Electromechanical project progress risk early warning and coping method based on BIM

Abstract

The invention provides a BIM (Building Information Modeling)-based electromechanical project progress risk early warning and coping method. The method comprises the following steps: step 1, establishing a dynamic BIM model; 2, risk factor quantification: extracting a design risk, a resource risk and a process risk based on a BIM model, respectively calculating risk scores, and comprehensively quantifying a total risk through weighting; 3, real-time data fusion: collecting position data of constructors and equipment through an Internet of Things sensor, associating the position data with the BIM model, and updating the process completion rate and the resource distribution state in real time; and 4, dynamic risk early warning: in combination with the BIM model and sensor data, quantifying design, resources and process risks through a weighting formula, realizing risk score dynamic updating, realizing multi-dimensional risk quantification, and mapping a physical construction state in real time by constructing a digital model of a construction site so as to realize dynamic risk early warning. The effectiveness of the optimization strategy is verified through 4D simulation time and space dimensions, and whether construction conflicts and construction period delay are reduced or not can be quickly checked after the optimization strategy is generated.

Description

Technical Field

[0001] This invention relates to the field of project monitoring and prediction technology, and in particular to a method for early warning and response to schedule risks in electromechanical projects based on BIM. Background Technology

[0002] Mechanical and electrical installation projects involve multiple disciplines (water supply and drainage, electrical, HVAC, fire protection, etc.), complex pipelines, and densely intersecting spaces. Schedule risks are easily caused by factors such as design conflicts, inefficient construction coordination, and delayed resource supply. During the construction of mechanical and electrical projects, schedule management faces multiple challenges. Traditional methods rely on two-dimensional drawings and manual experience, which have the following problems: Data fragmentation across design, construction, and operation and maintenance stages leads to delays in design changes, frequent on-site conflicts such as pipeline collisions and insufficient equipment installation space, affecting construction progress, creating information silos and insufficient collaboration. At the same time, construction plans are mostly based on experience and lack dynamic monitoring methods, making it difficult to cope with sudden risks.

[0003] Therefore, it is necessary to provide a new BIM-based method for early warning and response to schedule risks in electromechanical projects to solve the above-mentioned technical problems. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a BIM-based method for early warning and response to schedule risks in electromechanical projects. The BIM-based method for early warning of schedule risks in electromechanical projects provided by this invention includes the following steps: Step 1: Dynamic BIM Model Creation: Import the electromechanical engineering design model into the BIM software of the BIM platform, assign a unique ID to each component, and label the construction logic attributes, including installation sequence, process dependencies, resource requirements, and planned completion time. Step 2: Risk Factor Quantification: Based on the BIM model, design risks, resource risks, and process risks are extracted, and risk scores are calculated for each. The total risk is then quantified through weighted summation. Step 3: Real-time data fusion: Collect location data of construction personnel and equipment through IoT sensors and link it with the BIM model to update the process completion rate and resource distribution status in real time; Step 4: Dynamic Risk Warning: Update the risk score based on real-time construction data and compare it with the preset threshold to trigger a warning. Highlight the risk area and push the warning to relevant personnel.

[0005] A further method, wherein the dynamic BIM model establishment includes the following steps: Component attribute labeling: Assign a unique ID to each component and bind it to construction parameters; By using BIM software to link the construction plan with the model, construction process animations are generated, and critical paths and construction stages are highlighted.

[0006] A further method for calculating the risk score includes the following steps: Risk weights are dynamically adjusted based on the project stage. Risk level classification: Establish three risk levels: high, medium, and low.

[0007] A further method, the real-time data fusion includes the following steps: Environmental factor data collection: Temperature, humidity and wind speed data are collected through meteorological sensors and linked to the construction plan. If extreme weather affects the critical path, one point is added to the risk score. Equipment status monitoring: Collect data on the operating status of construction machinery and predict potential failures.

[0008] A further method, the risk warning method, also includes overlaying the BIM model onto a GIS map, analyzing the impact of the surrounding environment of the construction area on the progress, combining GIS data to predict the impact of environmental factors on the construction progress, and dynamically adjusting the risk score.

