining, and relates to the technical field of production technical improvement project cost prediction. The project cost influence coefficient comprises production technical improvement project equipment influence data and production technical improvement project construction influence data; constructing a production technical improvement project cost prediction coefficient by using the project cost influence coefficient based on machine learning; and performing comparative analysis according to the production technical improvement project cost prediction coefficient and preset historical production technical improvement project cost data, and judging whether the production technical improvement project cost is reasonable or not. Through the data mining technology, the problems that prediction is not comprehensive and prediction results are difficult to adjust due to the fact that production technical improvement project cost prediction cannot comprehensively consider various data influence factors are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of production technology transformation project cost prediction, and more specifically, to a production technology transformation project cost prediction method and system based on data mining. Background Art

[0002] With the rapid development of the economy and the continuous upgrading of the industrial structure, enterprises are facing an urgent need for technological transformation. The production technology transformation project aims to improve production efficiency, reduce costs and enhance market competitiveness by introducing new technologies, optimizing production processes and improving equipment levels. Against the backdrop of intensified global competition, enterprises are in urgent need of improving their competitiveness through efficient project management and scientific cost control. Accurate cost forecasting can not only help enterprises allocate funds reasonably and reduce project risks, but also improve overall operational efficiency and enhance return on investment. Therefore, the market demand for advanced forecasting technology is increasing. Therefore, accurately predicting the cost of these projects is of great significance to the resource allocation and investment decision-making of enterprises.

[0003] Data mining refers to the process of analyzing and mining a large amount of data to discover the laws, trends and correlations in it, in order to provide decision support and insights into business problems. In modern society, a large amount of data is stored in databases, and these data contain rich information and value. Through data mining techniques and methods, useful information and knowledge can be extracted from these data to help enterprises make more informed decisions.

[0004] Although the traditional cost forecasting methods for production technology transformation projects have significant effects, they often have some obvious shortcomings and limitations, which cause enterprises to face higher budget risks during project implementation. First of all, the traditional cost forecasting methods for production technology transformation projects often rely on expert experience, lack scientificity, and are prone to subjective bias. In addition, they cannot comprehensively consider a variety of influencing factors, resulting in incomplete forecasts and difficulty in adjusting the forecast results, which leads to inaccurate forecasts of the cost of production technology transformation projects. In view of the above problems, the present invention proposes a solution. 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 method and system for predicting the cost of production technology transformation projects based on data mining, which uses digital mining technology to solve the problem of inaccurate prediction of the cost of production technology transformation projects due to the inability to comprehensively consider multiple influencing factors.

[0006] To achieve the above object, the present invention provides the following technical solutions: The method and system for predicting the cost of production technology transformation projects based on data mining include the following steps: obtaining and collecting project cost impact data during the production technology transformation project, wherein the project cost impact coefficients include production technology transformation project equipment impact data and production technology transformation project construction impact data; constructing a production technology transformation project cost prediction coefficient based on the project cost impact coefficient based on machine learning; and comparing and analyzing the production technology transformation project cost prediction coefficient with preset historical production technology transformation project cost data to determine whether the production technology transformation project cost is reasonable.

[0007] In a preferred embodiment, the specific acquisition method of the equipment impact data of the production technology transformation project is as follows: the depreciation impact data of the equipment is obtained by detecting the service life of the equipment and the residual value of the equipment; and the market data of the equipment on the market is collected according to the data mining technology, and the required market data includes labor cost data and equipment maintenance cost data; the energy consumption cost data of the equipment is obtained according to the resource consumption factor and energy consumption of the equipment during operation; the equipment impact data of the production technology transformation project is obtained based on the project equipment impact data calculation formula through the depreciation impact data, energy consumption cost data and market data.

[0008] In a preferred embodiment, the construction impact data includes material impact data of the production technology transformation project and personnel impact data of the production technology transformation project.

[0009] In a preferred embodiment, a specific method for acquiring the material impact data of the production technical transformation project is as follows: analyzing the construction site through data mining and cleaning the data to acquire the first data of the material impact of the production technical transformation project, wherein the first data includes material cost data and engineering design data; acquiring the second data of the material impact of the production technical transformation project through real-time monitoring, wherein the second data of the material impact of the production technical transformation project includes material utilization data and damage degree; acquiring the material impact data of the production technical transformation project based on a preset material project impact calculation formula according to the first data and the second data of the material impact of the production technical transformation project.

