Petrochemical device full life cycle management system based on digital twinning

Abstract

The invention relates to petrochemical device management, in particular to a digital twinning-based petrochemical device full-life-cycle management system, which is characterized in that a control unit acquires equipment data of each equipment in a petrochemical device through an equipment data acquisition module and acquires mechanism models of each equipment in the petrochemical device by utilizing a mechanism model acquisition module; the control unit converts the equipment data into action data corresponding to the mechanism model through an action data generation module, and generates a digital twinborn model corresponding to each piece of equipment according to the mechanism model and the action data by utilizing a digital twinborn model generation module; a leakage result analysis unit analyzes the reference leakage frequency and the corrected leakage frequency to obtain leakage results of all leakage types of each device, and a hierarchical management module performs hierarchical full-life-cycle management on each device according to the leakage results; according to the technical scheme provided by the invention, the defects that digital twin modeling is difficult to carry out on multiple devices of the petrochemical device and the full life cycle management lacks a differential strategy based on leakage risks can be overcome.

Description

Technical Field

[0001] The present invention relates to petrochemical plant management, and in particular to a petrochemical plant full life cycle management system based on digital twins. Background Art

[0002] Petrochemical plants are characterized by their large scale, complex processes, diverse equipment types, and harsh operating environments. Their full lifecycle management encompasses multiple stages, including design, manufacturing, installation, operation, maintenance, and decommissioning. Traditional management models create severe information silos across various stages, hindering data flow and resulting in inaccurate, timely, and comprehensive decision-making. Furthermore, petrochemical plants present safety risks such as leakage, flammability, and explosion hazards during operation. Accidents can result in significant casualties and property losses. Therefore, achieving digital and intelligent management of petrochemical plants throughout their lifecycle to improve production efficiency, reduce safety risks, and optimize resource allocation has become a pressing challenge for the industry.

[0003] Currently, most petrochemical plant management systems on the market are based on traditional information technology architectures, which are difficult to meet the complex needs of full lifecycle management. These systems have the following shortcomings:

[0004] 1) Fragmented model building: The lack of a unified modeling approach for the coupled characteristics of multiple devices and multiple physical fields in petrochemical plants leads to serious data silos between devices, making it difficult to support cross-process collaborative optimization.

[0005] 2) Static risk assessment: Leakage risk analysis often relies on periodic testing or threshold alarms, which cannot integrate dynamically changing equipment operating information and process parameters in real time, making it difficult to accurately classify and dynamically adjust risk levels.

[0006] 3) Extensive management strategies: The full life cycle management lacks a differentiated strategy based on risk levels, resulting in the same maintenance cycle for high-risk equipment and low-risk equipment, causing waste of resources and omission of hidden dangers. Summary of the Invention

[0007] (1) Technical problems solved

[0008] In response to the above-mentioned shortcomings of the existing technology, the present invention provides a petrochemical plant full life cycle management system based on digital twins, which can effectively overcome the defects of the existing technology in that it is difficult to perform digital twin modeling on multiple equipment in petrochemical plants, and the lack of differentiated strategies based on leakage risks in the full life cycle management.

[0009] (2) Technical solution

[0010] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0011] A digital twin-based petrochemical plant lifecycle management system includes a control unit that acquires device data of each device in the petrochemical plant through a device data acquisition module and acquires a mechanism model of each device in the petrochemical plant through a mechanism model acquisition module. The control unit converts the device data into action data corresponding to the mechanism model through an action data generation module, and generates a digital twin model corresponding to each device based on the mechanism model and action data using a digital twin model generation module.

