An intelligent data analysis method and system based on digital construction

Abstract

The present invention relates to the field of intelligent building technology, specifically to an intelligent data analysis method and system based on digital construction, comprising the following steps: collecting data from the design stage of a construction project, including material usage, design specifications and construction drawings, classifying them into building area, material category and use, analyzing the relationship between the data and the project budget, and obtaining initial design data. In the present invention, by identifying key cost factors, optimizing resource allocation and construction strategies, cost control is strengthened, especially in terms of material waste and labor allocation, ensuring the maximization of economic benefits in the construction stage, and by simulating different maintenance strategies, not only can cost-effectiveness be predicted, but also operation and maintenance plans can be adjusted in a targeted manner, effectively extending the service life of the building and reducing maintenance costs, significantly improving the construction efficiency and quality of the construction project, while reducing long-term operating costs and enhancing the project's ability to adapt to future changes.

Description

Technical Field

[0001] The present invention relates to the field of intelligent building technology, and in particular to an intelligent data analysis method and system based on digital construction. Background Art

[0002] The field of smart building technology focuses on integrating advanced information technology and automatic control technology into the building environment to achieve efficient and automated building operations. This includes using sensors, actuators, control systems, and software platforms to manage a building's lighting, security, HVAC (heating, ventilation, and air conditioning) systems, and energy consumption to improve energy efficiency and occupant comfort. Smart building technology collects large amounts of data and uses artificial intelligence and machine learning algorithms to analyze the data to optimize building performance and operations. The application of this technology can greatly reduce energy waste, improve the environmental sustainability of buildings, and improve the living and working environment.

[0003] Among them, the intelligent data analysis method of digital construction refers to collecting data during the construction process by using digital technology, and then applying intelligent analysis technology to process and analyze these data. The purpose is to improve the construction efficiency and quality control of construction projects, reduce costs, and optimize subsequent construction management and maintenance. Through the analysis of real-time data, builders can more accurately monitor building materials, construction progress and resource usage, make more scientific decisions, and improve the overall benefits of construction projects. This method is an important application direction in the field of intelligent building technology and represents an important step in the transformation of the construction industry towards digitalization and intelligence.

[0004] Although existing smart building technologies have made progress in automation and data utilization, they are still insufficient in real-time data analysis and cost control applications. These technologies focus on optimizing existing operational processes and are not sufficient to handle dynamic changes in the construction phase, such as real-time material consumption and labor allocation issues. Existing methods are also weak in analyzing the deviation between predictions and actual construction, limiting the ability to respond quickly to cost overruns. The limitations of these technologies have resulted in the failure to fully realize the potential of construction projects in cost management and resource optimization, affecting the overall performance and life cycle cost of buildings. Without the support of detailed real-time data analysis, construction projects will face problems such as budget overruns, increased material waste, and construction schedule delays, which will directly affect the economic benefits and quality of construction projects. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose an intelligent data analysis method and system based on digital construction.

[0006] In order to achieve the above object, the present invention adopts the following technical solution, an intelligent data analysis method based on digital construction, comprising the following steps:

[0007] S1: Collect data from the design phase of a construction project, including material usage, design specifications, and construction drawings, categorize them into building area, material category, and usage, analyze the relationship between the data and the project budget, and obtain initial design data;

[0008] S2: By comparing the material consumption amount, construction progress and labor use records in the real-time construction phase with the initial design data, analyzing the reasons for exceeding the design budget, and generating construction implementation difference analysis results;

[0009] S3: Based on the construction implementation difference analysis results, identify material waste and labor overspending in cost factors, extract key factors affecting cost through data analysis, screen the priority of cost control, and obtain key cost factor identification results;

[0010] S4: Using the key cost factor identification results, based on digital construction, configure differentiated maintenance strategies, simulate the performance of maintenance strategies in actual operation and maintenance, and obtain operation and maintenance strategy simulation output by comparing the cost and maintenance cycle of differentiated solutions;

[0011] S5: using the operation and maintenance strategy simulation output, calculating the cost and maintenance benefit of the maintenance strategy within a predetermined time, and generating a strategy cost-benefit analysis result by comparing the predicted maintenance cost with the actual cost;

[0012] S6: Graphically display the cost-benefit analysis results of the strategy, analyze and evaluate the impact of the maintenance strategy on the building life cycle cost and performance, and obtain the strategy impact assessment results.

[0013] As a further solution of the present invention, the initial design data includes building area, types and amounts of key building materials used, and reference building design specifications; the construction implementation difference analysis results include types and quantities of material usage exceeding expectations, actual and planned differences in construction progress, and actual and budgeted deviations in labor use; the key cost factor identification results include types and degrees of material waste, links and amounts of labor overspending, and priority strategies for cost control; the operation and maintenance strategy simulation output includes differentiated maintenance strategies adopted, the impact of maintenance strategies on costs and maintenance cycles, and cost-effectiveness comparisons between maintenance strategies; the strategy cost-effectiveness analysis results include maintenance cost forecasts within a predetermined time, actual maintenance costs, and difference analysis between the two; the strategy impact assessment results include the impact of maintenance strategies on building life cycle costs, the degree of optimization of building performance, and cost-effectiveness assessment information for differentiated strategies.

