A gas engineering whole-process digitalization management and control method

Abstract

The application discloses a kind of gas engineering whole-process digitalization management and control method, comprising the following steps: S1: constructing whole-process data model, whole-process data model stores engineering object, stage information, attribute information and association information;S2: acquisition design stage, construction stage and the multi-source data of operation and maintenance stage;S3: to multi-source data is cleaned, standardized and space-time alignment, form the standardized data with unified coding;S4: the standardized data is mapped to whole-process data model, generates digital twin;S5: based on digital twin, whole-process control is executed.The application is by constructing whole-process data model and digital twin, and the data of each stage of design, construction, operation and maintenance is uniformly associated, realizes quality defect traceability, construction progress deviation analysis, safety risk early warning and operation and maintenance work order linkage, effectively improves gas engineering whole-process digitalization management and control level, reduces safety risk.

Description

Technical Field

[0001] This invention belongs to the field of gas engineering technology, specifically relating to a digital management and control method for the entire process of gas engineering. Background Technology

[0002] Gas engineering projects involve multiple stakeholders and stages from design and construction to operation and maintenance, generating massive amounts of complex data at each stage. Currently, BIM models and CAD drawings from the design stage, welding and non-destructive testing data from the construction stage, and SCADA monitoring data from the operation and maintenance stage are typically stored in different systems or files, lacking a unified data model and a comprehensive workflow linkage mechanism. This results in ineffective data integration across stages. When weld defects occur during construction or pressure anomalies occur during operation and maintenance, it is difficult to quickly trace upstream information such as design parameters, construction records, and material batches. Furthermore, it is impossible to provide integrated early warnings for construction and operation and maintenance risks based on real-time monitoring data, making it difficult to promptly detect and manage quality and safety hazards in gas engineering projects.

[0003] Therefore, in order to address the aforementioned technical issues, it is necessary to provide a digital management and control method for the entire process of gas engineering.

[0004] The information disclosed in this background section is intended only to enhance the understanding of the overall background of the invention and should not be construed as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this invention is to provide a digital management and control method for the entire process of gas engineering, which can solve the problems mentioned in the background.

[0006] To achieve the above objectives, a specific embodiment of the present invention provides the following technical solution:

[0007] A method for digital management and control of the entire gas engineering process includes the following steps:

[0008] S1: Construct a full-process data model, which stores project objects, stage information, attribute information, and relationship information;

[0009] S2: Collect multi-source data from the design, construction, and operation and maintenance phases;

[0010] S3: Clean, standardize, and spatiotemporally align the multi-source data to form standardized data with a unified code;

[0011] S4: Map the standardized data to the full-process data model to generate a digital twin;

[0012] S5: Perform full-process control based on the digital twin, which includes construction progress deviation analysis, quality defect tracing, safety risk early warning, and operation and maintenance work order linkage.

[0013] In one or more embodiments of the present invention, in step S1, the full-process data model includes a role-based multi-participant permission model, which is used to configure data access, editing and approval permissions for design units, construction units, supervision units, operation and maintenance units and owner units respectively. The full-process data model has a data change tracking mechanism, which records the modifier, modification time, modification content and modification reason for each data modification, and supports version rollback.

[0014] In one or more embodiments of the present invention, the data acquisition during the construction phase in step S2 includes the following steps:

[0015] S201. Deploy UWB positioning base stations at the construction site and have construction workers wear positioning tags to collect real-time location trajectory data of construction workers.

[0016] S202. Associate the location trajectory data with the construction area, construction process and work ticket information to form a record of personnel's spatiotemporal activities.

[0017] In one or more embodiments of the present invention, step S3 further includes a data security processing step, specifically including the following steps:

[0018] S301. De-identify data involving personal privacy and sensitive engineering information;

[0019] S302. Encrypt the transmitted and stored data using the national cryptographic algorithm;

[0020] S303. Implement hierarchical control over data access permissions for users with different roles, and record all data access logs.

