An industrial plant structure scheme engineering cost comparison and measurement method

Abstract

The application is a kind of industrial plant structure scheme engineering cost comparison and measurement method, relates to the technical field of building engineering cost management, and comprises the following steps: S01, obtaining BIM model data of at least two alternative structure schemes of a target industrial plant, performing component identification and classification processing on the BIM model data, extracting geometric parameters and material attribute parameters of each component, and performing automatic engineering quantity statistics according to the extracted parameters to obtain a sub-item engineering quantity list of each alternative structure scheme; S02, obtaining real-time market price data and historical price data, calculating the current cost of each alternative structure scheme according to the sub-item engineering quantity list and the real-time market price data, and performing trend prediction on the material price according to the historical price data to obtain a dynamic cost prediction result of each alternative structure scheme. The application solves the problems of data isolation, one-sided comparison, strong subjective dependence and lack of sensitivity analysis capability in the prior art.

Description

Technical Field

[0001] This invention relates to the field of construction project cost management technology, and in particular to a method for comparative calculation of the cost of industrial plant structural schemes. Background Technology

[0002] With the continuous expansion of the scale of industrial plant construction and the diversification of structural forms, how to scientifically and accurately compare the costs of different structural schemes during the design phase has become a key factor restricting the investment benefits of projects.

[0003] Traditional cost estimation methods have the following limitations:

[0004] Data isolation: Existing cost estimation methods mostly rely on single historical data or quota standards, lacking the ability to dynamically respond to real-time market prices. For example, when steel prices fluctuate by 10%, the total cost of a steel structure factory building can fluctuate by 4%-5%, but traditional methods cannot reflect such changes in a timely manner.

[0005] The comparison is one-sided: conventional methods often focus on comparing initial construction costs, neglecting life-cycle cost analysis. For industrial plants, factors such as operating energy consumption, maintenance costs, and service life have a significant impact on overall economics, but current technologies lack systematic integrated evaluation methods.

[0006] Subjective Dependence: Current cost estimation processes rely heavily on the personal experience of cost engineers, leading to inconsistent comparison bases between different schemes and reduced reliability of results. This is especially true when dealing with new structural systems (such as prefabricated steel structures and composite structures), where a lack of experience further exacerbates estimation errors.

[0007] In summary, existing BIM-based engineering cost estimation methods mostly focus on the extraction of engineering quantities or cost calculation for a single project, and have not yet formed a systematic cost comparison and calculation framework for multiple schemes. Summary of the Invention

[0008] To address the aforementioned technical problems, the present invention provides a method for comparing and calculating the engineering costs of industrial plant structural schemes. The method includes the following steps:

[0009] S01. Obtain BIM model data of at least two alternative structural schemes for the target industrial plant, perform component identification and classification processing on the BIM model data, extract the geometric parameters and material property parameters of each component, and automatically calculate the engineering quantity based on the extracted parameters to obtain the itemized engineering quantity list for each alternative structural scheme.

[0010] S02. Obtain real-time market price data and historical price data, calculate the current cost of each alternative structural scheme based on the itemized bill of quantities and the real-time market price data, and predict the trend of material prices based on the historical price data to obtain the dynamic cost prediction results of each alternative structural scheme.

[0011] S03. Based on the dynamic cost prediction results, select multiple sensitive factors to perform single-factor perturbation analysis, calculate the sensitivity coefficient of each sensitive factor to the total cost, and perform multi-factor joint probability simulation analysis on each sensitive factor to obtain the sensitivity matrix of each alternative structural scheme.

[0012] S04. Based on the preset full life cycle multi-dimensional cost comparison index system, according to the itemized bill of quantities, the dynamic cost prediction results and the sensitivity matrix, calculate the score of each alternative structural scheme under each index category, weight and summarize the scores of each index category to obtain the comprehensive score of each alternative structural scheme, and output the multi-dimensional comparison results of each alternative structural scheme.

[0013] Preferably, the step S01 of obtaining the itemized bill of quantities for each alternative structural scheme includes the following steps:

[0014] S11. Classify and identify the BIM model data according to component type to obtain a data list;

[0015] S12. Extract geometric parameters and material property parameters from the IfcProductDefinitionShape of each component based on the data list;

[0016] S13. Based on the extracted geometric parameters and material property parameters, calculate the engineering quantity of each component according to the component type;

[0017] S14. The calculated quantities of each component are automatically categorized according to the coding rules in the expert knowledge base to generate a list of sub-items of quantities. The list of sub-items of quantities is then checked for total quantity verification, logical verification, and manual comparison verification. The verified list of sub-items of quantities is then output.

[0018] The types of components include steel columns, steel beams, steel roof trusses, concrete foundations, concrete columns, concrete beams, concrete slabs, roof panels, wall panels, and doors and windows.

[0019] The geometric parameters include cross-sectional dimensions, length, and span; the material property parameters include steel grade and concrete grade.