[0012] The control unit generates a comprehensive leakage parameter based on the basic operation information and leakage basic information of each device through the leakage parameter acquisition unit, and uses the leakage impact correction unit to determine the device sensitivity correction factor of each device for each leakage type based on the comprehensive leakage parameter. At the same time, the detection effectiveness correction factor of each device for each leakage type is determined based on the acquired device detection effectiveness information. The control unit calculates the baseline leakage frequency and corrected leakage frequency of each device for each leakage type based on the comprehensive leakage parameter, the device sensitivity correction factor and the detection effectiveness correction factor through the leakage frequency calculation unit, and uses the leakage result analysis unit to analyze the baseline leakage frequency and the corrected leakage frequency to obtain the leakage results of each device for all leakage types. The control unit performs hierarchical full life cycle management of each device based on the leakage results through the hierarchical management module.

[0013] Preferably, the device data acquisition module acquires the device data of each device in the petrochemical device, and the mechanism model acquisition module acquires the mechanism model of each device in the petrochemical device, including:

[0014] The device data acquisition module obtains the device data of the target device from the IoT management platform through the WebSocket communication protocol according to the device identifier corresponding to the target device;

[0015] The mechanism model acquisition module obtains the mechanism model of the target device from the industrial mechanism platform according to the device identification corresponding to the target device.

[0016] Preferably, the action data generation module converts the device data into action data corresponding to the mechanism model, including:

[0017] The device data of the target device is converted into simulation data, and the simulation data is converted into action data corresponding to the mechanism model of the target device.

[0018] Preferably, the digital twin model generation module generates a digital twin model corresponding to each device according to the mechanism model and action data, including:

[0019] The action data corresponding to the mechanism model of the target device is fused with the model parameters in the mechanism model to generate a digital twin model corresponding to the target device.

[0020] Preferably, the system also includes a business data acquisition module and a business message generation and display module. The control unit obtains the business data of each equipment in the petrochemical device through the business data acquisition module, and uses the business message generation and display module to generate business messages corresponding to the digital twin model based on the business data, and displays them in the digital twin model.

[0021] Preferably, the business data acquisition module acquires the business data of each device in the petrochemical device, and the business message generation and display module generates a business message corresponding to the digital twin model based on the business data and displays it in the digital twin model, including:

[0022] The business data acquisition module obtains the business data of the target device from the IoT management platform according to the device identifier corresponding to the target device;

[0023] The business message generation and display module performs statistical processing on the business data of the target device according to the preset business demand data to generate business messages, and displays the business messages in the digital twin model corresponding to the target device.

[0024] Preferably, the leakage parameter acquisition unit includes a basic operation information acquisition module and a basic leakage information acquisition module;

[0025] The basic operation information acquisition module acquires the basic operation information of the target device including target device type information, target device process information and target device operation medium information;

[0026] The leakage basic information acquisition module obtains the basic leakage information of the target device, including leakage type information, leakage duration information and leakage aperture information, and generates comprehensive leakage parameters of the target device in combination with the basic operation information.

[0027] Preferably, the leakage impact correction unit includes an equipment sensitivity correction factor determination module and a detection effectiveness correction factor determination module;

[0028] The device sensitivity correction factor determination module quantifies the daily management information and comprehensive leakage parameters of the target device, and calls the corresponding leakage severity calculation formula based on the leakage type information to calculate the device sensitivity correction factor of the target device for each leakage type;

[0029] The detection effectiveness correction factor determination module quantifies and rates the acquired device detection effectiveness information to obtain the detection effectiveness correction factor of the target device for each leakage type.

[0030] Preferably, the leakage frequency calculation unit includes a reference leakage frequency calculation module and a modified leakage frequency calculation module;

[0031] The baseline leakage frequency calculation module calls the corresponding leakage frequency calculation model according to the leakage type information, and calculates the baseline leakage frequency of the target device for each leakage type in combination with the comprehensive leakage parameters;

[0032] The corrected leakage frequency calculation module couples the baseline leakage frequency with the device sensitivity correction factor and the detection effectiveness correction factor to further obtain the corrected leakage frequency of the target device for each leakage type.

