CN113919028A - Engineering cost evaluation management system based on big data analysis - Google Patents
Engineering cost evaluation management system based on big data analysis Download PDFInfo
- Publication number
- CN113919028A CN113919028A CN202111192355.1A CN202111192355A CN113919028A CN 113919028 A CN113919028 A CN 113919028A CN 202111192355 A CN202111192355 A CN 202111192355A CN 113919028 A CN113919028 A CN 113919028A
- Authority
- CN
- China
- Prior art keywords
- construction
- building
- cost
- module
- bim
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Withdrawn
Links
- 238000007405 data analysis Methods 0.000 title claims abstract description 24
- 238000011156 evaluation Methods 0.000 title claims abstract description 19
- 238000010276 construction Methods 0.000 claims abstract description 107
- 239000002994 raw material Substances 0.000 claims abstract description 41
- 239000000463 material Substances 0.000 claims abstract description 12
- 239000004566 building material Substances 0.000 claims abstract description 7
- 238000000034 method Methods 0.000 claims description 25
- 238000007726 management method Methods 0.000 claims description 19
- 238000012549 training Methods 0.000 claims description 17
- 238000004088 simulation Methods 0.000 claims description 16
- 238000012360 testing method Methods 0.000 claims description 16
- 238000013461 design Methods 0.000 claims description 10
- 238000009435 building construction Methods 0.000 claims description 8
- 238000012795 verification Methods 0.000 claims description 8
- 238000005457 optimization Methods 0.000 claims description 6
- 238000004364 calculation method Methods 0.000 claims description 5
- 238000010438 heat treatment Methods 0.000 claims description 4
- 238000004519 manufacturing process Methods 0.000 claims description 4
- 238000005070 sampling Methods 0.000 claims description 4
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 4
- 238000012423 maintenance Methods 0.000 claims description 3
- 230000008520 organization Effects 0.000 claims description 3
- 238000004458 analytical method Methods 0.000 claims description 2
- 238000009423 ventilation Methods 0.000 claims description 2
- 239000000284 extract Substances 0.000 claims 1
- 238000012545 processing Methods 0.000 description 6
- 238000005516 engineering process Methods 0.000 description 5
- 230000000694 effects Effects 0.000 description 4
- 238000013528 artificial neural network Methods 0.000 description 2
- 238000004422 calculation algorithm Methods 0.000 description 2
- 230000005611 electricity Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 239000000470 constituent Substances 0.000 description 1
- 238000003066 decision tree Methods 0.000 description 1
- 230000003111 delayed effect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- -1 manpower Substances 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 230000029305 taxis Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/10—Geometric CAD
- G06F30/13—Architectural design, e.g. computer-aided architectural design [CAAD] related to design of buildings, bridges, landscapes, production plants or roads
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
- G06Q30/0206—Price or cost determination based on market factors
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/08—Construction
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- Strategic Management (AREA)
- General Physics & Mathematics (AREA)
- Finance (AREA)
- Development Economics (AREA)
- Accounting & Taxation (AREA)
- Geometry (AREA)
- General Business, Economics & Management (AREA)
- Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- Computer Hardware Design (AREA)
- Marketing (AREA)
- Primary Health Care (AREA)
- Structural Engineering (AREA)
- Mathematical Analysis (AREA)
- General Health & Medical Sciences (AREA)
- Human Resources & Organizations (AREA)
- Mathematical Optimization (AREA)
- Pure & Applied Mathematics (AREA)
- Computational Mathematics (AREA)
- Tourism & Hospitality (AREA)
- Health & Medical Sciences (AREA)
- General Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Civil Engineering (AREA)
- Evolutionary Computation (AREA)
- Architecture (AREA)
- Game Theory and Decision Science (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a project cost evaluation management system based on big data analysis, relating to the technical field of project cost, comprising a drawing module: two-dimensional engineering drawings of the whole building are arranged, and a BIM module: building an integral BIM model of the building through a two-dimensional engineering drawing, and estimating a module: calculating the duration of each building of the whole building according to a building BIM model and the quantity of the raw building materials in different stages in the duration, and a prediction module: building a prediction model to predict the fluctuation coefficient of the price of the building raw materials within a certain time period in the construction period according to the prices of the building raw materials in different stages and the corresponding price fluctuation coefficients; the budget module is used for carrying out budget estimation on man-machine materials in the construction cost according to the prediction data and combining other cost budget total cost in the construction cost, and the invention realizes accurate construction cost.