[0020] The quantities of work include the weight of steel, the volume of concrete, and the area of ​​the retaining components.

[0021] Preferably, step S13, which involves calculating the quantity of work for each component according to its type based on the extracted geometric parameters and material property parameters, includes:

[0022] For the steel components, the volume of a single steel component is calculated based on the cross-sectional dimensions and length. The volume is then multiplied by the steel density to obtain the weight of a single steel component. The number of steel components of the same type is counted, and the total weight is calculated.

[0023] For the concrete member, the volume of a single concrete member is calculated based on the cross-sectional dimensions, the number of concrete members of the same type is counted, and the total volume is calculated.

[0024] For the aforementioned enclosure components, the roof panel area and wall panel area are calculated based on the span, column spacing, and eave height;

[0025] For the steel structure connection nodes, the number of bolts and the length of welds are automatically identified and counted separately.

[0026] Preferably, the step S02 of obtaining the dynamic cost prediction results of each alternative structural scheme includes the following steps:

[0027] S21. Obtain the real-time price data of the corresponding materials in the current market material list, multiply the quantities of each item in the bill of quantities by the real-time price of the corresponding materials, and summarize to obtain the current cost of each alternative structural scheme.

[0028] S22. Obtain the monthly price series of various major materials over the past 3 to 5 years from the market price platform, establish an ARIMA time series model for the price of each type of material, and use the ARIMA model to make short-term trend predictions of material prices to obtain the monthly predicted prices and confidence intervals of each material in the next 3 to 6 months.

[0029] S23. Based on the historical price data, an influencing factor is introduced to construct an LSTM neural network model. The LSTM neural network model is used to predict the medium- and long-term trends of material prices, and the predicted prices and confidence intervals of each material in the next 6 to 24 months are obtained.

[0030] S24. Based on the short-term trend forecast results and the medium- and long-term trend forecast results, calculate the impact of material price changes on the total cost of each alternative structural scheme, and output the dynamic cost forecast results of each alternative structural scheme.

[0031] Preferably, obtaining the sensitivity matrix of each candidate structural scheme in step S03 includes the following steps:

[0032] S31. Based on the benchmark cost of any alternative structural scheme, select multiple sensitive factors, including span, column spacing, eaves height, steel price, concrete price, and crane tonnage.

[0033] S32. For each sensitive factor, perform item-by-item perturbation according to a preset change range, keep other sensitive factors as the baseline value, calculate the total cost change rate after the change of each sensitive factor, divide the total cost change rate by the corresponding sensitive factor change rate to obtain the sensitivity coefficient of each sensitive factor, sort all the sensitivity coefficients of the sensitive factors, and output the sensitivity ranking result.

[0034] S33. Define the probability distribution type and distribution parameters of each sensitive factor, randomly sample each sensitive factor, obtain a set of sensitive factor values ​​each time, calculate the corresponding total cost based on the set of sensitive factor values, repeat the simulation multiple times to obtain the probability distribution of the total cost.

[0035] S34. Calculate the contribution ratio of each sensitive factor to the total cost variance based on the total cost probability distribution, and generate a sensitivity matrix for each alternative structural scheme by combining the sensitivity ranking results and the contribution ratio.

[0036] Preferably, the multi-dimensional cost comparison index system for the entire life cycle in step S04 includes initial construction cost indicators, operation and maintenance cost indicators, long-term benefit indicators, and environmental benefit indicators, wherein:

[0037] The initial construction cost index is calculated by dividing the total cost by the building area, with a weighting coefficient of 0.4.

[0038] The operation and maintenance cost index is calculated as the sum of heating energy consumption cost, lighting energy consumption cost, and ventilation energy consumption cost, with a weighting coefficient of 0.3.

[0039] The long-term benefit indicator is calculated by dividing the initial investment by the annual savings, with a weighting coefficient of 0.2.

[0040] The environmental benefit indicator is calculated as the sum of carbon emissions during material production and carbon emissions during construction, with a weighting coefficient of 0.1.

[0041] Preferably, in step S04, the scores of each indicator category are weighted and summarized to obtain the comprehensive score of each alternative structural scheme, specifically: comprehensive score = 0.4 × initial construction cost index score + 0.3 × operation and maintenance cost index score + 0.2 × long-term benefit index score + 0.1 × environmental benefit index score.

[0042] Preferably, the output of the multi-dimensional comparison results of each alternative structural scheme in step S04 includes:

[0043] Generate a comparison table of each alternative structural scheme under each indicator category;

[0044] Generate a comprehensive score ranking for each alternative structural scheme;

[0045] The BIM models of each alternative structural scheme are linked with the corresponding cost data to create a three-dimensional model display.

[0046] The present invention has at least the following beneficial effects:

[0047] 1. By automatically extracting data from BIM models, the error in quantity calculations is reduced from 5-8% using traditional methods to 1-2%, significantly improving the reliability of calculations. Especially for complex nodes (such as steel structure connection nodes and foundation reinforcement), automated quantity calculation avoids human oversights.

