Visual analysis and decision support method and system for project cost data

Abstract

The invention discloses a project cost data visualization analysis and decision support method and system, and relates to the technical field of project cost, and the method comprises the steps: carrying out the element extraction of a preliminary design drawing and a feasibility research report based on preset data elements, obtaining target data, and generating a preliminary cost estimation table based on the target data; constructing a basic model and associating the initial cost estimation table with components of the basic model to obtain an initial model; updating the initial cost estimation table and the initial model based on a dynamic recheck mechanism to obtain a current cost estimation table and a current model; performing cost deviation evaluation on the current cost estimation table based on a dynamic scoring mechanism to obtain a cost deviation evaluation result corresponding to the current cost estimation table; and generating a visual billboard based on the current model and the cost deviation evaluation result. According to the invention, coherent analysis of full-cycle data can be realized, so that the cost estimation deviation is reduced, and the accuracy of cost management and control is improved.

Description

Technical Field

[0001] This application relates to the field of engineering cost technology, and in particular to a method and system for engineering cost data visualization analysis and decision support. Background Technology

[0002] Construction project cost refers to the construction cost of a construction project. It is an important economic and technical indicator for measuring construction project costs. Controlling the cost of a construction project within a scientifically reasonable range is a complex and systematic project. Construction cost generally includes the comprehensive expenses required at each stage (e.g., investment decision-making, design, bidding, construction, and completion), spanning the entire construction project. However, traditional cost control models suffer from fragmented data, insufficient dynamism, and weak decision support, making it difficult to achieve coherent analysis of data throughout the entire lifecycle. This leads to significant deviations in cost estimation and affects the accuracy of cost control. Summary of the Invention

[0003] In view of the above-mentioned defects or deficiencies in the related technologies, the purpose of this application is to provide a method and system for engineering cost data visualization analysis and decision support, which can realize the coherent analysis of data throughout the entire cycle, so as to reduce cost prediction deviation and improve the accuracy of cost control.

[0004] To achieve the above objectives, this application provides the following solution:

[0005] Firstly, this application provides a method for visual analysis and decision support of engineering cost data. The method includes: extracting elements from preliminary design drawings and feasibility study reports based on preset data elements to obtain target data, and generating a preliminary cost estimation table based on the target data; constructing a basic model and associating the preliminary cost estimation table with the components of the basic model to obtain an initial model; updating the preliminary cost estimation table and the initial model based on a dynamic back-checking mechanism to obtain a current cost estimation table and a current model; the dynamic back-checking mechanism is a data collection process for due diligence back-checking the impact on costs at various nodes or preset periods during the construction phase; evaluating the cost deviation of the current cost estimation table based on a dynamic scoring mechanism to obtain a cost deviation evaluation result corresponding to the current cost estimation table; the dynamic scoring mechanism is a strategy for scoring the current cost estimation table at various nodes or preset periods during the construction phase; and generating a visual dashboard based on the current model and the cost deviation evaluation result, so that users can obtain the current engineering cost of each sub-item of the project based on the visual dashboard.

[0006] Optionally, the target data includes the quantities of sub-items, material specifications, and quota standards; generating a preliminary cost estimate table based on the target data includes: generating a basic cost estimate table based on the quantities of sub-items, the material specifications, and the quota standards; using a prediction model to predict material prices and labor costs within a preset time period to obtain material price curves and labor cost curves; calibrating the basic cost estimate table based on the average of the material price curves and the labor cost curves to obtain the preliminary cost estimate table.

[0007] Optionally, the step of constructing a basic model and associating the preliminary cost estimate with the components of the basic model to obtain an initial model includes: constructing the basic model using the BIM modeling tool Revit; and associating each component of the basic model with the corresponding sub-items of the preliminary cost estimate to obtain the initial model.

[0008] Optionally, updating the preliminary cost estimate table and the initial model based on the dynamic back-checking mechanism to obtain the current cost estimate table and the current model includes: performing due diligence back-checks on current material prices, construction efficiency, and change orders based on the dynamic back-checking mechanism to obtain the back-checking data; updating the corresponding data in the preliminary cost estimate table based on the back-checking data to obtain the current cost estimate table; and updating the valuation data of the corresponding components in the initial model based on the current cost estimate table to generate the current model.

[0009] Optionally, a cost deviation assessment is performed on the current cost estimate table based on a dynamic scoring mechanism to obtain a cost deviation assessment result corresponding to the current cost estimate table. This includes: assessing the cost deviation and schedule matching degree of the current cost estimate table to obtain a first assessment result; assessing the cost proportion of each component item, the rationality of main material consumption, and the frequency of change orders in the current cost estimate table to obtain a second assessment result; assessing the fluctuation range of material prices, the accuracy of quota application, and the deviation of human-machine efficiency in the current cost estimate table to obtain a third assessment result; and generating the cost deviation assessment result based on the first assessment result, the second assessment result, and the third assessment result.