[0014] As a further solution of the present invention, data in the design stage of a construction project, including material usage, design specifications and construction drawings, are collected and classified into building area, material category and use, and the relationship between the data and the project budget is analyzed. The steps for obtaining the initial design data are specifically as follows:

[0015] S101: Collect data from the design phase of a construction project, including material usage, design specifications, and construction drawings, and classify them according to building area, material type, and purpose to obtain data classification records;

[0016] S102: using the data classification records, calculating the usage and cost of multiple types of materials, and at the same time estimating the overall material demand by associating the building area, analyzing the relationship between material usage and cost, and obtaining a material usage cost analysis table;

[0017] S103: Based on the material usage cost analysis table, the project budget is compared, and the difference between the budget and the actual material cost is determined by setting a threshold, and cost control and budget adjustment are performed to obtain initial design data.

[0018] As a further solution of the present invention, the steps of comparing the material consumption amount, construction progress and labor use record of the real-time construction stage with the initial design data, analyzing the reasons for exceeding the design budget, and generating the construction implementation difference analysis results are specifically as follows:

[0019] S201: Collecting data of the real-time construction phase through the initial design data, including material consumption amount, construction progress and labor use records, verifying the integrity and continuity of the data, and obtaining real-time construction data records;

[0020] S202: Based on the real-time construction data record, compare the initial design data with the actual consumption data, calculate the material usage and cost differences of multiple stages, analyze the factors that lead to cost increase in multiple links, and obtain the initialization difference analysis results;

[0021] S203: Analyze the reasons for exceeding the design budget based on the initialization difference analysis results, combined with the construction progress, material consumption and labor usage, perform cost control, and obtain the construction implementation difference analysis results.

[0022] As a further solution of the present invention, according to the construction implementation difference analysis results, identifying material waste and labor overruns in cost factors, extracting key factors affecting costs through data analysis, screening the priority of cost control, and obtaining the key cost factor identification results are specifically as follows:

[0023] S301: Using the construction implementation difference analysis results, identifying and quantifying the cost factors of material waste and labor overspending, verifying the consistency of the data, and obtaining a record of excessive cost consumption;

[0024] S302: Using the cost overconsumption records, applying statistical analysis methods, extracting factors that affect project costs, including material selection errors, high procurement costs, and low construction efficiency, analyzing their contribution to the total cost, and obtaining contribution analysis results;

[0025] S303: Screen and prioritize cost control measures based on the contribution analysis results, analyze the priorities of key cost factors with reference to the improvement potential and implementation difficulty of multiple factors, and obtain key cost factor identification results.

[0026] As a further solution of the present invention, the key cost factor identification results are used to configure differentiated maintenance strategies based on digital construction, simulate the performance of the maintenance strategies in actual operation and maintenance, and obtain the operation and maintenance strategy simulation output by comparing the cost and maintenance cycle of the differentiated solutions. Specifically, the steps are as follows:

[0027] S401: Based on the identification results of the key cost factors, using the building information model, designing optimization measures for material utilization and labor allocation, and checking adaptability and implementation probability to obtain differentiated maintenance strategies;

[0028] S402: According to the differentiated maintenance strategy, a genetic algorithm is used to perform optimization iterations by encoding differentiated strategies of building maintenance solutions, and maintenance costs, cycles and effects are recorded to obtain an optimal maintenance strategy configuration;

[0029] S403: By configuring the optimal maintenance strategy, evaluating the cost-effectiveness and cycle efficiency of multiple maintenance strategies, selecting the optimal operation and maintenance solution, comparing the long-term maintenance cost and benefit of the differentiated solutions, and obtaining an operation and maintenance strategy simulation output.

[0030] As a further solution of the present invention, the formula of the genetic algorithm is as follows:

[0031] ;

[0032] Among them, C is the optimal maintenance strategy configuration, Represents maintenance cost efficiency, represents the maintenance cycle efficiency, Represents the efficiency of the maintenance effect, weight parameter , and Adjust the influence of cost, time and effect separately.

[0033] As a further solution of the present invention, the steps of using the operation and maintenance strategy simulation output to calculate the cost and maintenance benefit of the maintenance strategy within a predetermined time, and generating the strategy cost-benefit analysis result by comparing the predicted maintenance cost with the actual cost are specifically as follows:

[0034] S501: Calculate the cost and expected maintenance benefits of multiple maintenance strategies within a predetermined time through the operation and maintenance strategy simulation output, analyze and verify the economy and practicality of each strategy, and obtain cost-benefit prediction data;

[0035] S502: Based on the cost-benefit forecast data, the actual maintenance cost data recorded during the project implementation is compared, and the key factors of cost and benefit deviation are identified through difference analysis, and the maintenance strategy and cost control measures are optimized to obtain the difference analysis results between the strategy and the actual cost;

[0036] S503: Perform cost-benefit evaluation based on the analysis results of the difference between the strategy and the actual cost, analyze the cost and maintenance benefits of multiple strategies, check the correlation between cost and benefit, and obtain the strategy cost-benefit analysis results.

[0037] As a further solution of the present invention, the strategy cost-benefit analysis results are graphically displayed, and the impact of the maintenance strategy on the building life cycle cost and performance is analyzed and evaluated. The steps of obtaining the strategy impact evaluation results are specifically as follows:

[0038] S601: Based on the strategy cost-benefit analysis result, convert the cost-benefit data into a graphical format, including a bar graph, a curve graph and a scatter graph, to display the cost and benefit comparison of the differentiated maintenance strategy, and obtain a cost-benefit graph;

[0039] S602: Analyze the impact of differentiated maintenance strategies on building life cycle costs and performance based on the cost-benefit graph, and obtain life cycle impact analysis results by referring to the sustainability of the maintenance strategies and the long-term effects;

[0040] S603: Based on the life cycle impact analysis results, evaluate and integrate the overall impact of maintenance strategies on building performance and cost, optimize and recommend strategies, and obtain strategy impact assessment results.

