Engineering cost management system based on big data

Abstract

The invention relates to the technical field of construction industry project cost, and provides a project cost management system based on big data, the core of the system is to integrate a big data technology and comprehensively manage project cost, data management covers links of acquisition, storage, analysis and maintenance, data quality and safety are ensured, and a foundation is built for cost management; the accurate cost analysis uses multiple algorithm models to deeply mine data association, so that accurate prediction and fluctuation reason analysis are realized, and cost optimization is assisted; the efficient business logic layer and the user interaction layer cooperatively operate, the business function is complete, the process is smooth, the interface is friendly, the visualization is strong, and a feedback channel is arranged to promote optimization; data acquisition is wide, storage is efficient, and the method is far better than a traditional scheme; the analysis depth and the visualization effect are prominent, and a more valuable decision basis is provided; the system is complete in business function and scientific in management, improves enterprise cost control and decision-making capability, and is good in user experience and high in safety.

Description

Technical Field

[0001] The present invention relates to the technical field of construction cost in the construction industry, and in particular to a construction cost management system based on big data. Background Art

[0002] Project cost refers to the construction price of a project, which refers to the total amount of all costs expected or actually required to complete the construction of a project. With the continuous advancement of my country's economic construction, it has greatly promoted the vigorous development of urban construction. In the process of project construction, since the project construction itself is a complex implementation process, the investment is generally large, the actual construction period is long, and the project implementation process requires cooperation from many aspects, which increases the cost management of multiple engineering projects. Therefore, before the completion of the entire project, it is impossible to obtain the accurate project cost of the actual project.

[0003] When managing the data information of engineering cost, it is necessary to process and analyze the data. However, due to the many sources of information and the variety of information, users have high costs when processing and analyzing data, and the decision-making efficiency is low. Therefore, it is urgent to invent an engineering cost management system based on big data to solve the above problems by integrating big data technology. Summary of the invention

[0004] Technical issues solved

[0005] In view of the deficiencies of the prior art, the present invention provides a construction cost management system based on big data.

[0006] Technical Solution

[0007] In order to achieve the above-mentioned solution, the present invention provides the following technical solution: a construction cost management system based on big data, the system comprising the following modules:

[0008] S1. Engineering information management module:

[0009] Information collection and input: Collect basic information of engineering projects from engineering information and cost information websites and other data sources, including project name, location, owner, design unit, construction unit, project scale, and engineering type, and provide manual input and batch import functions;

[0010] Information classification and retrieval: Use a distributed database system suitable for big data storage, combine the Hadoop distributed file system HDFS with the HBase database, store data in a distributed manner according to information types, and establish a retrieval mechanism;

[0011] Information update and maintenance: real-time monitoring of changes in engineering information, timely updating of project information in the system, and archiving and management of historical project information;

[0012] S2. Cost data processing module

[0013] Cost data collection and integration: Use web crawler technology to regularly crawl updated engineering and cost information on the website according to predetermined rules, including project budget, settlement, and cost details. Use data interface calls to obtain key data in real time. Use ETL tools to extract, convert, and load some structured data, and associate and integrate them with engineering project information to form a complete cost data set;

[0014] Cost data analysis and evaluation: Use clustering algorithms to classify engineering projects, and based on historical cost data, use regression analysis and neural network algorithms to build cost prediction models, predict the cost range of new projects, analyze cost data, evaluate the rationality of project costs, analyze the reasons for cost fluctuations, predict future cost trends, and provide users with cost analysis reports;

[0015] Visual display of cost data: display cost data in the form of intuitive charts;

[0016] S3. Market dynamic monitoring module:

[0017] Market price data collection: real-time collection of market price information required for engineering construction, attention to market price fluctuation trends, and acquisition of data from multiple data sources to ensure the comprehensiveness and accuracy of price information;

[0018] Price trend analysis and forecasting: Use data analysis technology to analyze market price data and predict future price trends, provide reference for cost estimation and budget preparation of engineering projects, and visualize price trends through Tableau and PowerBI;

[0019] Market intelligence push and early warning: timely push market price change intelligence and early warning information to users based on the user-set focus range and early warning threshold;