[0033] Preferably, the leakage result analysis unit analyzes the baseline leakage frequency and the corrected leakage frequency to obtain leakage results of each device for all leakage types, including:

[0034] Call the corresponding benchmark grade standard model according to the leakage type information, score the benchmark leakage frequency of the target device for each leakage type, and obtain the benchmark leakage evaluation score;

[0035] Call the corresponding correction level standard model according to the leakage type information, score the correction leakage frequency of the target device for each leakage type, and obtain the correction leakage evaluation score;

[0036] A corresponding weight is set according to the impact of each leakage type, and the baseline leakage frequency and the corrected leakage frequency of the target device for each leakage type are weighted and summed to obtain the leakage score of the target device for all leakage types.

[0037] (3) Beneficial effects

[0038] Compared with the existing technology, the petrochemical plant full life cycle management system based on digital twins provided by the present invention has the following beneficial effects:

[0039] 1) The device data acquisition module acquires the device data of each device in the petrochemical plant. The mechanism model acquisition module acquires the mechanism model of each device in the petrochemical plant. The action data generation module converts the device data into action data corresponding to the mechanism model. The digital twin model generation module generates a digital twin model corresponding to each device based on the mechanism model and action data. Through a unified data architecture and modular modeling approach, full-factor mapping from single equipment to device-level systems is achieved. This enables digital twin modeling of multiple devices in the petrochemical plant and supports dynamic simulation of complex scenarios such as equipment performance degradation and process parameter coupling.

[0040] 2) The leakage parameter acquisition unit generates a comprehensive leakage parameter based on the basic operation information and leakage basic information of each device. The leakage impact correction unit determines the device sensitivity correction factor of each device for each leakage type based on the comprehensive leakage parameter, and at the same time determines the detection effectiveness correction factor of each device for each leakage type based on the acquired device detection effectiveness information. The leakage frequency calculation unit calculates the baseline leakage frequency and corrected leakage frequency of each device for each leakage type based on the comprehensive leakage parameter, the device sensitivity correction factor and the detection effectiveness correction factor. The leakage result analysis unit analyzes the baseline leakage frequency and the corrected leakage frequency to obtain the leakage results of each device for all leakage types. The hierarchical management module performs hierarchical full life cycle management of each device based on the leakage results. Based on the equipment operation information, leakage history and real-time monitoring information, a multi-dimensional leakage risk assessment model is established, and the equipment is graded and identified, so that each device can be graded and managed throughout its life cycle according to the leakage results, and a differentiated management and control mechanism based on leakage risk is constructed. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort.

[0042] Figure 1 A schematic diagram of the system of the present invention;

[0043] Figure 2 A schematic diagram of the process of generating a digital twin model corresponding to each device in a petrochemical plant in the present invention;

[0044] Figure 3 This is a flow chart of the hierarchical full life cycle management of each device according to the leakage results in the present invention. DETAILED DESCRIPTION

[0045] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0046] Petrochemical plant full life cycle management system based on digital twin, such as Figure 1As shown, it includes a control unit, which obtains the equipment data of each equipment in the petrochemical device through the equipment data acquisition module, and obtains the mechanism model of each equipment in the petrochemical device using the mechanism model acquisition module. The control unit converts the equipment data into action data corresponding to the mechanism model through the action data generation module, and uses the digital twin model generation module to generate a digital twin model corresponding to each equipment according to the mechanism model and action data.

[0047] ① The equipment data acquisition module acquires the equipment data of each equipment in the petrochemical device, and the mechanism model acquisition module acquires the mechanism model of each equipment in the petrochemical device, such as Figure 2 Shown, including:

[0048] The device data acquisition module obtains the device data of the target device from the IoT management platform through the WebSocket communication protocol according to the device identifier corresponding to the target device;

[0049] The mechanism model acquisition module obtains the mechanism model of the target device from the industrial mechanism platform according to the device identification corresponding to the target device.

[0050] ②The action data generation module converts the device data into action data corresponding to the mechanism model, such as Figure 2 Shown, including:

[0051] The device data of the target device is converted into simulation data, and the simulation data is converted into action data corresponding to the mechanism model of the target device.

