CN117057835A - Auxiliary analysis method and system for power grid engineering cost - Google Patents
Auxiliary analysis method and system for power grid engineering cost Download PDFInfo
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Abstract
The disclosure provides a method and a system for auxiliary analysis of power grid engineering cost, comprising the following steps: acquiring engineering data information of an engineering project to be analyzed, and carrying out corresponding pretreatment; determining engineering cost indexes required by engineering projects to be analyzed based on the preprocessed engineering data information and a pre-constructed engineering database; the method comprises the steps of taking engineering cost indexes required by similar historical engineering projects with similarity to the engineering projects to be analyzed meeting a preset threshold value in an engineering database as initial engineering cost indexes of the engineering projects to be analyzed; optimizing configuration is carried out on the initial engineering cost index based on visual modeling analysis, and the final engineering cost index is determined; acquiring corresponding pricing information according to the acquired engineering cost index, and optimizing the pricing information through a machine learning model; and based on the obtained engineering cost index and pricing information, predicting the engineering cost index and calculating the engineering cost result, thereby generating a power grid engineering cost table.
Description
Technical Field
The disclosure belongs to the technical field of power grid engineering cost analysis, and particularly relates to a power grid engineering cost auxiliary analysis method and system.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
The power grid engineering is an indispensable important part in the development of the power industry, the control level of the power grid engineering cost can also influence the power supply stability of a power system, the power grid engineering is directly related to the daily production and life of the national people, and in the process of carrying out the power grid engineering cost, a cost index analysis system is required to carry out auxiliary optimization on the engineering cost work.
The inventor finds that in the existing project cost analysis scheme, when project information is received, the calculation of the cost work is usually directly performed, and because the existing scheme needs a large amount of power grid project data and related economic, social and environmental index data, the acquisition and processing of the data can consume a large amount of time and labor cost, and certain expertise and skills are needed to be provided when the system is used, such as knowledge related to electric power projects and familiarity with data analysis methods, otherwise, the calculation result cannot be effectively judged due to the fact that the existing scheme is difficult to accurately understand and use, and the accuracy of index analysis is reduced; meanwhile, when the indexes are analyzed, the corresponding engineering databases are not needed to be effectively matched, so that the analysis efficiency is low, the actual requirements cannot be met, the existing engineering cost analysis schemes mostly adopt fixed engineering cost indexes, the similarity of the engineering cost indexes needed by different engineering projects is not effectively considered, certain differences still exist, and the existing schemes cannot flexibly configure the engineering cost indexes according to the actual requirements of the engineering projects.
Disclosure of Invention
In order to solve the problems, the present disclosure provides a method and a system for assisting in analyzing the construction cost of a power grid, wherein the method determines similar historical engineering projects with similarity to the engineering project to be analyzed meeting a preset threshold value through analog analysis based on a plurality of historical engineering project information stored in an engineering database, so as to obtain an initial construction cost index, effectively solve the problems that a large amount of power grid engineering data and related economic, social and environmental index data are required to be processed, and effectively improve the efficiency of index acquisition; meanwhile, based on the visual modeling analysis of project data information of the project to be analyzed and similar historical project data, the initial project cost index is optimally configured to determine the final project cost index, the historical project data is effectively utilized, meanwhile, the specific requirements of the project to be analyzed are effectively ensured, and the project cost index can be flexibly configured according to the actual requirements of the project.
According to a first aspect of an embodiment of the present disclosure, there is provided a power grid engineering cost auxiliary analysis method, including:
acquiring engineering data information of an engineering project to be analyzed, and carrying out corresponding pretreatment;
determining engineering cost indexes required by engineering projects to be analyzed based on the preprocessed engineering data information and a pre-constructed engineering database; the method comprises the steps of taking engineering cost indexes required by similar historical engineering projects with similarity to the engineering projects to be analyzed meeting a preset threshold value in an engineering database as initial engineering cost indexes of the engineering projects to be analyzed; optimizing configuration is carried out on the initial engineering cost index based on visual modeling analysis, and the final engineering cost index is determined;
acquiring corresponding pricing information according to the acquired engineering cost index, and optimizing the pricing information through a machine learning model;
and based on the obtained engineering cost index and pricing information, predicting the engineering cost index and calculating the engineering cost result, and generating a power grid engineering cost table based on the obtained engineering cost index, the engineering cost result and preset table items of the engineering cost table.
Further, the determining the engineering cost index required by the engineering project to be analyzed based on the preprocessed engineering data information and the pre-constructed engineering database specifically comprises the following steps: based on a plurality of pieces of history project information stored in the project database, determining similar history project items with similarity meeting a preset threshold value with the project items to be analyzed through analogy analysis, and taking project cost indexes required by the similar history project items as initial project cost indexes of the project items to be analyzed; and simultaneously, optimizing and configuring the initial engineering cost index based on the visual modeling analysis of engineering data information of the engineering project to be analyzed and similar historical engineering projects, and determining the final engineering cost index.