[0010] Optionally, the visualization dashboard includes an overview layer, a drill-down layer, and a trend layer. The overview layer uses a Sankey diagram to show the flow of funds from preliminary estimate to budget to settlement. The overview layer uses a heatmap to show the cost percentage of each component. The drill-down layer allows users to drill down to a specific section or component of the current model by clicking on a dashboard element. The trend layer uses a line chart to show the cost prediction curve and the actual consumption curve.

[0011] Optionally, the method further includes: generating a resource allocation strategy based on the cost deviation assessment result, and sending the resource allocation strategy to the user terminal.

[0012] Optionally, the method further includes: splitting the resource allocation strategy into executable tasks and obtaining the execution results of the executable tasks; updating the current model based on the execution results to obtain the updated current model.

[0013] Secondly, this application provides a system for visualizing and analyzing engineering cost data and providing decision support, the system comprising:

[0014] The cost estimation module is used to extract elements from the preliminary design drawings and feasibility study report based on preset data elements, obtain target data, and generate a preliminary cost estimation table based on the target data.

[0015] The model building module is used to build a basic model and associate the preliminary cost estimate table with the components of the basic model to obtain an initial model;

[0016] The dynamic back-check module is used to update the preliminary cost estimate table and the initial model based on the dynamic back-check mechanism to obtain the current cost estimate table and the current model; the dynamic back-check mechanism is a data collection process that performs due diligence back-checks at various nodes or at preset cycles during the construction phase to check the impact of costs.

[0017] The deviation assessment module is used to assess the cost deviation of the current cost estimate table based on a dynamic scoring mechanism, and obtain the cost deviation assessment result corresponding to the current cost estimate table; the dynamic scoring mechanism is a strategy for scoring the current cost estimate table at each node of the construction stage or at a preset period.

[0018] The visualization generation module is used to generate a visualization dashboard based on the current model and the cost deviation assessment results, so that users can obtain the current project cost of each sub-item of the project based on the visualization dashboard.

[0019] Optionally, the engineering cost data visualization analysis and decision support system further includes:

[0020] The resource allocation strategy generation module is used to generate a resource allocation strategy based on the cost deviation assessment results and send the resource allocation strategy to the user terminal.

[0021] The strategy splitting and review module is used to split the resource allocation strategy into executable tasks and obtain the execution results of the executable tasks; based on the execution results, the current model is updated to obtain the updated current model.

[0022] According to the specific embodiments provided in this application, the following technical effects are disclosed:

[0023] This application provides a method and system for visual analysis and decision support of engineering cost data. By extracting target data from preliminary design drawings and feasibility study reports using preset data elements, a preliminary cost estimation table is generated based on this target data. This standardizes and refines the source data of cost estimates, reducing estimation errors caused by information omissions or biases at the source. It provides a reliable basis for subsequent initial model construction, dynamic data review, and cost deviation assessment. A dynamic review mechanism continuously updates the cost estimation table and initial model, ensuring real-time synchronization of data with actual project progress. This effectively captures the impact of dynamic factors such as material price fluctuations and changes in project quantities on costs during construction, upgrading cost management from static estimation to dynamic control. A dynamic scoring mechanism assesses cost deviations in the current cost estimation table, enabling timely identification of cost anomalies in sub-items and providing data support for risk warning and strategy adjustment. A visual dashboard presents the costs of each sub-item, allowing users to intuitively grasp cost distribution and deviations. This not only improves the transparency and decision-making efficiency of cost management but also enables full-process cost control, reducing the risk of cost overruns and improving the accuracy of cost control. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is an application environment diagram of a method for visualizing and analyzing engineering cost data and providing decision support in one embodiment of this application;

[0026] Figure 2 A flowchart illustrating a method for visual analysis and decision support of engineering cost data provided in an embodiment of this application;

[0027] Figure 3 A schematic diagram of the functional modules of an engineering cost data visualization analysis and decision support system provided in an embodiment of this application;

