CN-121978905-A - Digital twinning-based heating system dynamic coupling modeling method and system

CN121978905ACN 121978905 ACN121978905 ACN 121978905ACN-121978905-A

Abstract

The application relates to the technical field of heating systems and discloses a digital twinning-based heating system dynamic coupling modeling method and a digital twinning-based heating system dynamic coupling modeling system, wherein the method comprises the steps of acquiring operation data of a heating system, and constructing and operating a plurality of sub-models according to the operation data; the method comprises the steps of obtaining state data of each sub-model, generating state correction factors of the sub-models, setting standard time steps for the sub-models, correcting according to the state correction factors to obtain correction time steps, setting standard synchronization points of the sub-models, obtaining the state data of the sub-models, setting correction synchronization points according to the state data and the standard synchronization points, obtaining transmission data packets among the sub-models, dividing intensity levels for data in the data packets, and setting transmission strategies based on the correction synchronization points and the intensity levels. The application improves the overall operation efficiency by coordinating the data exchange among the models, can accurately judge the communication frequency among the models by an event identification method, and can reduce the operation amount.

Inventors

WANG BIN
LIU HAITAO
SONG BO
SU YONGJIN

Assignees

华能威海发电有限责任公司

Dates

Publication Date: 20260505
Application Date: 20251201

Claims (10)

1. A dynamic coupling modeling method of a heating system based on digital twinning is characterized by comprising the following steps: acquiring operation data of a heating system, and constructing and operating a plurality of sub-models according to the operation data; Acquiring state data of each sub-model, and generating state correction factors of each sub-model; Setting standard time step for each sub-model, and correcting according to the state correction factors to obtain corrected time step; setting standard synchronization points of all sub-models; Acquiring state data of the sub-model, and setting a correction synchronization point according to the state data and a standard synchronization point; Acquiring data packets among all sub-models, and dividing intensity levels for data in the data packets; and setting a transmission strategy based on the corrected synchronization point and the intensity level.
2. A method of modeling dynamic coupling of a heating system based on digital twinning as defined in claim 1, wherein said generating state correction factors for each sub-model comprises: acquiring one type of state parameters of each sub-model in real time; setting a change rate threshold of each type of state parameter, and calculating the change rate of the type of state parameter; generating a state index value based on the rate of change and a rate of change threshold; and generating a state correction factor according to the state index value.
3. A method of modeling dynamic coupling of a heating system based on digital twinning as defined in claim 2, wherein generating the state index value comprises: A class of state parameter series C, z= (C1, C2.. Cm.. Cn, wherein C1 is a type 1 state parameter of the sub-model; C2 is the 2 nd class state parameter of the sub-model, cm is the m th class state parameter of the sub-model, n is the total class state parameter of the sub-model; acquiring the change rate and the change rate threshold value of each first state parameter in Z; calculating a state index value Z Finger means of the single sub-model; Wherein Z Finger means is a state index value of a single submodel, km is a weight coefficient of Cm, bm is a change rate of Cm, B Threshold value m is a change rate threshold of Cm, and n is a total amount of one type of state parameters of the submodel.
4. A method of modeling dynamic coupling of a heating system based on digital twinning as claimed in claim 3, wherein said generating a state modifier from a state index value comprises: setting a first state index value threshold value X1, a second state index value threshold value X2, a first correction factor threshold value Z1 and a second correction factor threshold value Z2; calculating a state correction factor Z Repair tool of each sub-model; For sub-models with Z Finger means greater than the first state index value threshold, Z Repair tool =z1; For sub-models with Z Finger means less than the second state index value threshold, Z Repair tool =z2; For a sub-model where Z refers to being greater than the second state index value threshold and less than the first state index value threshold, Z Repair tool ＝1-(X2-Z Finger means )/(Z Finger means -X1).
5. A method of modeling dynamic coupling of a heating system based on digital twinning as defined in claim 4, wherein said modifying based on said state modifier results in a modified time step comprising: Obtaining standard time step and state correction factors of each sub-model; Calculating a correction time step T Repair tool of each sub-model; T Repair tool ＝Z Repair tool *T Label (C) ; Wherein T Repair tool is the correction time step and T Label (C) is the standard time step.
6. A method of modeling dynamic coupling of a heating system based on digital twinning as defined in claim 1, wherein said setting a revised synchronization point comprises: Extracting standard synchronization points of each sub-model; setting event condition sets of all sub-models, and generating event synchronization points; Generating a fusion synchronization point according to the standard synchronization point and the event synchronization point; and correcting the fusion synchronization points according to the correction time step length of each sub-model to obtain correction synchronization points.
7. A method of modeling dynamic coupling of a heating system based on digital twinning as defined in claim 6, wherein said setting the event condition set of each sub-model to generate an event synchronization point comprises: Establishing an event condition set for each sub-model; dividing the event condition set according to event types to obtain a plurality of sub-event sets; setting triggering conditions for each sub-event set; and when the acquired operation data meet the triggering condition, setting the operation data as an event synchronization point.
8. A digital twinning-based heating system dynamic coupling modeling method as defined in claim 7, wherein said generating a fused synchronization point from said standard synchronization point and an event synchronization point comprises: Obtaining standard synchronization points of each sub-model, and setting each two standard synchronization points as a standard period; setting a synchronization point interval threshold; setting a standard period which satisfies that the interval of the synchronization points is smaller than the interval threshold value of the synchronization points as a problem period; Acquiring time synchronization point data on each problem period; setting priority weights of event synchronization points corresponding to various sub-event sets; when the weights of the event synchronization points are the same, the time synchronization point with the earliest time on the problem period is reserved, and when the event synchronization points are smaller than the interval threshold value of the synchronization points, the time synchronization point with the highest priority weight is reserved.
9. A method of modeling dynamic coupling of a heating system based on digital twinning as defined in claim 8, wherein said obtaining corrected synchronization points comprises: Obtaining a standard time step and a correction time step corresponding to each fusion synchronization point; acquiring time data of each fusion synchronization point; generating time data of a correction synchronization point according to the standard time step and the correction time step; T Repair tool ＝T Melting and melting /(1+Bm/B Threshold value m*β); Wherein, T Repair tool is the time data of the correction time step, T Melting and melting is the time data of the fusion time step, and β is the step correction coefficient.
10. A digital twin based heating system dynamic coupling modeling system for performing a digital twin based heating system dynamic coupling modeling method as defined in any of claims 1-9, comprising: the model unit is used for acquiring the operation data of the heating system, and constructing and operating a plurality of sub-models according to the operation data; The central control unit is used for setting a correction time step length and a correction synchronization point according to the submodel; The central control unit comprises: The first control module is used for acquiring the state data of each sub-model and generating the state correction factors of each sub-model; the second control module is used for setting standard time step length for each sub-model and correcting according to the state correction factors to obtain corrected time step length; the third control module is used for setting standard synchronization points of all the submodels; the fourth control module is used for acquiring state data of the sub-model and setting a correction synchronization point according to the state data and a standard synchronization point; a fifth control module, configured to obtain a data packet between each sub-model, and divide an intensity level for data in the data packet; And the execution unit is used for setting a transmission strategy based on the corrected synchronization point and the intensity level.