Further, the step of determining, through analog analysis, a similar historical engineering project with similarity to the engineering project to be analyzed meeting a preset threshold value specifically comprises: calculating the semantic similarity of the project basic information of the project to be analyzed and the project basic information of the historical project in the project database, and determining similar historical project based on the semantic similarity and a preset threshold;
or alternatively, the first and second heat exchangers may be,
the project basic information includes, but is not limited to, project name, project type, construction site, and construction unit.
Further, the method performs optimal configuration on the initial engineering cost index based on visual modeling analysis to determine the final engineering cost index, which specifically comprises the following steps: based on engineering data information of engineering projects to be analyzed and similar historical engineering projects, displaying analysis personnel in a statistical analysis and data visual display mode, carrying out data modeling analysis based on a linear regression model, identifying differences between the engineering projects to be analyzed and the similar historical engineering projects in an expert judgment mode based on analysis results, and carrying out adding and deleting processing on the initial engineering cost indexes based on the differences.
Further, the obtaining the corresponding pricing information specifically includes: constructing a pricing information database by acquiring pricing information corresponding to engineering cost indexes required by engineering projects in different areas and different types; for the project to be analyzed, inquiring pricing information from a pre-constructed pricing information database based on the project cost index; when the price information database does not exist, the price information corresponding to the current index is determined in a manual price inquiring and price comparing mode, and the price information database is added.
Further, for the pricing information of the engineering cost index required by the engineering project to be analyzed, a three-dimensional calculation amount and list pricing modeling mode is adopted, three-dimensional modeling is carried out on the pricing information of the engineering cost index required by the engineering project to be analyzed through modeling software, and the built model comprises the quantity of materials and the cost required by the engineering project to be analyzed;
or alternatively, the first and second heat exchangers may be,
and optimizing the pricing information through a machine learning model, and particularly optimizing the pricing information by adopting a Lasso regression model fitting mode.
Further, for the pricing information in the pricing information database, a corresponding rule base is formed in advance by carrying out inventory and quota analysis with a plurality of cost specialists, and prediction of engineering cost indexes and calculation of engineering cost results are carried out based on the formed rule base.
According to a second aspect of embodiments of the present disclosure, there is provided a grid project cost auxiliary analysis system, comprising:
the data acquisition unit is used for acquiring engineering data information of an engineering project to be analyzed and carrying out corresponding preprocessing;
the construction cost index determining unit is used for determining construction cost indexes required by the project to be analyzed based on the preprocessed project data information and a pre-constructed project database; the method comprises the steps of taking engineering cost indexes required by similar historical engineering projects with similarity to the engineering projects to be analyzed meeting a preset threshold value in an engineering database as initial engineering cost indexes of the engineering projects to be analyzed; optimizing configuration is carried out on the initial engineering cost index based on visual modeling analysis, and the final engineering cost index is determined;
the pricing information determining unit is used for acquiring corresponding pricing information according to the acquired engineering cost index and optimizing the pricing information through a machine learning model;
and the engineering cost table generating unit is used for predicting the engineering cost index and calculating the engineering cost result based on the obtained engineering cost index and pricing information and generating the power grid engineering cost table based on the obtained engineering cost index, the engineering cost result and preset table items of the engineering cost table.
According to a third aspect of the embodiments of the present disclosure, there is provided an electronic device, including a memory, a processor, and a computer program running on the memory, where the processor implements the method for assisting in analyzing the power grid engineering cost index when executing the program.
According to a fourth aspect of embodiments of the present disclosure, there is provided a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method of grid engineering cost indicator assisted analysis.
Compared with the prior art, the beneficial effects of the present disclosure are:
(1) The scheme is based on a plurality of pieces of history project information stored in a project database, a similar history project with similarity meeting a preset threshold value with the project to be analyzed is determined through analogy analysis, and project cost indexes required by the similar history project are used as initial project cost indexes of the project to be analyzed; by the method, the problem that a large amount of time and labor cost are required for processing a large amount of power grid engineering data and related economic, social and environmental index data can be effectively solved, and the efficiency of index acquisition is effectively improved; meanwhile, based on the visual modeling analysis of the project data information of the project to be analyzed and similar historical projects, the initial project cost index is optimally configured, and the final project cost index is determined.
(2) According to the scheme, for the pricing information in the pricing information database, the corresponding rule base is formed by carrying out inventory and quota analysis with a plurality of cost specialists in advance, the prediction of the engineering cost index and the calculation of the engineering cost result are carried out based on the formed rule base, and the problem that certain expertise and skills are required when the conventional scheme is used can be effectively avoided by the mode of presetting the rule base, so that the limit value of the use condition of a user can be effectively reduced.