[0009] Another aspect of the present invention provides a method for managing schedule risks in MEP (Mechanical and Electrical) projects based on BIM, the method comprising the following steps: Step 1: Adaptive optimization strategy generation: Use the random forest model to predict the risk evolution trend, generate optimization strategies, synchronize the optimized construction plan to the risk response strategy library of the BIM platform, and verify the effectiveness of the strategies; Step 2, BIM Collaborative Modification: Use Revit's BIM software to directly modify the model, update conflicting pipeline locations, or adjust process logic; Step 3: Dynamically adjust resources: Dynamically allocate human and equipment resources based on risk score priorities; Step 4: Multi-dimensional collaborative execution: Synchronize the modified model data to the construction management system via the BIM platform's API, and notify on-site personnel of change instructions via mobile devices to ensure consistent execution.

[0010] A further method, wherein the optimization strategy generation includes the following steps: Machine learning prediction: Train a random forest model, input historical construction data and current risk scores, and predict future risk evolution trends; Develop strategy generation rules: design risk, resource risk, and process risk rules. Design risk rules adjust pipeline routing or add supports. Resource risk rules allocate and use work teams or optimize equipment usage plans. Process risk rules allow for parallel construction or increase resource input.

[0011] A further method for training the random forest model includes the following steps: Data preprocessing: Standardizing historical construction data and real-time BIM data; Model training: The risk prediction model is trained using the random forest algorithm. Input features include design risk score, resource gap ratio, and process delay days. Model validation: Model performance was evaluated using the area under the ROC curve and the F1 score.

[0012] A further method involves generating a new version of the model after each BIM collaborative modification, and recording the modifier, modification time, and modification content. If the optimization strategy is ineffective, it rolls back to the historical version and regenerates the risk score.

[0013] A further method, the construction of the risk response strategy library includes the following steps: Pre-set response strategies for common risks and store them in JSON format; The system automatically matches strategies based on the current risk type, generates execution instructions, and pushes them to the BIM platform.

[0014] Compared with related technologies, the BIM-based method for early warning and response to schedule risks in electromechanical projects provided by this invention has the following beneficial effects: 1. By combining BIM models and sensor data, design, resource, and process risks are quantified through weighted formulas to achieve dynamic updates of risk scores and multi-dimensional risk quantification. By constructing a digital model of the construction site, the physical construction status is mapped in real time, and the effectiveness of optimization strategies is verified through 4D simulation of time and space dimensions. After the optimization strategies are generated, it is possible to quickly verify whether they reduce construction conflicts and schedule delays.

[0015] 2. This invention collects construction site data through GPS and personnel positioning devices, associates it with the BIM model, monitors the completion rate of processes and the distribution status of resources in real time, realizes real-time data fusion, avoids the formation of information silos and insufficient collaboration, and when the threshold is exceeded, the system automatically triggers an early warning and pushes it to the operation and maintenance personnel.

[0016] 3. This invention solves the problems of information silos, delayed response, and resource waste in traditional management by dynamically identifying, accurately warning, and intelligently responding to construction progress risks, and significantly improves the construction efficiency, cost control capabilities, and quality and safety levels of electromechanical projects. Attached Figure Description

[0017] Figure 1 A flowchart illustrating the BIM-based electromechanical project schedule risk early warning method provided by this invention; Figure 2 A flowchart illustrating the process of establishing the dynamic BIM model provided by this invention; Figure 3A flowchart illustrating the BIM-based method for mitigating schedule risks in electromechanical projects provided by this invention; Figure 4 A flowchart for the optimization strategy provided by this invention. Detailed Implementation

[0018] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0019] Please refer to the following: Figure 1 , Figure 2 , Figure 3 as well as Figure 4 ,in, Figure 1 A flowchart illustrating the BIM-based electromechanical project schedule risk early warning method provided by this invention; Figure 2 A flowchart illustrating the process of establishing the dynamic BIM model provided by this invention; Figure 3 A flowchart illustrating the BIM-based method for mitigating schedule risks in electromechanical projects provided by this invention; Figure 4 A flowchart for the optimization strategy provided by this invention.