[0041] An intelligent data analysis system based on digital construction, the intelligent data analysis system based on digital construction is used to execute the intelligent data analysis method based on digital construction, the system comprises:

[0042] The project design data collection module collects data from the design phase of a construction project, including material usage, design specifications, and construction drawings, and classifies and organizes the data to obtain an initial design data set;

[0043] The real-time construction monitoring module uses the initial design data set to monitor the material consumption, construction progress and labor use of the construction site in real time, compare the differences between the design and implementation, analyze the reasons for the overspending, and obtain the construction implementation difference analysis results;

[0044] The cost analysis module identifies the key factors of cost overruns based on the construction implementation difference analysis results, selects the cost control points that are adjusted first, and forms a key cost factor identification list;

[0045] The maintenance strategy analysis module configures differentiated maintenance strategies based on the key cost factor identification list, simulates the economic and time performance of the strategies in actual maintenance, and generates strategy simulation results;

[0046] The life cycle impact module uses the strategy simulation results to calculate the costs and benefits of multiple maintenance strategies, displays the long-term impact of the strategies through graphical tools, evaluates the advantages of maintenance strategies on the building life cycle cost and performance, and obtains the strategy impact assessment results.

[0047] Compared with the prior art, the advantages and positive effects of the present invention are:

[0048] In the present invention, by collecting data on material usage, specifications and drawings during the design phase and analyzing the relationship with the budget, the economy and accuracy of the initial design are significantly improved. Real-time comparison of data from the construction phase makes it more direct and accurate to identify the reasons for budget overruns, and further optimizes resource allocation and construction strategies by identifying key cost factors. Cost control is strengthened, especially in terms of material waste and labor allocation, to ensure maximum economic benefits during the construction phase. By simulating different maintenance strategies, not only can cost-effectiveness be predicted, but the operation and maintenance plan can also be adjusted in a targeted manner, effectively extending the service life of the building and reducing maintenance costs. The construction efficiency and quality of the construction project are significantly improved, while long-term operating costs are reduced and the project's adaptability to future changes is enhanced. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0050] Figure 2 This is a detailed flow chart of S1 of the present invention;

[0051] Figure 3 This is a detailed flow chart of S2 of the present invention;

[0052] Figure 4 This is a detailed flow chart of S3 of the present invention;

[0053] Figure 5 This is a detailed flow chart of S4 of the present invention;

[0054] Figure 6 This is a detailed flow chart of S5 of the present invention;

[0055] Figure 7 This is a detailed flow chart of S6 of the present invention;

[0056] Figure 8 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0058] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0059] See also Figure 1 The present invention provides a technical solution, an intelligent data analysis method based on digital construction, comprising the following steps:

[0060] S1: Collect data from the design phase of a construction project, including material usage, design specifications, and construction drawings, and classify them into building area, material category, and usage. Analyze the correspondence between the data and the project budget to obtain initial design data.

[0061] S2: Compare the initial design data with the data records of the real-time construction phase, including the amount of material consumption, construction progress and labor use records, compare the design budget with the current expenditure, analyze the reasons for exceeding the budget, and generate the construction implementation difference analysis results;

[0062] S3: Based on the construction implementation difference analysis results, identify cost factors, including material waste and labor overspending, extract key factors affecting costs through data analysis, screen the priority of cost control, and obtain the key cost factor identification results;

[0063] S4: Using the key cost factor identification results, based on digital construction, configure differentiated maintenance strategies and cost budget plans, simulate the performance of maintenance strategies in actual operation and maintenance, and obtain the operation and maintenance strategy simulation output by comparing the cost and maintenance cycle of differentiated solutions;

[0064] S5: Use the operation and maintenance strategy simulation output to calculate the cost and maintenance benefits of the maintenance strategy within the scheduled time. By comparing the predicted maintenance cost with the actual cost, analyze the economic benefits of long-term operation and generate the strategy cost-benefit analysis results;

[0065] S6: Graphically display the results of the strategy cost-benefit analysis, analyze and evaluate the impact of maintenance strategies on the building life cycle cost and performance, analyze the correlation between cost and maintenance strategy, and obtain the strategy impact assessment results.

[0066] The initial design data includes the building area, the types and quantities of key building materials used, and the referenced building design specifications. The results of the construction implementation difference analysis include the types and quantities of material usage that exceed expectations, the differences between the actual and planned construction progress, and the deviations between the actual and budgeted labor use. The results of the key cost factor identification include the types and degree of material waste, the links and quantities of labor overspending, and the priority strategy for cost control. The operation and maintenance strategy simulation output includes the differentiated maintenance strategies adopted, the impact of maintenance strategies on costs and maintenance cycles, and the cost-effectiveness comparison between maintenance strategies. The strategy cost-effectiveness analysis results include the maintenance cost forecast within the scheduled time, the actual maintenance cost and the difference analysis between the two. The strategy impact assessment results include the impact of maintenance strategies on the building life cycle cost, the degree of optimization of building performance, and the cost-effectiveness evaluation information of differentiated strategies.

[0067] See also Figure 2 , collect data from the design stage of the construction project, including material usage, design specifications and construction drawings, categorize them into building area, material category and use, analyze the relationship between the data and the project budget, and obtain the initial design data in the following steps:

[0068] S101: Collect data from the design phase of a construction project, including material usage, design specifications, and construction drawings, and classify them according to building area, material type, and purpose. The execution process for obtaining data classification records is as follows;

[0069] The data collected during the design phase of a building project involves material usage, design specifications, and construction drawings. Classification and management of this data is an important step in basic data processing. The purpose of classification is to organize the data more systematically for subsequent use and analysis. This process requires first roughly grouping according to the building area, and then more detailed classification according to the material type and purpose. When classifying, material usage must be accurately recorded, design specifications need to be updated to the latest, and construction drawings must be reviewed in detail according to project requirements. In this way, the classified data can be directly used for calculations and budget preparation to ensure the practicality and accuracy of the data and obtain data classification records.

