Project cost consultation management system and method based on big data

Abstract

The invention provides a project cost consultation management system and method based on big data, and belongs to the technical field of project cost consultation management, and the system comprises a data collection module, a data storage module, a data analysis module, a project cost estimation module, a cost risk early warning module, a project cost optimization module and a project cost tracking module. The modules of the project cost consultation management system based on the big data are clear in division of labor. The data acquisition and storage provides a data basis, and the analysis module mines data values. The cost estimation module can accurately estimate the cost, risk early warning can prevent risks in advance, the optimization module reduces the cost, and the tracking module ensures reasonable control of the cost. The method can improve the cost estimation accuracy, effectively control the risk, optimize the cost, guarantee the smooth proceeding of a project, improve the efficiency and quality of project cost consultation management, enhance the enterprise competitiveness, and adapt to the market change demand.

Description

Technical Field

[0001] The present invention relates to the technical field of engineering cost consulting management, and in particular to an engineering cost consulting management system and method based on big data. Background Art

[0002] Engineering cost consulting refers to the intellectual services provided by consulting activities in the decision-making and implementation management of engineering construction projects for government departments, project owners and other types of customers, following the principles of independence, science and fairness, and using knowledge and experience in multiple disciplines such as engineering technology, science and technology, economic management, and laws and regulations, including consulting consultation in the early stage of project establishment, consulting in the survey and design stage, consulting in the construction stage, and evaluation after commissioning or delivery. With the development of information technology, the way of conducting engineering consulting is becoming more and more convenient, and people often conduct engineering consulting through the Internet and other channels. However, the existing management methods of engineering cost consulting have the following problems:

[0003] Ineffective cost control: In actual engineering projects, there are often problems such as cost overruns and tight funds. This is because the various costs are not comprehensively planned and controlled in the early stages of the project, resulting in cost deviations. During the execution of the project, the costs of materials, labor and equipment fluctuate due to various reasons. If there is no timely monitoring and adjustment, it is easy to cause insufficient project funds and failure to complete on schedule.

[0004] Poor information transmission and insufficient communication: Since engineering projects involve many departments and links, if information transmission is not smooth, it will lead to poor collaboration between functional departments, affecting project progress and cost control. Moreover, due to the lack of communication between different departments and personnel in the engineering project, it is often difficult to discover and solve problems in time, thus affecting the smooth progress of the engineering project.

[0005] Insufficient risk assessment and irregular contract management: In the process of implementing engineering projects, various risks are often encountered, such as natural disasters, technical problems, breach of contract by partners, national policy adjustments, etc., and some engineering projects do not conduct comprehensive assessment and preparation for these risks in the early stages, resulting in the inability to smoothly proceed with the subsequent projects. Since engineering projects involve many types of contracts, such as design contracts, material supply contracts, and contracting contracts, irregular contract management will lead to increased project costs and inconvenience in management.

[0006] Lack of full-process monitoring of project cost management: For a long time, the number of enterprises engaged in the construction industry has increased significantly, resulting in an already fierce market competition that has become even more fierce. For the sake of immediate interests, many enterprises have unreasonably lowered their bids, advanced funds for construction, arbitrarily shortened construction periods, and not priced according to quality. There is no effective follow-up review during the implementation phase, resulting in poor quality, long construction periods, over-budget, and low efficiency of construction projects. Some enterprises even use collusion and bidding to obtain improper benefits, which has a very bad impact.

[0007] Project settlement affects the total project cost: In project construction management, many construction companies ignore the cost management of project settlement, which is very detrimental to the operation of the company. The overall cost of the project needs to be compiled during the project settlement stage, and the results of the settlement compilation shall be submitted to the relevant departments for review; project settlement is a comprehensive assessment and evaluation of the progress and quality of the project, involving project quantity, unit price, contract form, change management, payment, taxation and law, etc. These factors work together to ensure the rationality and accuracy of the project cost, so as to achieve the whole process management of the project cost; however, due to the low awareness of project cost management in enterprises, there are loopholes and cost waste in cost control during the project settlement stage, which affects the profitability of the enterprise and weakens the market competitiveness of the enterprise.

[0008] The information disclosed in this background technology section is only intended to enhance the understanding of the overall background of the invention and should not be regarded as an acknowledgment or any form of suggestion that the information constitutes the prior art already known to a person skilled in the art. Summary of the invention

[0009] The purpose of the present invention is to provide a technical solution that can solve the problems existing in the existing engineering cost consulting management.

