CN112523818A - Monitoring method, system, server and storage medium based on digital twin information - Google Patents
Monitoring method, system, server and storage medium based on digital twin information Download PDFInfo
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- CN112523818A CN112523818A CN202011302379.3A CN202011302379A CN112523818A CN 112523818 A CN112523818 A CN 112523818A CN 202011302379 A CN202011302379 A CN 202011302379A CN 112523818 A CN112523818 A CN 112523818A
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F01—MACHINES OR ENGINES IN GENERAL; ENGINE PLANTS IN GENERAL; STEAM ENGINES
- F01D—NON-POSITIVE DISPLACEMENT MACHINES OR ENGINES, e.g. STEAM TURBINES
- F01D21/00—Shutting-down of machines or engines, e.g. in emergency; Regulating, controlling, or safety means not otherwise provided for
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F01—MACHINES OR ENGINES IN GENERAL; ENGINE PLANTS IN GENERAL; STEAM ENGINES
- F01D—NON-POSITIVE DISPLACEMENT MACHINES OR ENGINES, e.g. STEAM TURBINES
- F01D21/00—Shutting-down of machines or engines, e.g. in emergency; Regulating, controlling, or safety means not otherwise provided for
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Abstract
The invention provides a monitoring method, a system, a server and a storage medium based on digital twin body information, wherein the monitoring method comprises the following steps: acquiring a digital twin model of a steam turbine key component, converting the digital twin model into a digital model, and performing mesh division on the digital model to acquire geometric information and node information of the steam turbine key component; establishing a real-time state quantity corresponding relation model of each node on a digital twin model of a steam turbine critical component; inputting the production real-time data of the steam turbine into a real-time state quantity corresponding relation model of a key part of the steam turbine so as to read a state quantity real-time value; and judging whether the state quantity real-time values of all nodes on the digital twin model of the steam turbine critical component can be used for constructing a cloud picture of the real-time state quantity of the steam turbine critical component. The invention realizes the construction, monitoring and calculation precision control of the three-dimensional digital twin body of the key part of the steam turbine and the monitoring of the state of the key part of the steam turbine.
Description
Technical Field
The invention belongs to the technical field of steam turbines, relates to a monitoring method, and particularly relates to a monitoring method, a monitoring system, a server and a storage medium based on digital twin information.
Background
China is the country with the most coal electrical installations in the world. By the end of 2019, the installed capacity of electricity generation in China is 201066 thousands of kilowatts, and the coal-fired unit accounts for more than half of the total installed capacity. The steam turbine is one of three main machines of a coal-fired unit and is core equipment of a coal-fired power plant, wherein key parts of the steam turbine, such as a high-pressure rotor, a medium-pressure rotor, a low-pressure rotor, a high-pressure inner cylinder, a medium-pressure inner cylinder, a low-pressure inner cylinder, a high-pressure outer cylinder and a medium-pressure outer cylinder, are high in replacement cost, once problems occur, the shutdown time is long, economic loss is huge, how to ensure the operation safety of key parts of the steam turbine is guaranteed, and the service life of reasonably using the key parts is a great concern of.
The traditional technology mainly monitors the state of the steam turbine by installing measuring points on a steam turbine cylinder body, but because the installing measuring points are limited, the technical problem that the state of key components of the steam turbine cannot be comprehensively and accurately mastered is faced.
Therefore, how to provide a monitoring method, a system, a server and a storage medium based on digital twin biological information to solve the defects that the state of the key components of the steam turbine cannot be comprehensively and accurately mastered due to limited installation measuring points in the prior art and the like becomes a technical problem to be solved by those skilled in the art.
Disclosure of Invention
In view of the above-mentioned shortcomings of the prior art, an object of the present invention is to provide a monitoring method, a system, a server and a storage medium based on digital twin body information, which are used to solve the problem that the state of a critical component of a steam turbine cannot be comprehensively and accurately mastered due to limited installation measuring points in the prior art.
