Efficient and accurate engineering cost estimation method
By building an engineering cost estimation model based on data mining and machine learning, combined with real-time data updates and expert experience, the problems of low accuracy and efficiency in traditional methods are solved, and efficient and accurate engineering cost estimation is achieved.
Patent Information
- Application Number
- CN202510930011.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-07-07
- Publication Date
- 2025-10-24
AI Technical Summary
Traditional engineering cost estimation methods rely on manual experience and quota calculations, which have poor accuracy and low efficiency. They are difficult to adapt to market changes and cannot meet the needs of rapid advancement of modern engineering projects.
Using data mining and machine learning algorithms, we build a project cost estimation model. By combining real-time data updates with expert experience, we optimize the estimation process through multi-source data analysis and visualization.
It improves the accuracy and efficiency of project cost estimation, can adapt to market changes in a timely manner, provide reliable investment decision support, and reduce manual operation and calculation time.
Smart Images
Figure CN120833192A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The application relates to the technical field of engineering cost, and in particular to an efficient and accurate engineering cost estimation method. BACKGROUND
[0002] In various projects such as construction engineering and municipal engineering, engineering cost estimation plays a crucial role in the early planning, investment decision-making and cost control of the project. The traditional engineering cost estimation method mainly relies on manual experience and simple quota calculation, which has many defects. On the one hand, the subjectivity of manual experience is strong, and the estimation results of different estimators for the same project may have large differences, which makes it difficult to guarantee the accuracy of the estimation results. On the other hand, the updating speed of the quota calculation method is slow, and it is difficult to adapt to the rapid changes of market material prices, labor costs and new construction technologies and other factors, resulting in a large deviation between the estimation results and the actual cost. In addition, the traditional method needs to spend a lot of time and manpower to collect, organize and calculate data when dealing with complex projects, and the estimation efficiency is low, which cannot meet the needs of the rapid progress of modern engineering projects.
[0003] With the development of information technology, although some computer-based cost estimation software has appeared, most of these software only electronicize the traditional quota calculation process, lack of deep mining and intelligent analysis ability of historical data, and it is difficult to realize efficient and accurate engineering cost estimation. Therefore, we propose an efficient and accurate engineering cost estimation method. SUMMARY
[0004] The purpose of the application is to solve the problems in the prior art and provide an efficient and accurate engineering cost estimation method.
[0005] In order to achieve the above purpose, the application adopts the following technical scheme:
[0006] An efficient and accurate engineering cost estimation method comprises the following steps:
[0007] S1, data collection and preprocessing: collect historical engineering cost data, including project basic information, engineering drawing information, material price data, labor cost data, construction technology data; clean the collected data to remove duplicate and incorrect data, and perform interpolation or filling processing on missing data; standardize the processed data according to certain rules, unify the data format and unit;
[0008] S2, feature extraction and analysis: based on the historical engineering cost data, extract the key features related to the engineering cost, including project features, material features, construction technology features; use data mining algorithm to analyze the correlation between each feature and engineering cost, and determine the key features that have greater impact on engineering cost;
[0009] S3, constructing an estimation model: using machine learning algorithms, taking the extracted key features as input variables and historical engineering cost as output variables, constructing an engineering cost estimation model; training and optimizing the model through cross-validation, adjusting the model parameters and improving the prediction accuracy of the model;
[0010] S4, real-time data updating: establishing a real-time data acquisition system to obtain market material price fluctuation data, artificial cost adjustment data and new construction technology information in real time; integrating real-time data with historical data, updating and optimizing the estimation model regularly to ensure that the model can timely reflect market changes;
[0011] S5, engineering cost estimation: input the basic information and engineering drawing information of the project to be estimated, obtain the key features of the project to be estimated through data preprocessing and feature extraction steps; input the key features of the project to be estimated into the trained estimation model, output the engineering cost estimation result; analyze the rationality of the estimation result, combine expert experience and actual situation, and correct and adjust the estimation result.
[0012] Preferably, in step S3, the machine learning algorithm adopts an ensemble learning strategy, combines multiple different machine learning models, determines the final estimation result through voting or weighted average, and improves the stability and accuracy of the estimation model.