[0070] S102: Using data classification records, calculating the usage and cost of multiple types of materials, and at the same time associating the building area to estimate the overall material demand, analyzing the relationship between material usage and cost, and obtaining the material usage cost analysis table. The execution process is as follows;

[0071] Use data classification records, according to the formula , calculate the total cost of multiple types of materials. Represents the amount of material of type a, Represents the unit price of the material in category a. Formula explanation and formula calculation derivation process: In order to estimate the overall material demand and cost, the following steps are used: The amount of each type of material Obtain the price of each material through field measurement and design documents Determine through market research or supplier quotations. Multiply the amount of each material by the corresponding price to get their respective costs, and add up all costs to get the total cost. Set the amount of material A to 500 units and the price to 2 yuan per unit, and the amount of material B to 300 units and the price to 5 yuan per unit. The calculation process is as follows: . . This calculation shows that the material cost for the entire project is $2,500.

[0072] S103: Based on the material usage cost analysis table, the project budget is compared, the difference between the budget and the actual material cost is determined by setting a threshold, and cost control and budget adjustment are performed to obtain the execution process of the initial design data as follows;

[0073] Based on the material usage cost analysis table, comparing the project budget and analyzing the difference between the budget and the actual material cost is a task in project management. This process includes judging whether the budget is exceeded through the set cost threshold. If the budget is exceeded, cost control and budget adjustment are required. Through such control and adjustment, the management team can adjust the procurement strategy and construction plan in time to reduce costs, or apply for additional funds from investors when necessary. The threshold setting should be accurately calculated based on historical data and market trends to ensure that the economic benefits of the project are maximized and the initial design data is obtained.

[0074] See also Figure 3 , by comparing the initial design data with the material consumption, construction progress and labor use records of the real-time construction phase, analyzing the reasons for exceeding the design budget, and generating the construction implementation difference analysis results in the following steps:

[0075] S201: Collect the data of the real-time construction stage through the initial design data, including the amount of material consumption, construction progress and labor use records, verify the integrity and continuity of the data, and obtain the execution process of the real-time construction data record as follows;

[0076] Through the initial design data, the real-time construction phase data is collected to ensure that the data-driven decision-making of project management has sufficient information support, which includes the amount of material consumption, construction progress and labor utilization records. The real-time data collection process requires special monitoring equipment and recording systems to ensure the integrity and continuity of the data. The integrity verification is to check whether the data records are missing or inconsistent. The continuity verification is to ensure that the data is recorded completely in time series without time breakpoints. Such data records can effectively support the needs of construction management and cost control and obtain real-time construction data records.

[0077] S202: Based on the real-time construction data record, compare the initial design data with the actual consumption data, calculate the material usage and cost differences of multiple stages, analyze the factors that lead to cost increase in multiple links, and obtain the execution process of the initial difference analysis result as follows;

[0078] Compare the initial design data with the actual consumption data, according to the formula , calculate the multi-stage material usage and cost difference. In the formula, represents the estimated material usage in stage i, represents the actual material usage in stage i, Represents the unit price of materials in the i-th phase. Detailed explanation of the formula and the process of formula calculation: In order to obtain the initial difference analysis results, it is necessary to extract the expected and actual material usage of each phase and the corresponding material prices from the project database. Set the expected usage of the first phase to 1000 units, the actual usage to 1200 units, and the unit price to 10 yuan. Set the expected usage of the second phase to 500 units, the actual usage to 450 units, and the unit price to 12 yuan. The calculation process is as follows: . . This calculation shows that the project had a total overrun of $1,400 over the two phases due to the difference in material usage.

[0079] S203: Based on the initialization difference analysis results, combined with the construction progress, material consumption and labor usage, the reasons for exceeding the design budget are analyzed, and cost control is performed to obtain the construction implementation difference analysis results. The execution process is as follows;

[0080] Based on the results of the initial variance analysis, it is crucial to analyze the reasons why the project is over budget in combination with the construction progress, material consumption and labor usage. Through this analysis, the key factors that lead to cost increases can be identified, such as fluctuations in material prices, changes in construction efficiency or rising labor costs. Cost control measures include renegotiating material supply contracts, optimizing construction plans or adjusting labor allocation. These measures can have a direct impact on costs. Through effective cost control, project costs can be kept within the budget, ensuring the financial health and successful implementation of the project, and obtaining the results of the construction implementation variance analysis.

[0081] See also Figure 4 According to the construction implementation difference analysis results, identify the material waste and labor overspending in the cost factors, extract the key factors affecting the cost through data analysis, screen the priority of cost control, and obtain the key cost factor identification results in the following steps:

[0082] S301: Using the construction implementation variance analysis results, identify and quantify the cost factors of material waste and labor overruns, verify the consistency of the data, and obtain the execution process of the cost overconsumption record as follows;

[0083] Using the results of construction implementation variance analysis to identify and quantify material waste and labor overruns is a key step, which involves a detailed examination of the cost factors of the project. Verifying the consistency of the data requires checking the collected data to ensure the reliability of the data source and the correctness of the data input. By checking the consistency of the data, errors caused by data errors can be eliminated. Identifying and quantifying material waste includes checking the actual use of materials compared with the planned schedule. Labor overruns require analyzing the difference between actual working hours and planned working hours. Such analysis helps the project team understand the specific reasons for excessive costs and obtain records of excessive cost consumption.