Description
Technical Field
The invention belongs to the technical field of construction cost, in particular to a construction cost evaluation management system based on big data analysis,
background
The project cost refers to the construction cost of the project predicted or actually paid in the construction period, the knowledge and skill in the aspects of management, economics, engineering technology and the like are comprehensively applied, the working process of predicting, planning, controlling, accounting, analyzing, evaluating and the like of the project cost is called project cost management, according to the program, method and basis specified by laws, regulations and standards and the like, the prediction or determination of the project cost and the constituent content thereof is called project price, the project price is recommended according to the popular terms including project metering price standard, project price rating quota, project cost information and the like related to the price content, price method and price standard, namely, the project cost is predicted by a scientific mode before the building is built, all funds needed in the construction project of the building are predicted, the most accurate shutdown is provided for a building party, the problem that the problem of the fund raising of the building party to cause the tail rot of the building and the like is avoided in the construction process, scientific engineering cost is a step which is necessary to be carried out before each important building is built.
The existing engineering cost is firstly combined with the current cost to carry out price budget before construction according to the construction drawing of a building, the adopted drawing is a two-dimensional drawing, in the traditional two-dimensional drawing design, after professional design drawings such as a structure, water heating and electricity are gathered, a general chart engineer manually finds and solves the problem of incongruity, a large amount of time and energy are consumed by the construction structure designer and an installation engineer designer, the engineering progress and quality are influenced, the construction cost engineer cannot accurately judge the drawings when carrying out the general chart engineering, the budget can be carried out only by combining the current two-dimensional drawing when carrying out the construction cost budget, and certain operation is needed because rework caused by the incongruity of the professional design drawings such as the structure, the water heating and electricity and the like, so that the operation is inaccurate.
The existing construction cost is usually calculated according to the price of the building raw materials at the current stage, the construction period of a large building is long, the price of the building materials can fluctuate in a long construction period and can fluctuate greatly, and therefore the budget deviation is large when the current price of the building materials is adopted for cost budget.
Disclosure of Invention
The invention aims to provide a project cost evaluation management system based on big data analysis, which comprises a main system, wherein the main system comprises a drawing module: two-dimensional engineering drawings of the whole building are arranged, and a BIM module: building an integral BIM model of the building through a two-dimensional engineering drawing, and estimating a module: calculating the construction time of the whole building according to a building BIM model and the required building raw materials in different stages in the time, and a prediction module: building a prediction model to predict the fluctuation coefficient of the price of the building raw materials within a certain time period in the construction period according to the prices of the building raw materials in different stages and the corresponding price fluctuation coefficients; the budget module is used for carrying out budget estimation on man-machine materials in the construction cost according to the prediction data and budgeting the total cost by combining other costs in the construction cost;
carrying out simulation construction through a BIM model, and intuitively and quickly knowing a building plan, a construction process and the building raw material demand within a period of time in a building construction period at any time and any place through the simulation construction;
the method comprises the steps that a prediction module collects and establishes a corresponding database for all past price data of building raw materials, processes the data in a big data analysis mode, establishes a prediction model during data processing, in the prediction model, firstly, original sampling data are preprocessed, and a training set, a verification set and a test set are divided, wherein the training set is used for training common parameters of connection values of the prediction model, optimizing the super parameters such as iteration times and the like according to the evaluation effect of the verification set, the test set is used for an actual data test, independently predicting dates to be predicted in the test set after model training and parameter optimizing, and finally obtaining a model prediction result through an optimal weighting method so as to predict the fluctuation coefficient of the prices of the building raw materials within a period of a building;
and the budget module is used for obtaining the cost of the man-machine cost required by the building in a period of time according to the fact that the fluctuation coefficient of the price of the building raw materials in the predicted building period of time is multiplied by the current unit price of the raw materials and the demand of the raw materials in the BIM prediction model in the period of time, finally accumulating the predicted cost of a plurality of construction stages to obtain the total cost of the materials in the whole building period, and then combining other cost required by the whole building to budget the cost required by the whole building.