[0021] In one or more embodiments of the present invention, in step S4, the engineering objects in the digital twin have bidirectional indexes with the three-dimensional model components, two-dimensional drawing elements and attribute information, supporting associated queries from any entry point in the three-dimensional scene, two-dimensional drawings and data tables.

[0022] In one or more embodiments of the present invention, step S5 specifically includes the following steps:

[0023] Based on earned value management, the Schedule Performance Index (SPI) and Cost Performance Index (CPI) are calculated according to the planned workload and the actual workload completed.

[0024] The SPI and CPI are compared with the preset first and second thresholds, respectively. When the SPI is less than the first threshold or the CPI is less than the second threshold, a progress warning or cost warning is automatically generated and pushed to the project management personnel. At the same time, based on the critical path data in the digital twin, the impact of the deviation on the total project duration is analyzed.

[0025] In one or more embodiments of the present invention, step S5 specifically includes the following steps:

[0026] Establish a quality defect knowledge base, which includes the types, causes, handling measures, and prevention suggestions of historical quality defects;

[0027] Based on the aforementioned quality defect knowledge base, a defect root cause classification model is trained. When a new quality defect occurs, the defect features are input into the defect root cause classification model, which automatically matches similar historical cases and recommends root cause analysis results and solutions.

[0028] In one or more embodiments of the present invention, step S5 specifically includes the following steps:

[0029] Establish a tiered early warning mechanism, classifying early warnings into Level 1, Level 2, and Level 3 based on the degree to which the risk prediction value deviates from the safety threshold;

[0030] Different levels of early warning correspond to different handling procedures and response time limits. Among them, the first-level early warning will automatically shut down the associated valves and notify the emergency command center; the second-level early warning will be pushed to the regional person in charge and require feedback on the handling results within the specified time limit; and the third-level early warning will be pushed to the on-duty personnel and recorded and observed.

[0031] In one or more embodiments of the present invention, step S5 specifically includes the following steps:

[0032] Based on the equipment's operating data and maintenance records in the digital twin, a reliability analysis model is used to calculate the equipment's remaining lifespan and failure probability.

[0033] When the probability of equipment failure exceeds a preset threshold or the remaining lifespan is less than a preset number of days, a preventive maintenance work order is automatically generated, and the work order is intelligently dispatched by comprehensively considering the location of maintenance personnel, skill matching degree, and urgency of the work order.

[0034] In one or more embodiments of the present invention, step S6 is further included: generating a visualization dashboard based on the digital twin. The visualization dashboard displays key indicators of the entire gas engineering process in the form of visualization charts. The key indicators include construction progress achievement rate, quality pass rate, safety hazard rectification rate, equipment integrity rate, and maintenance work order timeliness rate. The visualization dashboard supports linked drill-down analysis by project segment, time range, and indicator dimension, generates management analysis reports, and pushes them to the management terminal.

[0035] Compared with existing technologies, the present invention provides a digital management and control method for the entire process of gas engineering. By constructing a full-process data model and digital twin, it unifies and links data from the design, construction, and operation and maintenance stages, enabling traceability of quality defects, analysis of construction progress deviations, early warning of safety risks, and linkage of operation and maintenance work orders. This effectively improves the level of digital management and control of the entire gas engineering process and reduces safety risks. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 This is a flowchart of a method for digital management and control of the entire gas engineering process according to an embodiment of the present invention;

[0038] Figure 2 This is a structural block diagram of the full-process data model in one embodiment of the present invention;

[0039] Figure 3 This is a flowchart of a data acquisition method during the construction phase in one embodiment of the present invention;

[0040] Figure 4 This is a flowchart of data security processing steps in one embodiment of the present invention. Detailed Implementation

[0041] To enable those skilled in the art to better understand the technical solutions in this disclosure, the technical solutions in the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. Based on the embodiments in this disclosure, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this disclosure.