[0013] Preferably, in step S4, the real-time data acquisition system establishes a data interface with the building material supplier database, the human resource market database and the industry technology information platform to realize automatic acquisition and updating of real-time data.
[0014] Preferably, in step S5, the rationality analysis includes comparing the cost indicators of similar projects, analyzing whether the proportion of each cost component conforms to the industry standard, and checking whether the estimation result is within a reasonable fluctuation range.
[0015] Preferably, in step S5, an engineering cost estimation database is established to store historical engineering cost data, training data of the estimation model and related data of the project to be estimated, facilitating data query and management.
[0016] Preferably, in step S2, the data mining algorithm adopts principal component analysis or correlation analysis algorithm to determine the key features.
[0017] Preferably, in step S5, when the estimation result deviates greatly from the cost indicators of similar projects, a re-evaluation process is automatically triggered to recheck and correct the input data and re-estimate.
[0018] Preferably, in the step S5, the visualization display module is used to visually display the engineering cost estimation results in the form of charts and reports, so that the user can intuitively understand the composition and distribution of the engineering cost.
[0019] Preferably, in the step S1, the image recognition technology is used to process the engineering drawing information, and the key information in the drawing is automatically extracted, so as to reduce the workload and errors of manual input.
[0020] Preferably, in the step S1, when the material price data is preprocessed, the time series analysis is introduced, a material price fluctuation prediction model is established, the material price trend in the future period is estimated, and the prediction result is included in the estimation data system.
[0021] Compared with the prior art, the present application has the following advantages:
[0022] 1、In the present application, by collecting multi-source data including project basic information, engineering drawing information, material price data, etc., and using data mining algorithm to deeply analyze the correlation between each feature and engineering cost, the key influencing factors can be accurately extracted. On this basis, the machine learning estimation model is constructed, which is optimized by cross-validation and regularization, effectively reduces the model error, avoids overfitting problem, and compared with traditional method, can more accurately reflect the actual situation of engineering cost, and provides reliable basis for project investment decision.
[0023] 2、In the present application, the data acquisition and preprocessing link realizes automatic processing, the engineering drawing information is quickly extracted by the image recognition technology, the workload and errors of manual input are reduced; the real-time data acquisition system automatically obtains market dynamic data, ensuring the timeliness of data; the estimation model can quickly process the input project feature data and output the estimation result. The whole process greatly reduces the manual operation and calculation time, significantly improves the efficiency of engineering cost estimation, and meets the needs of rapid development of modern engineering projects.
[0024] 3、In the present application, the real-time data updating mechanism establishes an interface with multiple databases and platforms, real-time obtains market material price fluctuation, manual cost adjustment and new construction technology information, and integrates them into the estimation model in time. This makes the estimation model can follow the market changes, adjust the estimation result in time, effectively adapt to the rapidly changing market environment, and avoid the estimation deviation caused by market factor changes.
[0025] 4、In the present application, the rationality analysis process is to compare the cost indicators of similar projects, analyze the proportion of cost components, etc. to comprehensively evaluate the estimation results, and make corrections combined with expert experience. At the same time, the visualization display module presents the estimation results in the form of intuitive charts and reports, which facilitates project managers to clearly understand the composition and distribution of engineering cost, so as to more scientifically carry out project planning, cost control and investment decision-making.
[0026] 5、In the present application, the established engineering cost estimation database centrally stores historical engineering cost data, estimation model training data and related data of the to-be-estimated project, forming a standardized data resource library. This not only facilitates data query, calling and management, but also provides a good data foundation for subsequent model optimization, new data supplement and experience summary, which helps to continuously improve the quality and level of engineering cost estimation. BRIEF DESCRIPTION OF DRAWINGS
[0027] Figure 1 A flowchart of an efficient and accurate engineering cost estimation method is proposed in the present application. DETAILED DESCRIPTION
[0028] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all.