Preferably, the two-dimensional drawing comprises a first page drawing, a general design description, a building construction drawing, a structure construction drawing, a water supply and drainage construction drawing, a heating and ventilation construction drawing and a building drawing required in the building process of an electrical construction drawing, and the three-dimensional building model is built according to the corresponding two-dimensional drawing.
Preferably, in the BIM module, it is ensured that the picture conflict, the plane and the elevation are consistent, and the drawing is not missed in the same region due to two different components in the BIM modeling according to the two-dimensional drawing, so as to avoid the inaccuracy in the construction of the BIM model, the simplification and omission caused by the general 2D plane view are made up through the BIM modeling, the misjudgment and loss in the construction work are caused by the oversimplified drawing, and further the project is delayed, the information exchange of the earlier drawing is mainly performed by the traditional 2D plane view, the longitudinal and transverse sections, and various elevation views, so that the related information of the drawing cannot be corrected in time, other related section elevations are required to be corrected after the modification of the plane view is completed, the information of the drawing is not completely conveyed to the construction side to understand, the real project and the information standardization are simulated in advance in the virtual application space of the computer by using the BIM technology, the life cycle planning for assisting the whole construction includes various management measures and new technology of engineering operation in planning design, construction, operation and maintenance work at the initial construction stage.
Preferably, the construction raw materials required in the construction of the building further include man-hours and production costs due to maintenance of equipment, and the wages of the man-hours are different from those in ordinary times in holidays.
Preferably, the other expenses required by the whole building in the budget module include construction measure project expenses, the construction measure project expenses in the list pricing mode include construction technical measure project expenses and construction organization measure project expenses, and special expenses corresponding to the content of expenses in the comprehensive pricing, and further include social insurance expenses and project quota determination expenses and taxes same as the provisions of the comprehensive pricing, and all expenses constitute the construction cost expenses of the building.
Preferably, the BIM model in the BIM module contains complete geometric, physical and performance information of the project, at each stage of project implementation, relevant data required for analysis, simulation and optimization are automatically extracted from the BIM model for calculation and demonstration, the project solution is adjusted according to the result and the simulation calculation is immediately carried out on the new solution until the optimized solution is formed, in the design stage, the BIM can carry out effective simulation experiments on sunlight, emergency evacuation, heat energy conduction and the like, 4D simulation can be carried out in the bidding and construction stage, namely the three-dimensional model increases the time dimension to form a four-dimensional model, the established construction organization design is used as a blue book, the actual construction is simulated, a reasonable construction method is guided and formulated by the simulated construction period, and the total amount of materials such as manpower, materials and machinery needed in different stages in the construction process is accurately predicted through simulation construction.
Preferably, in the prediction module, the data in the database includes price data and date data corresponding to the price of the building material in a period of time before, the database adopts an SQL database and establishes corresponding protection measures for the database, and the database protection is performed by establishing a corresponding firewall.
Preferably, the other expenses required by the whole building in the budget module further include certain expenses required in an emergency state, including rework and unexpected emergency reserve money, so that the price budget is more accurate in planning the labor cost of the expenses required in the emergency state.
Preferably, the host system is an APP installed in a windows system.