To achieve the above and other related objects, an aspect of the present invention provides a monitoring method based on digital twin information, for monitoring a critical component of a steam turbine; the monitoring method based on the digital twin information comprises the following steps: acquiring a digital twin model of the key part of the steam turbine, converting the digital twin model into a digital model, and performing mesh division on the digital model to acquire geometric information and node information of the key part of the steam turbine; training the digital model to establish a real-time state quantity corresponding relation model of each node on a digital twin model of the steam turbine critical component; acquiring production real-time data of a steam turbine, and inputting the production real-time data of the steam turbine into a real-time state quantity corresponding relation model of a steam turbine critical component so as to read state quantity real-time values of all nodes on a digital twin model of the steam turbine critical component; screening wall temperature online measuring point data of the steam turbine key component from the production real-time data, and comparing the wall temperature online measuring point data with node temperature data in the state quantity real-time values of all nodes to judge whether the state quantity real-time values of all nodes on the digital twin model of the steam turbine key component can be used for constructing a cloud picture of the real-time state quantity of the steam turbine key component.
In an embodiment of the invention, the digital model is a finite element model; and converting the digital twin model into the finite element model, and acquiring the geometric information and the node information of the steam turbine critical component from the finite element model.
In an embodiment of the invention, before the step of establishing the real-time state quantity corresponding relation model of each node on the digital twin model of the critical component of the steam turbine, the step of obtaining a start-stop curve of the power plant running along with time based on the digital twin information further includes obtaining a start-stop curve of the power plant running along with time, determining a thermal boundary and a force boundary of the finite element calculation of the critical component by using the start-stop curve, and performing the finite element calculation on the finite element model to obtain training data.
In an embodiment of the invention, the time-varying parameters included in the time-varying start-stop curves of the power plant operation include: the system comprises main steam temperature, main steam pressure, reheat steam temperature, reheat steam pressure, main steam flow, reheat steam flow, unit power, rotating speed, high-pressure valve shell metal measuring point temperature, high-pressure inner cylinder metal measuring point temperature, medium-pressure inner cylinder metal measuring point temperature, high-pressure inner cylinder surface steam temperature, medium-pressure inner cylinder surface steam temperature, high-medium-pressure outer cylinder surface metal temperature, high-medium-pressure outer cylinder surface steam temperature, first-stage rear steam temperature, first pumping temperature and pressure, second pumping temperature and pressure, third pumping temperature and pressure, and fourth pumping temperature and pressure.
In an embodiment of the present invention, the step of comparing the wall temperature online measurement point data with the node temperature data in the state quantity real-time values of the nodes includes: judging whether the temperature error between the wall temperature online measuring point data and the node temperature data in the state quantity real-time value of each node is smaller than a preset error threshold value or not; if so, the state quantity real-time values of all nodes on the digital twin model of the steam turbine critical component can be used for constructing a cloud picture of the real-time state quantity of the steam turbine critical component; and if not, the training data needs to be retrained and optimized until the temperature error between the wall temperature online measuring point data and the node temperature data in the state quantity real-time value of each node is smaller than a preset error threshold value.
In an embodiment of the invention, the monitoring method based on the digital twin information includes constructing a cloud graph of the real-time state quantity of the steam turbine key component by using the real-time state quantity value of each node on the digital twin model of the steam turbine key component and the node information of the steam turbine key component.
In an embodiment of the present invention, the monitoring method based on the digital twin information further includes: and acquiring the offline data of the steam turbine, integrating the offline data of the steam turbine on the digital twin model of the key component corresponding to the offline data of the steam turbine, and classifying the offline data of the steam turbine so as to display the classified offline data on a client.
The invention provides a monitoring system based on digital twin body information, which is used for monitoring a turbine key component; the monitoring system based on the digital twin information comprises: the conversion module is used for acquiring a digital twin model of the key part of the steam turbine, converting the digital twin model into a digital model and carrying out mesh division on the digital model so as to acquire geometric information and node information of the key part of the steam turbine; the model establishing module is used for training the digital model to establish a real-time state quantity corresponding relation model of each node on the digital twin model of the steam turbine critical component; the reading module is used for acquiring real-time production data of a steam turbine and inputting the real-time production data of the steam turbine into the real-time state quantity corresponding relation model of the steam turbine critical component so as to read the state quantity real-time values of all nodes on the digital twin model of the steam turbine critical component; and the measuring point data center module is used for screening wall temperature online measuring point data of the steam turbine key component from the production real-time data, and comparing the wall temperature online measuring point data with node temperature data in the state quantity real-time values of all nodes to judge whether the state quantity real-time values of all nodes on the digital twin model of the steam turbine key component can construct a cloud picture of the real-time state quantity of the steam turbine key component through a cloud picture construction module.
