WO2021227468A1 - 核电厂关键设备的状态预演方法、系统、设备及存储介质 - Google Patents

核电厂关键设备的状态预演方法、系统、设备及存储介质 Download PDF

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WO2021227468A1
WO2021227468A1 PCT/CN2020/134722 CN2020134722W WO2021227468A1 WO 2021227468 A1 WO2021227468 A1 WO 2021227468A1 CN 2020134722 W CN2020134722 W CN 2020134722W WO 2021227468 A1 WO2021227468 A1 WO 2021227468A1
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key equipment
equipment
state
identification
data
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PCT/CN2020/134722
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English (en)
French (fr)
Inventor
顾海霞
刘高俊
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中广核工程有限公司
中国广核集团有限公司
中国广核电力股份有限公司
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Priority to EP20935501.5A priority Critical patent/EP4235482A1/en
Publication of WO2021227468A1 publication Critical patent/WO2021227468A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/10—Pre-processing; Data cleansing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/23—Design optimisation, verification or simulation using finite element methods [FEM] or finite difference methods [FDM]
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
    • G05B23/0221—Preprocessing measurements, e.g. data collection rate adjustment; Standardization of measurements; Time series or signal analysis, e.g. frequency analysis or wavelets; Trustworthiness of measurements; Indexes therefor; Measurements using easily measured parameters to estimate parameters difficult to measure; Virtual sensor creation; De-noising; Sensor fusion; Unconventional preprocessing inherently present in specific fault detection methods like PCA-based methods
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
    • G05B23/0224—Process history based detection method, e.g. whereby history implies the availability of large amounts of data
    • G05B23/0227—Qualitative history assessment, whereby the type of data acted upon, e.g. waveforms, images or patterns, is not relevant, e.g. rule based assessment; if-then decisions
    • G05B23/0232—Qualitative history assessment, whereby the type of data acted upon, e.g. waveforms, images or patterns, is not relevant, e.g. rule based assessment; if-then decisions based on qualitative trend analysis, e.g. system evolution
    • G—PHYSICS
    • G21—NUCLEAR PHYSICS; NUCLEAR ENGINEERING
    • G21D—NUCLEAR POWER PLANT
    • G21D3/00—Control of nuclear power plant
    • G21D3/001—Computer implemented control
    • G—PHYSICS
    • G21—NUCLEAR PHYSICS; NUCLEAR ENGINEERING
    • G21D—NUCLEAR POWER PLANT
    • G21D3/00—Control of nuclear power plant
    • G21D3/04—Safety arrangements
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]

Definitions

  • This application belongs to the field of nuclear power technology, and in particular relates to a state preview method, system, equipment and storage medium of key equipment of a nuclear power plant.
  • Nuclear power plants have a wide variety of equipment, complex structures, and harsh operating environmental conditions. Once the equipment fails, it will not only damage the equipment, but also cause system-level failures, causing the unit to trip and stack, causing huge economic losses. Among them, the failure of safety-critical equipment may even lead to nuclear safety accidents. How to realize the timely detection and diagnosis of the safety of key equipment in service units is a problem that needs to be solved and studied urgently.
  • the inventor found that the existing technology has at least the following problems: the existing equipment state prediction and maintenance management mainly rely on personal subjective knowledge and experience.
  • the three-dimensional visualization technology is relatively mature, due to the wide variety of nuclear power plant equipment , The structure is complex, and it is difficult to directly apply the three-dimensional visualization technology to the visualization of the state of the key equipment of the nuclear power plant. How to quickly and intuitively obtain the change trend of the state of each key equipment of the nuclear power plant has become an urgent problem to be solved.
  • the purpose of the embodiments of the present application is to provide a method, device, computer equipment, and storage medium for rehearsing the state of key equipment in a nuclear power plant, so as to improve the intuitiveness of rehearsing the state of key equipment in a nuclear power plant.
  • an embodiment of the present application provides a method for rehearsing the state of key equipment in a nuclear power plant, including:
  • the state trend prediction model is used to perform the state prediction calculation of the key equipment, and the calculation result is mapped to the three-dimensional visualization model for demonstration.
  • the obtaining historical operation data of each key equipment identification includes:
  • the initial operating data includes key equipment identification, equipment parameters, and equipment parameter values, and the equipment parameters are used to characterize the operating state of the equipment corresponding to the key equipment identification;
  • historical reference data corresponding to the key equipment identification is obtained from the historical operation database as the historical operation data.
  • periodically collecting data on the key equipment to obtain the initial operation data identified by the key equipment includes:
  • the key equipment state parameter table includes at least one equipment parameter and at least one key equipment identifier
  • the target parameter in the target identification is detected to obtain the parameter value corresponding to the target parameter, and the target identification, the target parameter, and the parameter value corresponding to the target parameter are used as the initial Operating data.
  • a state trend prediction model to perform state prediction calculations for key equipment based on a preset device mechanism model library, and mapping the calculation results to the three-dimensional visualization model for demonstration includes:
  • the prediction result is demonstrated using the three-dimensional visualization model.
  • the state rehearsal method of the equipment also includes:
  • the mechanism model, the key equipment identification and the equipment parameters corresponding to the key equipment identification are correlated to obtain the preset equipment mechanism model library.
  • the state rehearsal method of the equipment also includes:
  • the identification of the equipment to be maintained and the reference trend information are sent to the monitoring terminal.
  • an embodiment of the present application also provides a state preview device for key equipment of a nuclear power plant, including:
  • the historical data acquisition module is used to acquire the historical operating data of each key equipment identification
  • a predictive model construction module configured to construct a state trend predictive model of the key equipment identification based on the historical operating data
  • the visualization model building module is used to establish a three-dimensional visualization model of the key equipment identification through a finite element mesh modeling method
  • the three-dimensional visualization demonstration module is used to use the state trend prediction model to perform state prediction calculations of key equipment according to the preset equipment mechanism model library, and to map the calculation results to the three-dimensional visualization model for demonstration.
  • the historical data acquisition module includes:
  • the list obtaining unit is configured to obtain a preset list of key equipment identifications, where the preset list of key equipment identifications includes key equipment identifications;
  • the data collection unit is used to obtain the full life cycle corresponding to the key equipment identification for each key equipment identification, and periodically collect data on the key equipment during the life cycle to obtain the initial operation of the key equipment identification Data, wherein the initial operating data includes a key equipment identification, equipment parameters, and equipment parameter values, and the equipment parameters are used to characterize the operating state of the equipment corresponding to the key equipment identification;
  • a data processing unit configured to perform data preprocessing on the initial operation data to obtain historical reference data, and store the historical reference data in the historical operation database;
  • the data generating unit is configured to obtain historical reference data corresponding to the key equipment identifier from the historical operation database according to the acquired key equipment identifier as the historical operation data.
  • the data collection unit includes:
  • Testing system construction sub-unit used to build key equipment testing system
  • the parameter table receiving subunit is configured to receive a key equipment state parameter table, wherein the key equipment state parameter table includes at least one equipment parameter and at least one key equipment identifier;
  • the data confirmation subunit is configured to use the equipment parameters included in the key equipment state parameter table as target parameters, and use the key equipment identifiers contained in the key equipment state parameter table as target identifiers;
  • the data detection subunit is used to detect the target parameter in the target identifier through the key equipment detection system, to obtain the parameter value corresponding to the target parameter, and to compare the target identifier, the target parameter, and the target parameter to the target parameter.
  • the parameter value is used as the initial operating data.
  • the three-dimensional visualization demonstration module includes:
  • the mapping relationship construction unit is configured to use the received key equipment identification as the target equipment identification, and based on the target equipment identification, construct the state trend prediction model, the preset equipment mechanism model library, and the three-dimensional visualization model The mapping relationship;
  • a state obtaining unit configured to obtain the state of each part corresponding to the target device identifier based on the preset device mechanism model library
  • the result prediction unit is configured to input the state of each part corresponding to the target device identifier into the state trend prediction model based on the mapping relationship for prediction calculation to obtain a prediction result;
  • the three-dimensional demonstration unit is used to demonstrate the prediction result by using the three-dimensional visualization model.
  • the state preview device of the key equipment of the nuclear power plant further includes:
  • the characteristic data obtaining module is used to obtain the device characteristic data corresponding to each of the key device identifiers
  • the mechanism model building module is used to build a mechanism model based on the device characteristic data
  • the mechanism model library generating module is used to associate the mechanism model, the key equipment identification and the equipment parameters corresponding to the key equipment identification to obtain the preset equipment mechanism model library.