[0084] S302: Using the cost overconsumption records, applying statistical analysis methods, extracting factors that affect project costs, including material selection errors, high procurement costs, and low construction efficiency, analyzing the contribution to the total cost, and obtaining the contribution analysis results. The execution process is as follows;

[0085] Apply statistical analysis methods, according to the formula , extract the factors that affect project cost. In the formula, represents the contribution of factor b to the project cost, Represents the weight of factor b. Detailed explanation of the formula and formula calculation derivation process: In order to analyze the contribution of each factor to the total cost, the actual impact data of each factor is obtained through the data acquisition system, such as specific cases and values ​​of material selection errors, high procurement costs, and low construction efficiency. Set weights for each factor, which are set based on the experience of previous projects and industry standards. Set the cost impact of material selection errors to 8,000 yuan, with a weight of 0.3, high procurement costs to 10,000 yuan, with a weight of 0.4, and additional costs caused by low construction efficiency to 12,000 yuan, with a weight of 0.3. The calculation process is as follows: . . . This calculation shows the contribution of each factor to the total project cost and provides a basis for cost control.

[0086] S303: Based on the contribution analysis results, cost control measures are screened and prioritized, and the priority of key cost factors is analyzed with reference to the improvement potential and implementation difficulty of multiple factors. The execution process of obtaining the key cost factor identification results is as follows;

[0087] Based on the results of contribution analysis, screening and prioritizing cost control measures is a routine operation for the management team when facing cost issues. By analyzing the improvement potential and implementation difficulty of multiple factors, the team can identify which cost factors are the most critical and decide which measures should be implemented first. If material selection errors are the main cost driver, improving procurement processes and review standards is the top priority. If low construction efficiency is an important factor, optimizing construction plans and improving worker training can generate greater cost savings. The formulation and implementation of these strategies require detailed cost-benefit analysis and feasibility assessment of actual operations to obtain key cost factor identification results.

[0088] See also Figure 5 , using the key cost factor identification results, based on digital construction, configure differentiated maintenance strategies, simulate the performance of maintenance strategies in actual operation and maintenance, and obtain the operation and maintenance strategy simulation output by comparing the cost and maintenance cycle of differentiated solutions. The specific steps are as follows:

[0089] S401: Based on the identification results of key cost factors, using the building information model, design optimization measures for material utilization and labor allocation, and check the adaptability and implementation probability, and obtain the execution process of the differentiated maintenance strategy as follows;

[0090] Based on the results of key cost factor identification, using building information models to optimize material utilization and labor allocation is a refined process, which includes adjusting material usage plans to reduce waste and optimizing labor allocation to improve efficiency. Building Information Modeling (BIM) technology provides a three-dimensional perspective to check the adaptability of the design and ensure the feasibility of the design solution in practical applications. The evaluation of implementation probability is carried out by simulating various operation and maintenance conditions and scenarios. This evaluation helps decision makers understand which design optimization measures have the greatest probability of success under different conditions, as well as the challenges they face. Through these detailed analyses and simulations, cost control is ensured while maintaining the quality and sustainability of the project, resulting in differentiated maintenance strategies.

[0091] S402: According to the differentiated maintenance strategy, a genetic algorithm is used to perform optimization iterations by encoding the differentiated strategy of the building maintenance plan, and the maintenance cost, cycle and effect are recorded to obtain the execution process of the optimal maintenance strategy configuration as follows;

[0092] The formula of genetic algorithm is as follows: ;

[0093] Among them, C is the optimal maintenance strategy configuration, Represents maintenance cost efficiency, represents the maintenance cycle efficiency, Represents the efficiency of the maintenance effect, weight parameter , and Adjust the influence of cost, time and effect separately.

[0094] Parameter definition and data acquisition:

[0095] (Maintenance Cost Efficiency): The contribution of each unit cost to the reduction of maintenance costs obtained through historical data analysis. By analyzing the maintenance records of the past 5 years, the average percentage of maintenance cost reduction for every 1,000 units of currency spent is calculated.

[0096] (Maintenance cycle efficiency): The ability to complete maintenance per unit time. Past maintenance cycle data is extracted from the maintenance log and the average number of maintenance tasks completed per day is calculated.

[0097] (Maintenance effect efficiency): Quantify the contribution of maintenance to performance improvement by comparing the difference in equipment operating efficiency before and after maintenance. This data is extracted from the equipment performance monitoring system, and the average performance improvement rate is calculated by analyzing the operating data before and after maintenance.

[0098] Weight parameter settings:

[0099] They are the weight parameters of cost, time, and effect, which are adjusted according to the focus of the project. For example, in a cost-sensitive project, Higher than and These weights are based on an analysis of priorities from similar past projects and can be adjusted with expert input to match current project needs.

[0100] Numerical setting and actual calculation example:

[0101] set up (15% reduction in maintenance cost per unit cost), (The number of maintenance tasks that can be completed every day is 10% more than the standard), (Maintenance effect increased by 10%). .

[0102] Specific calculation process:

[0103] ;

[0104] Overall Rating It shows that under the current weight setting, the adopted maintenance strategy has a relatively good balance in cost efficiency, cycle efficiency and effect efficiency. This result indicates that the direction of maintenance strategy configuration optimization should focus on further improving the efficiency of the effect, while slightly adjusting the weights of cost and time to achieve higher comprehensive efficiency. This score helps decision makers clarify the focus of future maintenance strategy adjustments.