[0042] like Figure 1As shown, an embodiment of the present invention provides a method for digital management and control of the entire process of a gas engineering project, comprising the following steps:

[0043] S1. Construct a full-process data model.

[0044] like Figure 2 As shown, the end-to-end data model stores project objects, phase information, attribute information, and relationship information. The end-to-end data model can be implemented based on one or more combinations of relational databases, graph databases, or time-series databases, and is used to uniformly organize and represent data objects and their relationships in the design, construction, and operation and maintenance phases.

[0045] Specifically, gas engineering objects can be categorized according to their characteristics into pipeline, equipment, testing, personnel, and space categories. Pipeline objects include pipe sections, elbows, tees, and valves. Equipment objects include pressure regulating boxes, metering cabinets, and leak monitoring equipment. Testing objects include non-destructive testing records, pressure test records, and leak detection records. Personnel objects include designers, construction workers, welders, supervisors, and maintenance personnel. Space objects include engineering sections, construction areas, stations, and buried areas.

[0046] In addition, the types of relationships include, but are not limited to, spatial inclusion, physical connection, logical dependency, parameter reference, stage progression, causal relationship, and version relationship. For example, weld joint objects can be associated with upstream and downstream pipe segments through "physical connection", with design parameters and welding process parameters through "parameter reference", and with design weld joint number, construction weld joint number, and maintenance defect record through "stage progression".

[0047] In this embodiment, the full-process data model also includes a role-based multi-participant permission model, which is used to configure data access, editing and approval permissions for design units, construction units, supervision units, operation and maintenance units and owner units respectively.

[0048] For example, design firms can edit and submit design documents, BIM models, and design changes, which take effect after approval by the supervision and owner units. Construction firms can enter construction process records, welding and non-destructive testing data, but cannot modify design parameters. Supervision firms can review and approve construction quality data. Operation and maintenance firms can view design parameters and construction records but can only edit operation and maintenance data. Owner units have global query permissions and approval permissions for major matters.

[0049] In addition, the end-to-end data model includes a data change tracking mechanism. Each data modification records the modifier, modification time, modification content, reason for modification, and terminal information, and supports version rollback. Operation logs are retained during version rollback to prevent accidental operations and malicious tampering.

[0050] S2. Collect multi-source data from the design, construction, and operation and maintenance phases.

[0051] Data acquisition during the design phase can include data collection from design management systems, BIM collaboration platforms, and CAD systems. BIM models can parse IFC files to extract component geometry, material properties, design parameters, equipment codes, etc. CAD drawings can parse layers, blocks, and extended attributes from DWG / DXF files. Parameters in design files, such as pipe diameter, wall thickness, material, pressure rating, corrosion resistance rating, and weld number, are mapped using rules to form structured data. Unstructured drawings or PDF files can be extracted and stored in the database using OCR and manual assistance.

[0052] In addition, data collection during the construction phase can include collecting welding records, non-destructive testing data, material batches, images of concealed works, and construction environment data through mobile terminals, IoT sensors, and positioning systems.

[0053] For example, welding data can be automatically uploaded by the intelligent welding machine via an IoT interface, including parameters such as current, voltage, welding speed, and heat input, and associated with the weld number and welder's number. Non-destructive testing data can include reports and film images from radiographic testing, ultrasonic testing, magnetic particle testing, and penetrant testing, with the test results linked to the weld number. Material batch data includes material certificates, re-inspection reports, and requisition records for pipes, welding materials, and anti-corrosion materials.

[0054] like Figure 2 As shown, data collection during the construction phase includes the following steps:

[0055] S201. Deploy UWB positioning base stations at the construction site and have construction workers wear positioning tags to collect their location trajectory data in real time. Specifically, deploy at least three UWB positioning base stations at the construction site, and have construction workers wear positioning tags. The positioning base stations receive the ultra-wideband pulse signals emitted by the tags and calculate the tags' three-dimensional coordinates using either Time Difference of Arrival (TDOA) or Time of Arrival (TOA) algorithms. The positioning data is uploaded to the management and control platform in real time via the on-site network.