[0020] S4. User management and authority control module

[0021] User registration and authentication: Provide user registration function and authenticate user identity;

[0022] User authority management: assign different system permissions based on user roles;

[0023] User operation records and auditing: record all user operations in the system;

[0024] S5, system configuration and management module

[0025] System parameter setting: configure the system's operating parameters and flexibly adjust them according to the system's operating conditions and user needs;

[0026] Data backup and recovery: regularly back up data in the system and formulate backup strategies;

[0027] System log management: record various log information during system operation;

[0028] S6. Data quality management module

[0029] Establish data quality standards: clarify the quality requirements for engineering information and cost data;

[0030] Data quality monitoring and assessment: Regularly check data quality, use data audit tools and techniques to assess the accuracy and completeness of data, and make corrections in a timely manner;

[0031] Data quality problem handling process: Establish a data quality problem handling mechanism and negotiate with the data source provider to resolve data collection source issues;

[0032] S7, data security management module

[0033] Network security protection: deploy network security equipment to prevent external network attacks;

[0034] Application of data encryption technology: Encrypted storage and transmission of sensitive data;

[0035] User authentication and authorization management: Strengthen the user authentication mechanism, adopt multi-factor authentication to improve the security of user identity authentication, and strictly manage user authorization to ensure that users can only access data within their authority to prevent data leakage and illegal operations;

[0036] Data access logging and auditing: Record all users’ data access operations in detail, conduct regular data access audits, and check for abnormal access behaviors;

[0037] S8, Data update and maintenance strategy module

[0038] Real-time data update mechanism: establish a real-time data push mechanism with the engineering information and cost information website to regularly update data from other data sources;

[0039] Data maintenance team and process: Establish a professional data maintenance team to be responsible for the daily maintenance of data;

[0040] Historical data management: Rationally archive and manage historical data, and determine the storage method and period based on the importance and frequency of use of the data.

[0041] Furthermore, data management covers the entire data process from collection, storage, analysis to update and maintenance. All links work closely together to establish a strict data quality management system, including quality standard setting, monitoring and evaluation, and problem handling procedures, focusing on data security management to prevent data leakage and illegal access.

[0042] Furthermore, cost assessment and analysis uses cost forecasting models to conduct project cost assessment based on project characteristics and market data, analyzes the costs of completed projects, identifies the reasons for cost fluctuations, and provides lessons for subsequent projects. In terms of data comparison and benchmark setting, cost data from different projects are compared and analyzed, and industry cost benchmarks are set to help companies evaluate the cost competitiveness of their own projects.

[0043] Furthermore, by monitoring market price fluctuations and changes in policies and regulations, early warnings can be issued for risks that affect project costs.

[0044] Furthermore, formulate the business rules of the system, define the business processes, and ensure smooth collaboration between the various functional modules of the system.

[0045] Beneficial Effects

[0046] Compared with the prior art, the present invention provides a construction cost management system based on big data, which has the following beneficial effects:

[0047] 1. The engineering cost management system based on big data integrates big data technology to manage engineering costs in all aspects; data management covers collection, storage, analysis and maintenance to ensure data quality and security, laying a solid foundation for cost management; accurate cost analysis uses a variety of algorithm models to deeply explore data associations, achieve accurate predictions and analyze fluctuation causes, and help optimize costs.

[0048] 2. The engineering cost management system based on big data operates in coordination with the efficient business logic layer and the user interaction layer. It has complete business functions and smooth processes, a friendly interface, strong visualization, and feedback channels to promote optimization. In terms of data collection, it is extensive and stored efficiently, far exceeding traditional solutions. The analysis depth and visualization effects are outstanding, providing more valuable decision-making basis. In terms of business, it has complete functions and scientific management, which helps to improve the company's cost control and decision-making capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a schematic diagram of the system framework of the present invention;

[0050] Figure 2 This is a schematic diagram of the functional modules of the present invention based on big data. DETAILED DESCRIPTION

[0051] 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.