[0010] In a preferred embodiment, the specific method for obtaining the personnel impact data of the production technology transformation project is as follows: real-time monitoring to obtain the construction data of the construction personnel, the construction data including work efficiency and working hours; obtaining the management expenses of the personnel based on the investigation of market data; combining the construction data and personnel management expenses to obtain the personnel impact data of the production technology transformation project based on data analysis.

[0011] In a preferred embodiment, the specific method for obtaining the cost prediction coefficient of the production technology transformation project is as follows: obtaining the cost impact data of the production technology transformation project through data processing and past data analysis; and obtaining the weight ratio through the weight of the cost impact data of the production technology transformation project; and constructing the cost prediction coefficient of the production technology transformation project based on the machine algorithm using the equipment impact data of the production technology transformation project and the construction impact data of the production technology transformation project.

[0012] In a preferred embodiment, the cost prediction coefficient of the production technical transformation project is compared and analyzed with the preset historical cost data of the production technical transformation project to determine whether the cost of the production technical transformation project is reasonable, as follows: if the comparison result is greater than 1 and the data is getting larger and larger, it means that the cost of the production technical transformation project is becoming more and more unreasonable, resulting in the overall production technical transformation project cost prediction data being higher than the preset historical data; if the comparison result is less than or close to 1, it means that in the cost process of the production technical transformation project, the cost prediction data of the production technical transformation project is lower than the historical cost data of the production technical transformation project, indicating that the cost of each link is within a reasonable range.

[0013] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. Through digital mining technology, a large amount of information and knowledge can be extracted to help enterprises make more informed decisions. This effectively solves the problem of incomplete predictions caused by the inability to comprehensively consider multiple data influencing factors in existing technologies.

[0014] 2. Through data mining and cleaning technology, important relevant data in the cost of production technical transformation projects is obtained to reduce the complexity of calculations and improve the accuracy of calculations, thereby solving the problem of inaccurate prediction of the cost of production technical transformation projects. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A schematic diagram of the structure of a method for predicting the cost of a production technology transformation project based on data mining provided in an embodiment of the present application.

[0016] Figure 2 A schematic diagram of the structure of a production technology transformation project cost prediction system based on data mining provided in an embodiment of the present application. DETAILED DESCRIPTION

[0017] 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.

[0018] Embodiment 1, Figure 1 A schematic diagram of the structure of a method for predicting the cost of a production technology transformation project based on data mining is provided, which includes the following steps: S1, obtaining and collecting project cost impact data during the production technology transformation project, wherein the project cost impact coefficient includes the production technology transformation project equipment impact data and the production technology transformation project construction impact data.

[0019] In this embodiment, data mining analyzes and mines a large amount of data to discover the rules, trends and correlations therein, cleans and transforms the original data, processes missing values ​​and outliers, ensures the quality of the data, and extracts useful information.

[0020] Obtaining the equipment impact data of production technology transformation projects has the following advantages for cost forecasting of production technology transformation projects: Improve accuracy: Equipment impact data can provide detailed cost information, including the market price of original equipment, procurement channels and their changing trends, making cost forecasts more accurate.

[0021] Cost control: By analyzing the price fluctuations of equipment, budgets and plans can be adjusted in a timely manner, costs can be effectively controlled, and budget overruns caused by rising equipment prices can be avoided.

[0022] Risk assessment: Obtaining equipment data helps identify and assess potential price risks, so companies can take measures in advance, such as locking in prices or choosing alternative equipment, to reduce uncertainty.

[0023] Optimize resource allocation: Understanding equipment demand and its impact on construction costs can help companies allocate resources more reasonably, optimize inventory management, and reduce capital occupation.

[0024] Support decision making: By analyzing equipment data, management can make more reasonable decisions based on actual conditions, such as selecting appropriate suppliers or determining the timing of purchases.

[0025] The specific method for obtaining the equipment impact data of the production technology transformation project is as follows: Obtain equipment depreciation impact data by testing the equipment's service life and residual value; and collecting market data of equipment on the market through data mining technology, wherein the market data includes labor cost data and equipment maintenance cost data; Obtain the energy consumption cost data of the equipment according to the resource consumption factor and energy consumption of the equipment during operation; The equipment impact data of the production technology transformation project is obtained based on the project equipment impact data calculation formula through depreciation impact data, energy consumption cost data and market data.

