EPC full-process technology management method

Abstract

The invention discloses an EPC full-process technology management method, and the method comprises the following steps: S1, taking a BIM model as a core, integrating geometric data, cost data, progress data and operation and maintenance data, and forming a digital twinborn model; s2, structuring the national standard, the industrial standard and the enterprise construction method into executable rules, and embedding the executable rules into the digital twin model to form a construction specification knowledge graph; s3, collecting construction site data in real time through an IoT sensor network, fusing the construction site data into the digital twinborn model, and driving the digital twinborn model to dynamically update; s4, performing progress prediction, resource optimization and risk early warning through an artificial intelligence AI algorithm based on the dynamically updated digital twinborn model, and outputting a decision instruction; and S5, the acceptance records and quality data of the key processes are stored based on the block chain technology, so that whole-process quality tracing and closed-loop management are realized, and the problems of information isolated island, progress lag, cost increase, quality tracing difficulty and the like in a traditional EPC project are effectively solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of engineering management, and particularly relates to an EPC full-process technical management method. BACKGROUND

[0002] In recent years, the EPC (Engineering, Procurement, Construction) general contracting mode has been widely promoted and applied. This mode covers the whole process management and service from the early stage of project design to the late stage of delivery of practical use, and has obvious advantages, such as single-point responsibility system, high specialization and collaboration, time and cost controllability, etc. In China, the EPC general contracting mode is mainly applied in public infrastructure, housing construction, energy, environmental protection, transportation and other fields. The EPC general contracting mode has also been widely applied internationally, especially in developing countries and emerging markets, and is widely used in large and medium-sized complex engineering projects in the fields of petrochemical industry, power energy, infrastructure, etc. These projects are usually large in scale and complex in technology, and the EPC general contracting mode can provide a full range of solutions to ensure the smooth completion of the project.

[0003] However, the practical project management of the EPC general contracting mode still faces the following problems:

[0004] (1) Information island problem: the data of design, procurement, construction and other links are fragmented, resulting in low communication efficiency and slow change response;

[0005] (2) Schedule uncontrollable: traditional management relies on manual coordination, and it is difficult to monitor the cross-operation of multiple specialties in real time, which is easy to delay the construction period;

[0006] (3) Cost overruns risk: material procurement is out of sync with construction progress, and redundant inventory or shortage occurs frequently;

[0007] (4) Quality traceability difficulty: construction problems are difficult to associate with design sources, and the rectification cycle is long.

[0008] And the traditional BIM+ERP system can only realize local data integration, and lacks full-process dynamic collaboration and intelligent decision support. SUMMARY

[0009] In order to solve the above problems, the present application designs an EPC full-process technical management method, and the specific technical scheme is as follows:

[0010] An EPC full-process technical management method, comprising the following steps:

[0011] S1: Taking BIM model as the core, integrating geometric data, cost data, progress data and operation and maintenance data to form a digital twin model;

[0012] S2: Structurize the national standards, industry standards and enterprise construction methods into executable rules and embed them into the digital twin model to form a construction specification knowledge graph;

[0013] S3: Real-time collection of construction site data through an Internet of Things (IoT) sensor network and fusion into the digital twin model to drive dynamic updates of the digital twin model;

[0014] S4: Based on the dynamically updated digital twin model, progress prediction, resource optimization and risk warning are performed through artificial intelligence (AI) algorithms, and decision instructions are output;

[0015] S5: Based on blockchain technology, the acceptance records and quality data of key processes are stored, and full-process quality traceability and closed-loop management are realized.

[0016] In a preferred implementation, S1 specifically includes:

[0017] S11: Based on design drawings, point cloud scanning data and process equipment parameters, a standardized three-dimensional geometric model with LOD400 and above detail level is constructed;

[0018] S12: The BIM model components are associated with the work breakdown structure (WBS), and the enterprise quota library, historical project cost database and supplier real-time quotation API are connected to automatically generate a bill of quantities (BOQ) and automatically calculate the construction cost change when design changes occur;

[0019] S13: The BIM model is synchronized with the progress management software Primavera P6 / MS Project in both directions, and the construction tasks are decomposed to the component level to realize dynamic binding and visualization of progress data;

[0020] S14: An operation and maintenance field is added to the BIM model component properties, which includes at least device manufacturer manual, warranty period, replacement model, vibration or temperature sensor threshold.