[0048] 2. Traditional cost comparisons take 5-7 working days, while this invention shortens that time to 2-3 hours, significantly improving efficiency. Moreover, the time advantage is even more pronounced when comparing multiple options.

[0049] 3. The full life cycle cost analysis covers the economic evaluation of the entire process from construction to operation, avoiding long-term operational burdens caused by short-term cost optimization. Attached Figure Description

[0050] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0051] Figure 1 This is a flowchart of a method for comparing and calculating the engineering cost of an industrial plant structure scheme, provided in Embodiment 1 of the present invention.

[0052] Figure 2 This is a flowchart of S01 provided in Embodiment 1 of the present invention;

[0053] Figure 3 This is a flowchart of S02 provided in Embodiment 1 of the present invention;

[0054] Figure 4 The flowchart of S03 provided in Embodiment 1 of the present invention. Detailed Implementation

[0055] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0056] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0057] Example 1

[0058] This embodiment provides a method for comparing and calculating the engineering cost of industrial plant structural schemes. The method includes the following steps: Figure 1 As shown:

[0059] S01. Obtain BIM model data for at least two alternative structural schemes of the target industrial plant. Perform component identification and classification processing on the BIM model data, extract the geometric parameters and material property parameters of each component, and automatically calculate the quantities based on the extracted parameters to obtain a bill of quantities for each alternative structural scheme. This includes the following detailed steps (e.g., Figure 2 (as shown)

[0060] S11. Classify and identify the BIM model data according to component type to obtain a data list;

[0061] S12. Extract geometric parameters and material property parameters from the IfcProductDefinitionShape of each component based on the data list;

[0062] S13. Based on the extracted geometric parameters and material property parameters, calculate the engineering quantity of each component according to the component type;

[0063] S14. Automatically classify the calculated quantities of each component according to the coding rules in the expert knowledge base, generate a list of sub-items, and perform total quantity verification, logical verification, and manual comparison verification on the list of sub-items, and output the verified list of sub-items.

[0064] The component types include steel columns, steel beams, steel roof trusses, concrete foundations, concrete columns, concrete beams, concrete slabs, roof panels, wall panels, and doors and windows.

[0065] Geometric parameters include cross-sectional dimensions, length, and span; material property parameters include steel grade and concrete grade.

[0066] The quantities of work include the weight of steel, the volume of concrete, and the area of ​​the enclosure components.

[0067] Secondly, in step S13, based on the extracted geometric parameters and material property parameters, the quantities of each component are calculated according to its type, including:

[0068] For steel components, calculate the volume of a single steel component based on its cross-sectional dimensions and length, multiply the volume by the density of the steel to obtain the weight of a single steel component, count the number of steel components of the same type, and calculate the total weight.

[0069] For concrete members, calculate the volume of a single concrete member based on its cross-sectional dimensions, count the number of concrete members of the same type, and calculate the total volume.

[0070] For enclosure components, the area of ​​the roof panel and the area of ​​the wall panel are calculated based on the span, column spacing and eave height;

[0071] For steel structure connection nodes, the number of bolts and weld lengths are automatically identified and counted separately.

[0072] Specifically, at the data acquisition and component identification level, the system reads the BIM model data of each alternative structural scheme through the IFC standard interface, classifies and merges all components in the model according to component type, and forms a data list to be processed. Considering the structural characteristics of industrial plants, the system focuses on identifying the following component types: steel components such as steel columns, steel beams, and steel roof trusses; concrete components such as concrete foundations, concrete columns, concrete beams, and concrete slabs; and enclosure components such as roof panels, wall panels, doors, and windows. Through this classification and identification process, the system transforms the originally unstructured BIM model data into a structured component list organized by type, laying the foundation for subsequent parameter extraction.

[0073] Secondly, in the parameter extraction stage, based on the aforementioned data list, the system extracts geometric and material property parameters from the IfcProductDefinitionShape of each component. Geometric parameters include cross-sectional dimensions (such as the cross-sectional height, flange width, web thickness, and flange thickness of H-beams), component length, and span; material property parameters include steel grades (such as Q235 and Q345) and concrete grades (such as C30 and C40). The system extracts corresponding parameter sets for different types of components to ensure the accuracy of subsequent quantity calculations.

[0074] Secondly, in the automatic quantity calculation stage, the system executes differentiated calculation logic according to component type based on the extracted geometric and material property parameters. For steel components, the volume of a single steel component is calculated based on its cross-sectional dimensions and length, multiplied by the steel density (e.g., 7850 kg / m³) to obtain the weight of a single component, and then the number of similar steel components is counted and summed to obtain the total weight. For concrete components, the volume of a single component is calculated based on its cross-sectional dimensions, the number of similar components is counted, and the total volume is summed. For enclosure components, the roof panel area and wall panel area are calculated based on parameters such as span, column spacing, and eaves height. In addition, for complex parts such as steel structure connection nodes, the system automatically identifies and separately counts details such as the number of bolts and weld lengths to avoid human omissions. For example, for a single-story steel structure factory building with a span of 24m, a column spacing of 6m, and an eaves height of 12m, the system automatically calculates key quantities such as the total weight of the steel columns, the total volume of the independent foundation concrete, and the roof panel area.