[0020] Example 1 In the specific implementation process, such as Figure 1 As shown, the BIM-based method for early warning of schedule risks in MEP (Mechanical, Electrical, and Plumbing) projects includes the following steps: Step 1: Dynamic BIM Model Creation: Import the electromechanical engineering design model into the BIM software of the BIM platform, assign a unique ID to each component, and label the construction logic attributes, including installation sequence, process dependencies, resource requirements, and planned completion time. Step 2: Risk Factor Quantification: Based on the BIM model, design risks (such as pipeline collisions), resource risks (such as manpower / machinery shortages), and process risks (such as critical path delays) are extracted, and risk scores are calculated for each. The total risk is then quantified by weighted summation. The weighted formula is The total risk is comprehensively quantified, among which , , These are design, resource, and process risks. ; Step 3: Real-time data fusion: Collect location data of construction personnel and equipment through IoT sensors and link it with the BIM model to update the process completion rate and resource distribution status in real time; Step 4: Dynamic Risk Warning: Update the risk score based on real-time construction data and compare it with the preset threshold to trigger a warning. Highlight the risk area and push the warning to relevant personnel.

[0021] refer to Figure 2 As shown, the creation of a dynamic BIM model includes the following steps: Component attribute labeling: Assign a unique ID to each component and bind it to construction parameters (such as installation time and required resources); By using BIM software to link the construction plan with the model, construction process animations are generated, and critical paths and construction stages are highlighted.

[0022] It should be noted that calculating the risk score includes the following steps: Dynamic adjustment of risk weights: Risk weights are dynamically adjusted according to project phases (e.g., design risk weights during the design phase). Risk weighting of construction phase procedures ); Weighting adjustment: ; Risk level classification: Establish three risk levels: high, medium, and low; High risk (≥80): Immediate intervention and adjustment of the construction plan are required; Medium risk (50-80): Develop and monitor backup plans; Low risk (<50): Routine management.

[0023] It should be noted that real-time data fusion includes the following steps: Environmental factor data collection: Temperature, humidity and wind speed data are collected through meteorological sensors and linked to the construction plan. If extreme weather (such as rainstorms and strong winds) affects the critical path, one point is added to the risk score. Equipment status monitoring: The operating status of construction machinery (such as welding machines and cranes) is collected through IoT devices to predict failure risks. If the probability of equipment failure is >30%, one point is added to the risk score.

[0024] It should be noted that IoT sensors include: Current and voltage sensors: monitor welding current and voltage fluctuations; Temperature sensor: detects the internal temperature of the welding machine; Load sensor: measures the load rate of the welding machine; Vibration sensor: monitors the vibration frequency of the welding machine motor; Vibration sensor: Detects vibrations in crane structures; Displacement sensor: monitors boom angle and telescopic length; Weight sensor: measures the weight of the load being lifted; GPS / BeiDou positioning: Tracking the location and movement trajectory of cranes and construction workers; Environmental sensors: monitor wind speed, temperature, and humidity.

[0025] Optionally, risk warning methods also include overlaying the BIM model onto a GIS map, analyzing the impact of the surrounding environment (such as traffic and terrain) on the construction progress, combining GIS data to predict the impact of environmental factors (such as traffic congestion) on the construction progress, and dynamically adjusting the risk score.

[0026] Example 2 In a specific implementation process, refer to Figure 3 As shown, the BIM-based method for addressing schedule risks in MEP (Mechanical, Electrical, and Plumbing) projects includes the following steps: Step 1: Adaptive optimization strategy generation: Use the random forest model to predict the risk evolution trend, generate optimization strategies (such as adjusting the sequence of work processes and allocating resources), synchronize the optimized construction plan to the risk response strategy library of the BIM platform, and verify the effectiveness of the strategies. Step 2, BIM Collaborative Modification: Use Revit's BIM software to directly modify the model, update conflicting pipeline locations, or adjust process logic; Step 3: Dynamic resource adjustment: Dynamically allocate human and equipment resources according to the risk score priority, with critical path processes having a priority of ≥3 and ordinary processes having a priority of 1; Step 4: Multi-dimensional collaborative execution: Synchronize the modified model data to the construction management system via the BIM platform's API, and notify on-site personnel of change instructions via mobile devices to ensure consistent execution.

[0027] It should be noted that Revit is an option among the BIM software mentioned above.

[0028] refer to Figure 4 As shown, the optimization strategy generation includes the following steps: Machine learning prediction: Train a random forest model, input historical construction data (such as process delays and resource gaps) and current risk scores, and predict future risk evolution trends; Develop strategy generation rules: design risk, resource risk, and process risk rules. Design risk rules adjust pipeline routing or add supports. Resource risk rules allocate and use work teams or optimize equipment usage plans. Process risk rules allow for parallel construction or increase resource input.