[0052] ③ The digital twin model generation module generates the digital twin model corresponding to each device based on the mechanism model and action data, such as Figure 2 Shown, including:

[0053] The action data corresponding to the mechanism model of the target device is fused with the model parameters in the mechanism model to generate a digital twin model corresponding to the target device.

[0054] In the above technical solution, the equipment data acquisition module acquires the equipment data of each equipment in the petrochemical plant, the mechanism model acquisition module acquires the mechanism model of each equipment in the petrochemical plant, the action data generation module converts the equipment data into action data corresponding to the mechanism model, and the digital twin model generation module generates a digital twin model corresponding to each equipment based on the mechanism model and action data. Through a unified data architecture and modular modeling method, full-factor mapping from single equipment to device-level systems is achieved, thereby enabling digital twin modeling of multiple equipment in the petrochemical plant and supporting dynamic simulation of complex scenarios such as equipment performance degradation and process parameter coupling.

[0055] In the technical solution of this application, Figure 1As shown, the digital twin-based petrochemical plant life cycle management system also includes a business data acquisition module and a business message generation and display module. The control unit obtains the business data of each device in the petrochemical plant through the business data acquisition module, and uses the business message generation and display module to generate business messages corresponding to the digital twin model based on the business data, and displays them in the digital twin model.

[0056] Specifically, the business data acquisition module obtains the business data of each device in the petrochemical plant, and the business message generation and display module generates business messages corresponding to the digital twin model based on the business data and displays them in the digital twin model, such as Figure 2 Shown, including:

[0057] The business data acquisition module obtains the business data of the target device from the IoT management platform according to the device identifier corresponding to the target device;

[0058] The business message generation and display module performs statistical processing on the business data of the target device according to the preset business demand data to generate business messages, and displays the business messages in the digital twin model corresponding to the target device.

[0059] like Figure 1 As shown, the control unit generates a comprehensive leakage parameter based on the basic operating information and leakage basic information of each device through the leakage parameter acquisition unit, and uses the leakage impact correction unit to determine the device sensitivity correction factor of each device for each leakage type based on the comprehensive leakage parameter. At the same time, the detection effectiveness correction factor of each device for each leakage type is determined based on the acquired device detection effectiveness information. The control unit calculates the baseline leakage frequency and corrected leakage frequency of each device for each leakage type based on the comprehensive leakage parameter, the device sensitivity correction factor and the detection effectiveness correction factor through the leakage frequency calculation unit, and uses the leakage result analysis unit to analyze the baseline leakage frequency and the corrected leakage frequency to obtain the leakage results of each device for all leakage types. The control unit performs hierarchical full life cycle management of each device based on the leakage results through the hierarchical management module.

[0060] ① Such as Figure 3 As shown, the leakage parameter acquisition unit includes a running basic information acquisition module and a leakage basic information acquisition module;

[0061] The basic operation information acquisition module acquires the basic operation information of the target device including target device type information, target device process information and target device operation medium information;

[0062] The leakage basic information acquisition module obtains the basic leakage information of the target device, including leakage type information, leakage duration information and leakage aperture information, and generates comprehensive leakage parameters of the target device in combination with the basic operation information.

[0063] ② If Figure 3 As shown, the leakage impact correction unit includes an equipment sensitivity correction factor determination module and a detection effectiveness correction factor determination module;

[0064] The device sensitivity correction factor determination module quantifies the daily management information and comprehensive leakage parameters of the target device, and calls the corresponding leakage severity calculation formula based on the leakage type information to calculate the device sensitivity correction factor of the target device for each leakage type;

[0065] The detection effectiveness correction factor determination module quantifies and rates the acquired device detection effectiveness information to obtain the detection effectiveness correction factor of the target device for each leakage type.