Yet another aspect of the present invention provides a storage medium having stored thereon a computer program, which when executed by a processor, implements the digital twin information-based monitoring method.
A final aspect of the present invention provides a server, including: a processor and a memory; the memory is used for storing a computer program, and the processor is used for executing the computer program stored by the memory so as to enable the server to execute the monitoring method based on the digital twin information.
As described above, the monitoring method, system, server and storage medium based on digital twin information according to the present invention have the following advantages:
firstly, the invention provides a digital twin body information system of a key part of a steam turbine, and the invention realizes the construction of the three-dimensional digital twin body of the key part of the steam turbine and the implementation of on-line display, monitoring and calculation precision control of a temperature field, a stress field and a displacement field by adopting an artificial intelligence technology, a digital twin body technology and a cloud picture reconstruction and display technology, and realizes the monitoring of the state of the key part of the steam turbine
Secondly, the invention integrates the design data, the on-line data and the off-line data of the steam turbine critical component, realizes the construction of the digital twin body of the steam turbine critical component, comprehensively understands the equipment state of the steam turbine critical component, scientifically optimizes the operation and maintenance of the critical component and improves the safety and the economical efficiency of the steam turbine.
Drawings
Fig. 1 is a schematic view of an application scenario of the present invention.
Fig. 2 is a flowchart illustrating a monitoring method based on digital twin information according to an embodiment of the present invention.
Fig. 3 is a schematic structural diagram of a monitoring system based on digital twin information according to an embodiment of the present invention.
Description of the element reference numerals
1 service end
2 client
3 monitoring system based on digital twin information
31 conversion module
32 model center module
33 model building module
34 algorithm module
35 real-time data center module
36 reading module
37 measuring point data center module
38 cloud picture construction module
39 offline data module
S21-S26
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict.
It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention, and the components related to the present invention are only shown in the drawings rather than drawn according to the number, shape and size of the components in actual implementation, and the type, quantity and proportion of the components in actual implementation may be changed freely, and the layout of the components may be more complicated.
Example one
The embodiment provides a monitoring method based on digital twin body information, which is used for monitoring a critical part of a steam turbine; the monitoring method based on the digital twin information comprises the following steps:
acquiring a digital twin model of the key part of the steam turbine, converting the digital twin model into a digital model, and performing mesh division on the digital model to acquire geometric information and node information of the key part of the steam turbine;
training the digital model to establish a real-time state quantity corresponding relation model of each node on a digital twin model of the steam turbine critical component;
acquiring production real-time data of a steam turbine, and inputting the production real-time data of the steam turbine into a real-time state quantity corresponding relation model of a steam turbine critical component so as to read state quantity real-time values of all nodes on a digital twin model of the steam turbine critical component;
screening wall temperature online measuring point data of the steam turbine key component from the production real-time data, and comparing the wall temperature online measuring point data with node temperature data in the state quantity real-time values of all nodes to judge whether the state quantity real-time values of all nodes on the digital twin model of the steam turbine key component can be used for constructing a cloud picture of the real-time state quantity of the steam turbine key component.
The digital twin information-based monitoring method provided by the present embodiment will be described in detail with reference to the drawings. The monitoring method based on the digital twin information in this embodiment can be applied to the application scenario shown in fig. 1, and includes that the monitoring method based on the digital twin information is operated by the server 1, and the real-time state quantities of each key component of the steam turbine are displayed and checked by the client 2.
In the embodiment, the 600MW steam turbine is monitored based on the digital twin information, the steam inlet temperature is 537 ℃, and the steam inlet pressure is 16.7 MPa. The 600MW steam turbine key parts include: high-medium pressure rotor, high-pressure inner cylinder, medium-pressure inner cylinder, high-pressure valve casing, medium-pressure valve casing, high-medium pressure outer cylinder, etc.
Or the 300MW steam turbine is monitored based on the digital twin information, the low-pressure steam inlet temperature is 311.05 ℃, and the steam inlet pressure is 0.77 MPa. The low-pressure part key components of the 300MW steam turbine comprise: low-pressure rotor and low-pressure inner cylinder.
Please refer to fig. 2, which is a flowchart illustrating a monitoring method based on digital twin information according to an embodiment. As shown in fig. 2, the monitoring method based on digital twin information specifically includes the following steps:
s21, acquiring a digital twin model of the turbine key component, converting the digital twin model into a digital model, and performing mesh division on the digital model to acquire geometric information and node information of the turbine key component.