[0052] See also Figure 1-2 This application proposes a big data-based construction cost management system designed using a big data-based system architecture logic, and its system framework includes the following contents:

[0053] 1. Data Collection Layer

[0054] Data source connection: connect with various databases and data interfaces of engineering information and cost information websites to obtain data including detailed information of engineering projects (such as project name, location, scale, type, etc.), cost data (budget, settlement, cost details, etc.), market price dynamics (material, equipment, labor price fluctuations), industry policy and regulation updates, etc. At the same time, establish data acquisition channels with relevant government department websites (such as Construction Bureau, Finance Bureau, etc.), industry association websites and other authoritative data platforms to ensure that data sources are extensive and reliable.

[0055] Data collection tools and technologies: Use web crawler technology to regularly capture updated engineering and cost information on the website according to predetermined rules; use data interface calls to obtain key data in real time; use ETL tools to extract, convert and load some structured data to ensure that the data format is unified and meets system requirements.

[0056] Data preprocessing: Perform preliminary cleaning of the collected data to remove duplicate, invalid or erroneous data records, such as checking the integrity of the data and supplementing missing key information; correcting format errors in the data, such as date format, numeric precision, etc.; identifying and processing outliers to ensure data quality.

[0057] 2. Data storage layer

[0058] Distributed database selection and architecture design: Use a distributed database system suitable for big data storage, such as the Hadoop Distributed File System (HDFS) combined with the HBase database, to distribute data according to project type, region, time and other dimensions to ensure the scalability and efficiency of data storage.

[0059] Data warehouse construction: Establish a data warehouse, integrate, summarize and convert the original data to form a subject-oriented data mart. For example, build a project-themed data mart, which includes basic project information, cost information, progress information, etc.; establish a market price-themed data mart, which summarizes data such as price trends of various materials, equipment, and labor, to provide efficient support for data analysis and query.

[0060] Application of caching mechanism: Use caching technologies such as Redis to cache frequently accessed data, such as basic information of popular engineering projects and prices of materials with large market price fluctuations in recent times, to reduce data reading delays and improve system performance.

[0061] 3. Data Analysis Layer

[0062] Project classification and cluster analysis: Use clustering algorithms to classify engineering projects. For example, projects can be divided into residential buildings, commercial buildings, infrastructure and other categories based on project type, scale, complexity and other characteristics, so as to conduct targeted analysis and management.

[0063] Construction of cost prediction model: Based on historical cost data, a cost prediction model is constructed using regression analysis, neural network and other algorithms to predict the cost range of new projects and provide a reference for project decision-making.

[0064] Association rule mining: Analyze the relationship between engineering features and cost elements, discover the relationship between a specific building structure type and the amount of a certain material used, and provide a basis for cost control.

[0065] Data cleaning and conversion: Before data analysis, the data is further cleaned, missing values ​​and outliers are processed, and the data is standardized, normalized and other conversion operations are performed to ensure that the data is suitable for the input requirements of the analysis model.

[0066] Model training and optimization: Divide the cleaned data into training sets and test sets, use the training set to train the data analysis model, and use the test set to evaluate the accuracy and performance of the model; optimize and adjust the model based on the evaluation results, such as adjusting model parameters, selecting a more appropriate algorithm, etc.

[0067] Integration of visual analysis tools: Use data visualization tools, such as Tableau and PowerBI, to display analysis results in the form of intuitive charts (such as line charts, bar charts, scatter charts, etc.), making it easier for users to understand and discover patterns and trends in the data.

[0068] 4. Business logic layer

[0069] Engineering information management: classify, store, query and update the collected engineering project information to ensure the integrity and accuracy of the project information.

[0070] Cost assessment and analysis: Based on project characteristics and market data, use cost forecasting models to conduct project cost assessment; analyze the costs of completed projects to identify the reasons for cost fluctuations and provide lessons for subsequent projects.

[0071] Data comparison and benchmark setting: Compare and analyze the cost data of different projects, set industry cost benchmarks, and help companies evaluate the cost competitiveness of their own projects.

[0072] Risk warning and control: By monitoring factors such as market price fluctuations and changes in policies and regulations, we can warn of risks that may affect project costs and provide corresponding risk control strategies.