Additional aspects of the disclosure will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the disclosure.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure, illustrate and explain the exemplary embodiments of the disclosure and together with the description serve to explain the disclosure, and do not constitute an undue limitation on the disclosure.
FIG. 1 is a flowchart illustrating an auxiliary analysis method for power grid engineering cost index according to an embodiment of the present disclosure;
FIG. 2 is a basic flow chart of project cost prediction as described in an embodiment of the present disclosure;
FIG. 3 is a basic flow chart of result upload recording in an embodiment of the present disclosure;
FIG. 4 is a basic flow diagram of an import analysis system according to an embodiment of the present disclosure;
FIG. 5 is a basic flow chart of pricing information fusion according to embodiments of the present disclosure;
fig. 6 is a basic flowchart of database fusion described in an embodiment of the present disclosure.
Detailed Description
The disclosure is further described below with reference to the drawings and examples.
It should be noted that the following detailed description is illustrative and is intended to provide further explanation of the present disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments in accordance with the present disclosure. As used herein, the singular is also intended to include the plural unless the context clearly indicates otherwise, and furthermore, it is to be understood that the terms "comprises" and/or "comprising" when used in this specification are taken to specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof.
Embodiments of the present disclosure and features of embodiments may be combined with each other without conflict.
Term interpretation:
pricing information: the latest market prices for building materials, finishing materials, installation materials, labor wages, construction tools, and the like, specifically include: manual price information: the real object engineering quantity manual price information of the building engineering and the construction work type manual cost information; material price information: the material category, specification, unit price (whether tax is included or not), supply area and unit, release date should be disclosed; price information of construction equipment: the device market price information and the device lease market price information are included, the latter is more important, and the device market price information comprises the contents of machine type, specification model, supplier name, lease price, release date and the like.
Engineering cost index: the cost value of various cost information of the completed or built engineering is processed in a unified format or standardized manner, so that a basis can be provided for the cost of the built engineering or the built engineering;
engineering cost index: an index for reflecting the influence degree of the price change on the engineering cost in a certain period, wherein the index comprises manual price information, material price information and construction tool price information price index; single engineering cost index and construction engineering cost comprehensive index.
Embodiment one:
the embodiment aims to provide an auxiliary analysis method for power grid engineering cost indexes.
An auxiliary analysis method for power grid engineering cost comprises the following steps:
acquiring engineering data information of an engineering project to be analyzed, and carrying out corresponding pretreatment;
determining engineering cost indexes required by engineering projects to be analyzed based on the preprocessed engineering data information and a pre-constructed engineering database; the method comprises the steps of taking engineering cost indexes required by similar historical engineering projects with similarity to the engineering projects to be analyzed meeting a preset threshold value in an engineering database as initial engineering cost indexes of the engineering projects to be analyzed; optimizing configuration is carried out on the initial engineering cost index based on visual modeling analysis, and the final engineering cost index is determined;
acquiring corresponding pricing information according to the acquired engineering cost index, and optimizing the pricing information through a machine learning model;
and based on the obtained engineering cost index and pricing information, predicting the engineering cost index and calculating the engineering cost result, and generating a power grid engineering cost table based on the obtained engineering cost index, the engineering cost result and preset table items of the engineering cost table.
In a specific implementation, the determining the engineering cost index required by the engineering project to be analyzed based on the preprocessed engineering data information and the pre-constructed engineering database specifically includes: based on a plurality of pieces of history project information stored in the project database, determining similar history project items with similarity meeting a preset threshold value with the project items to be analyzed through analogy analysis, and taking project cost indexes required by the similar history project items as initial project cost indexes of the project items to be analyzed; and simultaneously, optimizing and configuring the initial engineering cost index based on the visual modeling analysis of engineering data information of the engineering project to be analyzed and similar historical engineering projects, and determining the final engineering cost index.
In a specific implementation, the determining, through analogy analysis, a similar historical engineering project with similarity to the engineering project to be analyzed meeting a preset threshold value specifically includes: calculating the semantic similarity of the project basic information of the project to be analyzed and the project basic information of the historical project in the project database, and determining similar historical project based on the semantic similarity and a preset threshold;
or alternatively, the first and second heat exchangers may be,
the project basic information includes, but is not limited to, project name, project type, construction site, and construction unit.
In a specific implementation, the method performs optimal configuration on the initial engineering cost index based on visual modeling analysis, and determines the final engineering cost index, which specifically comprises the following steps: based on engineering data information of engineering projects to be analyzed and similar historical engineering projects, displaying analysis personnel in a statistical analysis and data visual display mode, carrying out data modeling analysis based on a linear regression model, identifying differences between the engineering projects to be analyzed and the similar historical engineering projects in an expert judgment mode based on analysis results, and carrying out adding and deleting processing on the initial engineering cost indexes based on the differences.