[0010] To achieve the above object, the present invention provides the following solutions:

[0011] A construction cost consulting management system based on big data, including:

[0012] A data collection module, used to collect data from multiple data sources;

[0013] Data storage module, used to build a storage system combining large relational database and non-relational database;

[0014] Data analysis module, which is used to pre-process data, extract features related to project cost, analyze using machine learning algorithms, and automatically identify and analyze project drawings using deep learning algorithms;

[0015] Data query module, used to retrieve and query data;

[0016] The project cost estimation module can quickly estimate the total project cost and the cost indicators of each individual project based on the results of data analysis and the specific characteristics of the current project.

[0017] The cost risk early warning module is used to monitor the changes of various factors affecting the cost in real time. When the risk factor reaches the set threshold, the system automatically issues an early warning message to remind the cost consultant and project stakeholders;

[0018] The project cost optimization module provides cost optimization suggestions for the project by analyzing the cost differences of different construction design schemes;

[0019] The project cost tracking module continuously tracks the actual cost of the project during the project implementation process and compares and analyzes it with the budgeted cost.

[0020] Optionally, the data collected from multiple data sources includes: collecting data from construction project drawings, price lists of project material suppliers, salary data of the labor market, information guidance prices issued by government departments, measurement and pricing specifications, construction specifications and tax policies; and also includes integrating data from social media and industry forums, mining industry trends, word-of-mouth evaluations of new materials and construction technologies in these data through natural language processing technology, as reference factors for project cost; using intelligent agent technology to allow the agent program to automatically search and collect data from different data sources according to preset rules, and the intelligent agent program can automatically identify the price update mode of the material supplier's website and obtain the latest price data in a timely manner;

[0021] Use image recognition technology to analyze photos and videos of project sites to obtain real-time project progress and site conditions data. By analyzing the proportion of completed work in site photos and comparing it with the planned progress, cost overruns or savings can be more accurately estimated.

[0022] Optionally, the relational database is used to store structured data, including basic project information, material price historical data, and labor cost historical data;

[0023] Non-relational databases are used to store semi-structured and unstructured data, including project drawings and documents generated during the project process.

[0024] Optionally, the data storage module is also used to construct a distributed file system and a distributed database to store a large amount of engineering cost data, and adopts data sharding and copy strategies to ensure high availability and fault tolerance of the data;

[0025] Establish a data caching mechanism to cache frequently accessed data and improve the system's response speed;

[0026] Data version management and traceability: record the version of each data update and modification in order to trace the historical changes of the data;

[0027] Establishing a data traceability system can clearly track the source of each data item, from the original data source to the entire link of final storage, to ensure the credibility and compliance of the data.

[0028] Optionally, the data preprocessing is to clean the collected data to remove duplicate, erroneous and incomplete data, including screening the material price data to remove data points that obviously deviate from the normal market price range;

[0029] The features extracted and related to the construction cost include project type, building structure, and the policies and human geography environment of the project location;

[0030] The machine learning algorithm includes: analyzing the relationship between material cost, labor cost and project scale, type and other factors using a multivariate linear regression model; using a decision tree algorithm to assess project risk and determine the risk factors and their weights that may affect the cost;

[0031] The automatic recognition and analysis of project drawings using a deep learning algorithm includes recognizing building components in the drawings through a convolutional neural network, and then estimating their engineering quantities.

[0032] A construction cost consulting management method based on big data, comprising:

[0033] Initial data entry for the project;

[0034] Data collection and updating;

[0035] Cost estimation and analysis.

[0036] Optionally, the initial data entry of the project includes: the cost consultant enters the basic information of the project into the system, including the project name, location, scale, construction unit, construction unit and design unit, etc.;

[0037] Upload the electronic drawings of the project to the system, which will automatically identify and perform preliminary analysis of the drawings to extract the project's architectural structure and building area.

[0038] Optionally, the data collection and updating includes: the system collects data from various data sources at set time intervals, and updates the material prices, machinery costs, labor costs and policies and regulations in the database;

[0039] When there are special circumstances in the project, the cost consultants manually add relevant data to the system.

[0040] Optionally, the cost estimation and analysis includes: based on the basic information of the project and the latest database data, the system uses an established data analysis model to perform cost estimation;

[0041] Conduct detailed analysis of the estimated results, including the proportion of various costs and comparison with the cost indicators of similar projects;

[0042] Generate a cost estimation report, which includes the total project cost, detailed cost breakdown and cost risk factor analysis.