[0026] The specific calculation formula for the depreciation impact data of the equipment is as follows:

[0027] The specific calculation formula for the energy consumption cost data of the equipment is as follows:

[0028] The specific calculation formula for the equipment impact data of the production technology transformation project is as follows:

[0029] In the formula, S is the equipment impact data, RE is the labor cost data, W is the equipment maintenance cost data, z is the depreciation impact data, a is the equipment purchase cost, b is the estimated residual value of the equipment, and t is the service life. is the actual useful life, is the energy consumption cost data, V is the voltage, I is the current, T is the usage time of the equipment, d is the electricity price, For the efficiency of the equipment, is the resource consumption factor, Q is the resource consumption, J is the unit price of the resource, For resource utilization.

[0030] It should be noted that the depreciation impact data of equipment refers to the residual value of the equipment, and the energy consumption cost data refers to the resource costs consumed by the equipment due to the use of related resources during the production technology transformation process. These data play a significant role in obtaining the equipment impact data of the production technology transformation project.

[0031] The construction impact data of the production technology transformation project refers to a series of data that affects the construction progress, quality and cost during the cost estimation process of the production technology transformation project. The construction impact data includes the material impact data of the production technology transformation project and the personnel impact data of the production technology transformation project.

[0032] Analyzing the construction impact data of production technology transformation projects has the following advantages for cost forecasting of production technology transformation projects: Cost control: Through in-depth analysis of data, we can optimize resource allocation, reduce waste, and achieve more effective cost control.

[0033] Risk identification: Analyzing construction impact data helps to identify potential risk factors in advance, formulate corresponding response measures, and reduce uncertainty during project implementation.

[0034] Evaluation of improvement measures: Through continuous monitoring and analysis of data, the effectiveness of technical improvement measures can be evaluated, strategies can be adjusted in a timely manner, and the overall performance of the project can be improved.

[0035] Improve accuracy: Through in-depth analysis of various influencing factors (such as material costs, labor costs, equipment usage, etc.), the uncertainty in cost forecasting can be reduced and accuracy can be improved.

[0036] Improve transparency: Through data analysis, relevant stakeholders can more clearly understand the cost composition and reasons for changes, thereby enhancing the transparency and trust of the project.

[0037] Promote continuous improvement: Through regular analysis of construction impact data, the management process and cost control strategy of production technology transformation projects can be continuously optimized to achieve continuous improvement.

[0038] The specific method for obtaining the material impact data of the production technology transformation project is as follows: Analyze the construction site through data mining and clean up the data to obtain first data on the impact of materials on the production technical transformation project, where the first data includes material cost data and engineering design data; Acquire the second data of the material impact of the production technology transformation project through real-time monitoring, wherein the second data of the material impact of the production technology transformation project includes the utilization data and the degree of damage of the material; The material impact data of the production technical transformation project is obtained based on the first data and the second data of the material impact of the production technical transformation project and a preset material project impact calculation formula. The specific calculation formula for the utilization data of the material is as follows:

[0039] The specific calculation formula for the material impact data of the production and technology transformation project is as follows:

[0040] In the formula, is the material utilization data, x is the total material cost data, f is the weathering loss data, q is the actual material data, and E is the construction impact data of the production technology transformation project. is the unit price of materials, Y is the cost of material damage, For engineering design fees, Design time for engineering.

[0041] It should be noted that material utilization data refers to the use of materials during the construction process, and weathering loss data refers to the materials being left in the construction environment and becoming unusable due to weathering, thus causing additional cost losses. The price and changes of materials will have a great impact on the construction of production technical transformation projects.

[0042] In this example, the specific method for obtaining the personnel impact data of the production technology transformation project is as follows: Real-time monitoring and acquisition of construction data of construction personnel, the construction data including work efficiency and working time; Obtain personnel management expenses based on market survey data; Combining construction data and personnel management costs, data analysis is used to obtain data on the impact of personnel on production technology transformation projects.

[0043] The specific calculation formula for the personnel impact data of the production technology transformation project is as follows:

[0044] In the formula, To manage data, is the number of personnel, Working hours for staff, For work efficiency, For personnel expenses.

[0045] S2, constructs the cost prediction coefficient of the production technology transformation project based on the project cost impact coefficient and machine learning.

[0046] In this example, the cost prediction coefficient of the production technology transformation project is obtained by using data mining technology to clean and screen a large amount of data during the production technology transformation process, obtain the data that affects the cost of the production technology transformation project among various projects, and obtain the cost prediction coefficient of the production technology transformation project through machine learning, so as to analyze and predict the cost.