  • the state preview device of the key equipment of the nuclear power plant further includes:
  • the information acquisition module is used to acquire the identification of the key equipment that has failed within the preset time interval displayed in the demonstration as the identification of the equipment to be maintained, and acquire the change information of the demonstration state of the identification of the equipment to be maintained as the reference trend information;
  • the maintenance reminder module is used to send the identification of the equipment to be maintained and the reference trend information to the monitoring terminal.
  • an embodiment of the present application also provides a computer device, including a memory and a processor, the memory stores a computer program, and the processor realizes the state of the key equipment of the nuclear power plant when the computer program is executed. Steps of the rehearsal method.
  • an embodiment of the present application also provides a computer-readable storage medium having a computer program stored on the computer-readable storage medium, and when the computer program is executed by a processor, the above-mentioned interface display method is implemented. step.
  • the model library uses the state trend prediction model to perform state prediction calculations for key equipment, and maps the calculation results to a three-dimensional visualization model for demonstration, which is intuitive and convenient to see the change trend of key equipment and the time point of possible failure, which is beneficial to improve The efficiency and timeliness of equipment maintenance.
  • Figure 1 is an exemplary system architecture diagram to which the present application can be applied;
  • FIG. 2 is a flowchart of an embodiment of the state preview method of key equipment of a nuclear power plant according to the present application
  • Fig. 3 is a schematic structural diagram of an embodiment of a state preview device for key equipment of a nuclear power plant according to the present application
  • Fig. 4 is a schematic structural diagram of an embodiment of a computer device according to the present application.
  • the system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105.
  • the network 104 is used to provide a medium for communication links between the terminal devices 101, 102, 103 and the server 105.
  • the network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, and so on.
  • the user can use the terminal devices 101, 102, and 103 to interact with the server 105 through the network 104 to receive or send messages and so on.
  • the terminal devices 101, 102, 103 may be various electronic devices with a display screen and support web browsing, including but not limited to smart phones, tablets, e-book readers, MP3 players (Moving Picture E interface display perts Group Audio Layer III. The moving picture expert compresses the standard audio layer 3), MP4 (Moving Picture E interface displays perts Group Audio Layer IV, the moving picture expert compresses the standard audio layer 4) player, laptop portable computer and desktop computer, etc.
  • the server 105 may be a server that provides various services, for example, a background server that provides support for pages displayed on the terminal devices 101, 102, and 103.
  • the method for rehearsing the state of the key equipment of the nuclear power plant provided by the embodiment of the present application is executed by the server, and accordingly, the state rehearsing device of the key equipment of the nuclear power plant is set in the server.
  • terminal devices, networks, and servers in FIG. 1 are merely illustrative. According to implementation needs, there may be any number of terminal devices, networks, and servers.
  • the terminal devices 101, 102, and 103 in the embodiments of the present application may specifically correspond to application systems in actual production.
  • FIG. 2 shows a flowchart of an embodiment of an interface display method according to the present application.
  • the state preview method of the key equipment of the nuclear power plant includes the following steps:
  • the key equipment identification refers to a character string used to uniquely identify the key equipment, which can specifically be one or more combinations of Chinese characters, letters, and symbols.
  • the historical operation data refers to the equipment corresponding to the key equipment identification, and various status data recorded during the operation process.
  • the historical operating data includes time attributes, that is, the same key equipment identification, at different time points, the corresponding historical operating data may be the same or different.
  • a state trend prediction model for key equipment identification is constructed.
  • the state trend prediction model is obtained based on internal correlation training between a large amount of historical operating data.
  • the state trend prediction model specifically includes but is not limited to: time series prediction model, regression prediction model, Kalman filter prediction model, and The BP neural network (Back-ProPagation Network) prediction model, etc., the specific model selected can be flexibly selected according to the actual situation, and it is not limited here.
  • the state trend prediction model is used to calculate the state evolution trend of the key equipment in the future according to the state change of the key equipment identification.
  • S203 Establish a three-dimensional visualization model of key equipment identification through a finite element mesh modeling method.
  • this embodiment adopts the method of finite element mesh modeling, and uses the identification of each key device as a finite element.
  • finite element Finite Element Method
  • Finite element modeling Finite Element Modeling
  • the unit corresponding to each key equipment identifier is obtained, and then these units are meshed according to actual needs to obtain a finite element network. Grid, and use the three-dimensional visualization method to put these finite element grids into the three-dimensional model to obtain the three-dimensional visualization model of the key equipment identification.
  • the three-dimensional visualization model is a model that can perform three-dimensional visualization of the equipment status changes corresponding to the key equipment identification.
  • Three-dimensional visualization is a representation form of the data volume. It can use a large amount of data to check the continuity of the data and identify The authenticity of the data, the discovery and proposal of useful anomalies, provide useful tools for analysis, understanding and repetition of data, which is conducive to rapid and intuitive demonstration of the various states of key equipment.
  • the state trend prediction model is used to perform the state prediction calculation of the key equipment, and the calculation result is mapped to the three-dimensional visualization model for demonstration.
  • the state trend prediction model is used to perform the state prediction calculation of the key equipment, and the calculation result is mapped to the 3D visualization model for demonstration.
  • the specific 3D visualization demonstration process please refer to the description of the subsequent embodiments. To avoid repetition, I won’t repeat it here.
  • the preset equipment mechanism model library refers to a pre-configured database containing key equipment mechanism models, the mechanism model, also known as the white box model.
  • the specific generation method of the preset device mechanism model library please refer to the description of the subsequent embodiments. To avoid repetition, the details are not repeated here.
  • the state trend prediction model of the key equipment identification is constructed, and the three-dimensional visualization model of the key equipment identification is established through the finite element mesh modeling method.
  • the state trend prediction model is used to calculate the state prediction of key equipment, and the calculation results are mapped to the three-dimensional visualization model for demonstration, so as to realize intuitive and convenient viewing of the change trend and possible failure of key equipment
  • the time point is conducive to improving the efficiency and timeliness of equipment maintenance.
  • step S201 obtaining historical operation data of each key device identifier includes:
  • the initial operation data includes the key equipment identification, Equipment parameters and equipment parameter values, equipment parameters are used to characterize the operating status of the equipment corresponding to the key equipment identification;
  • historical reference data corresponding to the key equipment identification is obtained from the historical operation database as historical operation data.
  • the initial operating data refers to the data collected by the key equipment inspection system.
  • the whole life cycle is all the links in the life course of the equipment.
  • data preprocessing mainly refers to the standardized processing of data and intensive data screening for the initial operating data.
  • Standardized processing refers to the unification of data format and scope, etc.
  • intensive data screening refers to the selection of conditions according to the preset data density.
  • the data is filtered to reduce the amount of data that is too dense, which is conducive to improving the efficiency of subsequent model predictions.
  • the device parameter value refers to the specific quantized value corresponding to the device parameter.
  • the historical reference data is obtained by collecting data on key equipment and performing data and data on the collected data.
  • the obtained data is obtained based on the real operating state, which is more realistic and objective, and is beneficial to improve subsequent passes.
  • the prediction accuracy rate of the state trend prediction model constructed by historical reference data.
  • data collection is performed on key equipment at regular intervals, and the initial operation data of the key equipment identification is obtained including:
  • the key equipment state parameter table includes at least one equipment parameter and at least one key equipment identifier
  • the target parameter in the target identification is detected to obtain the parameter value corresponding to the target parameter, and the target identification, the target parameter, and the parameter value corresponding to the target parameter are used as the initial operating data.
  • a key equipment detection system After receiving a key equipment state parameter table, the key equipment identification and equipment parameters in the pair of tables are detected to obtain initial operating data.
  • the key equipment status parameter table refers to the parameter table used to identify the detection target, including at least one equipment parameter and at least one key equipment identification.
  • the equipment parameters are the data items that need to be collected.
  • the key equipment detection system refers to a data collection system used to detect the equipment to be tested and the data items to be tested.
  • a key equipment detection system by constructing a key equipment detection system, data collection is performed on key equipment at regular intervals throughout the life cycle to obtain the initial operating data of the key equipment identification, so that the acquired data is true and objective, and the state trend prediction model is constructed using the data , Is conducive to improving the prediction accuracy of the state trend prediction model.