[0075] Finally, in the bill of quantities generation and verification stage, the system automatically categorizes the calculated quantities according to the coding rules built into the expert knowledge base (the coding rules are set according to the "Construction Engineering Quantity List Pricing Specification" (GB50500)). The system maps the quantities of each component to the corresponding sub-item codes, project names, and units of measurement, automatically generating a standard format bill of quantities. Simultaneously, the system incorporates multiple verification mechanisms to ensure data quality: total quantity verification compares the total weight of steel with the total weight of all steel components in the model; logical verification checks whether the concrete volume is positive and whether the cross-sectional dimensions are within a reasonable range; for complex nodes, the system automatically extracts detailed items, which are then manually compared. After the above verifications are successful, the system outputs the final bill of quantities, providing accurate data input for subsequent cost calculations.

[0076] S02. Obtain real-time market price data and historical price data. Calculate the current cost of each alternative structural scheme based on the bill of quantities and real-time market price data. Then, predict material prices based on historical price data to obtain the dynamic cost prediction results for each alternative structural scheme. This includes the following detailed steps (e.g., ...). Figure 3 (as shown)

[0077] S21. Obtain the real-time price data of the corresponding materials in the current market material list, multiply the quantities of each item in the bill of quantities by the real-time price of the corresponding materials, and summarize to obtain the current cost of each alternative structural scheme.

[0078] S22. Obtain the monthly price series of various major materials over the past 3 to 5 years from the market price platform, establish an ARIMA time series model for the price of each type of material, and use the ARIMA model to make short-term trend predictions of material prices, so as to obtain the monthly predicted price and confidence interval of each material in the next 3 to 6 months.

[0079] S23. Based on historical price data, introduce influencing factors, construct an LSTM neural network model, and use the LSTM neural network model to predict the medium- and long-term trends of material prices, and obtain the predicted prices and confidence intervals of each material in the next 6 to 24 months.

[0080] S24. Based on the short-term trend forecast results and the medium- and long-term trend forecast results, calculate the impact of material price changes on the total cost of each alternative structural scheme, and output the dynamic cost forecast results of each alternative structural scheme.

[0081] Specifically, firstly, at the real-time cost calculation level, the system maintains real-time data interaction with external market price platforms through standardized data interfaces. For major building materials involved in industrial plants (such as Q235 / Q345 steel, C30 / C40 concrete, cement, etc.), the system automatically obtains the current market unit price. Based on this, the system matches and multiplies the generated bills of quantities for each alternative structural scheme (including the weight of various steel materials, concrete volume, and area of ​​enclosure components) with the corresponding real-time price data item by item, and then sums them up for each item. This quickly yields the initial construction cost of each scheme at the current market point in time, achieving an immediate response to market price fluctuations in cost estimation.

[0082] Secondly, regarding material price trend forecasting, the system constructs a two-layer forecasting architecture of "time series analysis + machine learning". The first layer uses the ARIMA time series model for short-term forecasting: the system automatically collects historical monthly price data of major materials from the market price platform over the past 3 to 5 years, uses the ADF test to determine the stationarity of the price series and the difference order, and combines the ACF and PACF plots to determine the autoregression order and the moving average order. A suitable ARIMA(p,d,q) model is established for each type of material to output the monthly forecast price of materials for the next 3 to 6 months and its 95% confidence interval. The second layer uses an LSTM neural network model for medium- to long-term forecasting: Based on historical price data, the system additionally introduces macroeconomic indicators (such as PMI index, iron ore futures price, and rebar futures price), regional indicators (such as local steel market inventory and transportation costs), and seasonal factors (construction peak / off-season indicators) and other multi-dimensional influencing factors. The input layer receives 12 features, which are processed by two LSTM layers with 64 neurons each, and the Dense layer outputs the predicted price for the next month. The model is trained with data from the past 5 years and validated with data from the most recent 12 months to capture the non-linear patterns of price changes and output the predicted material prices and confidence intervals for the next 6 to 24 months.

[0083] Secondly, in the cost impact calculation stage, the system correlates the predicted material prices output by the ARIMA and LSTM models with the bill of quantities. Specifically, the system calculates the price change of each material during the forecast period, multiplies this change by the corresponding material usage to obtain the individual material cost change value, then sums all material cost changes and divides them by the total cost of the plan to obtain the magnitude and specific amount of the impact of material price fluctuations on the total cost. For example, when the price of Q345 steel is predicted to rise by 5% in the next 6 months, the system automatically calculates the increase in steel costs and its proportion of the total cost, outputting a quantitative prompt such as "Steel prices are predicted to rise by 5%, and the total cost of this plan is expected to increase by approximately 1.5% (approximately RMB 24,600)," providing decision-makers with a reference for material procurement timing.