[0047] The specific method for obtaining the cost prediction coefficient of the production technology transformation project is as follows: Obtain the cost impact data of production technology transformation projects through data processing and past data analysis; And obtain the weight ratio through the weight of the historical production technology transformation project cost impact data; The equipment impact data and construction impact data of the production technology transformation project are used based on machine algorithms to construct the cost prediction coefficient of the production technology transformation project.

[0048] The specific calculation formula for the cost prediction coefficient of the production technology transformation project is as follows:

[0049] In the formula, To manage data, To manage quantity, is the management fee, m, v, h are the weight ratios, S is the equipment impact data of the production technical transformation project, and E is the construction impact data of the production technical transformation project.

[0050] S3, compare and analyze the cost forecast coefficient of the production technology transformation project with the preset historical production technology transformation project cost data to determine whether the cost of the production technology transformation project is reasonable.

[0051] In this example, the production technology transformation project cost prediction coefficient is compared and analyzed with the preset historical production technology transformation project cost data. The specific comparison formula is as follows:

[0052] In the formula, For comparison results, A is the cost prediction coefficient of the production technology transformation project, and li is the cost data of the historical production technology transformation project.

[0053] It should be noted that the preset historical production technical transformation project cost data are usually obtained by searching the technical transformation standard documents of the relevant industry, which will list the cost range of typical projects, or some professional institutions or databases that provide historical data of industry projects, such as the information released by the China Construction Engineering Cost Management Association.

[0054] The cost forecast coefficient of the production technology transformation project is compared and analyzed with the preset historical production technology transformation project cost data to determine whether the cost of the production technology transformation project is reasonable, as follows: Analyze the comparison results. When the comparison result is greater than 1 and the data is getting larger and larger, it means that the cost of the production technology transformation project is becoming more and more unreasonable, indicating that in the process of the production technology transformation project, the project cost value of a certain link has become prominently unstable, resulting in the overall production technology transformation project cost forecast data being higher than the preset historical data. When the comparison result is less than or close to 1, it means that in the process of the production technology transformation project cost, the production technology transformation project cost forecast data is lower than the historical production technology transformation project cost data, indicating that the cost of each link is within a reasonable range, and when the result is getting smaller and smaller, it means that in the current production technology transformation project cost process, the optimization of each project link has played an important role in lowering the production technology transformation project cost forecast data, which also maximizes the benefits of the enterprise to the production technology transformation project.

[0055] Embodiment 2, Figure 2 The present invention provides a production technology transformation project cost prediction system based on data mining, including a project cost data processing module, a project cost data acquisition module, and a project cost data analysis module: A project cost data collection module is used to obtain and collect project cost impact data during the production technology transformation project, wherein the project cost impact coefficient includes equipment impact data of the production technology transformation project and construction impact data of the production technology transformation project; The project cost data processing module is used to construct the cost prediction coefficient of the production technology transformation project based on the project cost impact coefficient and machine learning; The project cost data analysis module is used to compare and analyze the production technology transformation project cost prediction coefficient with the preset historical production technology transformation project cost data to determine whether the production technology transformation project cost is reasonable.

[0056] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0057] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0058] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0059] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0060] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0061] 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. The method for predicting the cost of production technology transformation projects based on data mining is characterized by: The steps include: Obtain and collect project cost impact data during the production technology transformation project, where the project cost impact coefficient includes production technology transformation project equipment impact data and production technology transformation project construction impact data; The project cost impact coefficient is constructed based on machine learning to predict the cost of production technology transformation projects; By comparing and analyzing the cost forecast coefficient of the production technology transformation project with the preset historical production technology transformation project cost data, it is determined whether the cost of the production technology transformation project is reasonable.

2. The method for predicting the cost of production technology transformation projects based on data mining according to claim 1 is characterized in that: The specific method for obtaining the equipment impact data of the production technology transformation project is as follows: Obtain equipment depreciation impact data by testing the equipment's service life and residual value; and collecting market data of equipment on the market using data mining technology, wherein the market data includes labor cost data and equipment maintenance cost data; Obtain the energy consumption cost data of the equipment according to the resource consumption factor and energy consumption of the equipment during operation; The equipment impact data of the production technology transformation project is obtained based on the project equipment impact data calculation formula through depreciation impact data, energy consumption cost data and market data.

