Engineering cost risk control management system and method

Abstract

The invention discloses a project cost risk control management system and method, and the system comprises a potential risk analysis module which is used for recognizing project potential risks through historical project data mining, BIM three-dimensional model analysis and dynamic simulation; the risk probability evaluation module is used for calculating a risk influence index in combination with a three-dimensional dynamic simulation result, and the risk grade evaluation module is used for determining a risk grade based on a risk probability coefficient, the risk influence index and a risk propagation path; a dynamic simulation module; a risk response module; and a display terminal. Through historical data, a BIM model and dynamic simulation, potential risks such as spatial layout and construction processes are captured, and the coverage rate is high. Multi-dimensional data such as industry indexes and supplier scores are fused, the risk probability and the influence index are calculated through a formula, and subjective judgment is avoided. Risks are divided into high, medium and low levels in combination with risk propagation path complexity, and resources are preferentially projected to the high risk.

Description

Technical Field

[0001] The present invention relates to the technical field of engineering cost risk control, and in particular to an engineering cost risk control management system and method. Background Art

[0002] In the implementation of modern engineering projects, project cost risk management is a critical step in ensuring smooth project progress and controlling costs and schedules. As projects continue to expand in scale and cover a wider range of specialized fields, the risks faced during project implementation are becoming increasingly complex and diverse. Potential risks such as cost overruns, project delays, and substandard technical performance can have a serious impact on the overall project performance. Therefore, establishing a systematic and scientific project cost risk control management system is of great practical significance.

[0003] Existing engineering cost risk control and management solutions suffer from numerous deficiencies in practical application. For one thing, the lack of systematic analytical methods and procedures for potential risk identification makes it difficult to fully capture the full range of potential risks that may arise during project implementation. For example, traditional methods often rely solely on historical data or expert experience for risk identification, failing to fully incorporate the project's spatial layout, construction process, and other specific characteristics, resulting in insufficient coverage and accuracy in risk identification.

[0004] On the other hand, the risk assessment process lacks objective evaluation mechanisms and quantitative models. Most existing technologies fail to fully integrate multi-dimensional data such as industry risk indices, supplier stability, and market dynamics. This results in insufficient data support for risk assessment results, making it difficult to accurately reflect the actual risk situation. Furthermore, there is a lack of in-depth analytical methods and standards for assessing the impact of risks, making it impossible to fully understand the specific impact of different risks on project objectives such as cost, schedule, and technical performance, which in turn hinders the formulation and implementation of risk response strategies.

[0005] Furthermore, existing risk management and monitoring solutions lack an effective evaluation mechanism for risk response measures, making it difficult to optimize and iterate risk management measures, leading to wasted resources and accumulated risks. Furthermore, traditional risk monitoring methods lack intuitiveness and real-time capabilities, failing to promptly and accurately display the evolution of risks and their scope of impact, hindering project teams from making scientific and rational decisions.

[0006] To sum up, the existing engineering cost risk control management technology has obvious deficiencies in the comprehensiveness of risk identification, the scientific nature of assessment, the effectiveness of response, and the real-time nature of monitoring. It is urgent to propose a new engineering cost risk control management system and method that can integrate multi-dimensional data, combine dynamic simulation, and realize accurate risk assessment and scientific response. Therefore, this plan proposes an engineering cost risk control management system and method. Summary of the Invention

[0007] The purpose of the present invention is to solve at least one of the technical problems existing in the prior art, and to provide a construction cost risk control management system and method, which captures potential risks such as spatial layout and construction process through historical data, BIM models and dynamic simulation, with high coverage. Integrating multi-dimensional data such as industry indexes and supplier ratings, the risk probability and impact index are calculated using formulas, saying goodbye to subjective judgment. Combined with the complexity of the risk propagation path, risks are divided into high, medium and low levels, and resources are invested in high risks first. The effectiveness of strategies is verified through dynamic simulation, such as purchasing insurance, optimizing construction procedures, etc., to reduce risk losses. The three-dimensional model visualizes the risk area, and abnormalities such as displacement exceeding the limit trigger graded warnings in real time, and the disposal plan is pushed simultaneously. A knowledge base is constructed based on historical cases, and model parameters are dynamically adjusted according to the deviation of new projects to improve evaluation accuracy.

