CN-121050280-B - Digital twinning-based gas pressure regulating self-adjusting intelligent monitoring system

CN121050280BCN 121050280 BCN121050280 BCN 121050280BCN-121050280-B

Abstract

The invention relates to the technical field of intelligent monitoring of industrial processes, and provides a digital twinning-based gas pressure regulating self-regulating intelligent monitoring system which comprises a data acquisition unit, a digital twinning unit and an execution and feedback unit. The data acquisition unit acquires the operation data of the gas pressure regulating device in real time, and the operation data is preprocessed and then transmitted to the digital twin unit. The digital twin unit builds a physical mechanism sub-model based on the geometric topology and the heat flow coupling characteristic of the gas pressure regulating device, loads the data driving sub-model to form a hybrid digital twin model, and performs predictive iterative simulation by combining the real-time data mapping and the disturbance-safety boundary condition set to generate a plurality of groups of pressure regulating schemes and screens the optimal scheme. The execution and feedback unit converts the scheme into a standardized control instruction to drive the pressure regulating device and returns operation data to realize closed-loop optimization, so that the real-time monitoring, high-precision prediction and self-adaptive regulation and control of the gas pressure regulating process are realized under the complex working condition, and the safety, stability and energy utilization efficiency are improved.

Inventors

DONG LI
LU SHENGKANG
Shu Changzheng

Assignees

江苏长润智能燃气设备有限公司

Dates

Publication Date: 20260512
Application Date: 20250820

Claims (5)

1. Digital twinning-based gas pressure regulating self-regulating intelligent monitoring system, which is characterized by comprising: the data acquisition unit acquires gas operation data and transmits the gas operation data to the digital twin unit through preprocessing; the digital twin unit is combined with the physical topological structure and the heat flow coupling operation characteristics of the gas pressure regulating equipment to construct a physical mechanism sub-model, and the data driving sub-model trained based on the gas operation data is fused to generate a mixed digital twin model; The process of generating the hybrid digital twin model by the digital twin unit comprises the following steps: Loading a data driving sub-model obtained through gas operation data training to a computing environment of a mixed digital twin model, and establishing a data interaction channel with the constructed physical mechanism sub-model according to a preset interface protocol; in the operation stage, the preprocessing data from the data acquisition unit is synchronously input into a physical mechanism sub-model and a data driving sub-model, and the physical computation and the data driving prediction are simultaneously executed in a multi-thread computation and/or multi-core distributed operation mode, wherein the data driving sub-model is responsible for real-time prediction, and the physical mechanism sub-model is used for periodically calibrating the data driving model; After the dual-model calculation is completed, carrying out fusion processing on two groups of prediction results, wherein the fusion processing comprises the steps of distributing weights according to the precision evaluation result, carrying out weighted summation on different prediction values and carrying out consistency correction on an output sequence to generate a unified prediction result of a hybrid digital twin model; Introducing a gas disturbance-safety boundary condition set into the mixed digital twin model, extracting disturbance factors from the disturbance factor set, matching the disturbance factor set with the current state of the virtual equipment node, carrying out disturbance simulation of pressure regulating working conditions, generating a gas pressure regulating response curve, and combining valve control parameters to calculate to obtain a plurality of groups of gas pressure regulating schemes; Reading disturbance factors from a gas disturbance-safety boundary condition set, analyzing a pressure fluctuation range, a flow change amplitude and a temperature abnormal threshold value related in the disturbance factors into input parameters conforming to a simulation calculation format, and matching and binding with running state data of a current virtual equipment node; after binding is completed, starting a multi-working condition predictive iteration simulation calculation flow, generating a corresponding gas pressure regulating response curve for each disturbance factor and operation state combination, and carrying out correlation operation on the response curve and valve control parameters to obtain a gas pressure regulating scheme with multiple groups of valve openings; And screening candidate gas pressure regulating schemes according to safety threshold and stability requirements according to the weighted operation index scoring results, and selecting a comprehensive performance optimal scheme through multi-objective optimization to generate an encrypted standardized instruction data stream.
2. The intelligent monitoring system based on digital twin gas pressure regulation and self-adjustment according to claim 1, wherein the data acquisition unit acquires multidimensional operating parameters of a gas operation process, and performs noise filtering, time sequence alignment and deviation correction on multi-source data to generate standardized data meeting the calculation requirements of a physical mechanism submodel; the data acquisition unit comprises a data packaging and transmission module, the standardized data are packaged into data frames with time stamps and equipment identifiers, and the data frames are transmitted to the digital twin unit through a wired and/or wireless industrial communication link.
3. The intelligent monitoring system for gas pressure regulating and self-regulating based on digital twinning according to claim 1, wherein the process of constructing a physical mechanism submodel by the digital twinning unit comprises the following steps: Performing position analysis and size recognition on physical components of the gas pressure regulating equipment, digitally encoding analysis results according to actual space coordinates and connection sequences, and establishing three-dimensional geometry and connection topology in a virtual environment; And combining the heat flow coupling operation characteristic modeling, dividing a gas flow path into calculation units, endowing gas physical parameters and pressure regulation boundary conditions for each unit, and carrying out multi-physical field coupling solution on a fluid flow equation, a heat transfer equation and a pressure regulation dynamic control equation to generate a physical mechanism sub-model which reflects a real operation rule and can be called by the mixed digital twin model.
4. The intelligent monitoring system for gas pressure regulating and self-regulating based on digital twinning according to claim 1, wherein the process of performing real-time data mapping by the digital twinning unit comprises: After time synchronization is completed, analyzing the real-time data into node state parameters according to the equipment identification and the data type, and injecting an analysis result into a corresponding virtual equipment node object; In the injection process, according to the connection relation of the nodes in the virtual topological structure, the operation parameters related to the virtual equipment nodes, including pressure, flow and temperature, are updated to enable the equipment states in the virtual environment to be dynamically consistent with the physical equipment states, after the node state update is completed, the update result is written into a digital twin operation buffer zone, and the digital twin operation buffer zone is used as a disturbance condition to be introduced into an input data stream which is calculated through predictive iteration simulation.
5. The intelligent monitoring system based on digital twin gas pressure regulation and self-adjustment according to claim 1, wherein when performance evaluation and scheme screening are performed, the digital twin unit receives a valve opening gas pressure regulation scheme set which is output by disturbance condition introduction and predictive iteration simulation links, three operation indexes of response time, pressure fluctuation amplitude and safety margin corresponding to each scheme are read by a performance evaluation module, and weighting operation is performed on the operation indexes according to preset weight parameters to generate corresponding comprehensive performance scores; And further calling a multi-objective control parameter optimizing algorithm in the candidate scheme set, evaluating three operation indexes of response time, pressure stability and safety margin based on simulation results, screening candidate schemes with optimal comprehensive performance in a set threshold range, and converting the finally reserved optimal fuel gas pressure regulating scheme into a standardized instruction data stream.