[0029] It should be noted that training a random forest model includes the following steps: Data preprocessing: Standardizing historical construction data and real-time BIM data; Model training: The risk prediction model is trained using the random forest algorithm. Input features include design risk score, resource gap ratio, and process delay days. Model validation: Model performance was evaluated using the area under the ROC curve and the F1 score.

[0030] Optionally, BIM collaborative modifications generate a new version of the model after each modification and record the modifier, modification time, and modification content. If the optimization strategy is ineffective, it rolls back to the historical version and regenerates the risk score.

[0031] It should be noted that the construction of the risk response strategy library includes the following steps: Pre-set response strategies for common risks (such as collision adjustment, resource replenishment, and process optimization), and store them in JSON format; The system automatically matches strategies based on the current risk type, generates execution instructions, and pushes them to the BIM platform.

[0032] Optionally, the BIM-based approach to managing construction schedule risks in electromechanical projects can also combine the BIM platform with blockchain. By recording model modification history and risk management records through blockchain, data transparency and immutability can be ensured, supporting full traceability of model modification and risk management processes. This approach can be applied to legal disputes or quality audits.

[0033] According to embodiments of the present invention, a computing device that can be used to implement the above method includes a processor and a memory; The processor can be a multi-core processor or include multiple processors. In some embodiments, the processor may include a general-purpose main processor and one or more special coprocessors, such as a graphics processing unit (GPU), a digital signal processor (DSP), etc. In some embodiments, the processor may be implemented using custom circuitry, such as an application-specific integrated circuit (ASIC) or a field-programmable gate array (FPGA).

[0034] Memory can include various types of storage units, such as system memory, read-only memory (ROM), and permanent storage devices. ROM can store static data or instructions required by the processor or other modules of the computer. Permanent storage devices can be read-write storage devices. Permanent storage devices can be non-volatile storage devices that retain stored instructions and data even when the computer is powered off. In some embodiments, permanent storage devices use mass storage devices (e.g., magnetic or optical disks, flash memory) as permanent storage devices. In other embodiments, permanent storage devices can be removable storage devices (e.g., floppy disks, optical drives). System memory can be a read-write storage device or a volatile read-write storage device, such as dynamic random access memory. System memory can store some or all of the instructions and data required by the processor during operation. Furthermore, memory can include any combination of computer-readable storage media, including various types of semiconductor memory chips (DRAM, SRAM, SDRAM, flash memory, programmable read-only memory), and disks and / or optical disks can also be used. In some implementations, the memory may include removable storage devices that are readable and / or writable, such as laser discs (CDs), read-only digital versatile optical discs (e.g., DVD-ROMs, dual-layer DVD-ROMs), read-only Blu-ray discs, ultra-high density optical discs, flash memory cards (e.g., SD cards, mini SD cards, Micro-SD cards, etc.), magnetic floppy disks, etc. Computer-readable storage media do not include carrier waves and transient electronic signals transmitted wirelessly or via wired connections.

[0035] It should be understood that, unless otherwise expressly stated herein, there is no strict order restriction on the execution of the above steps, and these steps may be executed in other orders. Moreover, at least some steps in the processes involved in the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but may be executed at different times. The execution order of these steps or stages is not necessarily sequential, but may be performed alternately or in turn with other steps or at least some of the steps or stages in other steps.

[0036] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or basic characteristics. Therefore, the embodiments should be considered illustrative and non-limiting in all respects. The scope of the invention is defined by the appended claims rather than the foregoing description. Therefore, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention, and no reference numerals in the claims should be construed as limiting the scope of the claims.

[0037] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment includes only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A BIM-based method for early warning of schedule risks in MEP (Mechanical, Electrical, and Plumbing) projects, characterized in that... Includes the following steps: Step 1: Dynamic BIM Model Creation: Import the electromechanical engineering design model into the BIM software of the BIM platform, assign a unique ID to each component, and label the construction logic attributes, including installation sequence, process dependencies, resource requirements, and planned completion time. Step 2: Risk Factor Quantification: Based on the BIM model, design risks, resource risks, and process risks are extracted, and risk scores are calculated for each. The total risk is then quantified through weighted summation. Step 3: Real-time data fusion: Collect location data of construction personnel and equipment through IoT sensors and link it with the BIM model to update the process completion rate and resource distribution status in real time; Step 4: Dynamic Risk Warning: Update the risk score based on real-time construction data and compare it with the preset threshold to trigger a warning. Highlight the risk area and push the warning to relevant personnel.