In the embodiment, according to a design drawing, in combination with field component dimension measurement, a digital twin model of key components such as a high-medium pressure rotor, a high-pressure inner cylinder, a medium-pressure inner cylinder, a high-pressure valve casing, a medium-pressure valve casing, a high-medium pressure outer cylinder and the like of a steam turbine is established. The digital twin model is a virtual model of a physical entity created in a digital mode, a technology of the physical entity in a real environment is simulated by means of data, a digital twin technology is adopted, a digital twin information platform consistent with the physical characteristics of a steam turbine key component is established, and the state of the steam turbine key component is comprehensively mastered, so that the technical effects of improving the reliability and safety of the steam turbine and ensuring long-period safe operation of the steam turbine are achieved.
Specifically, the digital twin model is converted into a finite element model, the finite element model is subjected to meshing to obtain geometric information and node information of the steam turbine key component obtained in the finite element model, and the geometric information and the node information of the steam turbine key component are stored.
S22, acquiring a start-stop curve of the power plant operation changing along with time, determining a thermal boundary and a force boundary of the finite element calculation of the key component by using the start-stop curve, and performing finite element calculation on the finite element model to acquire training data; and training the digital model to establish a real-time state quantity corresponding relation model of each node on the digital twin model of the steam turbine critical component, and packaging the model into an algorithm module.
In this embodiment, the start-stop curves of the power plant operation changing with time include the curves of the actual operation of the power plant, such as cold start, warm start, hot start, and normal shutdown.
The time-varying parameters included in the time-varying start-stop curve of the power plant operation include: the system comprises main steam temperature, main steam pressure, reheat steam temperature, reheat steam pressure, main steam flow, reheat steam flow, unit power, rotating speed, high-pressure valve shell metal measuring point temperature, high-pressure inner cylinder metal measuring point temperature, medium-pressure inner cylinder metal measuring point temperature, high-pressure inner cylinder surface steam temperature, medium-pressure inner cylinder surface steam temperature, high-medium-pressure outer cylinder surface metal temperature, high-medium-pressure outer cylinder surface steam temperature, first-stage rear steam temperature, first pumping temperature and pressure, second pumping temperature and pressure, third pumping temperature and pressure, fourth pumping temperature and pressure and the like.
In this embodiment, a real-time state quantity correspondence model, such as a temperature of each node, a maximum principal stress, a minimum principal stress, an equivalent stress, an axial displacement, a radial displacement, a tangential displacement, and the like, on the digital twin model of the turbine critical component is specifically established.
And S23, acquiring real-time production data of the steam turbine, and inputting the real-time production data of the steam turbine into the real-time state quantity corresponding relation model of the steam turbine critical component so as to read the state quantity real-time values of all nodes on the digital twin model of the steam turbine critical component.
Specifically, the S23 reads a state quantity real-time value of each node temperature, stress, displacement, and the like on the digital twin model of the turbine critical component, and inputs the state quantity real-time value into the measurement point data center module.
S24, wall temperature online measuring point data of the steam turbine key component (specifically, wall temperature online measuring point data such as high-pressure valve shell metal measuring point temperature, high-pressure inner cylinder metal measuring point temperature, medium-pressure inner cylinder metal measuring point temperature, high-medium-pressure outer cylinder surface metal temperature and the like) are screened out from the production real-time data, and the wall temperature online measuring point data are compared with node temperature data in the state quantity real-time values of all the nodes to judge whether the state quantity real-time values of all the nodes on the digital twin biological model of the steam turbine key component can be used for constructing a cloud picture of the real-time state quantity of the steam turbine key component.
Specifically, the step of comparing the wall temperature online measurement point data with the node temperature data in the state quantity real-time values of the nodes includes:
judging whether the temperature error between the wall temperature online measuring point data and the node temperature data in the state quantity real-time value of each node is smaller than a preset error threshold (for example, 15%); if so, the state quantity real-time values of all nodes on the digital twin model of the steam turbine critical component can be used for constructing a cloud picture of the real-time state quantity of the steam turbine critical component; and if not, the training data needs to be retrained and optimized until the temperature error between the wall temperature online measuring point data and the node temperature data in the state quantity real-time value of each node is smaller than a preset error threshold value.