[0028] Figure 4 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0029] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0030] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0031] The engineering cost data visualization analysis and decision support method provided in this application embodiment can be applied to, for example... Figure 1 The application environment shown is illustrated. Terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be set up independently, integrated into server 104, or placed in the cloud or on another server. Terminal 102 can send preliminary design drawings and feasibility study reports to server 104. After receiving the preliminary design drawings and feasibility study reports, server 104 extracts elements from the preliminary design drawings and feasibility study reports based on preset data elements to obtain target data, and generates a preliminary cost estimate table based on the target data; constructs a basic model and associates the preliminary cost estimate table with the components of the basic model to obtain an initial model; updates the preliminary cost estimate table and the initial model based on a dynamic back-check mechanism to obtain the current cost estimate table and the current model; the dynamic back-check mechanism is the data collection process for due diligence back-checking the impact on costs at various nodes or preset periods during the construction phase; performs cost deviation assessment on the current cost estimate table based on a dynamic scoring mechanism to obtain the cost deviation assessment result corresponding to the current cost estimate table; the dynamic scoring mechanism is the strategy for scoring the current cost estimate table at various nodes or preset periods during the construction phase; and generates a visual dashboard based on the current model and cost deviation assessment result so that users can obtain the current project cost of each sub-item of the project based on the visual dashboard. In addition, in some embodiments, the engineering cost data visualization analysis and decision support method can also be implemented by the server 104 or the terminal 102 separately. For example, the terminal 102 can directly perform engineering cost data visualization analysis and decision support on the preliminary design drawings and feasibility study report, or the server 104 can obtain the preliminary design drawings and feasibility study report from the data storage system and perform engineering cost data visualization analysis and decision support on the preliminary design drawings and feasibility study report.

[0032] The terminal 102 can be, but is not limited to, various desktop computers, laptops, smartphones, tablets, etc. The server 104 can be implemented using a standalone server or a server cluster composed of multiple servers, or it can be a cloud server.

[0033] In one exemplary embodiment, such as Figure 2 As shown, a method for visual analysis and decision support of engineering cost data is provided. This method is executed by computer equipment, specifically by a terminal or server alone, or by both a terminal and a server. In this embodiment, the method is applied to... Figure 1 Taking server 104 as an example, the explanation includes the following steps S201 to S205. Wherein:

[0034] Step S201: Extract elements from the preliminary design drawings and feasibility study report based on preset data elements to obtain target data, and generate a preliminary cost estimate table based on the target data.

[0035] In the example implementation, the preset data elements include basic engineering elements, quantity-related elements, cost calculation elements, and process control elements. The basic engineering elements include basic project information and design standard requirements. Basic project information includes, for example: project name, construction location, engineering type (e.g., residential, municipal, industrial), structural form (e.g., frame, shear wall), and construction scale (building area, number of floors, height, etc.). Design standard requirements include, for example: seismic resistance rating, fire resistance rating, decoration standard (unfinished / simple / fully decorated), and energy-saving indicators (e.g., insulation material thickness, energy-saving rating).

[0036] The related elements of the project quantity include the division of sub-items and the technical parameters of components. The division of sub-items is, for example, by profession (architecture, structure, installation) to clarify sub-projects (such as foundation and main structure) and key sub-items (such as concrete components, reinforcement, wall masonry); the technical parameters of components are, for example, component dimensions (such as beam cross-section, column spacing, floor slab thickness), material type and specifications (such as concrete strength grade C30 / C40, steel reinforcement type HRB400, pipe diameter DN100), and construction process requirements (such as pumped concrete, hoisting of precast components).

[0037] Cost calculation elements include resource consumption standards, price-related information, and cost calculation rules. Resource consumption standards include, for example, the reference basis for the quota consumption of labor, materials, and machinery (such as the latest local quota number). Price-related information includes, for example, the specifications of main materials (such as steel bar diameter Φ12mm, floor tile size 600×600mm) and the price benchmark period (such as the market price in Q3 2025). Cost calculation rules include, for example, the calculation standards for management fees, profit base, regulatory fees, and taxes (such as the value-added tax rate of 9%).

[0038] Process control elements include key node parameters and risk-related conditions. Key node parameters include, for example, construction period (e.g., number of days for main structure construction) and cost control thresholds for each stage (e.g., deviation warning line ±10%). Risk-related conditions include, for example, geological conditions (e.g., foundation bearing capacity), climate impact (e.g., construction areas during the rainy season), and policy restrictions (e.g., environmental protection material requirements).

[0039] The target data includes the quantities of each component of the project, material specifications, and quota standards. It should be noted that, based on preset data elements, basic project data, quantity-related data, cost calculation data, and process control data are extracted to generate the quantities of each component of the project, material specifications, and quota standards corresponding to that project. The quota standards serve as the cost control benchmark for each node or stage of the project.

[0040] Optionally, the step S201 above, which generates a preliminary cost estimate table based on the target data, may include: generating a basic cost estimate table based on the quantities of sub-items, material specifications, and quota standards; using a prediction model to predict material prices and labor costs within a preset time period to obtain material price curves and labor cost curves; and calibrating the basic cost estimate table based on the average of the material price curves and labor cost curves to obtain a preliminary cost estimate table.

[0041] Understandably, the prediction model can be a neural network model, such as an LSTM time series model. The LSTM time series model is trained using historical similar projects to obtain a trained LSTM time series model. This trained LSTM time series model is then used to predict the material prices or labor costs of the current project, resulting in material price curves and labor cost curves. The preset time period can be a specific stage in the construction process or the entire project cycle from design to completion.