[0008] The present invention also provides the above-mentioned engineering cost risk control management system comprising:

[0009] Potential risk analysis module, used to identify potential project risks through historical project data mining, BIM 3D model analysis and dynamic simulation;

[0010] Risk Assessment Unit, including:

[0011] The risk probability assessment module is used based on the industry risk index, supplier stability score, historical risk probability and market volatility coefficient, using the following formula:

[0012]

[0013] Calculate the risk probability coefficient, where Pr i is the probability of historical risk occurrence, W is the supplier stability score, k is the market volatility coefficient, Pr' and W' are standard control values, and t1, t2, and t3 are weight factors;

[0014] The risk impact assessment module is used to combine the three-dimensional dynamic simulation results through the following formula:

[0015]

[0016] Calculate the risk impact index, where M i 、T i 、J i 、R i are cost overrun amount, construction delay duration, technical performance degradation value, and risk propagation range parameter, respectively. μ1, μ2, μ3, and μ4 are weight factors.

[0017] Risk level assessment module, used to determine the risk level based on risk probability coefficient, impact index and risk propagation path;

[0018] Dynamic simulation module, used to build a three-dimensional model of the project and output risk propagation range parameters;

[0019] Risk response module, used to generate and verify risk response strategies based on simulation results;

[0020] Display terminal, used for three-dimensional visualization of risk levels and evolution processes.

[0021] According to the engineering cost risk control management system provided by the present invention, the potential risk analysis module generates risk scenarios through historical data mining, BIM model space layout analysis and Monte Carlo simulation.

[0022] According to the engineering cost risk control management system provided by the present invention, the supplier stability scoring Among them S j Including financial health, on-time delivery rate and other dimension scores, w j is the corresponding weight factor.

[0023] The risk propagation range parameter R of the risk impact assessment module of the engineering cost risk control management system provided by the present invention is i Obtained through 3D model analysis of the dynamic simulation module, including:

[0024] Identification of risk diffusion paths;

[0025] Calculation of the number of work packages affected;

[0026] Chain reaction intensity assessment.

[0027] According to the engineering cost risk control management system provided by the present invention, the risk level assessment module determines the risk level through the following steps to calculate the comprehensive risk value F i =F pi ·F yi and the propagation path complexity C i Divide into high, medium and low risk levels i =f(F i ,C i ).

[0028] According to the present invention, the dynamic simulation module of the engineering cost risk control management system includes:

[0029] 3D model building unit, used to create geometric and semantic models of the project;

[0030] Risk injection unit, used to simulate the triggering of specific risk events;

[0031] Evolutionary analysis unit, used to track the propagation of risks in the project;

[0032] The parameter output unit is used to extract quantitative indicators such as the impact range and duration.

[0033] According to the present invention, the risk response module of the engineering cost risk control management system includes:

[0034] A strategy generation unit, used to automatically recommend response measures based on risk levels;

[0035] Effect verification unit, used to verify the effectiveness of response strategies through dynamic simulation;

[0036] Resource optimization unit to allocate risk response resources and minimize total costs.

[0037] According to the present invention, a method for controlling and managing project cost risks is provided, comprising:

[0038] Potential risk identification: Generate a risk list by combining historical data, BIM models and dynamic simulation;

[0039] Risk probability calculation: Substitute multidimensional data to calculate the risk probability coefficient;

[0040] Risk impact assessment: Obtain impact index and spread range through three-dimensional simulation;

[0041] Risk level determination: based on comprehensive risk value and transmission path;

[0042] Response strategy formulation: Generate strategies and verify their effectiveness through simulation;

[0043] Real-time monitoring and early warning: 3D visualization displays risk status and triggers graded early warnings.

[0044] According to a method for controlling and managing engineering cost risks provided by the present invention, in the response strategy formulation step, avoidance and mitigation strategies are preferentially adopted for high-risk events, while transfer and acceptance strategies are adopted for medium and low-risk events, and the cost-benefit ratio is compared through simulation.

[0045] According to the present invention, a method for engineering cost risk control and management also includes risk knowledge base construction and model dynamic optimization, training and evaluating models through historical data and continuously updating response strategies.