[0073] Business rules and process definition: formulate the business rules of the system, such as data update frequency, user authority management rules, cost assessment standards, etc.; define business processes to ensure smooth collaboration between the various functional modules of the system, such as the complete process from data collection to cost assessment.

[0074] System security and authority management: responsible for the security control of the system, including user authentication, authorization and access control, assigning different authorities according to user roles (such as website administrator, cost engineer, ordinary user, etc.) to ensure that users can only access data and functions within their authority.

[0075] 5. User Interaction Layer

[0076] Website interface design: Build a user-friendly engineering information and cost information website interface, design a simple and clear layout to help users quickly find the information they need, such as setting up an engineering project query entrance, cost data display area, market dynamics information section, etc.

[0077] Visual display and report generation: Display engineering cost data and analysis results in a visual way, such as project cost trend chart, cost composition pie chart, etc.; provide report generation function, users can generate customized reports according to needs, such as project cost detailed report, market price comparison report, etc., support export in multiple formats (such as PDF, Excel, etc.).

[0078] User interaction function design: realize the interactive function between users and the system, such as users can upload project information on the website, query the cost history data of specific projects, set cost warning thresholds, etc.; establish user feedback channels to collect user opinions and suggestions in order to continuously optimize system functions and user experience.

[0079] This application proposes a construction cost management system based on big data, including the following specific contents:

[0080] 1. Functional modules based on big data

[0081] 1. Engineering information management module

[0082] Information collection and input: Collect basic information of engineering projects from engineering information and cost information websites and other data sources, including project name, location, owner, design unit, construction unit, project scale, engineering type, etc., and provide manual entry and batch import functions to ensure the integrity and accuracy of the information.

[0083] Information classification and retrieval: Classify and store project information based on project type, region, time and other dimensions, and establish an efficient retrieval mechanism. Users can quickly query the required project information through keyword search and screening conditions (such as project type, time range, etc.).

[0084] Information update and maintenance: Real-time monitoring of changes in engineering information, timely update of project information in the system to ensure the timeliness of data, and archiving and management of historical project information to facilitate user tracing and query.

[0085] 2. Cost data processing module

[0086] Cost data collection and integration: Obtain various cost data from the website, including project budget, settlement, cost details (such as material cost, labor cost, equipment cost, etc.), and associate and integrate them with engineering project information to form a complete cost data set.

[0087] Cost data analysis and evaluation: Use the algorithms and models of the data analysis layer to analyze cost data, evaluate the rationality of project costs, analyze the reasons for cost fluctuations, and predict future cost trends. Provide users with cost analysis reports, including project cost comparison analysis, cost composition analysis, cost risk assessment, etc.

[0088] Visual display of cost data: Display cost data in the form of intuitive charts, such as drawing a curve showing the change of project cost over time, a comparative bar chart of the cost composition of different projects, etc., to help users understand the characteristics and rules of cost data more clearly.

[0089] 3. Market dynamic monitoring module

[0090] Market price data collection: Real-time collection of market price information of various materials, equipment, labor, etc. required for engineering construction, attention to market price fluctuation trends, and acquisition of data from multiple data sources to ensure the comprehensiveness and accuracy of price information.

[0091] Price trend analysis and forecasting: Use data analysis technology to analyze market price data and predict future price trends, provide a reference for cost estimation and budget preparation of engineering projects, and display price trends in a visual way, such as drawing material price fluctuation line graphs and labor cost change trend graphs.

[0092] Market intelligence push and early warning: According to the user-set focus range and early warning threshold, market price change intelligence and early warning information are pushed to users in a timely manner. For example, when the price of a key material rises above the set threshold, the system automatically sends a reminder notification to the relevant users to help users prepare countermeasures in advance.

[0093] 4. User management and authority control module

[0094] User registration and authentication: Provides user registration function, authenticates user identity, ensures the authenticity and legality of user information, and supports multiple authentication methods, such as username / password, mobile phone number verification code, third-party account login, etc.