In a specific implementation, the acquiring the corresponding pricing information is specifically: constructing a pricing information database by acquiring pricing information corresponding to engineering cost indexes required by engineering projects in different areas and different types; for the project to be analyzed, inquiring pricing information from a pre-constructed pricing information database based on the project cost index; when the price information database does not exist, the price information corresponding to the current index is determined in a manual price inquiring and price comparing mode, and the price information database is added.
In the specific implementation, the pricing information of the engineering cost index required by the engineering project to be analyzed is modeled in a three-dimensional calculation amount and list pricing modeling mode, and the pricing information of the engineering cost index required by the engineering project to be analyzed is modeled in a three-dimensional mode through modeling software, wherein the built model comprises the quantity and cost of materials required by the engineering project to be analyzed;
or alternatively, the first and second heat exchangers may be,
and optimizing the pricing information through a machine learning model, and particularly optimizing the pricing information by adopting a Lasso regression model fitting mode.
In specific implementation, for the pricing information in the pricing information database, corresponding rule bases are formed in advance through inventory and quota analysis with a plurality of cost specialists, and prediction of engineering cost indexes and calculation of engineering cost results are performed based on the formed rule bases.
In particular, for easy understanding, the following detailed description of the embodiments will be given with reference to the accompanying drawings:
in order to solve the problems existing in the prior art, the embodiment provides an auxiliary analysis method for the power grid project cost index, which comprises the steps of project acquisition, project information analysis, project cost prediction, project cost calculation processing, project cost table generation and result uploading and recording. The following detailed description is given respectively:
(1) Engineering project acquisition
Specifically, the following data information of the engineering project needs to be acquired:
1) Basic information of engineering projects: including information such as project name, project type, construction site, construction unit, etc. The information is the basis for building the power grid engineering cost model and is also a precondition for analysis and decision making.
2) Engineering project investment amount: i.e. the total amount of investment in the engineering project, which is one of the core factors affecting the construction costs. This data may be obtained from an engineering account or project budget.
3) Project progress: including the date of construction, date of completion, etc. of the project in order to analyze the duration and progress control required for the project. Project progress is another important factor affecting construction costs.
4) Investment sub-items of engineering projects: including funding and budgeting costs, which provide detailed information for grid engineering cost analysis.
5) Technical characteristics of engineering project: the method comprises the steps of equipment, technical process, scale and the like used by engineering projects; and environmental protection, safety, social benefit and other information of engineering projects.
(2) Engineering project information analysis
Specifically, the analysis of the engineering project information specifically includes the following processing procedures:
1) Import analysis system
As shown in fig. 4, the importing analysis system includes steps of fusing engineering data, analyzing and sorting indexes, and automatically generating indexes, and specifically includes the following processing steps:
firstly, designing a database, and pre-establishing an engineering database for storing information data required by the power grid engineering cost. In database design, association and interaction among various information are needed to be considered so as to realize data integration and analysis, then data acquisition is carried out, required information data is acquired from data sources with different sources and different formats through various means (such as questionnaires, role playing, analyzing existing data and the like), then data cleaning and processing are carried out, in the data acquisition process, data cleaning and processing are needed to be carried out so as to improve data quality and accuracy, then data integration is carried out, after the data acquisition and processing are completed, various information data are needed to be integrated into the same database so as to carry out analysis and comparison on the data, thus obtaining useful conclusions and decisions, and data analysis is carried out, and the analysis process can be realized through the following steps (only one concrete example is shown below):
# import necessary libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
# load data
data=pd.read_csv ('data.csv')#data.csv is a data file
# data cleansing and preprocessing
data. Dropna (place=true) # delete missing value
data [ 'cost' ] =data [ 'total_cost' ]/data [ 'total_area' ] # calculate cost per unit area
# data visualization
plt.figure(figsize＝(10,6))
sns.boxplot(x＝'region',y＝'cost',data＝data)
plt.title ('unit area cost for different regions comparative')
plt.xlabel ('region')
plt.ylabel ('cost per unit area')
Visual presentation of the plt.show () # data
Statistical analysis of #
grouped＝data.groupby('region')
mean_cost＝grouped['cost'].mean()
std_cost＝grouped['cost'].std()
print ('average unit area cost for different regions: \n', mean_cost)
print ('standard deviation of unit area of different areas: \n', std_cost)
# modeling analysis
from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score
# build simple linear regression model
X＝data[['building_area']]
Y＝data[['total_cost']]
model＝LinearRegression().fit(X,Y)
r2＝r2_score(Y,model.predict(X))
pr int ('model R2 score:', R2).