  • step S204 according to the preset equipment mechanism model library, the state trend prediction model is used to perform the state prediction calculation of the key equipment, and the calculation result is mapped to the three-dimensional visualization model for calculation.
  • the demonstration includes:
  • a three-dimensional visualization model is used to demonstrate the prediction results.
  • the received key equipment identification is used as the target equipment identification, and the target equipment identification is obtained from the state trend prediction model, the preset equipment mechanism model library, and the three-dimensional visualization model.
  • Target equipment identification construct the state trend prediction model, the preset equipment mechanism model library and the three-dimensional visualization model to obtain the mapping relationship between the target equipment identification, and then input the key equipment identification into the preset equipment mechanism model library to obtain the target equipment Identify the state of each part corresponding to the target device identification, and input the state of each part corresponding to the target device identification into the state trend prediction model.
  • the prediction calculation is performed to obtain the prediction result.
  • the three-dimensional visualization model is used to demonstrate the prediction result in three dimensions. .
  • the three-dimensional visualization model is used to demonstrate the prediction results of the state trend prediction model in real time. It is conducive to visually displaying the problems that may occur in the pre-rotation process of key equipment, and is conducive to improving the efficiency of key equipment maintenance.
  • the state preview method of the key equipment of the nuclear power plant further includes:
  • the mechanism model, the key equipment identification and the equipment parameters corresponding to the key equipment identification are correlated to obtain the preset equipment mechanism model library.
  • the equipment characteristic data corresponding to the key equipment identification is obtained from the manufacturers corresponding to these equipment, and the mechanism model corresponding to the key equipment identification is established through the equipment characteristic data, and then the mechanism model and the key equipment The identification and the equipment parameters corresponding to the key equipment identification are correlated to obtain a preset equipment mechanism model library.
  • network transmission protocols include but are not limited to: Internet Control Message Protocol (ICMP), Address Resolution Protocol (ARP Address Resolution Protocol, ARP), File Transfer Protocol (File Transfer Protocol, FTP), etc.
  • ICMP Internet Control Message Protocol
  • ARP Address Resolution Protocol ARP
  • FTP File Transfer Protocol
  • the mechanism model also known as the white box model, is an accurate mathematical model based on the object, the internal mechanism of the production process or the transfer mechanism of the material flow. It is based on the mass balance equation, energy balance equation, momentum balance equation, and phase balance equation. And some physical property equations, chemical reaction laws, basic laws of circuits, etc. to obtain mathematical models of objects or processes.
  • the advantage of the mechanism model is that the parameters have a very clear physical meaning.
  • the mechanism model between the device characteristic data and the state is obtained by simulating a large amount of device characteristic data.
  • the device characteristic data refers to parameters related to the performance state of the device, such as: manageability, security capabilities, support for adjustable transmit power, and support for MIMO functions, etc.
  • the state preview method of the key equipment of the nuclear power plant further includes:
  • the identification of the equipment to be maintained and the reference trend information are sent to the monitoring terminal.
  • a time interval is preset.
  • the identification of the key equipment that has failed in the preset time interval is used as the identification of the equipment to be maintained, and the equipment to be maintained is obtained.
  • the change information of the demonstration status of the device identification is used as the reference trend information, and the device identification and reference trend information to be maintained are sent to the monitoring terminal, so that the management personnel of the monitoring terminal can find the corresponding device according to the identification of the device to be maintained, and refer to the trend information , Determine the equipment location for maintenance.
  • the preset time interval can be set according to actual needs, which is not limited here.
  • the level of the fault and the specific content corresponding to each level of the fault can also be set according to actual needs.
  • the corresponding device identification and reference trend information to be maintained are obtained and sent to the monitoring terminal, which is beneficial to early maintenance of the subsequent failures that may be sent in time, and improves the maintenance of key equipment. Timeliness and efficiency.
  • the computer program can be stored in a computer readable storage medium. When executed, it may include the procedures of the above-mentioned method embodiments.
  • the aforementioned storage medium may be a non-volatile storage medium such as a magnetic disk, an optical disc, a read-only memory (Read-Only Memory, ROM), or a random access memory (Random Access Memory, RAM), etc.
  • this application provides an embodiment of a state preview device for key equipment of a nuclear power plant.
  • the device embodiment corresponds to the method embodiment shown in FIG. 2 ,
  • the device can be specifically applied to various electronic equipment.
  • the state rehearsal device for key equipment of a nuclear power plant in this embodiment includes: a historical data acquisition module 31, a prediction model construction module 32, a visualization model construction module 33, and a three-dimensional visualization demonstration module 34. in:
  • the historical data acquisition module 31 is used to acquire historical operating data of each key equipment identification
  • the predictive model construction module 32 is used to construct a state trend predictive model of key equipment identification based on historical operating data
  • the visualization model building module 33 is used to establish a three-dimensional visualization model of key equipment identification through a finite element mesh modeling method
  • the three-dimensional visualization demonstration module 34 is used to use the state trend prediction model to perform state prediction calculations of key equipment according to the preset equipment mechanism model library, and to map the calculation results to the three-dimensional visualization model for demonstration.
  • the historical data acquisition module 31 includes:
  • the list acquisition unit is used to acquire a preset list of key equipment identifications, where the preset list of key equipment identifications includes key equipment identifications;
  • the data collection unit is used to obtain the full life cycle corresponding to the key equipment identification for each key equipment identification, and periodically collect data on the key equipment during the entire life cycle to obtain the initial operation data of the key equipment identification.
  • the initial operation The data includes key equipment identification, equipment parameters, and equipment parameter values.
  • the equipment parameters are used to characterize the operating status of the equipment corresponding to the key equipment identification;
  • the data processing unit is used to perform data preprocessing on the initial operating data to obtain historical reference data, and store the historical reference data in the historical operating database;
  • the data generating unit is used to obtain historical reference data corresponding to the key equipment identifier from the historical operation database according to the acquired key equipment identifier, as historical operating data.
  • the data collection unit includes:
  • Testing system construction sub-unit used to build key equipment testing system
  • the parameter table receiving subunit is configured to receive a key equipment state parameter table, where the key equipment state parameter table includes at least one equipment parameter and at least one key equipment identifier;
  • the data confirmation subunit is used to take the equipment parameters contained in the key equipment state parameter table as the target parameter, and use the key equipment identification contained in the key equipment state parameter table as the target identification;
  • the data detection sub-unit is used to detect the target parameter in the target identification through the key equipment detection system to obtain the parameter value corresponding to the target parameter, and use the target identification, the target parameter, and the parameter value corresponding to the target parameter as the initial operating data.
  • the three-dimensional visual presentation module 34 includes:
  • the mapping relationship construction unit is used to use the received key equipment identification as the target equipment identification, and based on the target equipment identification, construct the state trend prediction model, the preset equipment mechanism model library and the mapping relationship of the three-dimensional visualization model;
  • the status acquisition unit is used to acquire the status of each part corresponding to the target device identifier based on the preset device mechanism model library;
  • the result prediction unit is used to input the state of each part corresponding to the target device identifier into the state trend prediction model for prediction calculation based on the mapping relationship, and obtain the prediction result;
  • the three-dimensional demonstration unit is used to demonstrate the prediction results using a three-dimensional visualization model.
  • the state rehearsal device of the key equipment of the nuclear power plant also includes:
  • the characteristic data acquisition module is used to acquire the characteristic data of the equipment corresponding to each key equipment identification
  • the mechanism model building module is used to build mechanism model based on equipment characteristic data
  • the mechanism model library generation module is used to associate the mechanism model, the key equipment identification and the equipment parameters corresponding to the key equipment identification to obtain the preset equipment mechanism model library.
  • the state rehearsal device of the key equipment of the nuclear power plant also includes:
  • the information acquisition module is used to acquire the identification of the key equipment that fails within the preset time interval displayed in the demonstration as the identification of the equipment to be maintained, and obtain the change information of the demonstration status of the identification of the equipment to be maintained as the reference trend information;
  • the maintenance reminder module is used to send the identification of the equipment to be maintained and the reference trend information to the monitoring terminal.
  • FIG. 4 is a block diagram of the basic structure of the computer device in this embodiment.