[0095] User authority management: Different system authorities are assigned according to user roles (such as administrator, cost engineer, ordinary user, etc.). Administrators have comprehensive management authority over the system, including data management, user management, system configuration, etc.; cost engineers can perform operations such as cost data analysis, evaluation and report generation; ordinary users can only query and browse public project information and cost data.

[0096] User operation records and audits: Record all user operations in the system, including data query, modification, upload, etc., to facilitate auditing and tracking, and ensure system security and data compliance.

[0097] 5. System configuration and management module

[0098] System parameter settings: Configure the system's operating parameters, such as data collection frequency, cache strategy, data analysis model parameters, etc., and flexibly adjust them according to system operation conditions and user needs.

[0099] Data backup and recovery: Regularly back up data in the system and formulate backup strategies to ensure data security and integrity. In the event of data loss or damage, data can be quickly restored to ensure the normal operation of the system.

[0100] System log management: records various log information during system operation, including system startup, stop, error information, user operation log, etc., to facilitate system maintenance and troubleshooting.

[0101] 2. Data Management Based on Big Data

[0102] 1. Data quality management module

[0103] Establish data quality standards: clarify the quality requirements for engineering information and cost data, including indicators such as data accuracy, completeness, consistency, and timeliness. For example, stipulate that the name of the engineering project must be unique and accurately describe the project characteristics, and the cost data must be accurate to two decimal places.

[0104] Data quality monitoring and assessment: Regularly conduct quality checks on data and use data auditing tools and techniques to assess the accuracy and completeness of data. For example, errors and omissions in data can be discovered through comparison with authoritative data sources and data logic verification, and corrected in a timely manner.

[0105] Data quality problem handling process: Establish a data quality problem handling mechanism so that when data quality problems are discovered, the cause of the problem can be quickly located and corresponding improvement measures can be taken. For example, for data entry errors, promptly notify the data entry personnel to make modifications; for data collection source problems, negotiate with the data source provider to resolve them.

[0106] 2. Data security management module

[0107] Network security protection: Deploy firewalls, intrusion detection systems (IDS), intrusion prevention systems (IPS) and other network security devices to prevent external network attacks and protect the security of system data.

[0108] Application of data encryption technology: Encrypted storage and transmission of sensitive data (such as user passwords, enterprise cost data, etc.), using encryption algorithms (such as AES, RSA, etc.) to ensure the confidentiality of data during storage and transmission.

[0109] User authentication and authorization management: Strengthen the user authentication mechanism and adopt multi-factor authentication to improve the security of user identity authentication. Strict user authorization management ensures that users can only access data within their authority to prevent data leakage and illegal operations.

[0110] Data access logging and auditing: Record all users’ data access operations in detail, including access time, accessed data content, operation type, and other information. Perform regular data access audits to check for abnormal access behaviors and ensure data security and compliance.

[0111] 3. Data update and maintenance strategy module

[0112] Real-time data update mechanism: Establish a real-time data push mechanism with the engineering information and cost information website to ensure that the system can obtain the latest engineering information and cost data in a timely manner, and regularly update data from other data sources, such as market price data, to maintain the timeliness of the data.

[0113] Data maintenance team and process: Establish a professional data maintenance team to be responsible for the daily maintenance of data, including data cleaning, updating, backup and other operations, formulate data maintenance processes and specifications, and ensure the efficiency and accuracy of data maintenance work.

[0114] Historical data management: Rationally archive and manage historical data, determine the storage method and period based on the importance and frequency of use of the data, and be able to quickly retrieve and call historical data when needed to provide support for project decision-making and data analysis.

[0115] By using the above system to analyze the data, the main reasons for cost fluctuations are shown in the following table:

[0116]

[0117]

[0118] Project characteristics include project type and scale, which are helpful for cluster analysis and the construction of prediction models; cost data include budget cost and actual cost, which are used to evaluate the effectiveness of cost control; market price dynamics such as material price index and labor cost are the main factors affecting cost; predicted cost is the cost predicted by the model, which is compared with the actual cost to evaluate the accuracy of the prediction.