Description

Digital twinning-based gas pressure regulating self-adjusting intelligent monitoring system Technical Field The invention relates to the technical field of intelligent monitoring of industrial processes, in particular to a digital twin-based intelligent monitoring system for gas pressure regulation and self-adjustment. Background The gas pressure regulating system is a key component in an urban gas transmission and distribution network and an industrial centralized gas supply system, and has the main task of maintaining stable downstream gas pressure in a set range under the conditions of gas source pressure and user load fluctuation. With the expansion of urban and industrial gas scale, the structure of the gas transmission and distribution network is increasingly complex, and the gas demand presents obvious dynamic fluctuation characteristics. In the case of peak hours, sudden load changes or abnormal upstream air source pressure, the pressure regulating equipment must respond quickly, otherwise, unbalance of the pipe network pressure, shutdown of the user side equipment and even safety accidents may be caused. The existing gas pressure regulation control mostly depends on manual duty, remote instructions or preset control logic based on fixed parameters. Although some systems have certain automatic adjustment capability, feedback adjustment lag is obvious when facing to rapidly-changing operation conditions, and the systems work in a single instruction or periodic adjustment mode, so that continuous closed-loop self-adaptive control is difficult to form. Especially under the condition of multi-source input, complex pipe network and multiple variable loads, the existing method depends on historical experience or static model to set pressure regulating parameters, and cannot consider response speed, pressure stability and operation safety margin. This not only limits the dynamic regulation capability of the voltage regulating system, but also increases the operating energy consumption and the operating and maintenance risks. Therefore, a digital twin-based gas pressure regulating self-regulating intelligent monitoring system is provided. Disclosure of Invention The invention aims to provide a digital twin-based gas pressure regulating self-regulating intelligent monitoring system which comprises a data acquisition unit, a digital twin unit and an execution and feedback unit. The data acquisition unit acquires the operation data of the gas pressure regulating device in real time, and the operation data is preprocessed and then transmitted to the digital twin unit. The digital twin unit builds a physical mechanism sub-model based on the geometric topology and the heat flow coupling characteristic of the gas pressure regulating device, loads the data driving sub-model to form a hybrid digital twin model, and performs predictive iterative simulation by combining the real-time data mapping and the disturbance-safety boundary condition set to generate a plurality of groups of pressure regulating schemes and screens the optimal scheme. The execution and feedback unit converts the scheme into a standardized control instruction to drive the pressure regulating device and returns operation data to realize closed-loop optimization, so that the real-time monitoring, high-precision prediction and self-adaptive regulation and control of the gas pressure regulating process are realized under the complex working condition, and the safety, stability and energy utilization efficiency are improved. In order to achieve the above purpose, the present invention provides the following technical solutions: a gas pressure regulating self-regulating intelligent monitoring system based on digital twinning comprises: the data acquisition unit acquires gas operation data and transmits the gas operation data to the digital twin unit through preprocessing; the digital twin unit is combined with the physical topological structure and the heat flow coupling operation characteristics of the gas pressure regulating equipment to construct a physical mechanism sub-model, and the data driving sub-model trained based on the gas operation data is fused to generate a mixed digital twin model; Introducing a gas disturbance-safety boundary condition set into the mixed digital twin model, extracting disturbance factors from the disturbance factor set, matching the disturbance factor set with the current state of the virtual equipment node, carrying out disturbance simulation of pressure regulating working conditions, generating a gas pressure regulating response curve, and combining valve control parameters to calculate to obtain a plurality of groups of gas pressure regulating schemes; And screening candidate gas pressure regulating schemes according to safety threshold and stability requirements according to the weighted operation index scoring results, and selecting a comprehensive performance optimal scheme through multi-objective optimization t