[0105] S403: By configuring the optimal maintenance strategy, evaluating the cost-effectiveness and cycle efficiency of multiple maintenance strategies, selecting the optimal operation and maintenance solution, comparing the long-term maintenance cost and benefit of the differentiated solutions, and obtaining the execution process of the operation and maintenance strategy simulation output is as follows;

[0106] Through the configuration of the optimal maintenance strategy, the cost-effectiveness and cycle efficiency of multiple maintenance strategies are evaluated in order to determine the most economical and efficient operation and maintenance plan. The process involves comparative analysis of the long-term costs and benefits of different maintenance strategies. Screening the optimal operation and maintenance plan requires consideration of the implementation cost of the strategy, the expected maintenance effect and the risk. This evaluation adopts the methods of cost-benefit analysis and cycle efficiency analysis. By calculating the ratio of the cost of each strategy to the benefit it brings, as well as the time efficiency of the maintenance operation, the most appropriate plan is identified, providing scientific decision-making information for project management, ensuring that the project can achieve the best state in terms of economy and efficiency, and obtaining the operation and maintenance strategy simulation output.

[0107] See also Figure 6, using the operation and maintenance strategy simulation output, calculate the cost and maintenance benefits of the maintenance strategy within the scheduled time, and generate the strategy cost-benefit analysis results by comparing the predicted maintenance cost with the actual cost. The specific steps are as follows:

[0108] S501: Through the operation and maintenance strategy simulation output, the cost and expected maintenance benefits of multiple maintenance strategies within the scheduled time are calculated, and the economy and practicality of each strategy are analyzed and verified. The execution process of obtaining the cost-benefit prediction data is as follows;

[0109] Calculating the cost and expected maintenance benefits of multiple maintenance strategies within a predetermined time through operation and maintenance strategy simulation output is a detailed analysis process, which includes evaluating the economy and practicality of each strategy. The data provided by the operation and maintenance strategy simulation must be verified for accuracy and reliability through detailed analysis to ensure the correctness of the strategy selection. The process involves quantifying and evaluating the expected cost and actual maintenance benefits of each strategy. In this way, the maintenance strategy with the highest cost-effectiveness ratio can be identified, making the decision more data-driven and result-oriented, and obtaining cost-effectiveness forecast data.

[0110] S502: Based on the cost-benefit forecast data, the actual maintenance cost data recorded during the project implementation is compared, and the key factors of cost and benefit deviation are identified through difference analysis, and the maintenance strategy and cost control measures are optimized. The execution process of obtaining the difference analysis results between the strategy and the actual cost is as follows;

[0111] Comparing the cost-benefit forecast data with the actual maintenance cost data recorded during the project implementation is a key control process. Variance analysis is used to identify the key factors of cost and benefit deviations. This analysis helps the project team understand the reasons for cost overruns or maintenance benefits not meeting expectations. Through these insights, maintenance strategies and cost control measures can be adjusted and optimized to ensure that project maintenance strategies are more in line with actual operational needs and budget constraints, provide precise adjustment directions for project management, improve the effectiveness of cost control and the accuracy of the budget, and obtain the results of the strategy and actual cost difference analysis.

[0112] S503: Perform cost-benefit evaluation based on the analysis results of the difference between the strategy and the actual cost. By analyzing the cost and maintenance benefits of multiple strategies and checking the correlation between the cost and the benefit, the execution process of obtaining the strategy cost-benefit analysis results is as follows;

[0113] Based on the results of the analysis of the difference between strategies and actual costs, a cost-benefit evaluation is conducted to gain a detailed understanding of the relationship between the costs and maintenance benefits of multiple strategies. The process involves an in-depth analysis of the degree of correlation between costs and benefits. The evaluation methods include a comparison of the cost input of the strategies and the maintenance results, as well as a prediction of long-term benefits. This analysis helps identify the balance point between cost input and benefit output, so that the project can achieve the best configuration between cost and benefit, providing a scientific basis for the future operation and maintenance and funding allocation of the project, ensuring the rational use of resources and maximizing the economic benefits of the project, and obtaining the results of the strategic cost-benefit analysis.

[0114] See also Figure 7 , graphically display the results of the strategy cost-benefit analysis, analyze and evaluate the impact of maintenance strategies on building life cycle costs and performance, and obtain the strategy impact assessment results in the following steps:

[0115] S601: Based on the results of the strategy cost-benefit analysis, the cost-benefit data is converted into a graphical format, including a bar chart, a curve chart, and a scatter plot, to display the cost-benefit comparison of the differentiated maintenance strategy. The execution process of obtaining the cost-benefit chart is as follows;

[0116] Based on the results of the strategic cost-benefit analysis, converting the cost-benefit data into a graphical format is a method of intuitively displaying data. This includes using bar charts, curve charts, and scatter plots to display the cost and benefit comparison of differentiated maintenance strategies. In the process, the bar chart can clearly show the cost and benefit level of each strategy, the curve chart is used to show the trend of cost and benefit over time, and the scatter plot is used to analyze the relationship between cost and benefit. Such graphical displays help project teams and decision makers quickly understand the data, make effective comparisons and decisions, and provide clear visual support for further strategic adjustments and decisions, and obtain a cost-benefit diagram.

[0117] S602: Based on the cost-benefit diagram, analyze the impact of differentiated maintenance strategies on the building life cycle cost and performance, and refer to the sustainability of the maintenance strategy and the long-term effect. The execution process of obtaining the life cycle impact analysis results is as follows;

[0118] According to the cost-benefit diagram, analyzing the impact of differentiated maintenance strategies on the cost and performance of the building life cycle is a key evaluation activity. This involves examining the sustainability of maintenance strategies and the long-term effectiveness, showing the economic and functional performance of different strategies throughout the building's life cycle. This analysis helps decision makers understand which strategies are more effective in the long run, supports sustainable development goals, provides strategic guidance for the long-term maintenance and management of buildings, ensures the optimization of building performance while controlling cost growth, and obtains life cycle impact analysis results.