  • the computer device 4 includes a memory 41, a processor 42, and a network interface 43 that are connected to each other in communication via a system bus. It should be pointed out that the figure only shows the computer device 4 with the components connected to the memory 41, the processor 42, and the network interface 43, but it should be understood that it is not required to implement all the shown components, and alternative implementations can be made. More or fewer components. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculation and/or information processing in accordance with pre-set or stored instructions.
  • Its hardware includes, but is not limited to, a microprocessor, a dedicated Integrated Circuit (Application Specific Integrated Circuit, ASIC), Programmable Gate Array (Field-Programmable Gate Array, FPGA), Digital Processor (Digital Signal Processor, DSP), embedded equipment, etc.
  • ASIC Application Specific Integrated Circuit
  • ASIC Application Specific Integrated Circuit
  • FPGA Field-Programmable Gate Array
  • DSP Digital Processor
  • the computer device may be a computing device such as a desktop computer, a notebook, a palmtop computer, and a cloud server.
  • the computer device can interact with the user through a keyboard, a mouse, a remote control, a touch panel, or a voice control device.
  • the memory 41 includes at least one type of readable storage medium, the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or D interface display memory, etc.), random access memory (RAM) , Static random access memory (SRAM), read only memory (ROM), electrically erasable programmable read only memory (EEPROM), programmable read only memory (PROM), magnetic memory, magnetic disks, optical disks, etc.
  • the memory 41 may be an internal storage unit of the computer device 4, such as a hard disk or memory of the computer device 4.
  • the memory 41 may also be an external storage device of the computer device 4, for example, a plug-in hard disk equipped on the computer device 4, a smart memory card (Smart Media Card, SMC), and a secure digital (Secure Digital, SD) card, Flash Card, etc.
  • the memory 41 may also include both the internal storage unit of the computer device 4 and its external storage device.
  • the memory 41 is generally used to store an operating system and various application software installed in the computer device 4, such as program code of a state preview method for key equipment of a nuclear power plant.
  • the memory 41 can also be used to temporarily store various types of data that have been output or will be output.
  • the processor 42 may be a central processing unit (Central Processing Unit, CPU), a controller, a microcontroller, a microprocessor, or other data processing chips in some embodiments.
  • the processor 42 is generally used to control the overall operation of the computer device 4.
  • the processor 42 is used to run the program code or processing data stored in the memory 41, for example, to run the program code of the state preview method of the key equipment of the nuclear power plant.
  • the network interface 43 may include a wireless network interface or a wired network interface, and the network interface 43 is generally used to establish a communication connection between the computer device 4 and other electronic devices.
  • This application also provides another implementation manner, that is, to provide a computer-readable storage medium that stores an interface display program, and the interface display program can be executed by at least one processor to enable all The at least one processor executes the steps of the state preview method of the key equipment of the nuclear power plant as described above.
  • the technical solution of this application essentially or the part that contributes to the existing technology can be embodied in the form of a software product, and the computer software product is stored in a storage medium (such as ROM/RAM, magnetic disk, The optical disc) includes several instructions to make a terminal device (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) execute the methods described in the various embodiments of the present application.