3. The method for predicting the cost of production technology transformation projects based on data mining according to claim 1 is characterized in that: The construction impact data include material impact data of production technology transformation projects and personnel impact data of production technology transformation projects.

4. The method for predicting the cost of production technology transformation projects based on data mining according to claim 3 is characterized in that: The specific method for obtaining the material impact data of the production technology transformation project is as follows: Analyze the construction site through data mining and clean up the data to obtain first data on the impact of materials on the production technical transformation project, where the first data includes material cost data and engineering design data; Acquire the second data of the material impact of the production technology transformation project through real-time monitoring, wherein the second data of the material impact of the production technology transformation project includes the utilization data and damage degree of the material; The material impact data of the production technical transformation project is obtained based on the first data and the second data of the material impact of the production technical transformation project and a preset material project impact calculation formula.

5. The method and system for predicting the cost of production technology transformation projects based on data mining according to claim 3 is characterized in that: The specific method for obtaining the personnel impact data of the production technology transformation project is as follows: Real-time monitoring and acquisition of construction data of construction personnel, the construction data including work efficiency and working time; Obtain personnel management expenses based on market survey data; Combining construction data and personnel management costs, data analysis is used to obtain data on the impact of personnel on production technology transformation projects.

6. The method for predicting the cost of production technology transformation projects based on data mining according to claim 1 is characterized in that: The specific method for obtaining the cost prediction coefficient of the production technology transformation project is as follows: Obtain the cost impact data of production technology transformation projects through data processing and past data analysis; And obtain the weight ratio through the weight of the historical production technology transformation project cost impact data; The equipment impact data and construction impact data of the production technology transformation project are used based on machine algorithms to construct the cost prediction coefficient of the production technology transformation project.

7. The method for predicting the cost of production technology transformation projects based on data mining according to claim 1 is characterized in that: The cost forecast coefficient of the production technology transformation project is compared and analyzed with the preset historical production technology transformation project cost data to determine whether the cost of the production technology transformation project is reasonable, as follows: If the comparison result is greater than 1 and the data is getting larger, it means that the cost of the production technology transformation project is becoming more and more unreasonable, resulting in the overall production technology transformation project cost forecast data being higher than the preset historical data; If the comparison result is less than or close to 1, it means that in the cost process of the production technical transformation project, the predicted cost data of the production technical transformation project is lower than the historical cost data of the production technical transformation project, which means that the cost of each link is within a reasonable range.

8. The method for predicting the cost of production technology transformation projects based on data mining according to claim 2 is characterized in that: The specific calculation formula for the depreciation impact data of the equipment is as follows: The specific calculation formula for the energy consumption cost data of the equipment is as follows: The specific calculation formula for the equipment impact data of the production technology transformation project is as follows: In the formula, S is the equipment impact data, RE is the labor cost data, W is the equipment maintenance cost data, z is the depreciation impact data, a is the equipment purchase cost, b is the estimated residual value of the equipment, and t is the service life. is the actual useful life, is the energy consumption cost data, V is the voltage, I is the current, T is the usage time of the equipment, d is the electricity price, For the efficiency of the equipment, is the resource consumption factor, Q is the resource consumption, J is the unit price of the resource, For resource utilization.

9. The method for predicting the cost of production technology transformation projects based on data mining according to claim 4 is characterized in that: The specific calculation formula for the utilization data of the material is as follows: The specific calculation formula for the material impact data of the production and technology transformation project is as follows: In the formula, is the material utilization data, x is the total material cost data, f is the weathering loss data, q is the actual material data, and E is the construction impact data of the production technology transformation project. is the unit price of materials, Y is the cost of material damage, For engineering design fees, Design time for engineering.

10. A system using the method for predicting the cost of production technology transformation projects according to any one of claims 1 to 9, comprising a project cost data processing module, a project cost data acquisition module, and a project cost data analysis module, wherein the modules are connected; A project cost data collection module is used to obtain and collect project cost impact data during the production technology transformation project, wherein the project cost impact coefficient includes equipment impact data of the production technology transformation project and construction impact data of the production technology transformation project; The project cost data processing module is used to construct the cost prediction coefficient of the production technology transformation project based on the project cost impact coefficient and machine learning; The project cost data analysis module is used to compare and analyze the production technology transformation project cost prediction coefficient with the preset historical production technology transformation project cost data to determine whether the production technology transformation project cost is reasonable.

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