[0066] ③ Such as Figure 3 As shown, the leakage frequency calculation unit includes a reference leakage frequency calculation module and a modified leakage frequency calculation module;

[0067] The baseline leakage frequency calculation module calls the corresponding leakage frequency calculation model according to the leakage type information, and calculates the baseline leakage frequency of the target device for each leakage type in combination with the comprehensive leakage parameters;

[0068] The corrected leakage frequency calculation module couples the baseline leakage frequency with the device sensitivity correction factor and the detection effectiveness correction factor to further obtain the corrected leakage frequency of the target device for each leakage type.

[0069] ④ Such as Figure 3 As shown, the leakage result analysis unit analyzes the baseline leakage frequency and the corrected leakage frequency to obtain the leakage results of each device for all leakage types, including:

[0070] Call the corresponding benchmark grade standard model according to the leakage type information, score the benchmark leakage frequency of the target device for each leakage type, and obtain the benchmark leakage evaluation score;

[0071] Call the corresponding correction level standard model according to the leakage type information, score the correction leakage frequency of the target device for each leakage type, and obtain the correction leakage evaluation score;

[0072] A corresponding weight is set according to the impact of each leakage type, and the baseline leakage frequency and the corrected leakage frequency of the target device for each leakage type are weighted and summed to obtain the leakage score of the target device for all leakage types.

[0073] In the above technical solution, the leakage parameter acquisition unit generates a comprehensive leakage parameter based on the basic operation information and leakage basic information of each device; the leakage impact correction unit determines the device sensitivity correction factor of each device for each leakage type based on the comprehensive leakage parameter, and at the same time determines the detection effectiveness correction factor of each device for each leakage type based on the acquired device detection effectiveness information; the leakage frequency calculation unit calculates the baseline leakage frequency and corrected leakage frequency of each device for each leakage type based on the comprehensive leakage parameter, the device sensitivity correction factor and the detection effectiveness correction factor; the leakage result analysis unit analyzes the baseline leakage frequency and the corrected leakage frequency to obtain the leakage results of each device for all leakage types; the hierarchical management module performs hierarchical full life cycle management of each device based on the leakage results, establishes a multi-dimensional leakage risk assessment model based on the equipment operation information, leakage history and real-time monitoring information, and classifies the equipment, so that the hierarchical full life cycle management of each device can be performed based on the leakage results, and a differentiated management and control mechanism based on leakage risk is constructed.

[0074] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A digital twin-based petrochemical plant full lifecycle management system, characterized by: The system comprises a control unit, which acquires the equipment data of each equipment in the petrochemical device through the equipment data acquisition module, and acquires the mechanism model of each equipment in the petrochemical device through the mechanism model acquisition module. The control unit converts the equipment data into action data corresponding to the mechanism model through the action data generation module, and generates a digital twin model corresponding to each equipment according to the mechanism model and the action data through the digital twin model generation module; The control unit generates a comprehensive leakage parameter based on the basic operation information and leakage basic information of each device through the leakage parameter acquisition unit, and uses the leakage impact correction unit to determine the device sensitivity correction factor of each device for each leakage type based on the comprehensive leakage parameter. At the same time, the detection effectiveness correction factor of each device for each leakage type is determined based on the acquired device detection effectiveness information. The control unit calculates the baseline leakage frequency and corrected leakage frequency of each device for each leakage type based on the comprehensive leakage parameter, the device sensitivity correction factor and the detection effectiveness correction factor through the leakage frequency calculation unit, and uses the leakage result analysis unit to analyze the baseline leakage frequency and the corrected leakage frequency to obtain the leakage results of each device for all leakage types. The control unit performs hierarchical full life cycle management of each device based on the leakage results through the hierarchical management module.

2. The digital twin-based petrochemical plant full life cycle management system according to claim 1 is characterized by: The device data acquisition module acquires the device data of each device in the petrochemical device, and the mechanism model acquisition module acquires the mechanism model of each device in the petrochemical device, including: The device data acquisition module obtains the device data of the target device from the IoT management platform through the WebSocket communication protocol according to the device identifier corresponding to the target device; The mechanism model acquisition module obtains the mechanism model of the target device from the industrial mechanism platform according to the device identification corresponding to the target device.