[0043] Optionally, the method further includes:

[0044] Cost risk monitoring and response: During the project implementation, the system continuously monitors cost risk factors, including: material price increases and labor shortages;

[0045] When risks occur, the system makes recommendations based on pre-set response strategies, including: finding alternative materials and renegotiating prices with suppliers when material prices rise;

[0046] Establish a real-time cost monitoring mechanism, regularly collect cost data from project sites, compare it with budget data, adopt earned value management methods, calculate the earned value, planned value and actual cost of the project, and evaluate the cost and schedule performance of the project by analyzing cost deviation and schedule deviation;

[0047] Cost optimization and decision support: Based on the actual situation of the project and the needs of the builder, the system optimizes and analyzes the project cost, including: adjusting the project schedule to reduce equipment rental costs, or optimizing the building design to reduce material usage;

[0048] Providing decision-making support to project stakeholders, including: when selecting construction units, providing the best selection suggestions based on the quotations and reputation of each construction unit and the system's prediction of project costs;

[0049] Project cost tracking and settlement: As the project progresses, the system tracks the actual cost of the project in real time and records every expense. After the project is completed, the system compares and analyzes the actual cost data and the budgeted cost, summarizes the lessons learned from cost control, and assists project stakeholders in cost settlement to ensure the accuracy of the final settlement amount.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] The present invention provides a construction cost consulting management system and method based on big data, the system includes: a data acquisition module, a data storage module, a data analysis module, a project cost estimation module, a cost risk warning module, a project cost optimization module and a project cost tracking module. The modules of the construction cost consulting management system based on big data have clear division of labor. Data acquisition and storage provide data foundation, and the analysis module mines data value. The cost estimation module can accurately estimate the cost and indicators, the risk warning can prevent risks in advance, the optimization module can reduce costs, and the tracking module can ensure reasonable cost control. It can improve the accuracy of cost estimation, effectively control risks, optimize costs, and ensure the smooth progress of projects. It can improve the efficiency and quality of construction cost consulting management, enhance the competitiveness of enterprises and adapt to market changes. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0053] Figure 1 A schematic diagram of the structure of an engineering cost consulting management system based on big data provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0054] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0055] The purpose of the present invention is to provide a system and method that can improve the accuracy of cost estimation, effectively control risks, optimize costs, ensure the smooth progress of projects, improve the efficiency and quality of engineering cost consulting management, enhance corporate competitiveness and adapt to market changes.

[0056] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0057] Embodiment 1:

[0058] This embodiment provides a construction cost consulting management system based on big data. Figure 1 As shown, it includes: a data acquisition module, used to collect data from multiple data sources;

[0059] Data storage module, used to build a storage system combining large relational database and non-relational database;

[0060] Data analysis module, which is used to pre-process data, extract features related to project cost, analyze using machine learning algorithms, and automatically identify and analyze project drawings using deep learning algorithms;

[0061] Data query module, used to retrieve and query data;

[0062] The project cost estimation module can quickly estimate the total project cost and the cost indicators of each individual project based on the results of data analysis and the specific characteristics of the current project.

[0063] The cost risk early warning module is used to monitor the changes of various factors affecting the cost in real time. When the risk factor reaches the set threshold, the system automatically issues an early warning message to remind the cost consultant and project stakeholders;

[0064] The project cost optimization module provides cost optimization suggestions for the project by analyzing the cost differences of different construction design schemes;

[0065] The project cost tracking module continuously tracks the actual cost of the project during the project implementation process and compares and analyzes it with the budgeted cost.

[0066] Furthermore, the data collected from multiple data sources includes: collecting data from construction project drawings, price lists of project material suppliers, salary data of the labor market, information guidance prices issued by government departments, measurement and pricing specifications, construction specifications and tax policies; it also includes integrating data from social media and industry forums, and using natural language processing technology to mine industry trends, word-of-mouth evaluations of new materials and construction technologies in these data as reference factors for project costs; using intelligent agent technology to allow the agent program to automatically search and collect data from different data sources according to preset rules. The intelligent agent program can automatically identify the price update mode of the material supplier's website and obtain the latest price data in a timely manner;

[0067] Use image recognition technology to analyze photos and videos of project sites to obtain real-time project progress and site conditions data. By analyzing the proportion of completed work in site photos and comparing it with the planned progress, cost overruns or savings can be more accurately estimated.

[0068] Furthermore, the relational database is used to store structured data, including basic project information, material price history data, and labor cost history data;

[0069] Non-relational databases are used to store semi-structured and unstructured data, including project drawings and documents generated during the project process.

[0070] Furthermore, the data storage module is also used to construct a distributed file system and a distributed database to store a large amount of engineering cost data, and adopts data sharding and copy strategies to ensure high availability and fault tolerance of data;

[0071] Establish a data caching mechanism to cache frequently accessed data and improve the system's response speed;

[0072] Data version management and traceability: record the version of each data update and modification in order to trace the historical changes of the data;

[0073] Establishing a data traceability system can clearly track the source of each data item, from the original data source to the entire link of final storage, to ensure the credibility and compliance of the data.