And S25, constructing a cloud picture of the real-time state quantities of the steam turbine key component by using the state quantity real-time values of all nodes on the digital twin model of the steam turbine key component (state quantities of all node temperatures, stresses, displacements and the like of the digital twin body of the steam turbine key component) and the node information of the steam turbine key component, and obtaining the cloud picture of the real-time state quantities of the digital twin body of the steam turbine key component such as a steam turbine high-and-medium-pressure rotor, a high-and-medium-pressure inner cylinder, a medium-and-medium-pressure inner cylinder, a high-and-medium-pressure outer cylinder, a high-and-pressure valve shell, a medium-pressure valve shell and the like, the maximum main stress, the minimum main stress, the equivalent stress.
And S26, acquiring the off-line data of the turbine, integrating the off-line data of the turbine on the digital twin model of the corresponding key component, and classifying the off-line data of the turbine so as to display the classified off-line data on a client.
Specifically, offline data such as inspection data, overhaul and overhaul records, equipment defect records, performance test reports and the like related to turbine key components such as a turbine high-medium pressure rotor, a turbine high-pressure inner cylinder, a turbine medium-pressure inner cylinder, a turbine high-medium pressure outer cylinder, a turbine high-pressure valve casing, a turbine medium-pressure valve casing and the like are collected.
The monitoring method based on the digital twin information has the following beneficial effects:
firstly, the embodiment provides a digital twin body information system of a steam turbine key component, and the artificial intelligence technology, the digital twin body technology and the cloud picture reconstruction and display technology are adopted, so that the construction of the three-dimensional digital twin body of the steam turbine key component and the implementation of online display, monitoring and calculation precision control of a temperature field, a stress field and a displacement field are realized, and the monitoring of the state of the steam turbine key component is realized
Secondly, the design data, the online data and the offline data of the steam turbine critical component are integrated, the digital twin body construction of the steam turbine critical component is realized, the equipment state of the steam turbine critical component is comprehensively known, the operation and maintenance of the critical component are scientifically optimized, and the safety and the economical efficiency of the steam turbine are improved.
The present embodiment also provides a storage medium (also referred to as a computer-readable storage medium) having stored thereon a computer program, which when executed by a processor, implements the digital twin information-based monitoring method.
One of ordinary skill in the art will appreciate that the computer-readable storage medium is: all or part of the steps for implementing the above method embodiments may be performed by hardware associated with a computer program. The aforementioned computer program may be stored in a computer readable storage medium. When executed, the program performs steps comprising the method embodiments described above; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.
Example two
The embodiment provides a monitoring system based on digital twin body information, which is used for monitoring a turbine key component; the monitoring system based on the digital twin information comprises:
the conversion module is used for acquiring a digital twin model of the key part of the steam turbine, converting the digital twin model into a digital model and carrying out mesh division on the digital model so as to acquire geometric information and node information of the key part of the steam turbine;
the model establishing module is used for training the digital model to establish a real-time state quantity corresponding relation model of each node on the digital twin model of the steam turbine critical component;
the reading module is used for acquiring real-time production data of a steam turbine and inputting the real-time production data of the steam turbine into the real-time state quantity corresponding relation model of the steam turbine critical component so as to read the state quantity real-time values of all nodes on the digital twin model of the steam turbine critical component;
and the measuring point data center module is used for screening wall temperature online measuring point data of the steam turbine key component from the production real-time data, and comparing the wall temperature online measuring point data with node temperature data in the state quantity real-time values of all nodes to judge whether the state quantity real-time values of all nodes on the digital twin model of the steam turbine key component can construct a cloud picture of the real-time state quantity of the steam turbine key component through a cloud picture construction module.
The digital twin information-based monitoring system provided by the present embodiment will be described in detail with reference to the drawings. Please refer to fig. 3, which is a schematic structural diagram of a monitoring system based on digital twin information in an embodiment. As shown in fig. 3, the monitoring system 3 based on digital twin information includes a conversion module 31, a model center module 32, a model building module 33, an algorithm module 34, a real-time data center module 35, a reading module 36, a measure point data center module 37, a cloud picture building module 38, and an offline data module 39.
The conversion module 31 is configured to obtain a digital twin model of the turbine key component, convert the digital twin model into a digital model, and perform mesh division on the digital model to obtain geometric information and node information of the turbine key component.