[0119] Through this system, the reasons for fluctuations are analyzed for six groups of data: In Example 1, effective material procurement management reduces material costs, which shows that the project has done a good job in material procurement and labor management and effectively controlled costs. In Example 2, the design change leads to additional costs, which may involve design adjustment, rework, etc. At the same time, both material prices and labor costs have increased, which has a greater impact on the total cost. It is necessary to strengthen control in the design stage to reduce unnecessary changes. In Example 3, policy changes increase material costs, such as adjustments to relevant tax policies and environmental protection requirements that lead to restrictions on material procurement. Although labor costs have not changed, they still lead to an increase in total costs. This shows that policy changes have a significant impact on project costs and policy trends need to be closely monitored. In Example 4, technological innovation brings cost reduction, such as the use of new construction processes or technologies, which improves production efficiency and offsets the impact of part of the increase in material and labor costs, making the actual cost lower than the budget. This shows that the project has achieved results in technological innovation and helps to reduce costs. In Example 5, the equipment procurement cost is higher than expected, which may be due to changes in equipment selection, changes in supplier prices, etc., which leads to an increase in the cost of equipment procurement and affects the total cost of the project. This shows that equipment procurement is a key link in cost control and needs to be strengthened. In Example 6, the geological conditions resulted in additional expenses, such as increased difficulty in foundation treatment and clearing of underground obstacles, which increased the construction cost. Although the material price increased slightly, the labor cost was well controlled and the overall cost fluctuation was small. This shows that the geological conditions have a significant impact on the project cost and that sufficient geological exploration and evaluation are required in the early stages of the project.

[0120] By comparing the system modules and specific measures of the engineering cost management system based on big data proposed by the present invention with those of the existing solutions, it is easy to see that the engineering cost management system based on big data proposed by the present invention has the following advantages:

[0121] Data processing and management: Extensive data source docking and diversified data collection technologies enable the system to obtain more comprehensive, timely and accurate data. Compared with existing solutions, the data volume is larger and the dimensions are richer, providing strong support for accurate analysis; using advanced data storage architecture and caching mechanism to improve data storage and query efficiency, with strong scalability, can adapt to the growing data needs in the big data era, and overcome the limitations of traditional solutions in data storage and processing capabilities.

[0122] Data analysis: In-depth data analysis capabilities, through the construction of prediction models and mining of association rules using a variety of algorithms, can provide more forward-looking and targeted cost analysis results. However, the existing analysis methods are relatively simple and it is difficult to provide such in-depth insights. Intuitive data visualization displays complex cost data in the form of charts, etc., to facilitate users to quickly understand data patterns and trends. The existing solutions have a low degree of visualization, which is not conducive to users' intuitive grasp of data information.

[0123] Business processing: Complete business logic functions, such as accurate cost assessment, industry benchmark comparison, risk warning, etc., will help enterprises manage project costs more scientifically, improve the accuracy of project decisions and cost control capabilities. Existing solutions are insufficient in these business functions; reasonable user authority management and security control ensure safe and stable operation of the system.

[0124] The core of this system is to integrate big data technology to manage engineering costs in all aspects; data management covers all aspects of collection, storage, analysis and maintenance to ensure data quality and security, and lay a solid foundation for cost management; accurate cost analysis uses a variety of algorithm models to deeply explore data associations, achieve accurate predictions and analysis of fluctuation causes, and assist in cost optimization; the efficient business logic layer and user interaction layer work together, with complete business functions and smooth processes, a friendly interface, strong visualization, and feedback channels to promote optimization; data is collected extensively and stored efficiently, far exceeding traditional solutions; the analysis depth and visualization effects are outstanding, providing more valuable decision-making basis; the business functions are complete and the management is scientific, which improves the company's cost control and decision-making capabilities, and the user experience is good and the security is high.