[0046] Beneficial effects

[0047] Compared with the existing technology, the engineering cost risk control management system and method of the present invention captures potential risks such as spatial layout and construction process through historical data, BIM models and dynamic simulation, with a high coverage rate. It integrates multi-dimensional data such as industry indexes and supplier ratings, and uses formulas to calculate risk probability and impact index, saying goodbye to subjective judgment. Combined with the complexity of the risk propagation path, risks are divided into high, medium and low levels, and resources are prioritized for high risks. The effectiveness of strategies is verified through dynamic simulation, such as purchasing insurance, optimizing construction processes, etc., to reduce risk losses. The three-dimensional model visualizes risk areas, and abnormalities such as displacement exceeding the limit trigger graded warnings in real time, and disposal plans are pushed simultaneously. A knowledge base is constructed based on historical cases, and model parameters are dynamically adjusted according to new project deviations to improve evaluation accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The present invention will be further described below with reference to the accompanying drawings and embodiments;

[0049] Figure 1 This is an architecture diagram of a project cost risk control management system according to the present invention;

[0050] Figure 2 This is a flow chart of a project cost risk control management method of the present invention. DETAILED DESCRIPTION

[0051] This section will describe in detail the specific embodiments of the present invention. The preferred embodiments of the present invention are shown in the accompanying drawings. The purpose of the accompanying drawings is to supplement the description of the text part of the specification with graphics, so that people can intuitively and vividly understand each technical feature and the overall technical solution of the present invention, but it should not be understood as a limitation on the scope of protection of the present invention.

[0052] Reference Figure 1 、 2 The embodiment of the present invention provides a construction cost risk control management system and method, which is built and initialized through the following steps:

[0053] Deployment of the potential risk analysis module: The system integrates historical data mining, BIM 3D analysis, and dynamic simulation. For example, for bridge projects, the historical data mining module extracts historical risk data for similar bridge projects (such as foundation treatment risks and concrete cracking risks) from the database. Simultaneously, the BIM 3D analysis module imports the project's BIM model to identify construction space risks at complex nodes in the bridge structure (such as the connection between piers and beams).

[0054] Dynamic Simulation Module Construction: Utilizing tools like Revit, the project's 3D model is constructed, defining the geometric parameters, material properties, and construction sequence of each component. For example, in a subway station project, the Dynamic Simulation Module generates a 3D scene of the station's main structure, ancillary facilities, and surrounding environment based on the BIM model, and sets the logical relationships between the various construction phases (e.g., "construction of retaining structures → excavation → pouring of the main structure").

[0055] Risk Assessment Unit Parameter Configuration: Set the weighting factors in the risk probability assessment formula. For example, for a decoration project with frequent material price fluctuations, set t1 = 0.3 (historical risk probability weight), t2 = 0.2 (supplier stability weight), and t3 = 0.5 (market volatility coefficient weight) to highlight the impact of market dynamics on risk.

[0056] Potential risk identification and analysis: The system identifies potential risks through multi-dimensional data fusion. The specific process is as follows:

[0057] Historical data mining: This system filters historical projects of the same type as the specified project from the construction cost risk management database. For example, for a high-rise residential project, risk records for similar projects from the past five years will be retrieved to identify historical risk types such as "rising steel bar prices" and "delays in exterior wall insulation construction." If a risk type appears more than a set threshold (e.g., three times), it will be included in the first part of the potential risks.

[0058] BIM 3D model analysis: Spatial layout risk identification based on BIM models. For example, in a hospital project, the BIM model revealed that the logistics corridor between the operating room and the ICU was too narrow, potentially leading to delays in the transportation of medical equipment. This identified "logistics corridor design flaws" as the second potential risk.

[0059] Dynamic simulation risk scenario generation: Monte Carlo simulation is used to generate potential risk scenarios. For example, for a commercial complex project, a scenario involving extreme weather causing a failure of the foundation pit dewatering system was simulated to analyze its impact on the excavation schedule, and the dewatering system failure was included in the third potential risk component.

[0060] Risk probability assessment and formula application, taking the "steel supply interruption" risk of an industrial plant project as an example, the risk probability coefficient calculation process is explained: Data collection: Historical risk probability Pr i : The historical probability of steel supply disruption in similar plant projects is 25%.

[0061] Supplier stability score W: by formula Calculation, where: financial health w1 = 0.3, S1 = 80 points), on-time delivery rate w2 = 0.25, S2 = 75 points), product quality (w3 = 0.2, S3 = 90 points), customer evaluation (w4 = 0.15, S24 = 85, points), and historical cooperation record (w5 = 0.1, S5 = 95 points). Calculated: W = 0.3 × 80 + 0.25 × 75 + 0.2 × 90 + 0.15 × 85 + 0.1 × 95 = 83 points. Market volatility coefficient k: The ratio of the standard deviation of recent steel price fluctuations to the mean is 0.15.