  • a terminal device which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.

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Abstract

一种核电厂关键设备的状态预演方法、装置、计算机设备及存储介质，所述方法包括：获取每个关键设备标识的历史运行数据(S201)，基于历史运行数据，构建关键设备标识的状态趋势预测模型(S202)，通过有限元网格建模方法，建立关键设备标识的三维可视化模型(S203)，根据预设的设备机理模型库，采用状态趋势预测模型进行关键设备的状态预测计算，并将计算结果映射到三维可视化模型中进行演示(S204)，实现直观便捷看到关键设备的变化趋势和可能出现故障的时间点，有利于提高设备维护的效率和及时性。

Description

核电厂关键设备的状态预演方法、系统、设备及存储介质 技术领域
本申请属于核电技术领域，尤其涉及一种核电厂关键设备的状态预演方法、系统、设备及存储介质。
背景技术
核电站设备种类繁多、结构复杂、运行环境条件严苛。一旦设备发生故障，不仅会使设备受损，还可能引发系统级故障，导致机组跳机、跳堆，造成巨大的经济损失，其中，安全级关键设备的故障，甚至会导致核安全事故发生，如何实现及时对役机组关键设备的安全进行检测诊断，是亟需解决和研究的问题。
发明人在实现本发明的过程中，发现现有技术至少存在如下问题：现有的设备状态预测和维护管理，主要依靠个人主观认识和经验，虽然三维可视化技术比较成熟，但由于核电站设备种类繁多、结构复杂，三维可视化技术很难直接运用到核电站关键设备的状态预测可视化之中，如何快速直观获取核电厂每个关键设备状态变化趋势，成了一个亟待解决的难题。
发明内容
本申请实施例的目的在于：提出一种核电厂关键设备的状态预演方法、装置、计算机设备及存储介质，以提高核电站关键设备状态预演的直观性。
为了解决上述技术问题，本申请实施例提供一种核电厂关键设备的状态预演方法，包括：
获取每个关键设备标识的历史运行数据；
基于所述历史运行数据，构建所述关键设备标识的状态趋势预测模型；
通过有限元网格建模方法，建立所述关键设备标识的三维可视化模型；
根据预设的设备机理模型库，采用状态趋势预测模型进行关键设备的状态预测计算，并将计算结果映射到所述三维可视化模型中进行演示。
可选地，所述获取每个关键设备标识的历史运行数据包括：
获取预设的关键设备标识清单，其中，所述预设的关键设备标识清单包括关键设备标识；
针对每个关键设备标识，获取所述关键设备标识对应的全寿命周期，并在全寿命周期，定时对所述关键设备进行数据采集，得到所述关键设备标识的初始运行数据，其中，所述初始运行数据包括关键设备标识、设备参数和设备参数值，所述设备参数用于对所述关键设备标识对应的设备的运行状态进行表征；
对所述初始运行数据进行数据预处理，得到历史参考数据，并将所述历史参考数据存储到历史运行数据库中；
根据获取到的关键设备标识，从所述历史运行数据库中获取所述关键设备标识对应的历史参考数据，作为所述历史运行数据。
可选地，所述在全寿命周期，定时对所述关键设备进行数据采集，得到所述关键设备标识的初始运行数据包括：
构建关键设备检测系统；
接收关键设备状态参数表，其中，所述关键设备状态参数表包括至少一项设备参数和至少一项关键设备标识；
将所述关键设备状态参数表中包含的设备参数作为目标参数，将所述关键设备状态参数表中包含的关键设备标识作为目标标识；
通过所述关键设备检测系统，对所述目标标识中的目标参数进行检测，得到所述目标参数对应的参数值，将所述目标标识、目标参数和目标参数对应的参数值，作为所述初始运行数据。
可选地，所述根据预设的设备机理模型库，采用状态趋势预测模型进行关键设备的状态预测计算，并将计算结果映射到所述三维可视化模型中进行演示包括：
将接收到的关键设备标识作为目标设备标识，并基于所述目标设备标识，构建所述状态趋势预测模型、所述预设的设备机理模型库和所述三维可视化 模型的映射关系；
基于所述预设的设备机理模型库，获取所述目标设备标识对应的各部位状态；
基于所述映射关系，将所述目标设备标识对应的各部位状态输入到所述状态趋势预测模型中进行预测计算，得到预测结果；
采用所述三维可视化模型对所述预测结果进行演示。
可选地，在所述根据预设的设备机理模型库，采用状态趋势预测模型进行关键设备的状态预测计算，并将计算结果映射到所述三维可视化模型中进行演示之前，所述核电厂关键设备的状态预演方法还包括：
获取每个所述关键设备标识对应的设备特性数据；
基于所述设备特征数据，构建机理模型；
将所述机理模型、关键设备标识和关键设备标识对应的设备参数进行关联，得到所述预设的设备机理模型库。
可选地，在所述根据预设的设备机理模型库，采用状态趋势预测模型进行关键设备的状态预测计算，并将计算结果映射到所述三维可视化模型中进行演示之后，所述核电厂关键设备的状态预演方法还包括：
获取演示中显示的在预设时间区间内出现故障的关键设备标识，作为待维护设备标识，并获取所述待维护设备标识的演示状态的变化信息，作为参考趋势信息；
将所述待维护设备标识和所述参考趋势信息发送给监控端。
为了解决上述技术问题，本申请实施例还提供一种核电厂关键设备的状态预演装置，包括：
历史数据获取模块，用于获取每个关键设备标识的历史运行数据；
预测模型构建模块，用于基于所述历史运行数据，构建所述关键设备标识的状态趋势预测模型；
可视化模型构建模块，用于通过有限元网格建模方法，建立所述关键设备标识的三维可视化模型；
三维可视化演示模块，用于根据预设的设备机理模型库，采用状态趋势预测模型进行关键设备的状态预测计算，并将计算结果映射到所述三维可视化模型中进行演示。
可选地，所述历史数据获取模块包括：
清单获取单元，用于获取预设的关键设备标识清单，其中，所述预设的关键设备标识清单包括关键设备标识；
数据采集单元，用于针对每个关键设备标识，获取所述关键设备标识对应的全寿命周期，并在全寿命周期，定时对所述关键设备进行数据采集，得到所述关键设备标识的初始运行数据，其中，所述初始运行数据包括关键设备标识、设备参数和设备参数值，所述设备参数用于对所述关键设备标识对应的设备的运行状态进行表征；
数据处理单元，用于对所述初始运行数据进行数据预处理，得到历史参考数据，并将所述历史参考数据存储到历史运行数据库中；
数据生成单元，用于根据获取到的关键设备标识，从所述历史运行数据库中获取所述关键设备标识对应的历史参考数据，作为所述历史运行数据。
可选地，所述数据采集单元包括：
检测系统构建子单元，用于构建关键设备检测系统；
参数表接收子单元，用于接收关键设备状态参数表，其中，所述关键设备状态参数表包括至少一项设备参数和至少一项关键设备标识；
数据确认子单元，用于将所述关键设备状态参数表中包含的设备参数作为目标参数，将所述关键设备状态参数表中包含的关键设备标识作为目标标识；
数据检测子单元，用于通过所述关键设备检测系统，对所述目标标识中的目标参数进行检测，得到所述目标参数对应的参数值，将所述目标标识、目标参数和目标参数对应的参数值，作为所述初始运行数据。
可选地，所述三维可视化演示模块包括：
映射关系构建单元，用于将接收到的关键设备标识作为目标设备标识， 并基于所述目标设备标识，构建所述状态趋势预测模型、所述预设的设备机理模型库和所述三维可视化模型的映射关系；
状态获取单元，用于基于所述预设的设备机理模型库，获取所述目标设备标识对应的各部位状态；
结果预测单元，用于基于所述映射关系，将所述目标设备标识对应的各部位状态输入到所述状态趋势预测模型中进行预测计算，得到预测结果；
三维演示单元，用于采用所述三维可视化模型对所述预测结果进行演示。
可选地，所述核电厂关键设备的状态预演装置还包括：
特性数据获取模块，用于获取每个所述关键设备标识对应的设备特性数据；
机构模型构建模块，用于基于所述设备特征数据，构建机理模型；
机理模型库生成模块，用于将所述机理模型、关键设备标识和关键设备标识对应的设备参数进行关联，得到所述预设的设备机理模型库。
可选地，所述核电厂关键设备的状态预演装置还包括：
信息获取模块，用于获取演示中显示的在预设时间区间内出现故障的关键设备标识，作为待维护设备标识，并获取所述待维护设备标识的演示状态的变化信息，作为参考趋势信息；
维护提醒模块，用于将所述待维护设备标识和所述参考趋势信息发送给监控端。
为了解决上述技术问题，本申请实施例还提供一种计算机设备，包括存储器和处理器，所述存储器中存储有计算机程序，所述处理器执行所述计算机程序时实现上述核电厂关键设备的状态预演方法的步骤。
为了解决上述技术问题，本申请实施例还提供一种计算机可读存储介质，所述计算机可读存储介质上存储有计算机程序，所述计算机程序被处理器执行时实现上述的界面显示的方法的步骤。
与现有技术相比，本申请实施例主要有以下有益效果：