[0084] Finally, the system features a dynamic update mechanism that automatically retrieves the latest price data from the market price platform weekly, reruns the ARIMA and LSTM models, and continuously updates the prediction results. Simultaneously, the system compares the deviation between actual prices and predicted values ​​in real time. When the deviation exceeds a set threshold (e.g., 10%), it automatically triggers model parameter tuning, refitting the model to adapt to current market trends, ensuring that the prediction results maintain high accuracy and reference value. Through this sequential data processing flow, the system ultimately outputs dynamic cost prediction results for each alternative structural scheme, incorporating the impact of price trends on the current cost basis, providing a data foundation for subsequent sensitivity analysis and comprehensive comparison.

[0085] S03. Based on the dynamic cost prediction results, select multiple sensitive factors and conduct single-factor perturbation analysis respectively. Calculate the sensitivity coefficient of each sensitive factor to the total cost, and perform multi-factor joint probability simulation analysis on each sensitive factor to obtain the sensitivity matrix of each alternative structural scheme. This includes the following details (e.g., Figure 4 (as shown)

[0086] S31. Based on the benchmark cost of any alternative structural scheme, select multiple sensitive factors, including span, column spacing, eaves height, steel price, concrete price, and crane tonnage.

[0087] S32. For each sensitive factor, perform item-by-item perturbation according to the preset change range, keep other sensitive factors as the baseline value, calculate the total cost change rate after the change of each sensitive factor, divide the total cost change rate by the corresponding sensitive factor change rate to obtain the sensitivity coefficient of each sensitive factor, sort all the sensitivity coefficients of the sensitive factors, and output the sensitivity ranking result.

[0088] S33. Define the probability distribution type and distribution parameters of each sensitive factor, randomly sample each sensitive factor, obtain a set of sensitive factor values ​​each time, calculate the corresponding total cost based on the set of sensitive factor values, repeat the simulation multiple times to obtain the probability distribution of the total cost.

[0089] S34. Calculate the contribution ratio of each sensitive factor to the variance of the total cost based on the probability distribution of the total cost, and generate the sensitivity matrix of each alternative structural scheme by combining the sensitivity ranking results and the contribution ratio.

[0090] Specifically, the system uses the benchmark cost of any alternative structural scheme as a benchmark and selects key parameters that have a significant impact on the cost of industrial plants as sensitive factors, including architectural design parameters such as span, column spacing, and eaves height, major building material price parameters such as steel price and concrete price, and process load parameters such as crane tonnage.

[0091] The system performs item-by-item perturbation on each sensitive factor at preset change ranges of ±10% and ±20%. While keeping other factors constant as baseline values, it automatically recalculates the project quantities based on the BIM model and combines this with market price data to calculate the new total cost. It then calculates the total cost change rate and divides it by the corresponding sensitive factor's change rate to obtain the sensitivity coefficient for that factor. For example, if the steel price increases by 10%, and the total cost increases from 5 million yuan to 5.15 million yuan (change rate 3%), the sensitivity coefficient is 0.30; if the span increases from 24m to 26m (change rate 8.3%), and the total cost increases to 5.35 million yuan (change rate 7%), the sensitivity coefficient is 0.84. The system sorts all sensitive factors by their sensitivity coefficients, outputs the sensitivity ranking results, and identifies the most sensitive factor. The system defines the corresponding probability distribution type and distribution parameters for each sensitive factor—steel prices typically follow a normal distribution (mean 4100 yuan / ton, standard deviation 200 yuan / ton), span deviation follows a triangular distribution (most likely value 24m, minimum value 22m, maximum value 26m), and concrete prices follow a uniform distribution (380-420 yuan / m²). 3 Based on this, random sampling is performed on each sensitive factor. Each sampling yields a set of factor values. The system automatically adjusts the BIM model parameters and recalculates the corresponding total cost based on these values. This process is repeated 10,000 times using Monte Carlo simulations to obtain the probability distribution of the total cost (mean, standard deviation, and P10 / P50 / P90 quantiles). The system performs variance decomposition on the simulation results, calculates the contribution ratio of each sensitive factor to the variance of the total cost, and combines the single-factor sensitivity ranking results with the contribution ratio of multiple factors to generate a sensitivity matrix for each alternative structural scheme. This matrix reflects both the independent influence strength of each factor and its variance contribution under combined effects, and can be visually presented through sensitivity spider diagrams and heatmaps, providing clear quantitative basis for design optimization.

[0092] S04. Based on the preset multi-dimensional cost comparison index system for the entire life cycle, calculate the score of each alternative structural scheme under each index category according to the bill of quantities for each item, the dynamic cost prediction results and the sensitivity matrix, weight and summarize the scores of each index category to obtain the comprehensive score of each alternative structural scheme, and output the multi-dimensional comparison results of each alternative structural scheme.