[0074] Furthermore, the data preprocessing is to clean the collected data and remove duplicate, erroneous and incomplete data, including screening the material price data and removing data points that obviously deviate from the normal market price range;

[0075] The extracted features related to the construction cost include project type, building structure, and the policies and cultural environment of the project location;

[0076] The machine learning algorithm includes: analyzing the relationship between material cost, labor cost and project scale, type and other factors using a multivariate linear regression model; using a decision tree algorithm to assess project risk and determine the risk factors and their weights that may affect the cost;

[0077] The automatic recognition and analysis of project drawings using a deep learning algorithm includes recognizing building components in the drawings through a convolutional neural network, and then estimating their engineering quantities.

[0078] A construction cost consulting management method based on big data, comprising:

[0079] Initial data entry for the project;

[0080] Data collection and updating;

[0081] Cost estimation and analysis.

[0082] Furthermore, the initial data entry of the project includes: the cost consultant enters the basic information of the project into the system, including the project name, location, scale, construction unit, construction unit and design unit;

[0083] Upload the electronic drawings of the project to the system, which will automatically identify and perform preliminary analysis of the drawings to extract the project's architectural structure and building area.

[0084] Further, the data collection and updating includes: the system collects data from various data sources at set time intervals and updates the material prices, machinery costs, labor costs and policies and regulations in the database;

[0085] When there are special circumstances in the project, the cost consultants manually add relevant data to the system.

[0086] Furthermore, the cost estimation and analysis includes: based on the basic information of the project and the latest database data, the system uses the established data analysis model to perform cost estimation;

[0087] Conduct detailed analysis of the estimated results, including the proportion of various costs and comparison with the costs of similar projects;

[0088] Generate a cost estimation report, which includes the total project cost, detailed cost breakdown and cost risk factor analysis.

[0089] Furthermore, cost risk monitoring and response: During the project implementation, the system continuously monitors cost risk factors, including: the extent of material price increases and labor shortages;

[0090] When risks occur, the system makes recommendations based on pre-set response strategies, including: finding alternative materials and renegotiating prices with suppliers when material prices rise;

[0091] Establish a real-time cost monitoring mechanism, regularly collect cost data from project sites, compare it with budget data, adopt earned value management methods, calculate the earned value, planned value and actual cost of the project, and evaluate the cost and schedule performance of the project by analyzing cost deviation and schedule deviation;

[0092] Cost optimization and decision support: Based on the actual situation of the project and the needs of the builder, the system optimizes and analyzes the project cost, including: adjusting the project schedule to reduce equipment rental costs, or optimizing the building design to reduce material usage;

[0093] Providing decision-making support to project stakeholders, including: when selecting construction units, providing the best selection suggestions based on the quotations and reputation of each construction unit and the system's prediction of project costs;

[0094] Project cost tracking and settlement: As the project progresses, the system tracks the actual cost of the project in real time and records every expense. After the project is completed, the system compares and analyzes the actual cost data and the budgeted cost, summarizes the lessons learned from cost control, and assists project stakeholders in cost settlement to ensure the accuracy of the final settlement amount.

[0095] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0096] The principles and implementation methods of the present invention are described in this article using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A construction cost consulting management system based on big data, characterized in that: include: A data collection module, used to collect data from multiple data sources; Data storage module, used to build a storage system combining large relational database and non-relational database; Data analysis module, which is used to pre-process data, extract features related to project cost, analyze using machine learning algorithms, and automatically identify and analyze project drawings using deep learning algorithms; Data query module, used to retrieve and query data; The project cost estimation module can quickly estimate the total project cost and the cost indicators of each individual project based on the results of data analysis and the specific characteristics of the current project. The cost risk early warning module is used to monitor the changes of various factors affecting the cost in real time. When the risk factor reaches the set threshold, the system automatically issues an early warning message to remind the cost consultant and project stakeholders; The project cost optimization module provides cost optimization suggestions for the project by analyzing the cost differences of different construction design schemes; The project cost tracking module continuously tracks the actual cost of the project during the project implementation process and compares and analyzes it with the budgeted cost.