获取每个关键设备标识的历史运行数据，基于历史运行数据，构建关键 设备标识的状态趋势预测模型，通过有限元网格建模方法，建立关键设备标识的三维可视化模型，根据预设的设备机理模型库，采用状态趋势预测模型进行关键设备的状态预测计算，并将计算结果映射到三维可视化模型中进行演示，实现直观便捷看到关键设备的变化趋势和可能出现故障的时间点，有利于提高设备维护的效率和及时性。
附图说明
为了更清楚地说明本申请中的方案，下面将对本申请实施例描述中所需要使用的附图作一个简单介绍，显而易见地，下面描述中的附图是本申请的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。
图1是本申请可以应用于其中的示例性系统架构图；
图2是本申请的核电厂关键设备的状态预演方法的一个实施例的流程图；
图3是根据本申请的核电厂关键设备的状态预演装置的一个实施例的结构示意图；
图4是根据本申请的计算机设备的一个实施例的结构示意图。
具体实施方式
除非另有定义，本文所使用的所有的技术和科学术语与属于本申请的技术领域的技术人员通常理解的含义相同；本文中在申请的说明书中所使用的术语只是为了描述具体的实施例的目的，不是旨在于限制本申请；本申请的说明书和权利要求书及上述附图说明中的术语“包括”和“具有”以及它们的任何变形，意图在于覆盖不排他的包含。本申请的说明书和权利要求书或上述附图中的术语“第一”、“第二”等是用于区别不同对象，而不是用于描述特定顺序。
在本文中提及“实施例”意味着，结合实施例描述的特定特征、结构或特性可以包含在本申请的至少一个实施例中。在说明书中的各个位置出现该短语 并不一定均是指相同的实施例，也不是与其它实施例互斥的独立的或备选的实施例。本领域技术人员显式地和隐式地理解的是，本文所描述的实施例可以与其它实施例相结合。
为了使本技术领域的人员更好地理解本申请方案，下面将结合附图，对本申请实施例中的技术方案进行清楚、完整地描述。
如图1所示，系统架构100可以包括终端设备101、102、103，网络104和服务器105。网络104用以在终端设备101、102、103和服务器105之间提供通信链路的介质。网络104可以包括各种连接类型，例如有线、无线通信链路或者光纤电缆等等。
用户可以使用终端设备101、102、103通过网络104与服务器105交互，以接收或发送消息等。
终端设备101、102、103可以是具有显示屏并且支持网页浏览的各种电子设备，包括但不限于智能手机、平板电脑、电子书阅读器、MP3播放器(Moving Picture E界面显示perts Group Audio Layer III，动态影像专家压缩标准音频层面3)、MP4(Moving Picture E界面显示perts Group Audio Layer IV，动态影像专家压缩标准音频层面4)播放器、膝上型便携计算机和台式计算机等等。
服务器105可以是提供各种服务的服务器，例如对终端设备101、102、103上显示的页面提供支持的后台服务器。
需要说明的是，本申请实施例所提供的核电厂关键设备的状态预演方法由服务器执行，相应地，核电厂关键设备的状态预演装置设置于服务器中。
应该理解，图1中的终端设备、网络和服务器的数目仅仅是示意性的。根据实现需要，可以具有任意数目的终端设备、网络和服务器，本申请实施例中的终端设备101、102、103具体可以对应的是实际生产中的应用系统。
请继续参考图2，示出了根据本申请的界面显示的方法的一个实施例的流程图。该核电厂关键设备的状态预演方法，包括以下步骤：
S201：获取每个关键设备标识的历史运行数据。
具体地，获取每个关键设备标识的历史运行数据。
其中，关键设备标识是指用于对关键设备进行唯一标识的字符串，其具体可以为汉字、字母和符号等中的一种或多种组合。
其中，历史运行数据是指关键设备标识对应的设备，在运行过程中被记录下的各项状态数据。
需要说明的是，历史运行数据包括时间属性，也即，同一关键设备标识，在不同时间点，对应的历史运行数据可能相同，也可能不相同。
S202：基于历史运行数据，构建关键设备标识的状态趋势预测模型。
具体地，通过历史运行数据，构建针对关键设备标识的状态趋势预测模型。
在本实施例中，状态趋势预测模型为根据大量的历史运行数据之间的内部关联训练得到，状态趋势预测模型具体包括但不限于：时间序列预测模型、回归预测模型、卡尔曼滤波预测模型和BP神经网络(Back-ProPagation Network)预测模型等，具体选用的模型，可根据实际情况进行灵活选取，此处不做限定。
容易理解地，状态趋势预测模型用于根据关键设备标识的状态变化，来推算其在未来一段时间内状态演变趋势。
S203：通过有限元网格建模方法，建立关键设备标识的三维可视化模型。
具体地，鉴于核电厂设备种类繁多、结构复杂的特性，本实施例采用有限元网格建模的方法，将每个关键设备标识作为一个有限元，通过有限元网格建模的方式，构建关键设备标识的三维可视化模型。
其中，有限元(FEM，Finite Element Method)是一种为求解偏微分方程边值问题近似解的数值技术，有限元建模(Finite Element Modeling)是建立有限元模型的过程，建模的中心任务是离散，但围绕离散还需要完成很多与之相关的工作，如单结构形式处理、几何模型建立、单元类型和数量选择、单元特性定义、单元质量检查、编号顺序优化以及模型边界条件的定义等。
本实施例中的，通过对每个关键设备标识对应的设备，进行有限元建模， 得到每个关键设备标识对应的单元，进而按照实际需要，对这些单元进行网格划分，得到有限元网格，并采用三维可视化的方式，将这些有限元网格投放到三维模型中，得到关键设备标识的三维可视化模型。
其中，三维可视化模型是一种能将关键设备标识对应的设备状态变化进行三维可视的模型，三维可视是数据体的一种表征形式，它能够利用大量数据，检查资料的连续性，辨认资料真伪，发现和提出有用异常，为分析、理解及重复数据提供了有用工具，从而有利于实现对关键设备的各种状态进行快速直观的演示。
S204：根据预设的设备机理模型库，采用状态趋势预测模型进行关键设备的状态预测计算，并将计算结果映射到三维可视化模型中进行演示。
具体地，根据预设的设备机理模型库，采用状态趋势预测模型进行关键设备的状态预测计算，并将计算结果映射到三维可视化模型中进行演示，具体三维可视化演示过程可参考后续实施例的描述，为避免重复，此处不再赘述。
其中，预设的设备机理模型库是指预先配置好的包含关键设备机理模型的资料库，机理模型，亦称白箱模型。根据对象、生产过程的内部机制或者物质流的传递机理建立起来的精确数学模型。预设的设备机理模型库的具体生成方式，可参考后续实施例的描述，为避免重复，此处不再赘述。
在本实施例中，通过获取每个关键设备标识的历史运行数据，基于历史运行数据，构建关键设备标识的状态趋势预测模型，通过有限元网格建模方法，建立关键设备标识的三维可视化模型，根据预设的设备机理模型库，采用状态趋势预测模型进行关键设备的状态预测计算，并将计算结果映射到三维可视化模型中进行演示，实现直观便捷看到关键设备的变化趋势和可能出现故障的时间点，有利于提高设备维护的效率和及时性。
在本实施例的一些可选的实现方式中，步骤S201中，获取每个关键设备标识的历史运行数据包括：
获取预设的关键设备标识清单，其中，预设的关键设备标识清单包括关 键设备标识；
针对每个关键设备标识，获取关键设备标识对应的全寿命周期，并在全寿命周期，定时对关键设备进行数据采集，得到关键设备标识的初始运行数据，其中，初始运行数据包括关键设备标识、设备参数和设备参数值，设备参数用于对关键设备标识对应的设备的运行状态进行表征；
对初始运行数据进行数据预处理，得到历史参考数据，并将历史参考数据存储到历史运行数据库中；
根据获取到的关键设备标识，从历史运行数据库中获取关键设备标识对应的历史参考数据，作为历史运行数据。
其中，初始运行数据是指关键设备检测系统采集到的数据。
其中，全寿命周期是设备寿命历程的所有环节。
其中，数据预处理主要是针对初始运行数据进行数据的规范化处理和密集数据筛选，规范化处理是指对数据格式、范围等进行统一，密集数据筛选是指按照预设数据密度选取条件，对过于密集的数据进行筛选，减少过于密集的数据量，有利于提高后续模型预测的效率。
应理解，设备参数值是指设备参数对应的具体量化值。
本实施例中，通过对关键设备进行数据采集，并对采集到的数据进行数据与数据，得到历史参考数据，获取到的数据为根据真实运行状态得到，更为真实客观，有利于提高后续通过历史参考数据构建的状态趋势预测模型的预测准确率。
在本实施例的一些可选的实现方式中，在全寿命周期，定时对关键设备进行数据采集，得到关键设备标识的初始运行数据包括：
构建关键设备检测系统；
接收关键设备状态参数表，其中，关键设备状态参数表包括至少一项设备参数和至少一项关键设备标识；
将关键设备状态参数表中包含的设备参数作为目标参数，将关键设备状态参数表中包含的关键设备标识作为目标标识；
通过关键设备检测系统，对目标标识中的目标参数进行检测，得到目标参数对应的参数值，将目标标识、目标参数和目标参数对应的参数值，作为初始运行数据。
具体地，通过构建关键设备检测系统，在接收到关键设备状态参数表后，针对该对表中的关键设备标识和设备参数进行检测，得到初始运行数据。
其中，关键设备状态参数表，是指用于标识检测目标的参数表，包括至少一项设备参数和至少一项关键设备标识，包含的关键设备标识为需要进行数据采集的关键设备标识，包含的设备参数为需要进行采集的数据项。
其中，关键设备检测系统是指用于对待检测设备和待检测数据项进行检测的数据采集系统。