[0021] In a preferred implementation, the BIM model and the progress management software Primavera P6 / MS Project are synchronized in both directions through IFC4.0 standard format or dedicated APIs.

[0022] In a preferred implementation, the structured processing in S2 is specifically: using natural language processing (NLP) technology to analyze national standards, industry standard texts and enterprise construction methods, and converting them into machine-readable rules in the form of IF-THEN.

[0023] In a preferred implementation, the S2 further comprises model automatic compliance verification, specifically: associating the construction specification knowledge graph with the BIM model components, and in the design or construction phase, the digital twin model automatically verifies in real time according to the rules in the construction specification knowledge graph, and triggers an early warning when a non-compliant situation is detected.

[0024] In a preferred implementation, the Internet of Things (IoT) sensor network includes RFID tags and GPS positioning devices for progress tracking, and temperature and humidity sensors and displacement sensors for quality monitoring.

[0025] In a preferred implementation, the RFID tags are installed on prefabricated components to track the progress of the components; the GPS positioning devices are installed on construction machinery to track the location of the construction machinery; the temperature and humidity sensors are embedded in the concrete to monitor the curing environment; and the displacement sensors are installed on the perimeter of the foundation pit to monitor the stability of the slope.

[0026] In a preferred implementation, the artificial intelligence (AI) algorithm in S4 includes:

[0027] S41: Train an LSTM model based on historical data, input current progress and external environment data, and output progress prediction and critical path risk warning;

[0028] S42: Dynamically adjust the resource scheduling plan using a genetic algorithm to optimize resource utilization.

[0029] In a preferred implementation, the closed-loop management in S5 includes:

[0030] S51: Diagnose the quality defect type and analyze the root cause through image recognition or IoT data AI;

[0031] S52: Automatically associate the BIM model location and responsible team, and push the rectification plan;

[0032] S53: After the rectification is completed, scan the code to upload the verification information, the system closes the problem and generates an electronic file.

[0033] An intelligent collaboration platform for the above-mentioned EPC full-process technical management method, comprising:

[0034] Unified data platform for integrating BIM, ERP, and IoT data;

[0035] AI decision engine for performing progress prediction, resource optimization, and risk warning in S4;

[0036] Multi-terminal collaboration interface for Web, mobile, and AR terminal access;

[0037] A blockchain storage module is configured to store the acceptance records and quality data of the key working procedures in S5.

[0038] Compared with the prior art, the present application has the following advantages:

[0039] (1) The present application realizes the data fusion of BIM+ERP+IOT by establishing a digital twin model based on a BIM model as the core, and breaks the information silos;

[0040] (2) The progress prediction and critical path risk early warning (LSTM model) are realized by using the artificial intelligence (AI) algorithm, the dynamic prediction and resource optimization are realized, and the decision instructions are output, and the progress lag is automatically pushed to the rush program;

[0041] (3) Based on the digital twin model, the cost change is automatically calculated and constructed when the design changes, and the procurement and progress dynamic matching and inventory optimization are realized;

[0042] (4) The present application stores the acceptance records and quality data of the key working procedures based on the blockchain technology, and realizes the non-tamperability and rapid traceability of the whole process quality data by combining the AI image recognition and BIM model correlation technology, and the quality accident traceability time is shortened from 72 hours to 1 hour, and the first acceptance pass rate is improved to 95%. BRIEF DESCRIPTION OF DRAWINGS

[0043] The present application will be further described in detail below in combination with the drawings and specific embodiments.

[0044] Figure 1 is a process schematic diagram of the EPC whole-process technical management method of the present application. DETAILED DESCRIPTION

[0045] The present application will be further described in detail below in combination with the drawings and specific embodiments.