3. The digital twin-based petrochemical plant life cycle management system according to claim 2 is characterized by: The action data generation module converts the device data into action data corresponding to the mechanism model, including: The device data of the target device is converted into simulation data, and the simulation data is converted into action data corresponding to the mechanism model of the target device.

4. The digital twin-based petrochemical plant life cycle management system according to claim 3 is characterized by: The digital twin model generation module generates a digital twin model corresponding to each device based on the mechanism model and action data, including: The action data corresponding to the mechanism model of the target device is fused with the model parameters in the mechanism model to generate a digital twin model corresponding to the target device.

5. The digital twin-based petrochemical plant full life cycle management system according to claim 4 is characterized by: The system also includes a business data acquisition module and a business message generation and display module. The control unit obtains the business data of each device in the petrochemical device through the business data acquisition module, and uses the business message generation and display module to generate business messages corresponding to the digital twin model based on the business data, and displays them in the digital twin model.

6. The digital twin-based petrochemical plant full life cycle management system according to claim 5 is characterized by: The business data acquisition module acquires the business data of each device in the petrochemical plant. The business message generation and display module generates business messages corresponding to the digital twin model based on the business data and displays them in the digital twin model, including: The business data acquisition module obtains the business data of the target device from the IoT management platform according to the device identifier corresponding to the target device; The business message generation and display module performs statistical processing on the business data of the target device according to the preset business demand data to generate business messages, and displays the business messages in the digital twin model corresponding to the target device.

7. The digital twin-based petrochemical plant life cycle management system according to claim 1 is characterized by: The leakage parameter acquisition unit includes a basic operation information acquisition module and a basic leakage information acquisition module; The basic operation information acquisition module acquires the basic operation information of the target device including target device type information, target device process information and target device operation medium information; The leakage basic information acquisition module obtains the basic leakage information of the target device, including leakage type information, leakage duration information and leakage aperture information, and generates comprehensive leakage parameters of the target device in combination with the basic operation information.

8. The digital twin-based petrochemical plant full life cycle management system according to claim 7 is characterized by: The leakage impact correction unit includes an equipment sensitivity correction factor determination module and a detection effectiveness correction factor determination module; The device sensitivity correction factor determination module quantifies the daily management information and comprehensive leakage parameters of the target device, and calls the corresponding leakage severity calculation formula based on the leakage type information to calculate the device sensitivity correction factor of the target device for each leakage type; The detection effectiveness correction factor determination module quantifies and rates the acquired device detection effectiveness information to obtain the detection effectiveness correction factor of the target device for each leakage type.

9. The digital twin-based petrochemical plant full life cycle management system according to claim 8, characterized in that: The leakage frequency calculation unit includes a reference leakage frequency calculation module and a modified leakage frequency calculation module; The baseline leakage frequency calculation module calls the corresponding leakage frequency calculation model according to the leakage type information, and calculates the baseline leakage frequency of the target device for each leakage type in combination with the comprehensive leakage parameters; The corrected leakage frequency calculation module couples the baseline leakage frequency with the device sensitivity correction factor and the detection effectiveness correction factor to further obtain the corrected leakage frequency of the target device for each leakage type.

10. The digital twin-based petrochemical plant full life cycle management system according to claim 9 is characterized in that: The leakage result analysis unit analyzes the baseline leakage frequency and the corrected leakage frequency to obtain leakage results of each device for all leakage types, including: Call the corresponding benchmark grade standard model according to the leakage type information, score the benchmark leakage frequency of the target device for each leakage type, and obtain the benchmark leakage evaluation score; Call the corresponding correction level standard model according to the leakage type information, score the correction leakage frequency of the target device for each leakage type, and obtain the correction leakage evaluation score; A corresponding weight is set according to the impact of each leakage type, and the baseline leakage frequency and the corrected leakage frequency of the target device for each leakage type are weighted and summed to obtain the leakage score of the target device for all leakage types.

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