[0062] Probability coefficient calculation: Substitute into the formula Where Pr' = 20% (standard control probability), W' = 80% (standard control score), set t1 = 0.4, t2 = 0.3, t3 = 0.3, then:

[0063] That is, the probability coefficient of this risk is 0.858, indicating that the probability of occurrence is high.

[0064] Risk impact assessment and dynamic simulation, taking the "bearing installation deviation" risk of a bridge project as an example, the impact index calculation process is explained: Dynamic simulation implementation:

[0065] The risk event of "support installation deviation of 5mm" was injected into the dynamic simulation module to simulate its impact on the subsequent bridge deck pavement and expansion joint installation: the bridge deck pavement needed to be reworked, resulting in a cost overrun of 120,000 yuan (M i = 120,000 yuan); construction period delayed by 7 days (T i =7 days); bridge bearing capacity decreases by 2% (J i =2%)); the scope of risk transmission involves 3 construction sections (R i =3). Impact index calculation:

[0066] Substitute into the formula Among them, M i =100,000 yuan, T i =5 days, J i =1%, R i =2, set μ1 = 0.4, μ2 = 0.3, μ3 = 0.2, μ4 = 0.1, then:

[0067] That is, the impact index of this risk is 1.45, indicating that it has a significant impact on the project.

[0068] Risk level determination and strategy generation, comprehensive risk value calculation: by F i =F pi ·F yi , if the F of a risk pi =0.8, i yi =1.2, then F i =0.96. Propagation path complexity evaluation:

[0069] Through dynamic simulation, it is found that the risk propagation path involves 5 work packages (P i =5), the chain reaction level is level 3 (H i =3), substitute (Assume α = 0.4, γ = 3, P' = 2), we get:

[0070] Risk level mapping: based on the mapping function L i =f(F i ,C i ), set when F i >0.8 and C i When it is >1.5, it is considered high risk, so the risk is classified as high risk level.

[0071] Response strategy generation: For high-risk events, the system automatically recommends a "risk transfer" strategy, such as purchasing engineering insurance, and verifies through dynamic simulation: after purchasing insurance, if the risk occurs, 80% of the loss cost can be transferred, reducing the comprehensive impact index to 0.7 and the risk level to medium risk.

[0072] Dynamic simulation verification and strategy optimization take the "interval tunnel collapse" risk of a subway project as an example:

[0073] Initial strategy setting: The initial response strategy is to "increase support strength", with an estimated cost of 2 million yuan and a construction period extension of 3 days.

[0074] Simulation verification process: The strategy was input into the dynamic simulation module. The simulation results showed that the probability of collapse risk dropped from 0.7 to 0.3, but the support construction caused the subsequent shield advancement to be delayed by 5 days, the total construction period was delayed to 8 days, and the cost was overspent by 3 million yuan. The comprehensive risk value did not decrease significantly. Strategy optimization:

[0075] The system's automatic adjustment strategy is "advanced geological forecast + microseismic monitoring + emergency support", with an estimated cost of 2.5 million yuan and a construction period extended by one day.

[0076] Another simulation showed that the probability of landslide risk dropped to 0.1, the construction period was delayed by 2 days, the cost overrun was 2.5 million yuan, the comprehensive risk value dropped from 0.9 to 0.4, and the risk level dropped from high risk to low risk, verifying that this strategy is better.

[0077] Real-time monitoring and early warning mechanism Early warning threshold setting: For the high-risk level of "deep foundation pit support failure" risk, set an early warning threshold: trigger an early warning when the displacement rate of the support structure exceeds 2mm / day or the settlement exceeds 10mm.

[0078] Real-time data collection: Displacement data is collected in real time through total stations and inclinometers deployed around the foundation pit. When the displacement rate of a monitoring point reaches 2.5mm / day, the system automatically calculates the risk probability coefficient from 0.6 to 0.8, the impact index from 1.0 to 1.2, and the comprehensive risk value from 0.6 to 0.96, triggering a red alert.

[0079] Visual early warning display: The display terminal highlights the risk area (such as the northeast corner of the foundation pit) through the 3D model and marks the displacement exceeding the limit point in red. At the same time, the early warning information is pushed to the mobile terminal of the project leader, accompanied by a risk evolution simulation animation and emergency response plan. 8. Risk knowledge base and model optimization knowledge base construction:

[0080] Collect risk data from historical projects, such as the risk of "steel structure hoisting safety accident" in a gymnasium project, record its probability of occurrence 0.3, impact index 1.5, response strategy (adding temporary support + drone monitoring) and implementation effect (risk level reduced from high to medium), and form structured cases to be stored in the knowledge base.