[0042] It should be noted that feasibility study reports are usually based on the engineering of similar historical projects. They use a trained LSTM time series model to predict material prices and labor costs, and calibrate the basic cost estimate table based on the average of the predicted values. This can improve the accuracy of the data and provide data support for subsequent monitoring of project costs.

[0043] Step S202: Construct a basic model and associate the preliminary cost estimate table with the components of the basic model to obtain the initial model.

[0044] In the example embodiment, the basic model is constructed including each component corresponding to the quantity of sub-items of the project, which provides support for subsequently marking the predicted cost, actual cost, project progress, and cost deviation assessment on each component.

[0045] Optionally, step S202 above may include: constructing a basic model using the BIM modeling tool Revit; associating each component of the basic model with the corresponding items in the preliminary cost estimate table to obtain an initial model.

[0046] It should be noted that in other embodiments, the initial model can be displayed layer by layer according to the hierarchical decomposition principle of "total project investment → individual project → unit project → sub-project → sub-item project", that is, the details such as the quantity of work, unit price, and reasons for deviation are displayed through the BIM model association.

[0047] Step S203: Update the preliminary cost estimate table and the initial model based on the dynamic back-checking mechanism to obtain the current cost estimate table and the current model.

[0048] In the example implementation, the dynamic review mechanism is a data collection process that involves due diligence and cost re-inspection at various stages of construction or at preset intervals. For example, at key stages such as the main structure's completion or masonry work, cost engineers conduct on-site reviews of change orders (such as wall openings or pipeline adjustments), supplementing detailed data such as "reason for change - responsible party - cost increase / decrease," and uploading this data to the server in real time via a mobile app. The preset intervals in the dynamic review mechanism, for example, involve collecting actual project quantities (such as the monthly steel reinforcement delivery volume and concrete pouring volume) and actual material purchase prices (connected to the supplier quotation system), comparing them with budget data to generate a quantity-price deviation table.

[0049] Optionally, step S203 may include: conducting due diligence and back-checking on current material prices, construction efficiency, and change orders based on a dynamic back-checking mechanism to obtain back-checking data; updating the corresponding data in the preliminary cost estimate table based on the back-checking data to obtain the current cost estimate table; and updating the valuation data of the corresponding components in the initial model based on the current cost estimate table to generate the current model.

[0050] It should be noted that the back-check data can be the current price data, price deviation data, cost data generated by manual scheduling, and cost data generated by construction efficiency.

[0051] Step S204: Based on the dynamic scoring mechanism, evaluate the cost deviation of the current cost estimate table to obtain the cost deviation evaluation result corresponding to the current cost estimate table.

[0052] In the example implementation, the dynamic scoring mechanism is a strategy for scoring the current cost estimate table for each node or preset period of the construction phase. Specifically, the dynamic scoring mechanism generates dynamic scoring rules based on three-level scoring indicators and constraints input by the cost engineer. For example, the first-level scoring indicators (weight 40%) are: cost deviation (budget vs. actual) and schedule matching (progress vs. cost payment); the second-level scoring indicators (weight 35%) are: cost proportion of sub-items, rationality of main material consumption, and frequency of change orders; and the third-level scoring indicators (weight 25%) are: material price fluctuation range, accuracy of quota application, and human-machine efficiency deviation. The constraint input by the cost engineer is that the deviation threshold for key projects is relaxed to ±8%. Therefore, the dynamic scoring mechanism assigns full marks for a deviation of ±5%, and deducts 2 points for each 1% exceeding this threshold.

[0053] It should be noted that in some embodiments, when any data of the three-level scoring indicators exceeds the threshold, an early warning mechanism is triggered, a special review is initiated for the early warning item, incremental data such as changes in market policies and design optimizations are added, and the dynamic scoring mechanism is updated.

[0054] Optionally, step S204 may include: evaluating the cost deviation and schedule matching of the current cost estimate to obtain a first evaluation result; evaluating the cost proportion of each item in the current cost estimate, the rationality of main material consumption, and the frequency of change orders to obtain a second evaluation result; evaluating the fluctuation range of material prices, the accuracy of quota application, and the deviation of human-machine efficiency in the current cost estimate to obtain a third evaluation result; and generating a cost deviation evaluation result based on the first, second, and third evaluation results.

[0055] Step S205: Generate a visual dashboard based on the current model and cost deviation assessment results, so that users can obtain the current project cost of each sub-item of the project based on the visual dashboard.

[0056] In the example implementation, the visualization dashboard includes an overview layer, a drill-down layer, and a trend layer. The overview layer uses a Sankey diagram to show the flow of funds from preliminary estimate to budget to settlement. The overview layer uses a heatmap to show the cost percentage of each component. The drill-down layer allows users to drill down to a specific section or component of the current model by clicking on a dashboard element. The trend layer uses a line chart to show the cost prediction curve and the actual consumption curve.