[0125] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A construction cost management system based on big data, characterized by: The system includes the following modules: S1. Engineering information management module: Information collection and input: Collect basic information of engineering projects from engineering information and cost information websites and other data sources, including project name, location, owner, design unit, construction unit, project scale, and engineering type, and provide manual input and batch import functions; Information classification and retrieval: Use a distributed database system suitable for big data storage, combine the Hadoop distributed file system HDFS with the HBase database, store data in a distributed manner according to information types, and establish a retrieval mechanism; Information update and maintenance: real-time monitoring of changes in engineering information, timely updating of project information in the system, and archiving and management of historical project information; S2. Cost data processing module Cost data collection and integration: Use web crawler technology to regularly crawl updated engineering and cost information on the website according to predetermined rules, including project budget, settlement, and cost details. Use data interface calls to obtain key data in real time. Use ETL tools to extract, convert, and load some structured data, and associate and integrate them with engineering project information to form a complete cost data set; Cost data analysis and evaluation: Use clustering algorithms to classify engineering projects, and based on historical cost data, use regression analysis and neural network algorithms to build cost prediction models, predict the cost range of new projects, analyze cost data, evaluate the rationality of project costs, analyze the reasons for cost fluctuations, predict future cost trends, and provide users with cost analysis reports; Visual display of cost data: display cost data in the form of intuitive charts; S3. Market dynamic monitoring module: Market price data collection: real-time collection of market price information required for engineering construction, attention to market price fluctuation trends, and acquisition of data from multiple data sources to ensure the comprehensiveness and accuracy of price information; Price trend analysis and forecasting: Use data analysis technology to analyze market price data and predict future price trends, provide reference for cost estimation and budget preparation of engineering projects, and visualize price trends through Tableau and PowerBI; Market intelligence push and early warning: timely push market price change intelligence and early warning information to users based on the user-set focus range and early warning threshold; S4. User management and authority control module User registration and authentication: Provide user registration function and authenticate user identity; User authority management: assign different system permissions based on user roles; User operation records and auditing: record all user operations in the system; S5, system configuration and management module System parameter setting: configure the system's operating parameters and flexibly adjust them according to the system's operating conditions and user needs; Data backup and recovery: regularly back up data in the system and formulate backup strategies; System log management: record various log information during system operation; S6. Data quality management module Establish data quality standards: clarify the quality requirements for engineering information and cost data; Data quality monitoring and assessment: Regularly check data quality, use data audit tools and techniques to assess the accuracy and completeness of data, and make corrections in a timely manner; Data quality problem handling process: Establish a data quality problem handling mechanism and negotiate with the data source provider to resolve data collection source issues; S7, data security management module Network security protection: deploy network security equipment to prevent external network attacks; Application of data encryption technology: Encrypted storage and transmission of sensitive data; User authentication and authorization management: Strengthen the user authentication mechanism, adopt multi-factor authentication to improve the security of user identity authentication, and strictly manage user authorization to ensure that users can only access data within their authority to prevent data leakage and illegal operations; Data access logging and auditing: Record all users’ data access operations in detail, conduct regular data access audits, and check for abnormal access behaviors; S8, Data update and maintenance strategy module Real-time data update mechanism: establish a real-time data push mechanism with the engineering information and cost information website to regularly update data from other data sources; Data maintenance team and process: Establish a professional data maintenance team to be responsible for the daily maintenance of data; Historical data management: Rationally archive and manage historical data, and determine the storage method and period based on the importance and frequency of use of the data.

2. According to the big data-based construction cost management system of claim 1, it is characterized by: The data management described covers the entire data process from collection, storage, analysis to update and maintenance. All links work closely together to establish a strict data quality management system, including quality standard setting, monitoring and evaluation, and problem handling procedures, focusing on data security management to prevent data leakage and illegal access.

3. The engineering cost management system based on big data according to claim 1 is characterized by: The cost assessment and analysis is based on project characteristics and market data, using a cost forecasting model to conduct project cost assessment, analyze the costs of completed projects, identify the causes of cost fluctuations, and provide lessons for subsequent projects. In terms of data comparison and benchmark setting, the cost data of different projects are compared and analyzed, and an industry cost benchmark is set to help companies evaluate the cost competitiveness of their own projects.

4. The engineering cost management system based on big data according to claim 3 is characterized by: By monitoring market price fluctuations and changes in policies and regulations, early warnings are issued for risks that affect project costs.

5. The engineering cost management system based on big data according to claim 1 is characterized by: Formulate business rules for the system, define business processes, and ensure smooth collaboration between the various functional modules of the system.

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