[0081] Dynamic Model Optimization: When the actual risk occurrence of a new project deviates by more than 20% from the predicted risk, the system automatically analyzes the cause of the deviation. For example, if the predicted probability of a "concrete supply interruption" risk for a project is 0.4, but the actual probability is 0.6, the system adjusts the historical probability weight t1 of this risk from 0.3 to 0.4 and updates the industry risk index calculation model to improve the accuracy of subsequent assessments.

[0082] The embodiments of the present invention are described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments. Various changes can be made within the scope of knowledge possessed by ordinary technicians in the technical field without departing from the scope of the present invention.

Claims

1. A project cost risk control management system, characterized in that: include: Potential risk analysis module, used to identify potential project risks through historical project data mining, BIM 3D model analysis and dynamic simulation; Risk Assessment Unit, including: The risk probability assessment module is used based on the industry risk index, supplier stability score, historical risk probability and market volatility coefficient, using the following formula: Calculate the risk probability coefficient, where Pr i is the probability of historical risk occurrence, W is the supplier stability score, k is the market volatility coefficient, Pr' and W' are standard control values, and t1, t2, and t3 are weight factors; The risk impact assessment module is used to combine the three-dimensional dynamic simulation results through the following formula: Calculate the risk impact index, where M i 、T i 、J i 、R i are cost overrun amount, construction delay duration, technical performance degradation value, and risk propagation range parameter, respectively. μ1, μ2, μ3, and μ4 are weight factors. Risk level assessment module, used to determine the risk level based on risk probability coefficient, impact index and risk propagation path; Dynamic simulation module, used to build a three-dimensional model of the project and output risk propagation range parameters; Risk response module, used to generate and verify risk response strategies based on simulation results; Display terminal, used for three-dimensional visualization of risk levels and evolution processes.

2. The engineering cost risk control management system according to claim 1, characterized in that: The potential risk analysis module generates risk scenarios through historical data mining, BIM model space layout analysis and Monte Carlo simulation.

3. The engineering cost risk control management system according to claim 1, characterized in that: Said vendor stability score Among them S j Including financial health, on-time delivery rate and other dimension scores, w j is the corresponding weight factor.

4. The engineering cost risk control and management system according to claim 1, characterized in that: The risk propagation range parameter R of the risk impact assessment module i Obtained through 3D model analysis of the dynamic simulation module, including: Identification of risk diffusion paths; Calculation of the number of work packages affected; Chain reaction intensity assessment.

5. The engineering cost risk control management system according to claim 1, characterized in that: The risk level assessment module determines the risk level through the following steps: i =F pi ·F yi and the propagation path complexity C i Divide into high, medium and low risk levels i =f(F i ,C i ).

6. The engineering cost risk control and management system according to claim 1, characterized in that: The dynamic simulation module includes: 3D model building unit, used to create geometric and semantic models of the project; Risk injection unit, used to simulate the triggering of specific risk events; Evolutionary analysis unit, used to track the propagation of risks in the project; The parameter output unit is used to extract quantitative indicators such as the impact range and duration.

7. The engineering cost risk control and management system according to claim 1, characterized in that: The risk response module includes: A strategy generation unit, used to automatically recommend response measures based on risk levels; Effect verification unit, used to verify the effectiveness of response strategies through dynamic simulation; Resource optimization unit to allocate risk response resources and minimize total costs.

8. A method for controlling and managing project cost risk, using a project cost risk control and management system as claimed in claims 1 to 7, characterized in that: include: Potential risk identification: Generate a risk list by combining historical data, BIM models and dynamic simulation; Risk probability calculation: Substitute multidimensional data to calculate the risk probability coefficient; Risk impact assessment: Obtain impact index and spread range through three-dimensional simulation; Risk level determination: based on comprehensive risk value and transmission path; Response strategy formulation: Generate strategies and verify their effectiveness through simulation; Real-time monitoring and early warning: 3D visualization displays risk status and triggers graded early warnings.

9. A method for controlling and managing engineering cost risks according to claim 8, wherein in the response strategy formulation step, avoidance and mitigation strategies are preferentially adopted for high-risk events, and transfer and acceptance strategies are adopted for medium- and low-risk events, and the cost-benefit ratio is compared through simulation.

10. The engineering cost risk control management method according to claim 8 further includes risk knowledge base construction and model dynamic optimization, training and evaluating models through historical data and continuously updating response strategies.

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