Data cleaning and data visualization, and statistical analysis and modeling analysis are performed on information data required by the power grid engineering cost. It can be understood that the code for data analysis needs to be written and modified correspondingly according to specific problems and data conditions, so that the accuracy and reliability of the data analysis result are ensured.
And then aiming at information data required by the power grid engineering cost, different analysis tools and methods are used for exploring the relation and rule among the data so as to make a reasonable decision, and finally, after the data analysis is completed, the analysis result is required to be output as a visual report and a data index so as to facilitate decision making and management of business personnel.
2) Pricing information fusion
As shown in fig. 5, the pricing information fusion includes the steps of receiving pricing information, processing data information, and optimizing the pricing information, specifically:
the acquiring of the pricing information specifically includes:
market research: through market research, engineering projects of different areas and different types are collected, generalized or arranged, so that standard pricing specifications of the industry are comprehensively known, and pricing information data required by manufacturing cost are mastered.
Price inquiry and price comparison: in the aspects of purchasing materials, equipment and the like, pricing information data can be obtained by inquiring or comparing prices to different suppliers, and a pricing database is built.
Professional consultation and training: pricing information data is obtained and updated by participating in training and consultation activities of related industries.
Pricing software: obtaining and collating from the pricing information data using the pricing software
The processing and optimizing of the pricing information are specifically as follows:
firstly, various pricing information data are standardized: because of the problems of different sources of pricing information data, format difference and the like, various pricing information needs to be standardized, data consistency and standardization are ensured, then the pricing information is arranged through data cleaning, processing and classifying, various pricing information is cleaned, processed and classified, the collected information is cleaned, processed and classified, so that the data can be more orderly, corresponding information data management systems are built for the data of different types, each data is carefully arranged and managed, three-dimensional modeling of the pricing information is performed through CAD technology, so that the data can be better displayed and managed, the accuracy and accuracy of data integration are improved, a large amount of pricing information is analyzed and mined through digital technology, deep research is performed on the data, effective decision suggestions and measures are provided, and finally the data analysis is performed through technologies such as machine learning and artificial intelligence: when the data volume is extremely large, technologies such as machine learning, artificial intelligence and the like can be adopted to analyze and predict massive pricing information data so as to make more reasonable decisions, and the analysis is realized through the following procedures:
# import necessary libraries
import pandas as pd
import numpy as np
from sklearn import preprocessing
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LassoCV
from sklearn.metrics import mean_squared_error
# load data
data=pd.read_csv ('data.csv')#data.csv is a data file
# data preprocessing
X＝data.iloc[:,-1]
Y＝data.iloc[:,-1]
X＝preprocessing.StandardScaler().fit_transform(X)
X_train,X_test,Y_train,Y_test＝train_test_split(X,Y,test_size＝0.2,random_state＝0)
Model fitting by # Lasso regression
lasso＝LassoCV(cv＝10).fit(X_train,Y_train)
# prediction result
Y_pred＝lasso.predict(X_test)
Model performance evaluation #
print ("mean square error:", mean_squared_error (y_test, y_pred)).
3) Database fusion
As shown in fig. 6, the database fusion includes the steps of establishing an index database, classifying the database, guiding the business, providing a solution, screening influencing factors, providing an understanding view, and the like, specifically: the construction of the index database is similar to that of the engineering database, the construction method is the same, the established database is classified, and then the database is fused with a rule base formed by analyzing and arranging a list and a quota by a plurality of cost experts, namely 'database fusion';
furthermore, the database classification decomposes a certain rule of the index database into a plurality of entity sub-tables with independent storage spaces, and each table corresponds to a MYD data file, a MYI index file and a FRM table structure file, thereby facilitating the calling of database solutions and screening and improving the speed.
The purpose of steps 903-906 as described in fig. 6 is to complete a rule base completed by a construction specialist, specifically by guiding the decision of construction costs to occur when finishing construction and according to the analyzed result, and when analyzing and controlling the costs, screening and analyzing various factors which may affect the costs in this process, determining which items or factors may affect the construction costs, then evaluating and predicting these items or factors to make a correct decision, and finally providing a more comprehensive and clear understanding of the aspects of construction, product or service costs and economic benefits, etc. by careful and thorough analysis and explanation. The problem that certain expertise and skills are required when the existing scheme is used can be solved through the rule base. Furthermore, the scheme in the embodiment supports the XML file derived by the mainstream pricing software while being compatible with the engineering data of the pricing (namely the pricing optimal engineering budget software), and the index analysis report is from thick to thin, so that the flat meter cost and the real object quantity can be analyzed at the same time, and the index data is more convenient to apply.