In the embodiment, according to a design drawing, in combination with field component dimension measurement, a digital twin model of key components such as a high-medium pressure rotor, a high-pressure inner cylinder, a medium-pressure inner cylinder, a high-pressure valve casing, a medium-pressure valve casing, a high-medium pressure outer cylinder and the like of a steam turbine is established. The digital twin model is a virtual model of a physical entity created in a digital mode, a technology of the physical entity in a real environment is simulated by means of data, a digital twin technology is adopted, a digital twin information platform consistent with the physical characteristics of a steam turbine key component is established, and the state of the steam turbine key component is comprehensively mastered, so that the technical effects of improving the reliability and safety of the steam turbine and ensuring long-period safe operation of the steam turbine are achieved.
Specifically, the conversion module 31 converts the digital twin model into a finite element model, and performs mesh division on the finite element model to obtain geometric information and node information of the steam turbine key component obtained in the finite element model, and stores the geometric information and the node information of the steam turbine key component in the model center module 32.
The model establishing module 33 is configured to train the digital model to establish a real-time state quantity correspondence model of each node on the digital twin model of the turbine critical component.
Specifically, the model establishing module 33 obtains a start-stop curve of the power plant operation changing with time, determines a thermal boundary and a force boundary of the finite element calculation of the key component by using the start-stop curve, and performs the finite element calculation on the finite element model to obtain training data; the digital model is trained to establish a real-time state quantity corresponding relation model of each node on the digital twin model of the steam turbine critical component, and the model is packaged into an algorithm module 34.
The reading module 36 obtains real-time production data of the steam turbine from the real-time data center module 35, and inputs the real-time production data of the steam turbine to the real-time state quantity correspondence model of the steam turbine critical component, so as to read the state quantity real-time values of each node on the digital twin model of the steam turbine critical component.
Specifically, the reading module 36 reads the state quantity real-time values of the temperature, stress, displacement, and the like of each node on the digital twin model of the turbine critical component, and inputs the state quantity real-time values into the measurement point data center module 37.
The measured point data center module 37 is configured to screen wall temperature online measured point data of the steam turbine key component from the production real-time data (specifically, wall temperature online measured point data such as a high-pressure valve casing metal measured point temperature, a high-pressure inner cylinder metal measured point temperature, a medium-pressure inner cylinder metal measured point temperature, a high-medium-pressure outer cylinder surface metal temperature, and the like), and compare the wall temperature online measured point data with node temperature data in the state quantity real-time values of each node to determine whether the state quantity real-time value of each node on the digital twin model of the steam turbine key component constructs a cloud map of the real-time state quantity of the steam turbine key component through the cloud map constructing module 38.
Specifically, the measuring point data center module 37 compares the wall temperature online measuring point data with the node temperature data in the state quantity real-time value of each node, that is, determines whether a temperature error between the wall temperature online measuring point data and the node temperature data in the state quantity real-time value of each node is smaller than a preset error threshold (for example, 15%); if so, the state quantity real-time values of all nodes on the digital twin model of the steam turbine critical component can be used for constructing a cloud picture of the real-time state quantity of the steam turbine critical component; and if not, the training data needs to be retrained and optimized until the temperature error between the wall temperature online measuring point data and the node temperature data in the state quantity real-time value of each node is smaller than a preset error threshold value.
The cloud map construction module 38 is configured to construct a cloud map of the real-time state quantities of the turbine critical component by using the real-time state quantity values of the nodes on the digital twin model of the turbine critical component (state quantities of the turbine critical component digital twin body such as temperature, stress, displacement, and the like of the nodes) and the node information of the turbine critical component, and obtain a cloud map of the real-time state quantities of the digital twin body of the turbine critical component such as a turbine high-and-medium-pressure rotor, a high-and-medium-pressure inner cylinder, a medium-and-medium-pressure inner cylinder, a high-and-medium-pressure outer cylinder, a high-and-medium-pressure valve casing, a medium-and-pressure valve casing, and the like, such as the temperature, the maximum main stress, the minimum main.
The offline data module 39 is configured to collect offline data of the steam turbine, integrate the offline data of the steam turbine on the digital twin model of the corresponding key component, and classify the offline data of the steam turbine, so as to display the classified offline data on a client.