[0093] Furthermore, the multi-dimensional cost comparison index system throughout the entire life cycle in the above embodiments includes initial construction cost indicators, operation and maintenance cost indicators, long-term benefit indicators, and environmental benefit indicators, wherein:

[0094] The initial construction cost index is calculated by dividing the total cost by the building area, with a weighting factor of 0.4.

[0095] The operation and maintenance cost indicator is calculated as the sum of heating energy consumption, lighting energy consumption, and ventilation energy consumption, with a weighting factor of 0.3.

[0096] The long-term benefit indicator is calculated by dividing the initial investment by the annual savings, with a weighting factor of 0.2.

[0097] The environmental benefit indicator is calculated as the sum of carbon emissions during material production and carbon emissions during construction, with a weighting coefficient of 0.1.

[0098] Secondly, the scores of each indicator category in the above embodiments are weighted and summarized to obtain the comprehensive score of each alternative structural scheme, specifically: comprehensive score = 0.4 × initial construction cost index score + 0.3 × operation and maintenance cost index score + 0.2 × long-term benefit index score + 0.1 × environmental benefit index score.

[0099] It should be noted that the output includes a multi-dimensional comparison of the alternative structural schemes, including:

[0100] Generate a comparison table of each alternative structural scheme under each indicator category;

[0101] Generate a comprehensive score ranking for each alternative structural scheme;

[0102] The BIM models of each alternative structural scheme are linked with the corresponding cost data to create a three-dimensional model display.

[0103] Specifically, after obtaining the bill of quantities for each alternative structural scheme, the system first multiplies each quantity in the bill of quantities by the real-time unit price data provided by the market price platform, summing the results to obtain the initial construction cost for each scheme. This initial cost is then divided by the corresponding building area to obtain the cost per unit area, which serves as the scoring basis for the initial construction cost indicator. Simultaneously, based on the building envelope thermal parameters, lighting area, and ventilation paths extracted from the BIM model, combined with meteorological data and energy prices at the industrial plant location, the system calculates and sums the annual heating, lighting, and ventilation energy consumption costs for each scheme under standard operating conditions, providing the scoring basis for the operation and maintenance cost indicator. Regarding the long-term benefit indicator, the system uses the initial investment difference between the scheme to be tested and the benchmark scheme as the numerator and the annual savings as the denominator to calculate the dynamic investment payback period, which serves as the scoring basis for this indicator. The environmental benefit index is calculated by the system multiplying the amount of each material in the bill of quantities by the corresponding value in the preset carbon emission factor database, and then summing the results to obtain the carbon emissions during the material production stage. This is then combined with construction simulation data to obtain the carbon emissions during the construction stage. The sum of the two is the basis for the score of this index.

[0104] After calculating the basic data across the four dimensions, the system normalizes the scores of each scheme under the same indicator. Then, it weights and sums the scores according to weights of 0.4, 0.3, 0.2, and 0.1 to obtain a comprehensive score for each alternative structural scheme. This weighting distribution is based on industry statistics of the distribution patterns of the entire life cycle cost of industrial plants in China. Construction costs account for approximately 40% to 50% of the total life cycle cost, while operation, energy consumption, and maintenance account for approximately 25% to 35%. Therefore, the weighting settings are largely consistent with the actual industry distribution. It is worth noting that the weighting coefficients are standardized parameters built into the system, allowing users to customize adjustments based on the specific project's focus.

[0105] Finally, in terms of results presentation, the system automatically generates a horizontal comparison table of each scheme under each indicator category and a comprehensive score ranking list. It also supports the linkage display of the BIM 3D model of each alternative structural scheme with the corresponding cost data. That is, when the user selects a component in the model view, the interface simultaneously displays the quantity of work and the corresponding cost information of that component, helping decision-makers to intuitively understand the differences between the schemes in terms of economy and structural layout.

[0106] Example 2

[0107] This invention provides a non-transitory computer-readable storage medium storing at least one instruction or at least one program segment, which is loaded and executed by a processor to implement the following steps:

[0108] Obtain BIM model data for at least two alternative structural schemes of the target industrial plant, perform component identification and classification processing on the BIM model data, extract the geometric parameters and material property parameters of each component, and automatically calculate the engineering quantity based on the extracted parameters to obtain the itemized engineering quantity list for each alternative structural scheme.

[0109] Obtain real-time market price data and historical price data, calculate the current cost of each alternative structural scheme based on the bill of quantities and real-time market price data, and make trend predictions on material prices based on historical price data to obtain the dynamic cost prediction results of each alternative structural scheme.

[0110] Based on the dynamic cost prediction results, multiple sensitive factors were selected for single-factor perturbation analysis, the sensitivity coefficient of each sensitive factor to the total cost was calculated, and a multi-factor joint probability simulation analysis was performed on each sensitive factor to obtain the sensitivity matrix of each alternative structural scheme.