(3) Project cost prediction
As shown in fig. 2, the project cost prediction includes inputting analysis information, organizing historical data, calculating cost indexes, measuring and calculating elastic coefficients, and researching index prediction, specifically:
in a specific implementation, the input analysis information comprises engineering information, pricing information and rule base information of engineering projects; the aim of the arrangement of the historical data is to collect and utilize the historical information data of the power grid engineering cost, so that the power grid engineering cost change trend, influence factors and future trend can be better known, and the accuracy and reliability of engineering budget are improved;
in the project cost prediction, firstly, the project cost index is arranged and analyzed, then, the classification result is analyzed, the primary weight and the secondary weight of each classified project are respectively given, the static investment of unit capacity is used as a reference value, the line project cost index is calculated, the relation between the cost index and the GDP average reduction index is analyzed, the elastic coefficient between the cost index and the GDP average reduction index is calculated, finally, the analysis and the prediction of the final cost index are integrated, and the prediction research of the final cost index is carried out by utilizing an elastic coefficient method, so that the defects of single traditional cost index prediction model, lack of explanation and low precision are effectively overcome.
The variation reasons, trends and characteristics of the cost indexes can be better known by classifying and analyzing CCI (Construction Cost Index: engineering cost indexes), scientific basis is provided for engineering budget and decision, firstly, data cleaning and preprocessing are carried out by classifying and analyzing, improper or incomplete data are removed, preprocessing is carried out on the data, such as calculating the base period price, removing seasonal and abnormal factor influences and the like, then the variation trend of each index in stages (month, quarter and year) is explored, the variation rule and trend of the CCI are observed, and each factor influencing the index variation is analyzed. Meanwhile, the correlation between the CCI index and the related index (such as object price, national economy, social factors and the like) is quantitatively analyzed, then the CCI data is clustered according to different classification factors, such as region, type, scale and the like, so as to explore the difference of the CCI index, analyze the influence of different factors on the CCI, and then according to the change trend of the CCI index, draw a related strategy, further establish a prediction model, predict the trend of the CCI index in the future, evaluate the reliability of the prediction, and feed the analysis and prediction result back to owners, design units and related institutions after the analysis is completed, thereby providing scientific basis for decision and cost control.
(4) Engineering cost calculation processing
Calculating according to the results obtained by the project information analysis and the project cost prediction in the previous step, specifically calculating each project quantity list project in the project, and adding all unit prices to obtain the total cost. Meanwhile, other fees of the whole construction project, such as charging, supervision fees, construction project planning licenses and the like, are taken into consideration in total price calculation, actual quotations are obtained by taking price calculation, based on unit price calculation or total price calculation results, reasonable profit and tax and the like into consideration, project calculation and detail budget are determined according to bidding documents and actual conditions in project cost prediction, cost control and emergency processing capability are enhanced, and finally project settlement accounting is carried out according to actual cost and quality conditions of project cost accounting, so that final project cost is defined.
(5) Generating engineering cost table
The generation of the engineering cost table is to determine the main structure of the table according to actual needs, wherein the main structure comprises contents such as a header, an item list, a price detail, a small account and a summation, the corresponding item list contents such as engineering names, engineering numbers, list names, engineering quantity, units, unit price, money and the like are added into the table, and the result is added into the corresponding position of the table according to the actual unit price calculation result. Typically including details of labor, materials, equipment, tax contracts, etc., and calculating sub-amounts and totals, including sub-amounts and totals of various materials, labor, equipment, tax, etc., according to the relevant formulas or algorithms in the form, and finally adding necessary notes and descriptions, such as correction data, descriptions of specific conditions such as assumptions, etc., to the form.
(6) Uploading and recording results
As shown in fig. 3, the result uploading record specifically includes steps of receiving result information, uploading cloud storage, cloud information classification, information output and use, and the like.
When the information analysis of engineering projects is carried out, the scheme of the embodiment provides a quick and accurate solution for operation by guiding operation activities for enterprises according to the required cost indexes, provides a view angle for understanding index characteristics and provides a view angle for fast screening influencing factors for project operation, and when the information analysis system is introduced, a rule base formed by analyzing and arranging a list and a quota through a plurality of cost experts in the system is used, so that the information analysis system is completely compatible with pricing and optimizing engineering data, simultaneously supports XML files led out by mainstream pricing software, and can analyze the cost and the real object quantity of flat meters at the same time, thereby ensuring that the index data is more convenient to apply; according to the scheme, after information analysis of engineering projects is carried out, firstly construction cost is predicted, in the process of prediction, the engineering cost indexes are arranged and analyzed, then classification results are analyzed, primary weights and secondary weights of the various projects are respectively given, static investment of unit capacity is used as a reference value, the engineering cost indexes of a line are calculated, the relation between the construction cost indexes and the GDP reduction indexes is analyzed, the elastic coefficient between the construction cost indexes and the GDP reduction indexes is calculated, finally the analysis and the prediction of the construction cost indexes and the GDP reduction indexes are integrated, and the prediction research of the final construction cost indexes is carried out by utilizing an elastic coefficient method, so that the defects of single traditional construction cost index prediction model, lack of explanation and low precision are overcome.