It should be noted that the division of the modules of the above system is only a logical division, and the actual implementation may be wholly or partially integrated into one physical entity, or may be physically separated. And the modules can be realized in a form that all software is called by the processing element, or in a form that all the modules are realized in a form that all the modules are called by the processing element, or in a form that part of the modules are called by the hardware. For example: the x module can be a separately established processing element, and can also be integrated in a certain chip of the system. In addition, the x-module may be stored in the memory of the system in the form of program codes, and may be called by one of the processing elements of the system to execute the functions of the x-module. Other modules are implemented similarly. All or part of the modules can be integrated together or can be independently realized. The processing element described herein may be an integrated circuit having signal processing capabilities. In implementation, each step of the above method or each module above may be implemented by an integrated logic circuit of hardware in a processor element or an instruction in the form of software. These above modules may be one or more integrated circuits configured to implement the above methods, such as: one or more Application Specific Integrated Circuits (ASICs), one or more microprocessors (DSPs), one or more Field Programmable Gate Arrays (FPGAs), and the like. When a module is implemented in the form of a Processing element scheduler code, the Processing element may be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor capable of calling program code. These modules may be integrated together and implemented in the form of a System-on-a-chip (SOC).
EXAMPLE III
This embodiment provides a server, the server includes: a processor, memory, transceiver, communication interface, or/and system bus; the memory and the communication interface are connected with the processor and the transceiver through a system bus and are used for achieving mutual communication, the memory is used for storing the computer program, the communication interface is used for communicating with other equipment, and the processor and the transceiver are used for running the computer program to enable the server to execute the steps of the monitoring method based on the digital twin information.
The above-mentioned system bus may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The system bus may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one thick line is shown, but this does not mean that there is only one bus or one type of bus. The communication interface is used for realizing communication between the database access device and other equipment (such as a client, a read-write library and a read-only library). The Memory may include a Random Access Memory (RAM), and may further include a non-volatile Memory (non-volatile Memory), such as at least one disk Memory.
The Processor may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; the Integrated Circuit may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, or discrete hardware components.
The protection scope of the monitoring method based on the digital twin information is not limited to the execution sequence of the steps listed in the embodiment, and all the schemes of adding, subtracting and replacing the steps in the prior art according to the principle of the invention are included in the protection scope of the invention.
The invention also provides a monitoring system based on digital twin information, which can realize the monitoring method based on digital twin information, but the realization device of the monitoring method based on digital twin information of the invention includes but is not limited to the structure of the monitoring system based on digital twin information listed in the embodiment, and all the structural modifications and replacements in the prior art made according to the principle of the invention are included in the protection scope of the invention.
In summary, the monitoring method, the monitoring system, the server and the storage medium based on the digital twin information according to the present invention have the following advantages:
firstly, the invention provides a digital twin body information system of a key part of a steam turbine, and the invention realizes the construction of the three-dimensional digital twin body of the key part of the steam turbine and the implementation of on-line display, monitoring and calculation precision control of a temperature field, a stress field and a displacement field by adopting an artificial intelligence technology, a digital twin body technology and a cloud picture reconstruction and display technology, and realizes the monitoring of the state of the key part of the steam turbine
Secondly, the invention integrates the design data, the on-line data and the off-line data of the steam turbine critical component, realizes the construction of the digital twin body of the steam turbine critical component, comprehensively understands the equipment state of the steam turbine critical component, scientifically optimizes the operation and maintenance of the critical component and improves the safety and the economical efficiency of the steam turbine. The invention effectively overcomes various defects in the prior art and has high industrial utilization value.
The foregoing embodiments are merely illustrative of the principles and utilities of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or change the above-mentioned embodiments without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which can be made by those skilled in the art without departing from the spirit and technical spirit of the present invention be covered by the claims of the present invention.
Claims (10)
1. A monitoring method based on digital twin information is characterized by being used for monitoring a turbine critical component; the monitoring method based on the digital twin information comprises the following steps:
acquiring a digital twin model of the key part of the steam turbine, converting the digital twin model into a digital model, and performing mesh division on the digital model to acquire geometric information and node information of the key part of the steam turbine;
training the digital model to establish a real-time state quantity corresponding relation model of each node on a digital twin model of the steam turbine critical component;
acquiring production real-time data of a steam turbine, and inputting the production real-time data of the steam turbine into a real-time state quantity corresponding relation model of a steam turbine critical component so as to read state quantity real-time values of all nodes on a digital twin model of the steam turbine critical component;
screening wall temperature online measuring point data of the steam turbine key component from the production real-time data, and comparing the wall temperature online measuring point data with node temperature data in the state quantity real-time values of all nodes to judge whether the state quantity real-time values of all nodes on the digital twin model of the steam turbine key component can be used for constructing a cloud picture of the real-time state quantity of the steam turbine key component.