[0046] Referring to Figure 1 , the present application provides an implementation as follows:

[0047] An EPC whole-process technical management method, comprising the following steps:

[0048] S1: Taking a BIM model as the core, integrating geometric data, cost data, progress data and operation and maintenance data to form a digital twin model;

[0049] The S1 specifically comprises:

[0050] S11: Based on design drawings, point cloud scanning data, and process equipment parameters, construct standardized 3D geometric models with a detail level of LOD400 or higher;

[0051] Specifically, Autodesk Revit was used as the core BIM modeling software, and a data platform was built based on Microsoft Azure Digital Twins. Semiconductor process equipment layout diagrams and on-site point cloud scan data were imported. Modeling was performed separately according to disciplines such as architecture, structure, MEP, and process piping. The Level of Detail (LOD) of the model reached LOD400 (prefabricated level) or higher. For example, the modeling accuracy of the wall panel joints in the cleanroom reached ±1mm to support subsequent airtightness simulation. The above model was constructed in accordance with the national standard GB / T51235-2017 "Standardization Manual for Building Information Modeling" and other relevant specifications, conforming to national standards and industry requirements. The 3D model was modeled according to discipline-system-construction type-number, with the unit uniformly in millimeters (mm), the coordinate system uniformly using the 2000 National Geodetic Coordinate System, and the elevation uniformly using the 1985 National Elevation Datum.

[0052] S12: Associate the BIM model components with the work breakdown structure (WBS), connect to the enterprise quota library, historical project cost database and supplier real-time quotation API, automatically generate the bill of materials (BOQ), and automatically calculate the change in construction cost when design changes occur.

[0053] In this embodiment, the model automatically updates the concrete usage when the design changes.

[0054] S13: Synchronize the BIM model with the schedule management software Primavera P6 / MS Project in both directions, and decompose the construction tasks to the component level to realize the dynamic binding and visualization of schedule data;

[0055] Specifically, through the IFC4.0 standard format or dedicated APIs, bidirectional synchronization between the BIM model and the schedule management software Primavera P6 / MS Project is achieved. When the schedule technology is adjusted, the highlighted status of the corresponding construction in the model and the estimated completion time are also dynamically updated.

[0056] S14: Add an operation and maintenance field to the component properties of the BIM6D model. The operation and maintenance field shall include at least the equipment manufacturer's manual, warranty period, alternative model, and vibration or temperature sensor threshold.

[0057] S2: National standards, industry standards, and enterprise construction methods are structured into executable rules and embedded into the digital twin model to form a construction specification knowledge graph;

[0058] The structured processing in S2 specifically involves using Natural Language Processing (NLP) technology to parse national standards, industry standards, and enterprise work methods, and converting them into machine-readable rules in IF-THEN format.

[0059] The S2 also includes automatic compliance verification of the model, specifically: associating the construction specification knowledge graph with the BIM model components, and during the design or construction phase, the digital twin model automatically performs real-time verification based on the rules in the construction specification knowledge graph, and triggers an early warning when non-compliance with the specifications is detected.

[0060] S3: Collect construction site data in real time through the Internet of Things (IoT) sensor network and integrate it into the digital twin model to drive the dynamic update of the digital twin model;

[0061] The Internet of Things (IoT) sensor network includes RFID tags and GPS positioning devices for progress tracking, as well as temperature and humidity sensors and displacement sensors for quality monitoring.

[0062] Specifically, RFID tags are affixed to precast components, and GPS positioning modules are installed on construction machinery to track the arrival of components and the location of construction machinery; temperature and humidity sensors are embedded in concrete to monitor the curing environment, and displacement sensors are installed around the foundation pit to monitor slope stability.

[0063] All real-time data is transmitted to the BIM model for overlay display. A progress checklist is generated daily based on the on-site progress, and areas with lagging progress and affected subsequent processes are automatically identified.

[0064] S4: Based on the dynamically updated digital twin model, use artificial intelligence (AI) algorithms to predict progress, optimize resources, and warn of risks, and output decision instructions;

[0065] The artificial intelligence (AI) algorithm in S4 includes:

[0066] S41: Train an LSTM model based on historical data, input current progress and external environment data, and output progress prediction and critical path risk warning;

[0067] External environmental data should include at least future weather data and resource input information;

[0068] S42: Use a genetic algorithm to dynamically adjust the resource scheduling plan and optimize resource utilization.

[0069] Specifically, this invention addresses the scheduling problem of multiple tower cranes by employing a genetic algorithm for optimization. The algorithm aims to minimize the total construction period and tower crane downtime, dynamically calculating and outputting the optimal lifting task sequence and location arrangement, which is updated and pushed to the tower crane operator terminal daily.