[0057] It's important to note that the visual dashboard presents project hierarchy using a tree structure or a collapsible list. When generating the visual dashboard, the dashboard page is built using front-end frameworks (such as Vue.js and React), and charts are drawn using visualization libraries (such as ECharts and D3.js) to achieve the "data → graph" conversion. For example, ECharts' bar component can be used to draw cost comparison charts, and the line component can be used to draw deviation trends. A lightweight BIM view is embedded, such as using Three.js to render a simplified model, enabling a linked effect where clicking on a sub-project in the visual dashboard highlights the corresponding component in the model, enhancing spatial awareness. The backend uses API interfaces, such as RESTful APIs, to retrieve and check data in real time, ensuring that the visual dashboard data is consistent with the current cost estimation table.

[0058] Steps S201 to S205 of this application extract target data from preliminary design drawings and feasibility study reports by pre-setting data elements, and generate a preliminary cost estimation table based on the target data. This achieves standardization and accuracy of cost source data, reducing estimation errors caused by information omissions or deviations at the source, and providing a reliable basis for subsequent initial model construction, dynamic data review, and cost deviation assessment. The dynamic review mechanism continuously updates the cost estimation table and initial model, ensuring real-time synchronization of data with actual project progress. This effectively captures the impact of dynamic factors such as material price fluctuations and changes in project quantities on costs during construction, upgrading cost management from static estimation to dynamic control. The dynamic scoring mechanism assesses cost deviations in the current cost estimation table, enabling timely identification of cost anomalies in sub-items and providing data support for risk warning and strategy adjustment. A visual dashboard presents the costs of each sub-item, allowing users to intuitively grasp cost distribution and deviations. This not only improves the transparency and decision-making efficiency of cost management but also enables full-process cost control, reducing the risk of cost overruns and improving the accuracy of cost control.

[0059] In another exemplary embodiment of this application, in order to improve the utilization rate of cost deviation assessment results and provide strategic support for users, the method further includes: generating a resource allocation strategy based on the cost deviation assessment results and sending the resource allocation strategy to the user terminal.

[0060] In the example implementation, the resource allocation strategy includes a material resource allocation strategy, a human resource allocation strategy, a machinery resource allocation strategy, and / or a management resource allocation strategy. For example, the material resource allocation strategy might involve: if the deviation is due to rising material prices, activating a pool of alternative suppliers and negotiating bulk purchase discounts (e.g., comparing prices with more than three suppliers to lock in a monthly purchase price); replacing materials with more cost-effective alternatives (e.g., using HRB500 steel bars instead of HRB400 to reduce usage and lower the overall cost); adjusting the procurement plan and adopting a "locked-in price procurement + tiered inventory" model for materials with large price fluctuations (e.g., steel, cement). If the deviation is due to excessive material loss, optimizing the material requisition process and implementing "limited material requisition + loss assessment" (e.g., issuing materials at 1.03 times the project quantity, with the construction team bearing 50% of the excess loss); strengthening on-site management and implementing "surplus material recycling and reuse" for steel bars, formwork, etc. (e.g., welding short steel bars to extend them).

[0061] Human resource allocation strategies, for example, if the deviation is due to the overspending of labor unit price, negotiate with the labor service company a "lump-sum price + performance bonus" model (such as agreeing on a template work unit price of 35 yuan / m). 2 A 5% bonus will be awarded for early completion; in-house skilled workers will be reassigned to replace high-priced outsourced teams, with priority given to stable, long-term cooperative teams (with minimal price fluctuations). If deviations are caused by increased downtime, the construction process will be optimized (e.g., completing the steel reinforcement binding and acceptance 3 days ahead of schedule to avoid waiting for the concrete team); the number of workers will be dynamically adjusted according to the progress plan (e.g., adding 20 carpenters during the main structure phase and reducing 10 workers during the masonry phase).

[0062] For example, if deviations are due to overdue machinery rentals, the machinery usage plan should be rescheduled, and the time spent on machinery in non-critical processes should be reduced (e.g., tower cranes should be prioritized for the main structure, and truck cranes should be used for assistance during the masonry stage); a "flexible rental period + reduced overdue rate" contract should be signed with the rental company (e.g., the original price should be charged if the rental period exceeds the plan by 5 days, and 80% of the rate should be charged if it exceeds 5 days). If deviations are due to low machinery efficiency, machinery maintenance should be strengthened (e.g., tower cranes should be inspected weekly to reduce downtime); old equipment should be replaced (e.g., new concrete pump trucks should be used to replace old equipment with high fuel consumption to reduce per-shift energy costs).

[0063] For example, management resource allocation strategies include establishing a "Rapid Response Team for Change Orders" if deviations are due to design changes or delays in approvals, requiring cost calculations to be completed within 48 hours of the change; implementing a "tiered change approval system" (minor changes (<50,000 RMB) require final review by the project manager, while major changes are reported to the client for record-keeping) to prevent changes from spiraling out of control. If deviations are due to delays in cost monitoring, increasing the frequency of re-inspections for high-risk items (e.g., from once a week to once every three days), dynamically updating cost estimates, and providing early warnings of potential cost overruns.