[0119] S603: Based on the results of the life cycle impact analysis, evaluate and integrate the overall impact of maintenance strategies on building performance and cost, optimize and recommend strategies, and obtain the execution process of the strategy impact assessment results as follows;

[0120] Based on the results of the life cycle impact analysis, evaluating and integrating the overall impact of maintenance strategies on building performance and cost is an important part of project management. Optimizing and recommending strategies includes identifying the most effective maintenance measures and adjusting strategies based on the specific needs and budget of the building. Such an assessment not only considers the short-term effects of the strategy, but also focuses on long-term cost-effectiveness, providing in-depth insights on how to balance performance improvement and cost control, and providing optimization solutions for the continuous operation and maintenance of the building, ensuring that the building achieves optimal performance and cost efficiency throughout its service life, and obtaining strategic impact assessment results.

[0121] See also Figure 8 , an intelligent data analysis system based on digital construction, the intelligent data analysis system based on digital construction is used to execute the above-mentioned intelligent data analysis method based on digital construction, the system includes:

[0122] The project design data collection module collects data from the design phase of a construction project, including material usage, design specifications, and construction drawings, and classifies and organizes the data to obtain an initial design data set;

[0123] The real-time construction monitoring module uses the initial design data set to monitor the material consumption, construction progress and labor use of the construction site in real time, compare the differences between design and implementation, analyze the reasons for overspending, and obtain the construction implementation difference analysis results;

[0124] The cost analysis module identifies the key factors of cost overruns based on the results of construction implementation variance analysis, selects the cost control points that need to be adjusted first, and forms a key cost factor identification list;

[0125] The maintenance strategy analysis module configures differentiated maintenance strategies based on the key cost factor identification list, simulates the economic and time performance of the strategies in actual maintenance, and generates strategy simulation results;

[0126] The life cycle impact module uses strategy simulation results to calculate the costs and benefits of multiple maintenance strategies, displays the long-term impact of strategies through graphical tools, evaluates the advantages of maintenance strategies on building life cycle costs and performance, and obtains strategy impact assessment results.

[0127] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. An intelligent data analysis method based on digital construction, characterized in that: The following steps are involved: Collect data from the design phase of a construction project, including material usage, design specifications, and construction drawings, categorize them into building area, material type, and usage, analyze the relationship between the data and the project budget, and obtain preliminary design data; By comparing the material consumption amount, construction progress and labor use records in the real-time construction phase with the initial design data, the reasons for exceeding the design budget are analyzed, and construction implementation difference analysis results are generated; According to the construction implementation difference analysis results, identify material waste and labor overspending in cost factors, extract key factors affecting cost through data analysis, screen the priority of cost control, and obtain key cost factor identification results; Using the key cost factor identification results, based on digital construction, differentiated maintenance strategies are configured, the performance of maintenance strategies in actual operation and maintenance is simulated, and the operation and maintenance strategy simulation output is obtained by comparing the cost and maintenance cycle of differentiated solutions; Utilizing the simulation output of the operation and maintenance strategy, calculating the cost and maintenance benefit of the maintenance strategy within a predetermined time, and generating a strategy cost-benefit analysis result by comparing the predicted maintenance cost with the actual cost; Graphically display the cost-benefit analysis results of the strategy, analyze and evaluate the impact of the maintenance strategy on the building life cycle cost and performance, and obtain the strategy impact assessment results; Collect data from the design phase of a construction project, including material usage, design specifications, and construction drawings, categorize them into building area, material category, and usage, analyze the relationship between the data and the project budget, and obtain the initial design data in the following steps: Collect data from the design phase of a construction project, including material usage, design specifications, and construction drawings, and classify them according to building area, material type, and purpose to obtain data classification records; The data classification records are used to calculate the usage and cost of multiple types of materials, and the overall material demand is estimated by associating the building area, analyzing the relationship between material usage and cost, and obtaining a material usage cost analysis table; Based on the material usage cost analysis table, the project budget is compared, and the difference between the budget and the actual material cost is determined by setting a threshold, so as to perform cost control and budget adjustment and obtain initial design data; By comparing the initial design data with the material consumption amount, construction progress and labor use records in the real-time construction phase, analyzing the reasons for exceeding the design budget, and generating the construction implementation difference analysis results, the specific steps are as follows: Through the initial design data, real-time construction data are collected, including material consumption, construction progress and labor use records, and the integrity and continuity of the data are verified to obtain real-time construction data records; Based on the real-time construction data records, the initial design data and actual consumption data are compared, the material usage and cost differences of multiple stages are calculated, the factors leading to cost increases in multiple links are analyzed, and the initialization difference analysis results are obtained; According to the initialization difference analysis results, combined with the construction progress, material consumption and labor use, the reasons for exceeding the design budget are analyzed, cost control is performed, and the construction implementation difference analysis results are obtained; The cost-benefit analysis results of the strategy are graphically displayed to analyze and evaluate the impact of the maintenance strategy on the building life cycle cost and performance. The specific steps for obtaining the strategy impact assessment results are as follows: Based on the cost-benefit analysis results of the strategies, convert the cost-benefit data into a graphical format, including a bar chart, a curve chart, and a scatter plot, to display the cost and benefit comparison of the differentiated maintenance strategies and obtain a cost-benefit graph; According to the cost-benefit diagram, analyze the impact of differentiated maintenance strategies on the life cycle cost and performance of the building, and obtain the life cycle impact analysis results by referring to the sustainability of the maintenance strategy and the long-term effect; Based on the life cycle impact analysis results, the overall impact of maintenance strategies on building performance and cost is evaluated and integrated, strategies are optimized and recommended, and strategy impact assessment results are obtained.