[0070] S5: Based on blockchain technology, the acceptance records and quality data of key processes are stored to achieve full-process quality traceability and closed-loop management.

[0071] The closed-loop management in S5 includes:

[0072] S51: Use image recognition or IoT data AI to diagnose quality defect types and analyze root causes;

[0073] S52: Automatically associates BIM model location with responsible work team and pushes rectification plan;

[0074] S53: After rectification is completed, scan the code to upload the verification information, the system closes the problem and generates an electronic file.

[0075] An intelligent collaborative platform for implementing the aforementioned EPC full-process technical management method includes:

[0076] A unified data platform for integrating BIM, ERP, and IoT data;

[0077] An AI decision engine is used to perform progress prediction, resource optimization, and risk warning in S4.

[0078] A multi-terminal collaborative interface for accessing from web, mobile, and AR terminals;

[0079] The blockchain evidence storage module is used to store the acceptance records and quality data of key processes in S5.

[0080] In this embodiment, the intelligent collaboration platform is mainly applied to collaboration during the design phase, procurement phase, and construction phase:

[0081] The design phase collaboration is mainly used to achieve multi-disciplinary collaborative design, automatic compliance review, and cost pre-control: designers from multiple disciplines design based on a unified model on the web interface of the intelligent collaboration platform; the intelligent collaboration platform implements conflict monitoring, and when it detects a collision between ducts and beams, it automatically prompts and suggests adjustments to the scheme. When the design scheme changes, the relevant professional models are automatically updated, and the engineering quantities are extracted based on the updated components, and a production cost budget report is generated according to the enterprise quota library.

[0082] The intelligent collaboration in the procurement phase is mainly used to achieve dynamic matching between procurement technology and construction progress, and reduce inventory costs. The intelligent collaboration platform extracts component information from the BIM model and automatically generates a procurement list with precise demand time according to the construction schedule. At the same time, the intelligent collaboration platform's API connects to the ERP systems of steel, cable and other suppliers to obtain inventory and prices. For critical path materials, the intelligent collaboration platform activates ghost and certificate planning (MIP) algorithms to optimize procurement strategies and ensure on-time delivery.

[0083] The intelligent collaboration during the construction phase is mainly used for real-time management and control. RFID tags are affixed to all important precast components, and GPS positioning modules are installed on construction machinery to automatically track the status of component arrival and hoisting, as well as the location and utilization rate of machinery. At the same time, drones are used to automatically take panoramic aerial photos of the site every day, and computer vision AI algorithms are used to automatically identify the completion percentage of each work surface with an accuracy of ±2%, thereby driving the BIM model to be updated in real time.

[0084] On-site personnel can intuitively obtain component installation guidelines and acceptance standards through AR devices (HoloLens 2 AR glasses), improving operational accuracy. Simultaneously, an IoT sensor network monitors quality and safety data in real time, automatically issuing warnings in case of anomalies. All key process data is stored on the blockchain, ensuring a complete and reliable quality traceability chain, forming a closed-loop management system of "monitoring-early warning-handling-storage," significantly improving construction efficiency and quality control.

[0085] This invention constructs a digital twin model centered on BIM, integrating AI decision-making and blockchain evidence storage technologies to achieve deep collaboration and intelligent management across the entire design-procurement-construction process. It effectively solves problems such as information silos, schedule delays, cost overruns, and difficulties in quality traceability in traditional EPC projects, ultimately achieving significant benefits including a 95% first-time acceptance rate, a quality traceability time reduced to 1 hour, a 40% reduction in rework costs, and a 35% increase in inventory turnover. In terms of efficiency improvements, design collaboration time is increased by 35%, and procurement order processing efficiency is improved by 60%, comprehensively enhancing the refinement, intelligence, and reliability of EPC project management. This invention represents an upgrade from static models to dynamic twins, supporting intelligent decision-making throughout the entire EPC process. Through a complete cycle of standard digitization → process monitoring → problem closure → knowledge accumulation, it achieves continuous improvement in EPC project quality. Simultaneously, the intelligent collaboration platform enables data-driven, real-time decision-making, and risk pre-control across the entire EPC chain, making it suitable for complex projects such as high-tech factories and energy infrastructure.