The invention has the following beneficial effects:
1. according to the method, a corresponding three-dimensional BIM model is established according to a two-dimensional building engineering drawing, the construction of a building is simulated through the three-dimensional BIM model, and real engineering is simulated in advance in a virtual application space of a computer by using a BIM technology, so that rework caused by incongruity of two-dimensional drawings is avoided in the building construction process, the information is rich, the dynamic and real-time effects are realized, the coordination and synchronization are realized, the management is convenient, the dimensionality is expanded, and the construction efficiency is increased.
2. In the invention, building construction is divided into stages in a BIM model, different building material demands in different stages are accurately budgeted through the BIM model, unit prices corresponding to each building material in continuous time periods are collected, a database containing price data and date data is established, data in the database is analyzed through a computer big data processing technology, corresponding prediction model processing data is established in big data analysis, data learning and budgeting are carried out through a neural network, finally, a model prediction result is obtained through an optimal weighting method, and then a fluctuation coefficient of the building raw material price in a period of time in a building construction period is predicted, so that the budget is more accurate when project cost is budgeted, and the budget deviation is reduced.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a system diagram of a large data analysis-based construction cost evaluation management system according to the present invention;
FIG. 2 is a flowchart of a first embodiment of a big data analysis-based engineering cost according to a first embodiment of the present invention;
fig. 3 is a flowchart of a second embodiment of the engineering cost based on big data analysis according to the first embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments, and all other embodiments obtained by a person of ordinary skill in the art without creative efforts based on the embodiments of the present invention belong to the protection scope of the present invention.
In the description of the present invention, it is to be understood that the terms "upper", "middle", "outer", "inner", "lower", "around", and the like, indicate orientations or positional relationships, are used merely to facilitate the description of the present invention and to simplify the description, and do not indicate or imply that the referenced components or elements must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be taken as limiting the present invention.
Example one
The embodiment provides a project cost evaluation management system based on big data analysis, as shown in fig. 1, including the drawing module: two-dimensional engineering drawings of the whole building are arranged, and a BIM module: building an integral BIM model of the building through a two-dimensional engineering drawing, and estimating a module: calculating the construction time of the whole building according to a building BIM model and the required building raw materials in different stages in the time, and a prediction module: building a prediction model to predict the fluctuation coefficient of the price of the building raw materials within a certain time period in the construction period according to the prices of the building raw materials in different stages and the corresponding price fluctuation coefficients; the budget module is used for carrying out budget estimation on man-machine materials in the construction cost according to the prediction data and budgeting the total cost by combining other costs in the construction cost;
carrying out simulation construction through a BIM model, visually and quickly knowing a building plan, a construction process and a construction period within a period of time of three months at any time and any place through the simulation construction, and predicting the demand of building raw materials within the period of time through the BIM model;
the method comprises the steps that a prediction module collects all past price data of building raw materials and establishes a corresponding database, the data are processed in a big data analysis mode, a LightGBM prediction model is established during data processing, in the prediction model, original sampling data are preprocessed firstly, a training set, a verification set and a test set are divided, the training set is used for training common parameters of connection values of the prediction model, the optimization is carried out on super parameters such as iteration times and the like according to the evaluation effect of the verification set, the test set is used for an actual data test, the date to be predicted in the test set is independently predicted after model training and parameter optimization, finally, the result of model prediction is obtained through an optimal weighting method, and the fluctuation coefficient of the price of the building raw materials within a period of a building is predicted;
and the budget module is used for obtaining the cost of the man-machine cost required by the building in a period of time according to the fact that the fluctuation coefficient of the price of the building raw materials in each three-month period in the predicted building period is multiplied by the current unit price of the raw materials and the demand of the raw materials in the period of time in the BIM prediction model, finally accumulating the predicted cost in a plurality of construction stages to obtain the total cost of the materials in the whole building period, and then combining other cost required by the whole building to budget the manufacturing cost required by the whole building.