[0056] S202. Link the location trajectory data with the construction area, construction process, and work permit information to form a record of personnel's spatiotemporal activities. For example, it can record the time a welder stays around a weld joint, the time period when entering a dangerous area, and whether they are active in an area without a work permit, and can perform time verification with the weld joint welding record.

[0057] Furthermore, data collection during the operation and maintenance phase can include data such as pressure, flow rate, temperature, leakage concentration, valve status, equipment operating status, inspection records, and maintenance work orders from inspection systems, equipment management systems, and video monitoring platforms.

[0058] S3. Clean, standardize, and spatiotemporally align multi-source data to form standardized data with unified coding.

[0059] Data cleaning includes removing duplicate records, correcting obvious outliers, filling in missing values, and standardizing null value identifiers. For example, when there are multiple inspection records for the same weld joint, duplicates are removed based on the inspection time, inspection method, and report number.

[0060] In addition, standardization includes standardization of object types and attribute names, standardization of units of measurement, standardization of time formats, and standardization of codes. For example, pressure is standardized to kPa, temperature to ℃, length to m, and time to Beijing time. The names of the same valve may differ in the design, construction, and operation and maintenance phases; these are mapped to a unified code throughout the entire process through a data dictionary.

[0061] Specifically, spatiotemporal alignment includes spatial alignment and temporal alignment. Spatial alignment transforms construction layout coordinates and maintenance equipment coordinates into the engineering coordinate system. Temporal alignment corrects for clock discrepancies in timestamps from different systems. Through spatiotemporal alignment, it is possible to ensure that data for the same engineering object at different stages and in different systems are consistent and comparable in both spatial and temporal dimensions.

[0062] After the above cleaning, standardization and spatiotemporal alignment processes, the entire data allocation process is uniformly coded and mapped to the primary key of the source system, retaining the source identifier and original data index for easy subsequent traceability.

[0063] like Figure 2 As shown, step S3 also includes a data security processing step, which specifically includes the following steps:

[0064] S301. Data involving personal privacy and sensitive engineering information should be anonymized. For example, the names, ID numbers, and mobile phone numbers of construction workers should be masked. Sensitive data such as the precise coordinates of pipelines and site security videos should be categorized, blurred, or have access restricted.

[0065] S302. Encrypt transmitted and stored data using national cryptographic algorithms. For example, the transport layer uses the SM4 symmetric encryption algorithm, the session key is negotiated using the SM2 asymmetric algorithm, and data integrity is verified using the SM3 hash algorithm; the storage layer performs transparent encryption or application layer encryption on key data fields.

[0066] S303. Implement hierarchical control over data access permissions for users with different roles, and record all data access logs. Access logs include access subject, access time, data object, operation type, access IP, whether it was exported, etc., and the log retention time meets management requirements.

[0067] S4. Map standardized data to the end-to-end data model to generate a digital twin.

[0068] This involves establishing mapping rules between standardized data and the end-to-end data model. These mapping rules can include direct field mapping, dictionary mapping, combined mapping, and spatial association mapping. For example, layer names in design drawings are mapped to standard object types using a dictionary.

[0069] In addition, the engineering objects in the digital twin have bidirectional indexes with the 3D model components, 2D drawing elements and attribute information, supporting related queries from any entry point in the 3D scene, 2D drawings and data tables.

[0070] For example, clicking on a valve object in a 3D scene allows users to view its design parameters, construction records, and operation and maintenance monitoring curves. Similarly, clicking on a weld number in the defect record list provides reverse location to the 3D model and 2D drawing elements. Through this bidirectional indexing, the digital twin enables linked retrieval of information across multiple stages of design, construction, and operation and maintenance.