[0029] REFERENCE Figure 1 An efficient and accurate engineering cost estimation method includes the following steps:
[0030] S1, data collection and preprocessing: collect historical engineering cost data, including project basic information, engineering drawing information, material price data, labor cost data, construction technology data; clean the collected data to remove duplicate and incorrect data, and perform interpolation or filling processing on missing data; standardize the processed data according to certain rules, unify the data format and unit;
[0031] S2, feature extraction and analysis: based on historical engineering cost data, extract key features related to engineering cost, including project features, material features, and construction technology features; use data mining algorithms to analyze the correlation between each feature and engineering cost, and determine the key features that have greater impact on engineering cost;
[0032] S3, construction of estimation model: adopt machine learning algorithm, take the extracted key features as input variables, and take historical engineering cost as output variables to construct engineering cost estimation model; train and optimize the model through cross-validation, adjust the model parameters, and improve the prediction accuracy of the model;
[0033] S4, Real-time data update: Establish a real-time data collection system to obtain market material price fluctuation data, labor cost adjustment data, and new construction technology information in real time. Integrate real-time data with historical data, update and optimize the estimation model regularly to ensure that the model can reflect market changes in a timely manner.
[0034] S5, Engineering cost estimation: Input the basic information and engineering drawing information of the project to be estimated. Through data preprocessing and feature extraction, the key features of the project to be estimated are obtained. The key features of the project to be estimated are input into the trained estimation model, and the engineering cost estimation result is output. Reasonable analysis is conducted on the estimation result, and the estimation result is corrected and adjusted in combination with expert experience and actual situation.
[0035] In step S3, the machine learning algorithm adopts an ensemble learning strategy, combining multiple different machine learning models to determine the final estimation result through voting or weighted average, to improve the stability and accuracy of the estimation model.
[0036] In step S4, the real-time data collection system establishes data interfaces with building material supplier databases, human resource market databases, and industry technology information platforms to realize automatic collection and update of real-time data.
[0037] In step S5, the reasonable analysis includes comparing the cost indicators of similar projects, analyzing whether the proportion of each cost component conforms to the industry standard, and checking whether the estimation result is within a reasonable fluctuation range.
[0038] In step S5, an engineering cost estimation database is established to store historical engineering cost data, training data of the estimation model, and related data of the project to be estimated, facilitating data query and management.
[0039] In step S2, the data mining algorithm uses principal component analysis or correlation analysis algorithm to determine the key features.
[0040] In step S5, when the estimation result deviates greatly from the cost indicators of similar projects, the re-evaluation process is automatically triggered to check and correct the input data again and re-estimate.
[0041] In step S5, a visual display module is added to visually display the engineering cost estimation result in the form of charts and reports, making it easy for users to intuitively understand the composition and distribution of engineering cost.
[0042] In step S1, the engineering drawing information is processed through image recognition technology to automatically extract key information from the drawing, reducing the workload and errors of manual input.
[0043] In step S1, when preprocessing the material price data, time series analysis is introduced to establish a material price fluctuation prediction model, estimate the material price trend in the future period of time, and incorporate the prediction results into the estimation data system.
[0044] The present application has the following effects:
[0045] Improve estimation accuracy: By collecting multi-source data, including project basic information, engineering drawing information, material price data, etc., and using data mining algorithms to analyze the correlation between each feature and engineering cost, key influencing factors can be accurately extracted. Based on this, the machine learning estimation model is optimized by cross-validation and regularization, effectively reducing the model error and avoiding overfitting problem. Compared with traditional methods, it can more accurately reflect the actual situation of engineering cost and provide reliable basis for project investment decision.
[0046] Improve estimation efficiency: Data collection and preprocessing are automated, engineering drawing information is quickly extracted by image recognition technology, reducing manual input workload and error; real-time data acquisition system automatically obtains market dynamic data, ensuring data timeliness; estimation model can quickly process input project feature data and output estimation results. The whole process greatly reduces manual operation and calculation time, significantly improves the efficiency of engineering cost estimation, and meets the needs of modern engineering project development.
[0047] Enhance adaptability: Real-time data update mechanism establishes interface with multiple databases and platforms to obtain market material price fluctuation, labor cost adjustment and new construction technology information in real time, and integrates them into the estimation model in time. This makes the estimation model keep up with market changes and adjust the estimation results in time, effectively adapting to the rapidly changing market environment and avoiding estimation deviation caused by market factor changes.