[0111] Based on a pre-defined multi-dimensional cost comparison index system for the entire life cycle, the scores of each alternative structural scheme under each index category are calculated according to the bill of quantities for each item, dynamic cost prediction results and sensitivity matrix. The scores of each index category are weighted and summarized to obtain the comprehensive score of each alternative structural scheme, and the multi-dimensional comparison results of each alternative structural scheme are output.

[0112] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0113] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0114] Example 3

[0115] This invention provides an electronic device, including a processor and a memory, wherein the memory stores at least one instruction or at least one program segment, and the at least one instruction or the at least one program segment is loaded and executed by the processor to implement the following steps:

[0116] Obtain BIM model data for at least two alternative structural schemes of the target industrial plant, perform component identification and classification processing on the BIM model data, extract the geometric parameters and material property parameters of each component, and automatically calculate the engineering quantity based on the extracted parameters to obtain the itemized engineering quantity list for each alternative structural scheme.

[0117] Obtain real-time market price data and historical price data, calculate the current cost of each alternative structural scheme based on the bill of quantities and real-time market price data, and make trend predictions on material prices based on historical price data to obtain the dynamic cost prediction results of each alternative structural scheme.

[0118] Based on the dynamic cost prediction results, multiple sensitive factors were selected for single-factor perturbation analysis, the sensitivity coefficient of each sensitive factor to the total cost was calculated, and a multi-factor joint probability simulation analysis was performed on each sensitive factor to obtain the sensitivity matrix of each alternative structural scheme.

[0119] Based on a pre-defined multi-dimensional cost comparison index system for the entire life cycle, the scores of each alternative structural scheme under each index category are calculated according to the bill of quantities for each item, dynamic cost prediction results and sensitivity matrix. The scores of each index category are weighted and summarized to obtain the comprehensive score of each alternative structural scheme, and the multi-dimensional comparison results of each alternative structural scheme are output.

[0120] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A method for comparing and calculating the engineering cost of industrial plant structural schemes, characterized in that, The method includes the following steps: S01. Obtain BIM model data of at least two alternative structural schemes for the target industrial plant, perform component identification and classification processing on the BIM model data, extract the geometric parameters and material property parameters of each component, and automatically calculate the engineering quantity based on the extracted parameters to obtain the itemized engineering quantity list for each alternative structural scheme. S02. Obtain real-time market price data and historical price data, calculate the current cost of each alternative structural scheme based on the itemized bill of quantities and the real-time market price data, and predict the trend of material prices based on the historical price data to obtain the dynamic cost prediction results of each alternative structural scheme. S03. Based on the dynamic cost prediction results, select multiple sensitive factors to perform single-factor perturbation analysis, calculate the sensitivity coefficient of each sensitive factor to the total cost, and perform multi-factor joint probability simulation analysis on each sensitive factor to obtain the sensitivity matrix of each alternative structural scheme. S04. Based on the preset full life cycle multi-dimensional cost comparison index system, according to the itemized bill of quantities, the dynamic cost prediction results and the sensitivity matrix, calculate the score of each alternative structural scheme under each index category, weight and summarize the scores of each index category to obtain the comprehensive score of each alternative structural scheme, and output the multi-dimensional comparison results of each alternative structural scheme.

2. The method for comparing and calculating the engineering cost of industrial plant structural schemes according to claim 1, characterized in that, The step S01 of obtaining the itemized bill of quantities for each alternative structural scheme includes the following steps: S11. Classify and identify the BIM model data according to component type to obtain a data list; S12. Extract geometric parameters and material property parameters from the IfcProductDefinitionShape of each component based on the data list; S13. Based on the extracted geometric parameters and material property parameters, calculate the engineering quantity of each component according to the component type; S14. The calculated quantities of each component are automatically categorized according to the coding rules in the expert knowledge base to generate a list of sub-items of quantities. The list of sub-items of quantities is then checked for total quantity verification, logical verification, and manual comparison verification. The verified list of sub-items of quantities is then output. The types of components include steel columns, steel beams, steel roof trusses, concrete foundations, concrete columns, concrete beams, concrete slabs, roof panels, wall panels, and doors and windows. The geometric parameters include cross-sectional dimensions, length, and span; the material property parameters include steel grade and concrete grade. The quantities of work include the weight of steel, the volume of concrete, and the area of ​​the retaining components.

3. The method for comparing and calculating the engineering cost of industrial plant structural schemes according to claim 2, characterized in that, Step S13, which involves calculating the quantity of work for each component based on the extracted geometric parameters and material property parameters, according to the component type, includes: For the steel components, the volume of a single steel component is calculated based on the cross-sectional dimensions and length. The volume is then multiplied by the steel density to obtain the weight of a single steel component. The number of steel components of the same type is counted, and the total weight is calculated. For the concrete member, the volume of a single concrete member is calculated based on the cross-sectional dimensions, the number of concrete members of the same type is counted, and the total volume is calculated. For the aforementioned enclosure components, the roof panel area and wall panel area are calculated based on the span, column spacing, and eave height; For the steel structure connection nodes, the number of bolts and the length of welds are automatically identified and counted separately.