The basic idea of the system is that M weak regression trees are linearly combined to form a strong regression tree, the Light GBM model is mainly improved to comprise a histogram algorithm and a leaf growing strategy with depth limitation, the histogram algorithm divides continuous data into K integers and constructs a histogram with the width of K, discretized values are accumulated in the histogram as indexes during traversal, and then an optimal decision tree division point is searched, and the leaf-wise strategy with depth limitation means that during each division, a leaf with the maximum gain is found to be divided and circulated, and meanwhile, the complexity of the model is reduced through the limitation of the depth of the tree and the number of the leaves, so that fitting is prevented, the Light GBM model greatly reduces the occupation of a memory, the training time is short, and the accuracy is high.
Example two
The embodiment provides a project cost evaluation management system based on big data analysis, as shown in fig. 3, including the drawing module: two-dimensional engineering drawings of the whole building are arranged, and a BIM module: building an integral BIM model of the building through a two-dimensional engineering drawing, and estimating a module: calculating the construction time of the whole building according to a building BIM model and the required building raw materials in different stages in the time, and a prediction module: building a prediction model to predict the fluctuation coefficient of the price of the building raw materials within a certain time period in the construction period according to the prices of the building raw materials in different stages and the corresponding price fluctuation coefficients; the budget module is used for carrying out budget estimation on man-machine materials in the construction cost according to the prediction data and budgeting the total cost by combining other costs in the construction cost;
carrying out simulation construction through a BIM model, visually and quickly knowing a building plan, a construction process and a construction period within every six months by simulating construction anytime and anywhere, and predicting the demand of building raw materials in the period through the BIM model;
the prediction module collects and establishes a corresponding database for all past price data of building raw materials, processes the data in a big data analysis mode, establishes an LSTM prediction model during data processing, preprocesses original sampling data in the prediction model, divides a training set, a verification set and a test set, wherein the training set is used for training common parameters of connection values of the prediction model, optimizes the super parameters such as iteration times and the like according to the evaluation effect of the verification set, the test set is used for an actual data test, independently predicts the date to be predicted in the test set after model training and parameter optimization, and finally obtains the result of model prediction by an optimal weighting method so as to predict the fluctuation coefficient of the price of the building raw materials within a period of a building;
and the budget module is used for obtaining the cost of the man-machine cost required by the construction in a period according to the fact that the fluctuation coefficient of the price of the construction raw materials in each six month period in the construction period is predicted and is multiplied by the current unit price of the raw materials and the demand of the raw materials in the BIM prediction model in the period, finally accumulating the predicted cost of a plurality of construction stages to obtain the total cost of the materials in the whole construction period, and then combining other cost required by the whole construction to budget the required manufacturing cost of the whole construction.
The LSTM prediction model firstly needs to determine an input sequence and an output sequence thereof, different from a traditional neural network, the input sequence and the output sequence need to be input by taking data of each lag time from t-1 to t-n as a plurality of characteristics, the LST M network can directly take the whole time sequence as a single characteristic due to a unique memory structure, the network structure is greatly reduced, the calculation efficiency of node parameters is improved, and input data x mainly comprises the following characteristics: historical price, time, denoted by x, and x ", respectively. Because the fluctuation coefficient data of the day to be predicted has great correlation with the load of the previous period of time, the characteristic data of the previous multiple periods of data is selected as input, the output is the number of price fluctuation words of each period of time in the construction period, and after the input data and the output data are determined, normalization processing needs to be carried out on the input data and the output data, so that the influence of different dimensions among the characteristics can be eliminated, the model can be rapidly converged, and an accurate price coefficient fluctuation model can be predicted.
While the preferred embodiments of the present invention have been disclosed for illustrative purposes only, and not for purposes of limiting the same to all details, it is to be understood that numerous modifications and variations may be made in the details of the present disclosure, which were chosen and described in order to best explain the principles of the invention and the practical application, thereby enabling others skilled in the art to best understand and utilize the invention, the invention being limited only by the claims and their full scope and equivalents.