Description

Digital twinning-based heating system dynamic coupling modeling method and system Technical Field The application relates to the technical field of heating systems, in particular to a digital twin-based dynamic coupling modeling method and system for a heating system. Background The digital twin modeling of the heating system is a key technology for realizing real-time monitoring, simulation prediction and optimal control of the running state of the heating system by constructing virtual mapping of a physical system. The existing modeling method mainly has the technical defects that firstly, the model construction process depends on manual experience, and a multimode coupling mechanism of a system is lacked. In the traditional method, boundary division and interface design of a hydraulic model, a thermal model and a load model are mainly based on subjective experience of engineers, and lack of objective and quantitative division basis leads to insufficient coupling precision among the models, so that the real dynamic characteristics of a complex heating system are difficult to accurately reflect. Secondly, the collaborative simulation capability of multiple time scales is insufficient. The hydraulic process, the thermal process and the load response in the heating system correspond to different time scales of millisecond, minute and hour levels respectively, and the organic coordination of the different scale models is difficult to realize by the existing method. The rough uniform time step can result in either wasted computing resources or lost key dynamic features, especially the inability to effectively capture transients such as water hammer. Disclosure of Invention In order to solve the technical problems, the application provides a dynamic coupling modeling method and a dynamic coupling modeling system for a heating system based on digital twinning, and aims to obtain a technical scheme which can link different models of each time step, improve the overall operation efficiency, accurately judge the data communication frequency between each model by an event identification method, enable the operation result to be closer to the real condition and reduce the operation amount. In some embodiments of the present application, a digital twin-based dynamic coupling modeling method for a heating system is provided, which is characterized in that the method includes: acquiring operation data of a heating system, and constructing and operating a plurality of sub-models according to the operation data; Acquiring state data of each sub-model, and generating state correction factors of each sub-model; Setting standard time step for each sub-model, and correcting according to the state correction factors to obtain corrected time step; setting standard synchronization points of all sub-models; Acquiring state data of the sub-model, and setting a correction synchronization point according to the state data and a standard synchronization point; acquiring transmission data packets among all sub-models, and dividing intensity levels for data in the data packets; and setting a transmission strategy based on the corrected synchronization point and the intensity level. In some embodiments of the present application, the generating the state correction factors of the respective sub-models includes: acquiring one type of state parameters of each sub-model in real time; setting a change rate threshold of each type of state parameter, and calculating the change rate of the type of state parameter; generating a state index value based on the rate of change and a rate of change threshold; and generating a state correction factor according to the state index value. In some embodiments of the application, the generating a state index value includes: A class of state parameter series C, z= (C1, C2.. Cm.. Cn, wherein C1 is a type 1 state parameter of the sub-model; C2 is the 2 nd class state parameter of the sub-model, cm is the m th class state parameter of the sub-model, n is the total class state parameter of the sub-model; acquiring the change rate and the change rate threshold value of each first state parameter in Z; calculating a state index value Z Finger means of the single sub-model; Wherein Z Finger means is a state index value of a single submodel, km is a weight coefficient of Cm, bm is a change rate of Cm, B Threshold value m is a change rate threshold of Cm, and n is a total amount of one type of state parameters of the submodel. In some embodiments of the present application, the generating a state correction factor according to a state index value includes: setting a first state index value threshold value X1, a second state index value threshold value X2, a first correction factor threshold value Z1 and a second correction factor threshold value Z2; calculating a state correction factor Z Repair tool of each sub-model; For sub-models with Z Finger means greater than the first state index value threshold,