本实施例中，通过构建关键设备检测系统，在全寿命周期，定时对关键设备进行数据采集，得到关键设备标识的初始运行数据，使得获取到的数据真实客观，使用该数据构建状态趋势预测模型，有利于提高状态趋势预测模型的预测准确率。
在本实施例的一些可选的实现方式中，步骤S204中，根据预设的设备机理模型库，采用状态趋势预测模型进行关键设备的状态预测计算，并将计算结果映射到三维可视化模型中进行演示包括：
将接收到的关键设备标识作为目标设备标识，并基于目标设备标识，构建状态趋势预测模型、预设的设备机理模型库和三维可视化模型的映射关系；
基于预设的设备机理模型库，获取目标设备标识对应的各部位状态；
基于映射关系，将目标设备标识对应的各部位状态输入到状态趋势预测模型中进行预测计算，得到预测结果；
采用三维可视化模型对预测结果进行演示。
具体地，在接收到至少一个关键设备标识时，将接收到的关键设备标识作为目标设备标识，并从状态趋势预测模型、预设的设备机理模型库和三维可视化模型中获取目标设备标识，通过目标设备标识，构建状态趋势预测模型、预设的设备机理模型库和三维可视化模型中获取目标设备标识之间的映 射关系，进而将关键设备标识输入到预设的设备机理模型库，获取目标设备标识对应的各部位状态，并将目标设备标识对应的各部位状态输入到状态趋势预测模型中，通过模拟状态的变化，进行预测计算，得到预测结果，最后采用三维可视化模型对预测结果进行三维演示。
在本实施例中，通过采用目标设备标识，构建状态趋势预测模型、预设的设备机理模型库和三维可视化模型的映射关系，实现采用三维可视化模型对状态趋势预测模型的预测结果进行实时演示，有利于直观显示关键设备在预转过程中可能出现的问题，有利于提高关键设备维护的效率。
在本实施例的一些可选的实现方式中，在步骤S204之前，该核电厂关键设备的状态预演方法还包括：
获取每个关键设备标识对应的设备特性数据；
基于设备特征数据，构建机理模型；
将机理模型、关键设备标识和关键设备标识对应的设备参数进行关联，得到预设的设备机理模型库。
具体地，根据关键设备标识，通过网络传输协议，从这些设备对应的厂商获取关键设备标识对应的设备特性数据，并通过设备特性数据建立关键设备标识对应的机理模型，再将机理模型、关键设备标识和关键设备标识对应的设备参数进行关联，得到预设的设备机理模型库。
其中，网络传输协议包括但不限于：互联网控制报文协议(Internet Control Message Protocol，ICMP)、地址解析协议(ARP Address Resolution Protocol，ARP)和文件传输协议(File Transfer Protocol，FTP)等。
其中，机理模型，亦称白箱模，根据对象、生产过程的内部机制或者物质流的传递机理建立起来的精确数学模型，它是基于质量平衡方程、能量平衡方程、动量平衡方程、相平衡方程以及某些物性方程、化学反应定律、电路基本定律等而获得对象或过程的数学模型。机理模型的优点是参数具有非常明确的物理意义，在本实施例中，是通过对大量设备特性数据进行模拟，从而获取设备特性数据与状态之间的机理模型。
其中，设备特性数据是指设备性能状态相关的参数，例如：可管理性、安全能力、支持发射功率可调整和支持MIMO功能等。
在本实施例中，通过从设备特性数据构建机理模型，进而得到预设的设备机理模型库，有利于后续通过该预设的设备机理模型库进行趋势预测，提高预测的效率和准确率。
在本实施例的一些可选的实现方式中，在步骤S204之后，该核电厂关键设备的状态预演方法还包括：
获取演示中显示的在预设时间区间内出现故障的关键设备标识，作为待维护设备标识，并获取待维护设备标识的演示状态的变化信息，作为参考趋势信息；
将待维护设备标识和参考趋势信息发送给监控端。
具体地，在三维可视化演示的过程中，存在时间的变化，预先设置一个时间区间，将在演示过程中，预设时间区间内出现故障的关键设备标识，作为待维护设备标识，并获取待维护设备标识的演示状态的变化信息，作为参考趋势信息，并将待维护设备标识和参考趋势信息发送给监控端，以使监控端的管理人员根据待维护设备标识找到对应的设备，并根据参考趋势信息，确定维护的设备部位。
其中，预设时间区间可以根据实际需要进行设定，此处不做限定。
需要说明的是，本实施例还可以根据实际需要，设置故障的等级和每个等级的故障具体对应的内容。
在本实施例中，通过对演示过程中故障状态的监控，获取对应的待维护设备标识和参考趋势信息并发送给监控端，有利于及时对后续可能发送的故障进行提前维护，提高关键设备维护的及时性和效率。
本领域普通技术人员可以理解实现上述实施例方法中的全部或部分流程，是可以通过计算机程序来指令相关的硬件来完成，该计算机程序可存储于一计算机可读取存储介质中，该程序在执行时，可包括如上述各方法的实施例的流程。其中，前述的存储介质可为磁碟、光盘、只读存储记忆体(Read-Only  Memory，ROM)等非易失性存储介质，或随机存储记忆体(Random Access Memory，RAM)等。
应该理解的是，虽然附图的流程图中的各个步骤按照箭头的指示依次显示，但是这些步骤并不是必然按照箭头指示的顺序依次执行。除非本文中有明确的说明，这些步骤的执行并没有严格的顺序限制，其可以以其他的顺序执行。而且，附图的流程图中的至少一部分步骤可以包括多个子步骤或者多个阶段，这些子步骤或者阶段并不必然是在同一时刻执行完成，而是可以在不同的时刻执行，其执行顺序也不必然是依次进行，而是可以与其他步骤或者其他步骤的子步骤或者阶段的至少一部分轮流或者交替地执行。
进一步参考图3，作为对上述图2所示方法的实现，本申请提供了一种核电厂关键设备的状态预演装置的一个实施例，该装置实施例与图2所示的方法实施例相对应，该装置具体可以应用于各种电子设备中。
如图3所示，本实施例所述的核电厂关键设备的状态预演装置包括：历史数据获取模块31、预测模型构建模块32、可视化模型构建模块33以及三维可视化演示模块34。其中：
历史数据获取模块31，用于获取每个关键设备标识的历史运行数据；
预测模型构建模块32，用于基于历史运行数据，构建关键设备标识的状态趋势预测模型；
可视化模型构建模块33，用于通过有限元网格建模方法，建立关键设备标识的三维可视化模型；
三维可视化演示模块34，用于根据预设的设备机理模型库，采用状态趋势预测模型进行关键设备的状态预测计算，并将计算结果映射到三维可视化模型中进行演示。
可选地，历史数据获取模块31包括：
清单获取单元，用于获取预设的关键设备标识清单，其中，预设的关键设备标识清单包括关键设备标识；
数据采集单元，用于针对每个关键设备标识，获取关键设备标识对应的 全寿命周期，并在全寿命周期，定时对关键设备进行数据采集，得到关键设备标识的初始运行数据，其中，初始运行数据包括关键设备标识、设备参数和设备参数值，设备参数用于对关键设备标识对应的设备的运行状态进行表征；
数据处理单元，用于对初始运行数据进行数据预处理，得到历史参考数据，并将历史参考数据存储到历史运行数据库中；
数据生成单元，用于根据获取到的关键设备标识，从历史运行数据库中获取关键设备标识对应的历史参考数据，作为历史运行数据。
可选地，数据采集单元包括：
检测系统构建子单元，用于构建关键设备检测系统；
参数表接收子单元，用于接收关键设备状态参数表，其中，关键设备状态参数表包括至少一项设备参数和至少一项关键设备标识；
数据确认子单元，用于将关键设备状态参数表中包含的设备参数作为目标参数，将关键设备状态参数表中包含的关键设备标识作为目标标识；
数据检测子单元，用于通过关键设备检测系统，对目标标识中的目标参数进行检测，得到目标参数对应的参数值，将目标标识、目标参数和目标参数对应的参数值，作为初始运行数据。
可选地，三维可视化演示模块34包括：
映射关系构建单元，用于将接收到的关键设备标识作为目标设备标识，并基于目标设备标识，构建状态趋势预测模型、预设的设备机理模型库和三维可视化模型的映射关系；
状态获取单元，用于基于预设的设备机理模型库，获取目标设备标识对应的各部位状态；
结果预测单元，用于基于映射关系，将目标设备标识对应的各部位状态输入到状态趋势预测模型中进行预测计算，得到预测结果；
三维演示单元，用于采用三维可视化模型对预测结果进行演示。
可选地，核电厂关键设备的状态预演装置还包括：
特性数据获取模块，用于获取每个关键设备标识对应的设备特性数据；
机构模型构建模块，用于基于设备特征数据，构建机理模型；
机理模型库生成模块，用于将机理模型、关键设备标识和关键设备标识对应的设备参数进行关联，得到预设的设备机理模型库。
可选地，核电厂关键设备的状态预演装置还包括：
信息获取模块，用于获取演示中显示的在预设时间区间内出现故障的关键设备标识，作为待维护设备标识，并获取待维护设备标识的演示状态的变化信息，作为参考趋势信息；
维护提醒模块，用于将待维护设备标识和参考趋势信息发送给监控端。
关于上述实施例中核电厂关键设备的状态预演装置，其中各个模块执行操作的具体方式已经在有关该方法的实施例中进行了详细描述，此处将不做详细阐述说明。
为解决上述技术问题，本申请实施例还提供计算机设备。具体请参阅图4，图4为本实施例计算机设备基本结构框图。
所述计算机设备4包括通过系统总线相互通信连接存储器41、处理器42、网络接口43。需要指出的是，图中仅示出了具有组件连接存储器41、处理器42、网络接口43的计算机设备4，但是应理解的是，并不要求实施所有示出的组件，可以替代的实施更多或者更少的组件。其中，本技术领域技术人员可以理解，这里的计算机设备是一种能够按照事先设定或存储的指令，自动进行数值计算和/或信息处理的设备，其硬件包括但不限于微处理器、专用集成电路(Application Specific Integrated Circuit，ASIC)、可编程门阵列(Field－Programmable Gate Array，FPGA)、数字处理器(Digital Signal Processor，DSP)、嵌入式设备等。