Claims (10)
1. The engineering cost evaluation management system based on big data analysis is characterized by comprising a main system, wherein the main system comprises a drawing module: two-dimensional engineering drawings of the whole building are arranged, and a BIM module: building an integral BIM model of the building through a two-dimensional engineering drawing, and estimating a module: calculating the building duration of the whole building according to a building BIM model and the quantity of building raw materials required by each stage in the duration, and a prediction module: building a prediction model according to the prices of the building raw materials at different stages in the past and the corresponding price fluctuation coefficients, and predicting the fluctuation coefficients of the prices of the building raw materials within a certain time period in a construction period; the budget module is used for carrying out budget estimation on man-machine materials in the construction cost according to the prediction data and combining other expenses in the construction cost to further estimate the total cost of the construction cost;
the BIM module is used for carrying out simulation construction through a BIM model, and obtaining a building plan, a construction process and the building raw material demand within a period of time in a building construction period through the simulation construction;
the prediction module collects all historical price data of building raw materials, establishes a corresponding database, establishes a prediction model to process data, preprocesses original sampling data in the prediction model, divides a training set, a verification set and a test set, the training set is used for training common parameters of connection values of the prediction model, optimizes the super parameters of iteration times according to the result of the verification set, the test set is used for an actual data test, independently predicts the date to be predicted in the test set after model training and parameter optimization, and finally obtains the result of model prediction by an optimal weighting method so as to predict the fluctuation coefficient of the price of the building raw materials within a period of time in a building construction period;
and the budgeting module is used for multiplying the current unit price of the raw material and the demand of the raw material in the BIM prediction model within a period of time according to the predicted fluctuation coefficient to obtain the cost of the man-machine cost required by the construction within the period of time, finally accumulating the predicted cost of a plurality of construction stages to obtain the total cost of the material within the whole construction period, and budgeting the construction cost required by the whole construction by combining other cost required by the whole construction.
2. The project cost evaluation management system based on big data analysis of claim 1, wherein the two-dimensional drawings comprise first page drawings, general design description, construction drawings, structural construction drawings, water supply and drainage construction drawings, heating and ventilation construction drawings, electrical construction drawings and construction drawings required in the construction process.
3. The project cost evaluation management system based on big data analysis of claim 1, wherein in the BIM module, in the BIM modeling according to the two-dimensional drawing, it is ensured that the plan view and the elevation view in the two-dimensional drawing of the same region are consistent without drawing shortage.
4. The big data analysis-based project cost evaluation and management system of claim 1, wherein the building raw materials required in the building construction process further include labor hours and production costs due to equipment maintenance.
5. The big data analysis-based project cost assessment and management system according to claim 1, wherein other expenses required for the whole building in said budget module include construction measure project expenses, construction measure project expenses in invoice valuation mode, social insurance expenses and project quota determination expenses and the same tax as specified in the integrated valuation.
6. The big data analysis-based engineering cost evaluation and management system according to claim 1, wherein the BIM model in the BIM module contains complete geometric, physical and performance information of the project, the BIM module automatically extracts relevant data required for analysis, simulation and optimization from the BIM model to perform calculation demonstration, adjusts the project plan according to the result and immediately performs simulation calculation on the new plan until the optimized plan is completed, the BIM performs effective simulation experiments on sunlight, emergency evacuation and heat energy conduction in the design stage, the BIM performs 4D simulation in the bidding and construction stage, the three-dimensional model increases time dimension to form a four-dimensional model, the three-dimensional model uses a given construction organization design as a blue book to simulate actual construction, the simulated construction period guides to make a reasonable construction party, and the artificial data required in different stages of the construction process are accurately predicted by simulating construction, Total amount of materials and machinery.