[0086] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the present invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. A method for managing the entire EPC process, characterized in that, Includes the following steps: S1: Using the BIM model as the core, integrate geometric data, cost data, schedule data and operation and maintenance data to form a digital twin model; S2: National standards, industry standards, and enterprise construction methods are structured into executable rules and embedded into the digital twin model to form a construction specification knowledge graph; S3: Collect construction site data in real time through the Internet of Things (IoT) sensor network and integrate it into the digital twin model to drive the dynamic update of the digital twin model; S4: Based on the dynamically updated digital twin model, use artificial intelligence (AI) algorithms to predict progress, optimize resources, and warn of risks, and output decision instructions; S5: Based on blockchain technology, the acceptance records and quality data of key processes are stored to achieve full-process quality traceability and closed-loop management.

2. The EPC full-process technology management method according to claim 1, characterized in that, S1 specifically includes: S11: Based on design drawings, point cloud scanning data, and process equipment parameters, construct standardized 3D geometric models with a detail level of LOD400 or higher; S12: Associate the BIM model components with the work breakdown structure (WBS), connect to the enterprise quota library, historical project cost database and supplier real-time quotation API, automatically generate the bill of materials (BOQ), and automatically calculate the change in construction cost when design changes occur. S13: Synchronize the BIM model with the schedule management software Primavera P6 / MS Project in both directions, and decompose the construction tasks to the component level to realize the dynamic binding and visualization of schedule data; S14: Add an operation and maintenance field to the BIM model component properties. The operation and maintenance field shall include at least the equipment manufacturer's manual, warranty period, alternative model, and vibration or temperature sensor threshold.

3. The EPC full-process technology management method according to claim 2, characterized in that, The BIM model and the progress management software Primavera P6 / MS Project are synchronized bidirectionally through the IFC4.0 standard format or dedicated APIs.

4. The EPC full-process technology management method according to claim 1, characterized in that, The structured processing in S2 specifically involves using Natural Language Processing (NLP) technology to parse national standards, industry standards, and enterprise work methods, and converting them into machine-readable rules in IF-THEN format.

5. The EPC full-process technology management method according to claim 4, characterized in that, The S2 also includes automatic compliance verification of the model, specifically: associating the construction specification knowledge graph with the BIM model components, and during the design or construction phase, the digital twin model automatically performs real-time verification based on the rules in the construction specification knowledge graph, and triggers an early warning when non-compliance with the specifications is detected.

6. The EPC full-process technology management method according to claim 1, characterized in that, The Internet of Things (IoT) sensor network includes RFID tags and GPS positioning devices for progress tracking, as well as temperature and humidity sensors and displacement sensors for quality monitoring.

7. The EPC full-process technology management method according to claim 6, characterized in that, The RFID tag is installed on the precast component to track the component's arrival on site; the GPS positioning device is installed on the construction machinery to track the machinery's location; the temperature and humidity sensor is embedded in the concrete to monitor the curing environment; and the displacement sensor is installed around the foundation pit to monitor slope stability.

8. The EPC full-process technology management method according to claim 1, characterized in that, The artificial intelligence (AI) algorithm in S4 includes: S41: Train an LSTM model based on historical data, input current progress and external environment data, and output progress prediction and critical path risk warning; S42: Use a genetic algorithm to dynamically adjust the resource scheduling plan and optimize resource utilization.

9. The EPC full-process technology management method according to claim 1, characterized in that, The closed-loop management in S5 includes: S51: Use image recognition or IoT data AI to diagnose quality defect types and analyze root causes; S52: Automatically associates BIM model location with responsible work team and pushes rectification plan; S53: After rectification is completed, scan the code to upload the verification information, the system closes the problem and generates an electronic file.

10. An intelligent collaborative platform for implementing the EPC full-process technical management method of claims 1-9, characterized in that, include: A unified data platform for integrating BIM, ERP, and IoT data; An AI decision engine is used to perform progress prediction, resource optimization, and risk warning in S4. A multi-terminal collaborative interface for accessing from web, mobile, and AR terminals; The blockchain evidence storage module is used to store the acceptance records and quality data of key processes in S5.

Images