2. The engineering cost consulting management system based on big data according to claim 1 is characterized in that: The data collection from multiple data sources includes: collecting data from construction project drawings, price lists of project material suppliers, salary data of the labor market, information guidance prices issued by government departments, measurement and pricing specifications, construction specifications and tax policies; it also includes integrating data from social media and industry forums, and using natural language processing technology to mine industry trends, word-of-mouth evaluations of new materials and construction technologies in these data as reference factors for project costs; using intelligent agent technology to allow the agent program to automatically search and collect data from different data sources according to preset rules. The intelligent agent program can automatically identify the price update mode of the material supplier's website and obtain the latest price data in a timely manner; Use image recognition technology to analyze photos and videos of project sites to obtain real-time project progress and site conditions data. By analyzing the proportion of completed work in site photos and comparing it with the planned progress, cost overruns or savings can be more accurately estimated.

3. The engineering cost consulting and management system based on big data according to claim 1 is characterized in that: The relational database is used to store structured data, including basic project information, material price history data, and labor cost history data; Non-relational databases are used to store semi-structured and unstructured data, including project drawings and documents generated during the project process.

4. The engineering cost consulting management system based on big data according to claim 1 is characterized in that: The data storage module is also used to construct a distributed file system and a distributed database to store a large amount of engineering cost data, using data sharding and copy strategies to ensure high availability and fault tolerance of data; Establish a data caching mechanism to cache frequently accessed data and improve the system's response speed; Data version management and traceability: record the version of each data update and modification in order to trace the historical changes of the data; Establishing a data traceability system can clearly track the source of each data item, from the original data source to the entire link of final storage, to ensure the credibility and compliance of the data.

5. The engineering cost consulting management system based on big data according to claim 1 is characterized in that: The data preprocessing is to clean the collected data and remove duplicate, erroneous and incomplete data, including screening the material price data and removing data points that obviously deviate from the normal market price range; The features extracted and related to the construction cost include project type, building structure, and the policies and human geography environment of the project location; The use of machine learning algorithms includes: analyzing a multivariate linear regression model to analyze the relationship between material costs, labor costs, and project scale, type, and other factors; The decision tree algorithm is used to assess project risks and determine the risk factors and their weights that may affect the cost; The automatic recognition and analysis of project drawings using a deep learning algorithm includes recognizing building components in the drawings through a convolutional neural network, and then estimating their engineering quantities.

6. A method for engineering cost consulting management based on big data, characterized in that: include: Initial data entry for the project; Data collection and updating; Cost estimation and analysis.

7. The engineering cost consulting management method based on big data according to claim 6 is characterized in that: The initial data entry of the project includes: the cost consultant enters the basic information of the project into the system, including the project name, location, scale, construction unit, construction unit and design unit, etc.; Upload the electronic drawings of the project to the system, which will automatically identify and perform preliminary analysis of the drawings to extract the project's architectural structure and building area.

8. The engineering cost consulting management method based on big data according to claim 6 is characterized in that: The data collection and update includes: the system collects data from various data sources at set time intervals and updates the material prices, machinery costs, labor costs and policies and regulations in the database; When there are special circumstances in the project, the cost consultants manually add relevant data to the system.

9. The engineering cost consulting management method based on big data according to claim 6 is characterized in that: The cost estimation and analysis include: based on the basic information of the project and the latest database data, the system uses the established data analysis model to perform cost estimation; Conduct detailed analysis of the estimated results, including the proportion of various costs and comparison with the cost indicators of similar projects; Generate a cost estimation report, which includes the total project cost, detailed cost breakdown and cost risk factor analysis.

10. The engineering cost consulting management method based on big data according to claim 6 is characterized in that: The method also includes: Cost risk monitoring and response: During the project implementation, the system continuously monitors cost risk factors, including: price increases of machinery and materials and labor shortages; When risks occur, the system makes recommendations based on pre-set response strategies, including: finding alternative materials and renegotiating prices with suppliers when material prices rise; Establish a real-time cost monitoring mechanism, regularly collect cost data from project sites, compare it with budget data, adopt earned value management methods, calculate the earned value, planned value and actual cost of the project, and evaluate the cost and schedule performance of the project by analyzing cost deviation and schedule deviation; Cost optimization and decision support: Based on the actual situation of the project and the needs of the builder, the system optimizes and analyzes the project cost, including: adjusting the project schedule to reduce equipment rental costs, or optimizing the building design to reduce material usage, etc. Providing decision-making support to project stakeholders, including: when selecting construction units, providing the best selection suggestions based on the quotations and reputation of each construction unit and the system's prediction of project costs; Project cost tracking and settlement: As the project progresses, the system tracks the actual cost of the project in real time and records every expense. After the project is completed, the system compares and analyzes the actual cost data and the budgeted cost, summarizes the lessons learned from cost control, and assists project stakeholders in cost settlement to ensure the accuracy of the final settlement amount.

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