2. The intelligent data analysis method based on digital construction according to claim 1 is characterized in that: The initial design data includes the building area, the types and quantities of key building materials used, and the referenced building design specifications; the construction implementation difference analysis results include the types and quantities of material usage that exceed expectations, the actual and planned differences in construction progress, and the deviations between actual and budgeted labor use; the key cost factor identification results include the types and extent of material waste, the links and quantities of labor overspending, and the priority strategy for cost control; the operation and maintenance strategy simulation output includes the differentiated maintenance strategies adopted, the impact of maintenance strategies on costs and maintenance cycles, and cost-effectiveness comparisons between maintenance strategies; the strategy cost-effectiveness analysis results include maintenance cost forecasts within a predetermined time, actual maintenance costs, and analysis of the differences between the two; the strategy impact assessment results include the impact of maintenance strategies on the life cycle cost of the building, the degree of optimization of building performance, and cost-effectiveness assessment information for differentiated strategies.

3. The intelligent data analysis method based on digital construction according to claim 1 is characterized in that: According to the construction implementation difference analysis results, identify material waste and labor overspending in cost factors, extract key factors affecting cost through data analysis, screen the priority of cost control, and obtain the key cost factor identification results in the following steps: Using the construction implementation variance analysis results, identify and quantify the cost factors of material waste and labor overruns, verify the consistency of the data, and obtain records of excessive cost consumption; By using the records of excessive cost consumption and applying statistical analysis methods, factors affecting project costs are extracted, including material selection errors, high procurement costs and low construction efficiency, and their contribution to the total cost is analyzed to obtain contribution analysis results; Based on the contribution analysis results, cost control measures are screened and prioritized, and the priorities of key cost factors are analyzed with reference to the improvement potential and implementation difficulty of multiple factors to obtain key cost factor identification results.

4. The intelligent data analysis method based on digital construction according to claim 1 is characterized in that: Using the key cost factor identification results, based on digital construction, differentiated maintenance strategies are configured, the performance of maintenance strategies in actual operation and maintenance is simulated, and the steps for obtaining the operation and maintenance strategy simulation output are as follows by comparing the cost and maintenance cycle of differentiated solutions: Based on the identification results of the key cost factors, using the building information model, design optimization measures for material utilization and labor allocation, and check the adaptability and implementation probability to obtain differentiated maintenance strategies; According to the differentiated maintenance strategy, a genetic algorithm is used to perform optimization iterations by encoding the differentiated strategy of the building maintenance plan, and the maintenance cost, cycle and effect are recorded to obtain the optimal maintenance strategy configuration; Through the optimal maintenance strategy configuration, the cost-effectiveness and cycle efficiency of multiple maintenance strategies are evaluated, the optimal operation and maintenance solution is screened, the long-term maintenance costs and benefits of differentiated solutions are compared, and the operation and maintenance strategy simulation output is obtained.

5. The intelligent data analysis method based on digital construction according to claim 4 is characterized in that: The formula of the genetic algorithm is as follows: Among them, C is the optimal maintenance strategy configuration, Represents maintenance cost efficiency, represents the maintenance cycle efficiency, Represents the efficiency of the maintenance effect, weight parameter , and Adjust the influence of cost, time and effect separately.

6. The intelligent data analysis method based on digital construction according to claim 1 is characterized in that: The steps of using the operation and maintenance strategy simulation output to calculate the cost and maintenance benefit of the maintenance strategy within a predetermined time and generating the strategy cost-benefit analysis result by comparing the predicted maintenance cost with the actual cost are as follows: By simulating the output of the operation and maintenance strategy, the cost and expected maintenance benefits of multiple maintenance strategies within a predetermined time are calculated, the economy and practicality of each strategy are analyzed and verified, and cost-effectiveness prediction data is obtained; Based on the cost-benefit forecast data, the actual maintenance cost data recorded during the project implementation is compared, and the key factors of cost and benefit deviation are identified through difference analysis, so as to optimize the maintenance strategy and cost control measures and obtain the difference analysis results between the strategy and the actual cost; Based on the analysis results of the difference between the strategy and the actual cost, a cost-benefit evaluation is conducted. By analyzing the costs and maintenance benefits of multiple strategies and checking the correlation between costs and benefits, the results of the strategy cost-benefit analysis are obtained.

7. An intelligent data analysis system based on digital construction, characterized in that: According to any one of claims 1 to 6, the intelligent data analysis method based on digital construction comprises: The project design data collection module collects data from the design phase of a construction project, including material usage, design specifications, and construction drawings, and classifies and organizes the data to obtain an initial design data set; The real-time construction monitoring module uses the initial design data set to monitor the material consumption, construction progress and labor use of the construction site in real time, compare the differences between the design and implementation, analyze the reasons for the overspending, and obtain the construction implementation difference analysis results; The cost analysis module identifies the key factors of cost overruns based on the construction implementation difference analysis results, selects the cost control points that are adjusted first, and forms a key cost factor identification list; The maintenance strategy analysis module configures differentiated maintenance strategies based on the key cost factor identification list, simulates the economic and time performance of the strategies in actual maintenance, and generates strategy simulation results; The life cycle impact module uses the strategy simulation results to calculate the costs and benefits of multiple maintenance strategies, displays the long-term impact of the strategies through graphical tools, evaluates the advantages of maintenance strategies on the building life cycle cost and performance, and obtains the strategy impact assessment results.

Images