[0071] S5. Implement full-process control based on digital twins. Full-process control includes construction progress deviation analysis, quality defect tracing, safety risk early warning, and operation and maintenance work order linkage.

[0072] (1) Construction progress deviation analysis.

[0073] In this embodiment, the construction schedule deviation analysis is based on earned value management. For each scheduled task, the planned value (PV), earned value (EV), and actual cost (AC) are determined. PV is determined based on the construction drawing budget and schedule plan, EV is calculated based on the actual completed work volume and the budgeted unit price, and AC is collected based on the actual cost ledger.

[0074] Calculate the Schedule Performance Index (SPI) and the Cost Performance Index (CPI), where SPI = EV / PV and CPI = EV / AC. Compare SPI and CPI with preset first and second thresholds, respectively. When SPI is less than the first threshold, a schedule delay is identified, and a schedule warning is automatically generated. When CPI is less than the second threshold, a cost overrun is identified, and a cost warning is automatically generated. The warning information is pushed to project management personnel. When both SPI and CPI are less than the thresholds, a combined warning can be generated and the warning level can be increased.

[0075] Simultaneously, based on the critical path data in the digital twin, the impact of deviations on the overall project duration is analyzed. Delays in tasks on the critical path will directly cause overall project delays. Delays in tasks on non-critical paths require calculation, in conjunction with total float, to determine whether a critical path shift will occur. The analysis results can be displayed in the digital twin's 3D construction progress scene using color highlighting, such as red for delayed critical path tasks, yellow for warning tasks, and green for normal tasks.

[0076] (2) Quality defect traceability.

[0077] Establish a quality defect knowledge base, recording the types, causes, handling measures, and prevention suggestions for historical quality defects. The knowledge base may include the following fields: defect number, defect name, defect type, stage of occurrence, project object type, defect characteristic description, characteristic parameters, root cause, handling measures, prevention suggestions, associated drawing / model / record number, and entry time. Defect types may include cracks, porosity, inclusions, leaks, deformation, corrosion, and third-party damage.

[0078] A defect root cause classification model is trained based on a quality defect knowledge base. Machine learning algorithms such as XGBoost and Random Forest can be used. Input features can include defect type, defect size, defect location, welding method, ambient temperature, and detection method. The model outputs possible root cause classifications and confidence scores. Simultaneously, the system retrieves similar historical cases from the knowledge base based on the defect feature vectors. Similarity can be calculated using Euclidean distance, cosine similarity, or nearest neighbor algorithms. The system then recommends several cases with the highest similarity, along with the corresponding root cause analysis results and treatment solutions.

[0079] When a new quality defect occurs, the defect characteristics are input into the defect root cause classification model, which automatically matches historical similar cases and recommends root cause analysis results and solutions. Quality engineers can confirm or correct the recommendations. After processing, the actual root cause and the processing results are fed back into the knowledge base for continuous optimization of the classification model and similar case retrieval performance.

[0080] (3) Safety risk warning.

[0081] Establish a tiered early warning mechanism, classifying warnings into Level 1, Level 2, and Level 3 based on the degree to which the predicted risk value deviates from the safety threshold. Different levels of warnings correspond to different handling procedures and response time limits.

[0082] Risk prediction values ​​can be derived from real-time monitoring data in a digital twin, construction process risk data, equipment reliability data, or third-party risk data. Taking pipeline pressure risk as an example, normal operating pressure range, attention threshold, warning threshold, and high-risk threshold can be set. For instance, when the pressure exceeds the upper limit of normal operation but does not reach the warning threshold, it is determined as a level three warning. When the pressure exceeds the warning threshold or the rate of change is abnormal, it is determined as a level two warning. When the pressure approaches or exceeds the maximum permissible operating pressure, or the leakage concentration reaches 20% of the lower explosive limit, it is determined as a level one warning.