[0064] By generating resource allocation strategies, we can enhance the ability to proactively correct cost deviations and reduce the risk of cost overruns; and by enhancing risk prediction capabilities, we can improve the project's resilience to risks.

[0065] In another exemplary embodiment of this application, in order to obtain data changes generated based on resource allocation strategies in a timely manner, the method further includes: splitting the resource allocation strategy into executable tasks and obtaining the execution results of the executable tasks; updating the current model based on the execution results to obtain the updated current model.

[0066] In the example implementation, the resource allocation strategy is broken down into executable tasks. For example, for the strategy of increasing the purchase price of steel bars, it is broken down into "completing the price comparison of 3 suppliers within 3 days (task 1)", "signing a new purchase contract before August 15 to lock in a unit price of ≤4800 yuan / ton (task 2)" and "establishing a steel bar loss ledger and recording the material requisition and actual usage daily (task 3)".

[0067] For example, the strategy to increase idle time is broken down into "optimizing the main structure process connection plan and clarifying that the interval between steel bar binding and concrete pouring is ≤24 hours (Task 1)" and "adjusting the number of carpentry team members to 25 people before August 10 to reduce idle people by 10 people (Task 2)".

[0068] For example, the strategy for overdue tower crane rentals can be broken down into "rescheduling the tower crane usage plan and reducing the daily occupation by 2 hours during the masonry phase (Task 1)" and "negotiating a flexible rental contract with the lessor and clarifying the overdue billing discount (Task 2)".

[0069] For example, the strategy of delaying change orders can be broken down into "establishing a change response team and ensuring that cost calculation is completed within 48 hours (Task 1)" and "summarizing the implementation of change orders every Friday and forming a deviation analysis report (Task 2)".

[0070] It should be noted that in other embodiments, tasks can also be broken down according to their attributes. For example, each task should include elements such as "task ID, name, responsible department / person, start and end time, core indicators (quantitative standards), and deliverables". For example, task ID "CL-001": name: steel bar supplier price comparison, responsible department: purchasing department, time: August 5th - August 8th, core indicator: "collect quotations from ≥3 suppliers, with the lowest quotation ≤ 4800 yuan / ton", deliverables: "price comparison table + supplier qualification documents".

[0071] By updating the current model through task execution results, it is possible to ensure that the current model parameters (such as the unit price of steel bars, the quantity of labor, and the number of machine shifts) are synchronized with the actual execution status, forming a linkage mechanism between execution results and model updates. This allows cost control to be upgraded from static accounting to dynamic adaptation, significantly improving the accuracy and timeliness of project cost management and effectively solving problems such as data fragmentation, insufficient dynamism, and weak decision support in traditional cost control models.

[0072] Based on the same inventive concept, this application also provides an engineering cost data visualization analysis and decision support system for implementing the above-mentioned engineering cost data visualization analysis and decision support method. The solution provided by this system is similar to the implementation scheme described in the above method. Therefore, the specific limitations of one or more engineering cost data visualization analysis and decision support system embodiments provided below can be found in the limitations of the engineering cost data visualization analysis and decision support method described above, and will not be repeated here.

[0073] In one exemplary embodiment, such as Figure 3 As shown, an engineering cost data visualization analysis and decision support system is provided. The engineering cost data visualization analysis and decision support system 300 includes: a cost estimation module 301, a model building module 302, a dynamic backtesting module 303, a deviation evaluation module 304, and a visualization generation module 305; wherein:

[0074] The cost estimation module 310 is used to extract elements from the preliminary design drawings and feasibility study report based on preset data elements, obtain target data, and generate a preliminary cost estimation table based on the target data.

[0075] The model building module 302 is used to build a basic model and associate the preliminary cost estimate table with the components of the basic model to obtain an initial model;

[0076] The dynamic back-check module 303 is used to update the preliminary cost estimate table and the initial model based on the dynamic back-check mechanism to obtain the current cost estimate table and the current model; the dynamic back-check mechanism is the data collection process for due diligence back-checking the impact on costs at various nodes or preset cycles during the construction phase;

[0077] The deviation assessment module 304 is used to assess the cost deviation of the current cost estimate table based on the dynamic scoring mechanism, and obtain the cost deviation assessment result corresponding to the current cost estimate table; the dynamic scoring mechanism is a strategy to score the current cost estimate table for each node of the construction stage or a preset period;

[0078] The visualization generation module 305 is used to generate a visualization dashboard based on the current model and the cost deviation assessment results, so that users can obtain the current project cost of each sub-item of the project based on the visualization dashboard.