Embodiment two:
the embodiment aims to provide an auxiliary analysis system for power grid engineering cost.
A grid engineering cost auxiliary analysis system, comprising:
the data acquisition unit is used for acquiring engineering data information of an engineering project to be analyzed and carrying out corresponding preprocessing;
the construction cost index determining unit is used for determining construction cost indexes required by the project to be analyzed based on the preprocessed project data information and a pre-constructed project database; the method comprises the steps of taking engineering cost indexes required by similar historical engineering projects with similarity to the engineering projects to be analyzed meeting a preset threshold value in an engineering database as initial engineering cost indexes of the engineering projects to be analyzed; optimizing configuration is carried out on the initial engineering cost index based on visual modeling analysis, and the final engineering cost index is determined;
the pricing information determining unit is used for acquiring corresponding pricing information according to the acquired engineering cost index and optimizing the pricing information through a machine learning model;
and the engineering cost table generating unit is used for predicting the engineering cost index and calculating the engineering cost result based on the obtained engineering cost index and pricing information and generating the power grid engineering cost table based on the obtained engineering cost index, the engineering cost result and preset table items of the engineering cost table.
Further, the system in this embodiment corresponds to the method in the first embodiment, and the technical details thereof are described in the first embodiment, so that they will not be described in detail herein.
In further embodiments, there is also provided:
an electronic device comprising a memory and a processor and computer instructions stored on the memory and running on the processor, which when executed by the processor, perform the method of embodiment one. For brevity, the description is omitted here.
It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general purpose processors, digital signal processors DSP, application specific integrated circuits ASIC, off-the-shelf programmable gate array FPGA or other programmable logic device, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory may include read only memory and random access memory and provide instructions and data to the processor, and a portion of the memory may also include non-volatile random access memory. For example, the memory may also store information of the device type.
A computer readable storage medium storing computer instructions which, when executed by a processor, perform the method of embodiment one.
The method in the first embodiment may be directly implemented as a hardware processor executing or implemented by a combination of hardware and software modules in the processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in a memory, and the processor reads the information in the memory and, in combination with its hardware, performs the steps of the above method. To avoid repetition, a detailed description is not provided herein.
Those of ordinary skill in the art will appreciate that the elements of the various examples described in connection with the present embodiments, i.e., the algorithm steps, can be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.
The auxiliary analysis method and the auxiliary analysis system for the power grid engineering cost can be realized, and have wide application prospects.
The foregoing description of the preferred embodiments of the present disclosure is provided only and not intended to limit the disclosure so that various modifications and changes may be made to the present disclosure by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present disclosure should be included in the protection scope of the present disclosure.
Claims (10)
1. The auxiliary analysis method for the construction cost of the power grid is characterized by comprising the following steps of:
acquiring engineering data information of an engineering project to be analyzed, and carrying out corresponding pretreatment;
determining engineering cost indexes required by engineering projects to be analyzed based on the preprocessed engineering data information and a pre-constructed engineering database; the method comprises the steps of taking engineering cost indexes required by similar historical engineering projects with similarity to the engineering projects to be analyzed meeting a preset threshold value in an engineering database as initial engineering cost indexes of the engineering projects to be analyzed; optimizing configuration is carried out on the initial engineering cost index based on visual modeling analysis, and the final engineering cost index is determined;
acquiring corresponding pricing information according to the acquired engineering cost index, and optimizing the pricing information through a machine learning model;
and based on the obtained engineering cost index and pricing information, predicting the engineering cost index and calculating the engineering cost result, and generating a power grid engineering cost table based on the obtained engineering cost index, the engineering cost result and preset table items of the engineering cost table.
2. The auxiliary analysis method for power grid engineering cost according to claim 1, wherein the determining the engineering cost index required by the engineering project to be analyzed based on the preprocessed engineering data information and a pre-constructed engineering database specifically comprises: based on a plurality of pieces of history project information stored in the project database, determining similar history project items with similarity meeting a preset threshold value with the project items to be analyzed through analogy analysis, and taking project cost indexes required by the similar history project items as initial project cost indexes of the project items to be analyzed; and simultaneously, optimizing and configuring the initial engineering cost index based on the visual modeling analysis of engineering data information of the engineering project to be analyzed and similar historical engineering projects, and determining the final engineering cost index.