7. The big data analysis-based project cost evaluation management system according to claim 1, wherein in the forecasting module, the data in the database comprises price data corresponding to prices of the building materials in past time periods and date data.
8. The big data analysis-based project cost evaluation and management system according to claim 1, wherein the database is a SQL database, and corresponding safety protection measures are established for the database.
9. The big data analysis-based project cost assessment management system according to claim 5, wherein other expenses required by the whole building in said budget module further comprises rework expenses and unexpected emergency reserve money.
10. The big data analysis-based project cost evaluation management system according to claim 1, wherein the host system is an APP installed in a windows system.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111192355.1A CN113919028A (en) | 2021-10-13 | 2021-10-13 | Engineering cost evaluation management system based on big data analysis |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111192355.1A CN113919028A (en) | 2021-10-13 | 2021-10-13 | Engineering cost evaluation management system based on big data analysis |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113919028A true CN113919028A (en) | 2022-01-11 |
Family
ID=79239911
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111192355.1A Withdrawn CN113919028A (en) | 2021-10-13 | 2021-10-13 | Engineering cost evaluation management system based on big data analysis |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113919028A (en) |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114116756A (en) * | 2022-01-26 | 2022-03-01 | 四川野马科技有限公司 | Engineering cost data correction system and method thereof |
| CN114819720A (en) * | 2022-05-19 | 2022-07-29 | 武汉有方设计有限公司 | Capital construction project cost analysis method, system and computer storage medium |
| CN114971327A (en) * | 2022-06-02 | 2022-08-30 | 贵州百胜数源工程技术管理有限公司 | Intelligent building material data management system based on characteristic analysis |
| CN115358456A (en) * | 2022-08-11 | 2022-11-18 | 广东省建筑工程监理有限公司 | BIM-based engineering cost evaluation method, system, device, equipment and medium |
| CN115841306A (en) * | 2022-11-30 | 2023-03-24 | 中诚智信工程咨询集团股份有限公司 | Method and system for checking quality of engineering cost result |
| CN116308067A (en) * | 2023-05-10 | 2023-06-23 | 深圳市亿嘉建筑系统有限公司 | Building design and building material management system |
| CN116843399A (en) * | 2023-07-27 | 2023-10-03 | 天栋建设管理有限公司 | Construction cost analysis method and system based on balance state |
| CN117273571A (en) * | 2023-10-12 | 2023-12-22 | 江苏泓鑫科技有限公司 | Intelligent port operation data management system and method based on blockchain |
| CN117876053A (en) * | 2024-03-11 | 2024-04-12 | 浙江立兴造价师事务所有限责任公司 | Intelligent mapping method and system for engineering cost |
-
2021
- 2021-10-13 CN CN202111192355.1A patent/CN113919028A/en not_active Withdrawn
Cited By (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114116756A (en) * | 2022-01-26 | 2022-03-01 | 四川野马科技有限公司 | Engineering cost data correction system and method thereof |
| CN114116756B (en) * | 2022-01-26 | 2022-07-19 | 四川野马科技有限公司 | Engineering cost data correction system and method thereof |
| CN114819720A (en) * | 2022-05-19 | 2022-07-29 | 武汉有方设计有限公司 | Capital construction project cost analysis method, system and computer storage medium |
| CN114971327A (en) * | 2022-06-02 | 2022-08-30 | 贵州百胜数源工程技术管理有限公司 | Intelligent building material data management system based on characteristic analysis |
| CN115358456A (en) * | 2022-08-11 | 2022-11-18 | 广东省建筑工程监理有限公司 | BIM-based engineering cost evaluation method, system, device, equipment and medium |
| CN115841306A (en) * | 2022-11-30 | 2023-03-24 | 中诚智信工程咨询集团股份有限公司 | Method and system for checking quality of engineering cost result |
| CN116308067A (en) * | 2023-05-10 | 2023-06-23 | 深圳市亿嘉建筑系统有限公司 | Building design and building material management system |