2. The monitoring method based on the digital twin information as claimed in claim 1, wherein the digital model adopts a finite element model; and converting the digital twin model into the finite element model, and acquiring the geometric information and the node information of the steam turbine critical component from the finite element model.
3. The monitoring method based on the digital twin information as claimed in claim 2, wherein before the step of establishing the real-time state quantity corresponding relation model of each node on the digital twin model of the critical component of the steam turbine, the monitoring method based on the digital twin information further comprises the steps of obtaining a start-stop curve of the power plant operation changing with time, determining a thermal boundary and a force boundary of the finite element calculation of the critical component by using the start-stop curve, and performing the finite element calculation on the finite element model to obtain training data.
4. The monitoring method based on the digital twin body information as claimed in claim 3, wherein the time-varying parameters included in the time-varying start-stop curve of the power plant operation include: the system comprises main steam temperature, main steam pressure, reheat steam temperature, reheat steam pressure, main steam flow, reheat steam flow, unit power, rotating speed, high-pressure valve shell metal measuring point temperature, high-pressure inner cylinder metal measuring point temperature, medium-pressure inner cylinder metal measuring point temperature, high-pressure inner cylinder surface steam temperature, medium-pressure inner cylinder surface steam temperature, high-medium-pressure outer cylinder surface metal temperature, high-medium-pressure outer cylinder surface steam temperature, first-stage rear steam temperature, first pumping temperature and pressure, second pumping temperature and pressure, third pumping temperature and pressure, and fourth pumping temperature and pressure.
5. The monitoring method based on the digital twin body information according to claim 3, wherein the step of comparing the wall temperature online measurement point data with the node temperature data in the state quantity real-time value of each node includes:
judging whether the temperature error between the wall temperature online measuring point data and the node temperature data in the state quantity real-time value of each node is smaller than a preset error threshold value or not; if so, the state quantity real-time values of all nodes on the digital twin model of the steam turbine critical component can be used for constructing a cloud picture of the real-time state quantity of the steam turbine critical component; and if not, the training data needs to be retrained and optimized until the temperature error between the wall temperature online measuring point data and the node temperature data in the state quantity real-time value of each node is smaller than a preset error threshold value.
6. The method according to claim 3, wherein the method comprises constructing a cloud map of the real-time state quantities of the steam turbine critical components by using the state quantity real-time values of the nodes on the digital twin model of the steam turbine critical components and the node information of the steam turbine critical components.
7. The digital twin information based monitoring method according to claim 6, further comprising:
and acquiring the offline data of the steam turbine, integrating the offline data of the steam turbine on the digital twin model of the key component corresponding to the offline data of the steam turbine, and classifying the offline data of the steam turbine so as to display the classified offline data on a client.
8. A monitoring system based on digital twin information is characterized by being used for monitoring a turbine critical component; the monitoring system based on the digital twin information comprises:
the conversion module is used for acquiring a digital twin model of the key part of the steam turbine, converting the digital twin model into a digital model and carrying out mesh division on the digital model so as to acquire geometric information and node information of the key part of the steam turbine;
the model establishing module is used for training the digital model to establish a real-time state quantity corresponding relation model of each node on the digital twin model of the steam turbine critical component;
the reading module is used for acquiring real-time production data of a steam turbine and inputting the real-time production data of the steam turbine into the real-time state quantity corresponding relation model of the steam turbine critical component so as to read the state quantity real-time values of all nodes on the digital twin model of the steam turbine critical component;
and the measuring point data center module is used for screening wall temperature online measuring point data of the steam turbine key component from the production real-time data, and comparing the wall temperature online measuring point data with node temperature data in the state quantity real-time values of all nodes to judge whether the state quantity real-time values of all nodes on the digital twin model of the steam turbine key component can construct a cloud picture of the real-time state quantity of the steam turbine key component through a cloud picture construction module.
9. A storage medium having stored thereon a computer program, characterized in that the program, when executed by a processor, implements the digital twin information-based monitoring method according to any one of claims 1 to 7.
10. A server, comprising: a processor and a memory;
the memory is used for storing a computer program, and the processor is used for executing the computer program stored by the memory so as to enable the server to execute the monitoring method based on the digital twin information according to any one of claims 1 to 7.
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