2. The BIM-based method for early warning of schedule risks in electromechanical projects according to claim 1, characterized in that, The creation of the dynamic BIM model includes the following steps: Component attribute labeling: Assign a unique ID to each component and bind it to construction parameters; By using BIM software to link the construction plan with the model, construction process animations are generated, and critical paths and construction stages are highlighted.

3. The BIM-based method for early warning of schedule risks in electromechanical projects according to claim 1, characterized in that, Calculating the risk score includes the following steps: Risk weights are dynamically adjusted based on the project stage. Risk level classification: Establish three risk levels: high, medium, and low.

4. The BIM-based method for early warning of schedule risks in electromechanical projects according to claim 1, characterized in that, The real-time data fusion includes the following steps: Environmental factor data collection: Temperature, humidity and wind speed data are collected through meteorological sensors and linked to the construction plan. If extreme weather affects the critical path, one point is added to the risk score. Equipment status monitoring: Collect data on the operating status of construction machinery and predict potential failures.

5. The BIM-based method for early warning of schedule risks in electromechanical projects according to claim 1, characterized in that, The risk warning method also includes overlaying the BIM model onto the GIS map, analyzing the impact of the surrounding environment of the construction area on the progress, combining GIS data to predict the impact of environmental factors on the construction progress, and dynamically adjusting the risk score.

6. A BIM-based method for managing schedule risks in MEP (Mechanical, Electrical, and Plumbing) projects, applied to the BIM-based method for early warning of schedule risks in MEP projects as described in any one of claims 1-5, characterized in that... The method includes the following steps: Step 1: Adaptive optimization strategy generation: Use the random forest model to predict the risk evolution trend, generate optimization strategies, synchronize the optimized construction plan to the risk response strategy library of the BIM platform, and verify the effectiveness of the strategies; Step 2, BIM Collaborative Modification: Directly modify the model using BIM software to update the location of conflicting pipelines or adjust process logic; Step 3: Dynamically adjust resources: Dynamically allocate human and equipment resources based on risk score priorities; Step 4: Multi-dimensional collaborative execution: Synchronize the modified model data to the construction management system via the BIM platform's API, and notify on-site personnel of change instructions via mobile devices to ensure consistent execution.

7. The method for managing schedule risks in MEP (Mechanical, Electrical, and Plumbing) projects based on BIM according to claim 6, characterized in that, The optimization strategy generation includes the following steps: Machine learning prediction: Train a random forest model, input historical construction data and current risk scores, and predict future risk evolution trends; Develop strategy generation rules: design risk, resource risk, and process risk rules. Design risk rules adjust pipeline routing or add supports. Resource risk rules allocate and use work teams or optimize equipment usage plans. Process risk rules allow for parallel construction or increase resource input.

8. The method for managing schedule risks in MEP (Mechanical and Electrical) projects based on BIM according to claim 7, characterized in that, Training the random forest model includes the following steps: Data preprocessing: Standardizing historical construction data and real-time BIM data; Model training: The risk prediction model is trained using the random forest algorithm. Input features include design risk score, resource gap ratio, and process delay days. Model validation: Model performance was evaluated using the area under the ROC curve and the F1 score.

9. The method for managing schedule risks in MEP (Mechanical, Electrical, and Plumbing) projects based on BIM according to claim 8, characterized in that, The BIM collaborative modification generates a new version of the model after each modification and records the modifier, modification time, and modification content. If the optimization strategy is ineffective, it rolls back to the historical version and regenerates the risk score.

10. The BIM-based method for managing schedule risks in MEP (Mechanical, Electrical, and Plumbing) projects according to claim 9, characterized in that, The construction of the risk response strategy library includes the following steps: Pre-set response strategies for common risks and store them in JSON format; The system automatically matches strategies based on the current risk type, generates execution instructions, and pushes them to the BIM platform.

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