3. The auxiliary analysis method for power grid engineering cost according to claim 2, wherein the step of determining similar historical engineering items with similarity to the engineering items to be analyzed meeting a preset threshold through analog analysis comprises the following steps: calculating the semantic similarity of the project basic information of the project to be analyzed and the project basic information of the historical project in the project database, and determining similar historical project based on the semantic similarity and a preset threshold;
or alternatively, the first and second heat exchangers may be,
the project basic information includes, but is not limited to, project name, project type, construction site, and construction unit.
4. The auxiliary analysis method for power grid engineering cost according to claim 1, wherein the method is characterized in that the initial engineering cost index is optimally configured based on visual modeling analysis, and the final engineering cost index is determined, specifically: based on engineering data information of engineering projects to be analyzed and similar historical engineering projects, displaying analysis personnel in a statistical analysis and data visual display mode, carrying out data modeling analysis based on a linear regression model, identifying differences between the engineering projects to be analyzed and the similar historical engineering projects in an expert judgment mode based on analysis results, and carrying out adding and deleting processing on the initial engineering cost indexes based on the differences.
5. The auxiliary analysis method for the construction cost of the power grid according to claim 1, wherein the obtaining of the corresponding pricing information is specifically as follows: constructing a pricing information database by acquiring pricing information corresponding to engineering cost indexes required by engineering projects in different areas and different types; for the project to be analyzed, inquiring pricing information from a pre-constructed pricing information database based on the project cost index; when the price information database does not exist, the price information corresponding to the current index is determined in a manual price inquiring and price comparing mode, and the price information database is added.
6. The auxiliary analysis method for the construction cost of the power grid according to claim 1, wherein the construction cost information of the construction cost index required by the construction project to be analyzed is modeled in a three-dimensional calculation amount and list calculation modeling mode, and the modeling software is used for carrying out three-dimensional modeling on the construction cost information of the construction cost index required by the construction project to be analyzed, wherein the built model comprises the quantity and cost of materials required by the construction project to be analyzed;
or alternatively, the first and second heat exchangers may be,
and optimizing the pricing information through a machine learning model, and particularly optimizing the pricing information by adopting a Lasso regression model fitting mode.
7. A method of auxiliary analysis of construction costs of a power grid according to claim 1, wherein the price information in the price information database is previously analyzed by performing a list and a quota with a plurality of construction cost experts to form a corresponding rule base, and the prediction of construction cost index and the calculation of construction cost result are performed based on the formed rule base.
8. An auxiliary analysis system for power grid engineering cost, comprising:
the data acquisition unit is used for acquiring engineering data information of an engineering project to be analyzed and carrying out corresponding preprocessing;
the construction cost index determining unit is used for determining construction cost indexes required by the project to be analyzed based on the preprocessed project data information and a pre-constructed project database; the method comprises the steps of taking engineering cost indexes required by similar historical engineering projects with similarity to the engineering projects to be analyzed meeting a preset threshold value in an engineering database as initial engineering cost indexes of the engineering projects to be analyzed; optimizing configuration is carried out on the initial engineering cost index based on visual modeling analysis, and the final engineering cost index is determined;
the pricing information determining unit is used for acquiring corresponding pricing information according to the acquired engineering cost index and optimizing the pricing information through a machine learning model;
and the engineering cost table generating unit is used for predicting the engineering cost index and calculating the engineering cost result based on the obtained engineering cost index and pricing information and generating the power grid engineering cost table based on the obtained engineering cost index, the engineering cost result and preset table items of the engineering cost table.
9. An electronic device comprising a memory, a processor and a computer program stored for execution on the memory, wherein the processor, when executing the program, implements a grid engineering cost indicator assisted analysis method according to any one of claims 1-7.
10. A non-transitory computer readable storage medium having stored thereon a computer program, which when executed by a processor implements a grid engineering cost indicator assisted analysis method according to any of claims 1-7.
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| CN117273394A (en) * | 2023-11-17 | 2023-12-22 | 中铁四局集团有限公司 | Intelligent equipment selection allocation analysis management method based on big data |
| CN117934035A (en) * | 2024-01-22 | 2024-04-26 | 苏州华星工程造价咨询有限公司 | Method, device and storage medium for predicting construction cost of building construction |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117273394A (en) * | 2023-11-17 | 2023-12-22 | 中铁四局集团有限公司 | Intelligent equipment selection allocation analysis management method based on big data |
| CN117273394B (en) * | 2023-11-17 | 2024-02-06 | 中铁四局集团有限公司 | Intelligent equipment selection allocation analysis management method based on big data |
| CN117934035A (en) * | 2024-01-22 | 2024-04-26 | 苏州华星工程造价咨询有限公司 | Method, device and storage medium for predicting construction cost of building construction |
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