[0083] In terms of the handling process, a Level 1 warning automatically shuts down the associated valves and notifies the emergency command center. A Level 2 warning is sent to the regional manager, who is required to provide feedback on the handling results within a specified time limit. A Level 3 warning is sent to on-duty personnel, who then record and observe the situation. Each Level 1 warning generates a warning record, which is linked to the corresponding engineering object and spatial location in the digital twin. The entire warning and handling process is traceable, forming a closed loop.

[0084] (4) Operation and maintenance work order linkage.

[0085] Based on the operational data and maintenance records of equipment in a digital twin, a reliability analysis model is used to calculate the remaining life and probability of failure of the equipment. The reliability analysis model can employ a Weibull distribution model or a machine learning-based remaining life prediction model. Taking the Weibull distribution as an example, shape and scale parameters are estimated based on the equipment's historical failure times, maintenance records, and operational parameters, and then the probability of failure and remaining life under a given operating time are calculated.

[0086] Set a failure probability threshold and a remaining lifespan threshold. When the equipment failure probability exceeds the preset threshold or the remaining lifespan is less than the preset number of days, a preventative maintenance work order is automatically generated. The work order includes the equipment code, equipment location, predicted failure probability, suggested maintenance items, and maintenance period.

[0087] Intelligent work order dispatch comprehensively considers the location of maintenance personnel, skill matching, and the urgency of the work order. A work order dispatch objective function can be established, such as minimizing response time or minimizing maintenance risk. Constraints include personnel skill requirements, personnel's current work order load not exceeding the limit, and priority given to urgent work orders. A genetic algorithm can be used to solve the problem. The work order dispatch result is pushed to the maintenance personnel's mobile terminal. After receiving the data, the maintenance personnel execute the maintenance task and update the processing results, completing the work order loop.

[0088] S6. Generate visual dashboards based on digital twins.

[0089] The visualization dashboard displays key indicators for the entire gas engineering process in the form of visual charts, including construction progress achievement rate, quality pass rate, safety hazard rectification rate, equipment integrity rate, and maintenance work order timeliness rate.

[0090] The following metrics are defined: Construction progress achievement rate = Actual completed work volume / Planned completed work volume. Quality pass rate = Number of qualified inspection batches / Total number of inspection batches. Safety hazard rectification rate = Number of hazards rectified in a closed loop / Total number of hazards found. Equipment integrity rate = Number of intact equipment units / Total number of equipment units. Maintenance work order timeliness rate = Number of work orders completed on time / Total number of work orders to be completed.

[0091] The dashboard supports drill-down analysis by project segment, time range, and indicator dimension. For example, it allows drilling down from the overall construction progress achievement rate to a specific project segment, and then down to the progress details of a specific construction process. It can also drill down from the quality pass rate to the pass rate trend and defect distribution for a specific inspection type. The dashboard can generate management analysis reports and push them to management terminals via email, SMS, and mobile messages.

[0092] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0093] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0094] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0095] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0096] It will be apparent to those skilled in the art that this disclosure is not limited to the details of the exemplary embodiments described above, and that this disclosure can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of this disclosure is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within this disclosure. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0097] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains 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 method for digital management and control of the entire process of a gas engineering project, characterized in that, Includes the following steps: S1. Construct a full-process data model, which stores project objects, stage information, attribute information, and relationship information; S2. Collect multi-source data from the design, construction, and operation and maintenance phases; S3. Clean, standardize, and spatiotemporally align the multi-source data to form standardized data with a unified code; S4. Map the standardized data to the full-process data model to generate a digital twin; S5. Perform full-process control based on the digital twin, which includes construction progress deviation analysis, quality defect tracing, safety risk early warning, and operation and maintenance work order linkage.

2. The method for full-process digital management and control of gas engineering according to claim 1, characterized in that, In step S1, the full-process data model includes a role-based multi-participant permission model, which is used to configure data access, editing and approval permissions for design units, construction units, supervision units, operation and maintenance units and owner units respectively. The full-process data model has a data change tracking mechanism, which records the modifier, modification time, modification content and modification reason for each data modification, and supports version rollback.