[0048] Optimize decision support: The rationality analysis process evaluates the estimation results by comparing similar project cost indicators and analyzing the proportion of cost components, and makes corrections based on expert experience. At the same time, the visualization display module presents the estimation results in the form of intuitive charts and reports, making it easy for project managers to clearly understand the composition and distribution of engineering cost, and making more scientific project planning, cost control and investment decisions.
[0049] Facilitate data management: The established engineering cost estimation database stores historical engineering cost data, estimation model training data and related data of projects to be estimated, forming a standardized data resource library. This not only facilitates data query, call and management, but also provides a good data foundation for subsequent model optimization, new data supplement and experience summary, which helps to continuously improve the quality and level of engineering cost estimation.
[0050] The above merely provides the preferred embodiment of the present application, and the protection scope of the present application is not limited thereto, and any person skilled in the art, according to the technical scheme and the inventive concept of the present application, can make equivalent substitutions or changes within the technical scope disclosed by the present application, which should be covered within the protection scope of the present application.
Claims
1. A high-efficiency and accurate engineering cost estimation method, characterized in that, The method comprises the following steps: S1, data collection and preprocessing: collect historical engineering cost data, including project basic information, engineering drawing information, material price data, labor cost data, construction technology data; clean the collected data, remove duplicate and incorrect data, and interpolate or fill in the missing data; standardize the processed data according to certain rules, unify the data format and unit; S2, feature extraction and analysis: based on historical engineering cost data, extract key features related to engineering cost, including project features, material features, and construction technology features; use data mining algorithms to analyze the correlation between each feature and engineering cost, and determine the key features that have a greater impact on engineering cost; S3, construction of estimation model: adopt machine learning algorithm, take the extracted key features as input variables, and take historical engineering cost as output variable to construct engineering cost estimation model; train and optimize the model through cross-validation, adjust the model parameters, and improve the prediction accuracy of the model; S4, real-time data update: establish a real-time data collection system to obtain market material price fluctuation data, labor cost adjustment data and new construction technology information in real time; integrate real-time data with historical data, update and optimize the estimation model regularly to ensure that the model can timely reflect market changes; S5, engineering cost estimation: input the basic information and engineering drawing information of the project to be estimated, obtain the key features of the project to be estimated through data preprocessing and feature extraction, input the key features of the project to be estimated into the trained estimation model, and output the engineering cost estimation result; analyze the rationality of the estimation result, and correct and adjust the estimation result according to expert experience and actual situation.
2. The method of claim 1, wherein, In step S3, the machine learning algorithm adopts an ensemble learning strategy, combines multiple different machine learning models, determines the final estimation result through voting or weighted average, and improves the stability and accuracy of the estimation model.
3. The method of claim 1, wherein, In step S4, the real-time data collection system realizes automatic collection and update of real-time data by establishing data interface with building material supplier database, human resource market database and industry technology information platform.
4. The method of claim 1, wherein, In step 5, the rationality analysis includes comparing the cost indicators of similar projects, analyzing whether the proportion of each cost component conforms to the industry standard, and checking whether the estimation result is within a reasonable fluctuation range.
5. The method of claim 1, wherein, In step S5, an engineering cost estimation database is established to store historical engineering cost data, training data of estimation model and related data of projects to be estimated, facilitating data query and management.
6. The method of claim 1, wherein, In step S2, the data mining algorithm adopts principal component analysis or correlation analysis algorithm to determine the key features.
7. The method of claim 1, wherein, In step S5, when the estimation result deviates greatly from the cost indicators of similar projects, a reevaluation process is automatically triggered to check and correct the input data again and reestimate.
8. The method of claim 1, wherein, In the step S5, the visualization display module is added to visually display the engineering cost estimation result in the form of charts and reports, so as to facilitate the user to intuitively understand the composition and distribution of the engineering cost.
9. The method of claim 1, wherein, In the step S1, the engineering drawing information is processed through the image recognition technology to automatically extract the key information in the drawing and reduce the workload and errors of manual input.
10. The method of claim 1, wherein, In the step S1, when the material price data is preprocessed, the time series analysis is introduced to establish a material price fluctuation prediction model, estimate the material price trend in a future period of time, and incorporate the prediction result into the estimation data system.