4. The method for comparing and calculating the engineering cost of industrial plant structure schemes according to claim 1, characterized in that, The step S02, which involves obtaining the dynamic cost prediction results for each alternative structural scheme, includes the following steps: S21. Obtain the real-time price data of the corresponding materials in the current market material list, multiply the quantities of each item in the bill of quantities by the real-time price of the corresponding materials, and summarize to obtain the current cost of each alternative structural scheme. S22. Obtain the monthly price series of various major materials over the past 3 to 5 years from the market price platform, establish an ARIMA time series model for the price of each type of material, and use the ARIMA model to make short-term trend predictions of material prices to obtain the monthly predicted prices and confidence intervals of each material in the next 3 to 6 months. S23. Based on the historical price data, an influencing factor is introduced to construct an LSTM neural network model. The LSTM neural network model is used to predict the medium- and long-term trends of material prices, and the predicted prices and confidence intervals of each material in the next 6 to 24 months are obtained. S24. Based on the short-term trend forecast results and the medium- and long-term trend forecast results, calculate the impact of material price changes on the total cost of each alternative structural scheme, and output the dynamic cost forecast results of each alternative structural scheme.

5. The method for comparing and calculating the engineering cost of industrial plant structural schemes according to claim 1, characterized in that, The process of obtaining the sensitivity matrix of each candidate structural scheme in step S03 includes the following steps: S31. Based on the benchmark cost of any alternative structural scheme, select multiple sensitive factors, including span, column spacing, eaves height, steel price, concrete price, and crane tonnage. S32. For each sensitive factor, perform item-by-item perturbation according to a preset change range, keep other sensitive factors as the baseline value, calculate the total cost change rate after the change of each sensitive factor, divide the total cost change rate by the corresponding sensitive factor change rate to obtain the sensitivity coefficient of each sensitive factor, sort all the sensitivity coefficients of the sensitive factors, and output the sensitivity ranking result. S33. Define the probability distribution type and distribution parameters of each sensitive factor, randomly sample each sensitive factor, obtain a set of sensitive factor values ​​each time, calculate the corresponding total cost based on the set of sensitive factor values, repeat the simulation multiple times to obtain the probability distribution of the total cost. S34. Calculate the contribution ratio of each sensitive factor to the total cost variance based on the total cost probability distribution, and generate a sensitivity matrix for each alternative structural scheme by combining the sensitivity ranking results and the contribution ratio.

6. The method for comparing and calculating the engineering cost of industrial plant structure schemes according to claim 1, characterized in that, The multi-dimensional cost comparison index system for the entire life cycle mentioned in step S04 includes initial construction cost indicators, operation and maintenance cost indicators, long-term benefit indicators, and environmental benefit indicators, among which: The initial construction cost index is calculated by dividing the total cost by the building area, with a weighting coefficient of 0.

4. The operation and maintenance cost index is calculated as the sum of heating energy consumption cost, lighting energy consumption cost, and ventilation energy consumption cost, with a weighting coefficient of 0.

3. The long-term benefit indicator is calculated by dividing the initial investment by the annual savings, with a weighting coefficient of 0.

2. The environmental benefit indicator is calculated as the sum of carbon emissions during material production and carbon emissions during construction, with a weighting coefficient of 0.

1.

7. The method for comparing and calculating the engineering cost of industrial plant structural schemes according to claim 1, characterized in that, In step S04, the scores of each indicator category are weighted and summarized to obtain the comprehensive score of each alternative structural scheme. Specifically, the comprehensive score is calculated as follows: Comprehensive score = 0.4 × initial construction cost index score + 0.3 × operation and maintenance cost index score + 0.2 × long-term benefit index score + 0.1 × environmental benefit index score.

8. The method for comparing and calculating the engineering cost of industrial plant structure schemes according to claim 1, characterized in that, The output of the multi-dimensional comparison results of each candidate structural scheme in step S04 includes: Generate a comparison table of each alternative structural scheme under each indicator category; Generate a comprehensive score ranking for each alternative structural scheme; The BIM models of each alternative structural scheme are linked with the corresponding cost data to create a three-dimensional model display.

9. A non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores at least one instruction or at least one program, characterized in that, The at least one instruction or the at least one program segment is loaded and executed by the processor to implement the steps of the method for comparing and calculating the engineering cost of industrial plant structure schemes as described in any one of claims 1-8.

10. An electronic device, characterized in that, The system includes a processor and a memory, wherein the memory stores at least one instruction or at least one program, and the at least one instruction or at least one program is loaded and executed by the processor to implement the steps of the method for comparing and calculating the engineering cost of industrial plant structure schemes as described in any one of claims 1-8.

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