3. The method for full-process digital management and control of gas engineering according to claim 1, characterized in that, In step S2, data collection during the construction phase includes the following steps: S201. Deploy UWB positioning base stations at the construction site and have construction workers wear positioning tags to collect real-time location trajectory data of construction workers. S202. Associate the location trajectory data with the construction area, construction process and work ticket information to form a record of personnel's spatiotemporal activities.

4. The method for full-process digital management and control of gas engineering according to claim 1, characterized in that, Step S3 also includes a data security processing step, specifically including the following steps: S301. De-identify data involving personal privacy and sensitive engineering information; S302. Encrypt the transmitted and stored data using the national cryptographic algorithm; S303. Implement hierarchical control over data access permissions for users with different roles, and record all data access logs.

5. The method for full-process digital management and control of gas engineering according to claim 1, characterized in that, In step S4, the engineering objects in the digital twin have bidirectional indexes with the 3D model components, 2D drawing elements and attribute information, supporting associated queries from any entry point in the 3D scene, 2D drawings and data tables.

6. The method for full-process digital management and control of gas engineering according to claim 1, characterized in that, In step S5, the construction schedule deviation analysis specifically includes the following steps: Based on earned value management, the Schedule Performance Index (SPI) and Cost Performance Index (CPI) are calculated according to the planned workload and the actual workload completed. The SPI and CPI are compared with the preset first and second thresholds, respectively. When the SPI is less than the first threshold or the CPI is less than the second threshold, a progress warning or cost warning is automatically generated and pushed to the project management personnel. At the same time, based on the critical path data in the digital twin, the impact of the deviation on the total project duration is analyzed.

7. The method for full-process digital management and control of gas engineering according to claim 1, characterized in that, In step S5, the quality defect tracing specifically includes the following steps: Establish a quality defect knowledge base, which includes the types, causes, handling measures, and prevention suggestions of historical quality defects; Based on the aforementioned quality defect knowledge base, a defect root cause classification model is trained. When a new quality defect occurs, the defect features are input into the defect root cause classification model, which automatically matches similar historical cases and recommends root cause analysis results and solutions.

8. The method for full-process digital management and control of gas engineering according to claim 1, characterized in that, In step S5, the security risk warning specifically includes the following steps: Establish a tiered early warning mechanism, classifying early warnings into Level 1, Level 2, and Level 3 based on the degree to which the risk prediction value deviates from the safety threshold; Different levels of early warning correspond to different handling procedures and response time limits. Among them, the first-level early warning will automatically shut down the associated valves and notify the emergency command center; the second-level early warning will be pushed to the regional person in charge and require feedback on the handling results within the specified time limit; and the third-level early warning will be pushed to the on-duty personnel and recorded and observed.

9. The method for full-process digital management and control of gas engineering according to claim 1, characterized in that, In step S5, the linkage of maintenance work orders specifically includes the following steps: Based on the equipment's operating data and maintenance records in the digital twin, a reliability analysis model is used to calculate the equipment's remaining lifespan and failure probability. When the probability of equipment failure exceeds a preset threshold or the remaining lifespan is less than a preset number of days, a preventive maintenance work order is automatically generated, and the work order is intelligently dispatched by comprehensively considering the location of maintenance personnel, skill matching degree, and urgency of the work order.

10. The method for full-process digital management and control of gas engineering according to claim 1, characterized in that, It also includes step S6: generating a visualization dashboard based on the digital twin. The visualization dashboard displays key indicators of the entire gas engineering process in the form of visual charts. The key indicators include construction progress achievement rate, quality pass rate, safety hazard rectification rate, equipment integrity rate, and maintenance work order timeliness rate. The visualization dashboard supports linked drill-down analysis by project segment, time range, and indicator dimension, generates management analysis reports, and pushes them to the management terminal.

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