[0079] As an optional implementation, the target data includes the quantities of sub-items, material specifications, and quota standards; the aforementioned cost estimation module 310 is specifically used to generate a basic cost estimation table based on the quantities of sub-items, material specifications, and quota standards; to predict the material prices and labor costs within a preset time period using a prediction model, thereby obtaining material price curves and labor cost curves; and to calibrate the basic cost estimation table based on the average of the material price curves and labor cost curves, thereby obtaining a preliminary cost estimation table.

[0080] As an optional implementation, the model building module 302 is specifically used to build a basic model using the BIM modeling tool Revit; and to associate each component of the basic model with the corresponding items in the preliminary cost estimate table to obtain an initial model.

[0081] As an optional implementation, the aforementioned dynamic back-check module 303 is specifically used to: perform due diligence back-checks on current material prices, construction efficiency, and change orders based on the dynamic back-check mechanism to obtain back-check data; update the corresponding data in the preliminary cost estimate table based on the back-check data to obtain the current cost estimate table; and update the valuation data of the corresponding components in the initial model based on the current cost estimate table to generate the current model.

[0082] As an optional implementation, the aforementioned deviation assessment module 304 is specifically used to: assess the cost deviation and schedule matching degree of the current cost estimate to obtain a first assessment result; assess the cost proportion of each component item, the rationality of main material consumption, and the frequency of change orders in the current cost estimate to obtain a second assessment result; assess the fluctuation range of material prices, the accuracy of quota application, and the deviation of human-machine efficiency in the current cost estimate to obtain a third assessment result; and generate a cost deviation assessment result based on the first, second, and third assessment results.

[0083] As an optional implementation, the aforementioned visualization dashboard includes an overview layer, a drill-down layer, and a trend layer. The overview layer uses a Sankey diagram to show the flow of funds from preliminary estimate to budget to settlement. The overview layer uses a heatmap to show the cost percentage of each component. The drill-down layer allows users to drill down to a specific section or component of the current model by clicking on dashboard elements. The trend layer uses a line chart to show the cost prediction curve and the actual consumption curve.

[0084] As an optional implementation, the above-mentioned engineering cost data visualization analysis and decision support system further includes a resource allocation strategy generation module and a strategy splitting and review module; wherein, the resource allocation strategy generation module is used to generate resource allocation strategies based on the cost deviation assessment results and send the resource allocation strategies to the user terminal; the strategy splitting and review module is used to split the resource allocation strategy into executable tasks and obtain the execution results of the executable tasks; and update the current model based on the execution results to obtain the updated current model.

[0085] This implementation method involves extracting target data from preliminary design drawings and feasibility study reports using preset data elements. A preliminary cost estimate table is then generated based on this target data, achieving standardization and accuracy of cost source data. This reduces estimation errors caused by information omissions or deviations at the source, providing a reliable basis for subsequent initial model construction, dynamic data review, and cost deviation assessment. The dynamic review mechanism continuously updates the cost estimate table and initial model, ensuring real-time synchronization of data with actual project progress. This effectively captures the impact of dynamic factors such as material price fluctuations and changes in project quantities on costs during construction, upgrading cost management from static estimation to dynamic control. The dynamic scoring mechanism assesses cost deviations in the current cost estimate table, promptly identifying cost anomalies in sub-items and providing data support for risk warnings and strategy adjustments. A visual dashboard presents the costs of each sub-item, allowing users to intuitively grasp cost distribution and deviations. This not only improves the transparency and decision-making efficiency of cost management but also enables full-process cost control, reducing the risk of cost overruns and improving the accuracy of cost control.

[0086] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 4As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores data for engineering cost data visualization analysis and decision support. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for engineering cost data visualization analysis and decision support.

[0087] Those skilled in the art will understand that Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0088] In one exemplary embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0089] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0090] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0091] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0092] Those skilled in the art will understand that all or part of the processes in the methods of 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, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0093] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchain. The processors involved in the embodiments provided in this application may be, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc.

[0094] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0095] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for visual analysis and decision support of engineering cost data, characterized in that, The methods for visualizing and analyzing engineering cost data and providing decision support include: Based on preset data elements, the preliminary design drawings and feasibility study report are used to extract elements to obtain target data, and a preliminary cost estimate table is generated based on the target data. A basic model is constructed and the preliminary cost estimate table is associated with the components of the basic model to obtain an initial model; The preliminary cost estimate table and the initial model are updated based on a dynamic back-checking mechanism to obtain the current cost estimate table and the current model; the dynamic back-checking mechanism is a data collection process that involves due diligence and back-checking the impact on costs at various nodes or preset periods during the construction phase. The current cost estimation table is evaluated based on a dynamic scoring mechanism to obtain the cost deviation evaluation result corresponding to the current cost estimation table; the dynamic scoring mechanism is a strategy to score the current cost estimation table for each node or preset period of the construction stage. A visual dashboard is generated based on the current model and the cost deviation assessment results, so that users can obtain the current project cost of each sub-item of the project based on the visual dashboard.