所述计算机设备可以是桌上型计算机、笔记本、掌上电脑及云端服务器等计算设备。所述计算机设备可以与用户通过键盘、鼠标、遥控器、触摸板或声控设备等方式进行人机交互。
所述存储器41至少包括一种类型的可读存储介质，所述可读存储介质包括闪存、硬盘、多媒体卡、卡型存储器(例如，SD或D界面显示存储器等)、随机访问存储器(RAM)、静态随机访问存储器(SRAM)、只读存储器(ROM)、电可擦除可编程只读存储器(EEPROM)、可编程只读存储器(PROM)、磁性存储器、磁盘、光盘等。在一些实施例中，所述存储器41可以是所述计算机设备4的内部存储单元，例如该计算机设备4的硬盘或内存。在另一些实施例中，所述存储器41也可以是所述计算机设备4的外部存储设备，例如该计算机设备4上配备的插接式硬盘，智能存储卡(Smart Media Card,SMC)，安全数字(Secure Digital,SD)卡，闪存卡(Flash Card)等。当然，所述存储器41还可以既包括所述计算机设备4的内部存储单元也包括其外部存储设备。本实施例中，所述存储器41通常用于存储安装于所述计算机设备4的操作系统和各类应用软件，例如核电厂关键设备的状态预演方法的程序代码等。此外，所述存储器41还可以用于暂时地存储已经输出或者将要输出的各类数据。
所述处理器42在一些实施例中可以是中央处理器(Central Processing Unit，CPU)、控制器、微控制器、微处理器、或其他数据处理芯片。该处理器42通常用于控制所述计算机设备4的总体操作。本实施例中，所述处理器42用于运行所述存储器41中存储的程序代码或者处理数据，例如运行所述核电厂关键设备的状态预演方法的程序代码。
所述网络接口43可包括无线网络接口或有线网络接口，该网络接口43通常用于在所述计算机设备4与其他电子设备之间建立通信连接。
本申请还提供了另一种实施方式，即提供一种计算机可读存储介质，所述计算机可读存储介质存储有界面显示程序，所述界面显示程序可被至少一个处理器执行，以使所述至少一个处理器执行如上述的核电厂关键设备的状态预演方法的步骤。
通过以上的实施方式的描述，本领域的技术人员可以清楚地了解到上述实施例方法可借助软件加必需的通用硬件平台的方式来实现，当然也可以通 过硬件，但很多情况下前者是更佳的实施方式。基于这样的理解，本申请的技术方案本质上或者说对现有技术做出贡献的部分可以以软件产品的形式体现出来，该计算机软件产品存储在一个存储介质(如ROM/RAM、磁碟、光盘)中，包括若干指令用以使得一台终端设备(可以是手机，计算机，服务器，空调器，或者网络设备等)执行本申请各个实施例所述的方法。
显然，以上所描述的实施例仅仅是本申请一部分实施例，而不是全部的实施例，附图中给出了本申请的较佳实施例，但并不限制本申请的专利范围。本申请可以以许多不同的形式来实现，相反地，提供这些实施例的目的是使对本申请的公开内容的理解更加透彻全面。尽管参照前述实施例对本申请进行了详细的说明，对于本领域的技术人员来而言，其依然可以对前述各具体实施方式所记载的技术方案进行修改，或者对其中部分技术特征进行等效替换。凡是利用本申请说明书及附图内容所做的等效结构，直接或间接运用在其他相关的技术领域，均同理在本申请专利保护范围之内。

Claims (10)

  1. 一种核电厂关键设备的状态预演方法，其特征在于，包括：
    获取每个关键设备标识的历史运行数据；
    基于所述历史运行数据，构建所述关键设备标识的状态趋势预测模型；
    通过有限元网格建模方法，建立所述关键设备标识的三维可视化模型；
    根据预设的设备机理模型库，采用状态趋势预测模型进行关键设备的状态预测计算，并将计算结果映射到所述三维可视化模型中进行演示。
  2. 根据权利要求1所述的核电厂关键设备的状态预演方法，其特征在于，所述获取每个关键设备标识的历史运行数据包括：
    获取预设的关键设备标识清单，其中，所述预设的关键设备标识清单包括关键设备标识；
    针对每个关键设备标识，获取所述关键设备标识对应的全寿命周期，并在全寿命周期，定时对所述关键设备进行数据采集，得到所述关键设备标识的初始运行数据，其中，所述初始运行数据包括关键设备标识、设备参数和设备参数值，所述设备参数用于对所述关键设备标识对应的设备的运行状态进行表征；
    对所述初始运行数据进行数据预处理，得到历史参考数据，并将所述历史参考数据存储到历史运行数据库中；
    根据获取到的关键设备标识，从所述历史运行数据库中获取所述关键设备标识对应的历史参考数据，作为所述历史运行数据。
  3. 根据权利要求2所述的核电厂关键设备的状态预演方法，其特征在于，所述在全寿命周期，定时对所述关键设备进行数据采集，得到所述关键设备标识的初始运行数据包括：
    构建关键设备检测系统；
    接收关键设备状态参数表，其中，所述关键设备状态参数表包括至少一项设备参数和至少一项关键设备标识；
    将所述关键设备状态参数表中包含的设备参数作为目标参数，将所述关 键设备状态参数表中包含的关键设备标识作为目标标识；
    通过所述关键设备检测系统，对所述目标标识中的目标参数进行检测，得到所述目标参数对应的参数值，将所述目标标识、目标参数和目标参数对应的参数值，作为所述初始运行数据。
  4. 根据权利要求2或3所述的核电厂关键设备的状态预演方法，其特征在于，所述根据预设的设备机理模型库，采用状态趋势预测模型进行关键设备的状态预测计算，并将计算结果映射到所述三维可视化模型中进行演示包括：
    将接收到的关键设备标识作为目标设备标识，并基于所述目标设备标识，构建所述状态趋势预测模型、所述预设的设备机理模型库和所述三维可视化模型的映射关系；
    基于所述预设的设备机理模型库，获取所述目标设备标识对应的各部位状态；
    基于所述映射关系，将所述目标设备标识对应的各部位状态输入到所述状态趋势预测模型中进行预测计算，得到预测结果；
    采用所述三维可视化模型对所述预测结果进行演示。
  5. 根据权利要求2所述的核电厂关键设备的状态预演方法，其特征在于，在所述根据预设的设备机理模型库，采用状态趋势预测模型进行关键设备的状态预测计算，并将计算结果映射到所述三维可视化模型中进行演示之前，所述核电厂关键设备的状态预演方法还包括：
    获取每个所述关键设备标识对应的设备特性数据；
    基于所述设备特征数据，构建机理模型；
    将所述机理模型、关键设备标识和关键设备标识对应的设备参数进行关联，得到所述预设的设备机理模型库。
  6. 根据权利要求1所述的核电厂关键设备的状态预演方法，其特征在于，在所述根据预设的设备机理模型库，采用状态趋势预测模型进行关键设备的状态预测计算，并将计算结果映射到所述三维可视化模型中进行演示之后， 所述核电厂关键设备的状态预演方法还包括：
    获取演示中显示的在预设时间区间内出现故障的关键设备标识，作为待维护设备标识，并获取所述待维护设备标识的演示状态的变化信息，作为参考趋势信息；
    将所述待维护设备标识和所述参考趋势信息发送给监控端。
  7. 一种核电厂关键设备的状态预演装置，其特征在于，包括：
    历史数据获取模块，用于获取每个关键设备标识的历史运行数据；
    预测模型构建模块，用于基于所述历史运行数据，构建所述关键设备标识的状态趋势预测模型；
    可视化模型构建模块，用于通过有限元网格建模方法，建立所述关键设备标识的三维可视化模型；
    三维可视化演示模块，用于根据预设的设备机理模型库，采用状态趋势预测模型进行关键设备的状态预测计算，并将计算结果映射到所述三维可视化模型中进行演示。
  8. 根据权利要求7所述的核电厂关键设备的状态预演装置，其特征在于，所述历史数据获取模块包括：
    清单获取单元，用于获取预设的关键设备标识清单，其中，所述预设的关键设备标识清单包括关键设备标识；
    数据采集单元，用于针对每个关键设备标识，获取所述关键设备标识对应的全寿命周期，并在全寿命周期，定时对所述关键设备进行数据采集，得到所述关键设备标识的初始运行数据，其中，所述初始运行数据包括关键设备标识、设备参数和设备参数值，所述设备参数用于对所述关键设备标识对应的设备的运行状态进行表征；
    数据处理单元，用于对所述初始运行数据进行数据预处理，得到历史参考数据，并将所述历史参考数据存储到历史运行数据库中；
    数据生成单元，用于根据获取到的关键设备标识，从所述历史运行数据库中获取所述关键设备标识对应的历史参考数据，作为所述历史运行数据。
  9. 一种计算机设备，其特征在于，包括存储器、处理器及存储在所述存储器上并可在所述处理器上运行的计算机程序，其特征在于，所述处理器执行所述计算机程序时实现权利要求1至6中任一项所述核电厂关键设备的状态预演方法的步骤。
  10. 一种计算机可读存储介质，其特征在于，所述计算机可读存储介质上存储有计算机程序，所述计算机程序被处理器执行时实现如权利要求1至6中任一项所述的核电厂关键设备的状态预演方法的步骤。
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