| CN116308067B (en) * | 2023-05-10 | 2023-08-08 | 深圳市亿嘉建筑系统有限公司 | Building design and building material management system |
| CN116843399A (en) * | 2023-07-27 | 2023-10-03 | 天栋建设管理有限公司 | Construction cost analysis method and system based on balance state |
| CN116843399B (en) * | 2023-07-27 | 2024-01-12 | 天栋建设管理有限公司 | Construction cost analysis method and system based on balance state |
| CN117273571A (en) * | 2023-10-12 | 2023-12-22 | 江苏泓鑫科技有限公司 | Intelligent port operation data management system and method based on blockchain |
| CN117273571B (en) * | 2023-10-12 | 2024-04-02 | 江苏泓鑫科技有限公司 | Intelligent port operation data management system and method based on blockchain |
| CN117876053A (en) * | 2024-03-11 | 2024-04-12 | 浙江立兴造价师事务所有限责任公司 | Intelligent mapping method and system for engineering cost |
| CN117876053B (en) * | 2024-03-11 | 2024-05-28 | 浙江立兴造价师事务所有限责任公司 | Intelligent mapping method and system for engineering cost |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113919028A (en) | Engineering cost evaluation management system based on big data analysis | |
| CN102183621B (en) | Aquaculture dissolved oxygen concentration online forecasting method and system | |
| Lim et al. | BIM and genetic algorithm optimisation for sustainable building envelope design | |
| CN111210093B (en) | Daily water consumption prediction method based on big data | |
| CN112700027A (en) | Multi-precision index comprehensive evaluation method and system for electric power prediction model | |
| CN104573851A (en) | Meteorological temperature forecast-based building hourly load forecasting method | |
| CN112446534A (en) | Construction period prediction method and device for power transmission and transformation project | |
| CN110796307A (en) | Distributed load prediction method and system for comprehensive energy system | |
| CN115130164A (en) | BIM-based building construction equipment, construction management system and management method | |
| CN115099511A (en) | Photovoltaic power probability estimation method and system based on optimized copula | |
| CN115982953A (en) | Method and device for evaluating influence of continuous high temperature on power supply capacity of new energy power grid | |
| CN115456472A (en) | Industrial product purchase supply chain carbon asset digital management method and system | |
| CN110084403B (en) | Multi-period joint probability density prediction method and system for current-carrying capacity of overhead conductor | |
| CN110895774A (en) | Thermal power plant cost fine management method | |
| WO2024082836A1 (en) | Optimization method for comprehensive benefit evaluation scheme for water-wind-photovoltaic energy storage multi-energy complementary system | |
| CN113537601A (en) | Distributed photovoltaic investment decision optimization method and system | |
| CN116596196A (en) | Carbon emission checking method and system | |
| CN115616333A (en) | Power distribution network line loss prediction method and system | |
| Reitberger et al. | Urban systems exploration: A generic process for multi-objective urban planning to support decision making in early design phases | |
| CN114358485A (en) | Source-load matching evaluation method, system, medium and electronic equipment | |
| Antoja et al. | Automated short-term load forecasting using modified stochastic hour ahead proportion (SHAP) analysis | |
| RU78970U1 (en) | ENERGY FLOW MANAGEMENT SYSTEM | |
| Ma et al. | Research on Power Grid Infrastructure Project Intelligent Management Platform Based on Digital Twin Technology | |
| Coelho et al. | Modelling the Growth of DG Market and the Impact of Incentives on its Deployment: Comparing Fixed Adoption and System Dynamics Methods in Brazil | |
| Venus et al. | Energy and cost optimization in the life cycle of nearly zero energy buildings using parametric calculations |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| WW01 | Invention patent application withdrawn after publication |
Application publication date: 20220111 |
|
| WW01 | Invention patent application withdrawn after publication |