2. The method for visual analysis and decision support of engineering cost data according to claim 1, characterized in that, The target data includes the quantities of each item of work, material specifications, and quota standards; the generation of a preliminary cost estimate table based on the target data includes: Based on the quantities of the sub-items, the material specifications, and the quota standards, a basic cost estimation table is generated. The material price and labor cost within a preset time period are predicted using a prediction model, resulting in material price curves and labor cost curves. The basic cost estimate table is calibrated based on the average of the material price curve and the labor cost curve to obtain the preliminary cost estimate table.

3. The method for visual analysis and decision support of engineering cost data according to claim 2, characterized in that, The process of constructing a basic model and associating the preliminary cost estimate table with the components of the basic model to obtain an initial model includes: The basic model is constructed using the BIM modeling tool Revit; The initial model is obtained by associating each component of the basic model with the corresponding sub-items of the preliminary cost estimate table.

4. The method for visual analysis and decision support of engineering cost data according to claim 1, characterized in that, The step of updating the preliminary cost estimate table and the initial model based on the dynamic back-checking mechanism to obtain the current cost estimate table and the current model includes: Based on the dynamic back-check mechanism, due diligence back-checks are performed on current material prices, construction efficiency, and change orders to obtain the back-check data; The corresponding data in the preliminary cost estimate table is updated based on the back-check data to obtain the current cost estimate table; The current model is generated by updating the estimated data of the corresponding components of the initial model based on the current cost estimation table.

5. The method for visual analysis and decision support of engineering cost data according to claim 1, characterized in that, A cost deviation assessment is performed on the current cost estimate table based on a dynamic scoring mechanism to obtain the cost deviation assessment result corresponding to the current cost estimate table, including: The cost deviation and schedule matching of the current cost estimate are evaluated to obtain the first evaluation result; The cost percentage of each item in the current cost estimate, the rationality of main material consumption, and the frequency of change orders are evaluated to obtain a second evaluation result. The fluctuation range of material prices, the accuracy of quota application, and the deviation of human-machine efficiency in the current cost estimate table are evaluated to obtain a third evaluation result; The cost deviation assessment result is generated based on the first assessment result, the second assessment result, and the third assessment result.

6. The method for visual analysis and decision support of engineering cost data according to claim 1, characterized in that, The visualization dashboard includes an overview layer, a drill-down layer, and a trend layer. The overview layer uses a Sankey diagram to show the flow of funds from preliminary estimate to budget to settlement. The overview layer also uses a heatmap to show the cost percentage of each component. The drill-down layer allows users to drill down to specific sections or components of the current model by clicking on dashboard elements; the trend layer presents the cost prediction curve and the actual consumption curve through a line chart.

7. The method for visual analysis and decision support of engineering cost data according to claim 1, characterized in that, The method further includes: Based on the cost deviation assessment results, a resource allocation strategy is generated and sent to the user terminal.

8. The method for visual analysis and decision support of engineering cost data according to claim 7, characterized in that, The method further includes: The resource allocation strategy is broken down into executable tasks, and the execution results of the executable tasks are obtained; The current model is updated based on the execution result to obtain the updated current model.

9. A system for visual analysis and decision support of engineering cost data, characterized in that, The engineering cost data visualization analysis and decision support system includes: The cost estimation module is used to extract elements from the preliminary design drawings and feasibility study report based on preset data elements, obtain target data, and generate a preliminary cost estimation table based on the target data. The model building module is used to build a basic model and associate the preliminary cost estimate table with the components of the basic model to obtain an initial model; The dynamic back-check module is used to update the preliminary cost estimate table and the initial model based on the dynamic back-check mechanism to obtain the current cost estimate table and the current model; the dynamic back-check mechanism is a data collection process that performs due diligence back-checks at various nodes or at preset cycles during the construction phase to check the impact of costs. The deviation assessment module is used to assess the cost deviation of the current cost estimate table based on a dynamic scoring mechanism, and obtain the cost deviation assessment result corresponding to the current cost estimate table; the dynamic scoring mechanism is a strategy for scoring the current cost estimate table at each node of the construction stage or at a preset period. The visualization generation module is used to generate a visualization dashboard based on the current model and the cost deviation assessment results, so that users can obtain the current project cost of each sub-item of the project based on the visualization dashboard.

10. The engineering cost data visualization analysis and decision support system according to claim 9, characterized in that, The engineering cost data visualization analysis and decision support system also includes: The resource allocation strategy generation module is used to generate a resource allocation strategy based on the cost deviation assessment results and send the resource allocation strategy to the user terminal. The strategy splitting and review module is used to split the resource allocation strategy into executable tasks and obtain the execution results of the executable tasks; based on the execution results, the current model is updated to obtain the updated current model.

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