WO2022257925A1 - Procédé et appareil de prédiction de défaillance basés sur un jumeau numérique, serveur et support de stockage - Google Patents

Procédé et appareil de prédiction de défaillance basés sur un jumeau numérique, serveur et support de stockage Download PDF

Info

Publication number
WO2022257925A1
WO2022257925A1 PCT/CN2022/097396 CN2022097396W WO2022257925A1 WO 2022257925 A1 WO2022257925 A1 WO 2022257925A1 CN 2022097396 W CN2022097396 W CN 2022097396W WO 2022257925 A1 WO2022257925 A1 WO 2022257925A1
Authority
WO
WIPO (PCT)
Prior art keywords
data
fault prediction
fault
real
trained
Prior art date
Application number
PCT/CN2022/097396
Other languages
English (en)
Chinese (zh)
Inventor
崔岩
常青玲
侯宇灿
揭英达
Original Assignee
五邑大学
广东四维看看智能设备有限公司
中德（珠海）人工智能研究院有限公司
珠海市四维时代网络科技有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 五邑大学, 广东四维看看智能设备有限公司, 中德（珠海）人工智能研究院有限公司, 珠海市四维时代网络科技有限公司 filed Critical 五邑大学
Publication of WO2022257925A1 publication Critical patent/WO2022257925A1/fr

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/044—Recurrent networks, e.g. Hopfield networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/084—Backpropagation, e.g. using gradient descent

Definitions

  • the present application belongs to the technical field of data mapping, and in particular relates to a digital twin-based fault prediction method, device, server and storage medium.
  • the embodiment of the present application provides a fault prediction method, device, server, and storage medium based on digital twins to realize early prediction of production line faults.
  • the embodiment of the present application provides a fault prediction method based on digital twins, including:
  • the simulation data is input into a pre-trained fault prediction model, and before the fault prediction result is obtained, the method further includes:
  • extracting the fault data in the real-time data includes:
  • the fault data in the real-time training data is extracted according to a preset fault data extraction model.
  • the pre-trained fault prediction model includes a generation network and a discrimination network
  • Import the fault data into the preset fault prediction model for training to obtain a pre-trained fault prediction model including:
  • the fault data is used as real data, the real data and the fake sample data are used as input, and the discrimination result is used as the output, and the discrimination result is used as the feedback input of the generation network, and according to the preset objective function
  • the discrimination network and the generation network are trained to obtain a pre-trained fault prediction model.
  • the preset objective function is:
  • D represents the generation network
  • G represents the confrontation network
  • E represents the expected function
  • x represents the fault data
  • x ⁇ pdata(x) represents the fault data obeys the pdata(x) distribution
  • z represents the noise data
  • z ⁇ p (z) indicates that the noise data obeys the p (z) distribution
  • D(x) indicates the output data after the fault data is imported into the generation network
  • G(z) indicates the output data after the noise data is imported into the judgment network.
  • the fault prediction result is obtained according to the simulation data and the pre-trained fault prediction model, including:
  • the simulation data and the data generated by the generating network according to the noise data are imported into the discriminant network, and the fault prediction result is output.
  • the method further includes:
  • the embodiment of the present application provides a digital twin-based fault prediction device, including:
  • the first acquisition module is used to acquire the real-time data of the target real object
  • a generating module configured to generate a digital twin virtual object according to the real-time data
  • the second acquisition module the simulation data in the digital twin virtual model
  • the fault prediction module is used to obtain the fault prediction result according to the simulation data and the pre-trained fault prediction model.
  • the device further includes:
  • the third obtaining module is used to obtain the real-time training data of the target real object
  • An extraction module configured to extract fault data in the real-time data samples
  • the training module is used to import fault data into a preset fault prediction model for training to obtain a pre-trained fault prediction model.
  • the extraction module includes:
  • the extraction unit is configured to extract fault data in the real-time training data according to a preset fault data extraction model.
  • the pre-trained fault prediction model includes a generation network and a discrimination network
  • the training modules include:
  • the fault data is used as real data, the real data and the fake sample data are used as input, and the discrimination result is used as the output, and the discrimination result is used as the feedback input of the generation network, and according to the preset objective function
  • the discrimination network and the generation network are trained to obtain a pre-trained fault prediction model.
  • the preset objective function is:
  • D represents the generation network
  • G represents the confrontation network
  • E represents the expected function
  • x represents the fault data
  • x ⁇ pdata(x) represents the fault data obeys the pdata(x) distribution
  • z represents the noise data
  • z ⁇ p (z) indicates that the noise data obeys the p (z) distribution
  • D(x) indicates the output data after the fault data is imported into the generation network
  • G(z) indicates the output data after the noise data is imported into the judgment network.
  • the fault prediction module includes:
  • the fault prediction unit is used to import the simulation data and the data generated by the generation network according to the noise data into the discrimination network, and output a fault prediction result.
  • the digital twin-based fault diagnosis equipment further includes:
  • the registration module is used to register simulation data and fault prediction results.
  • a sending module configured to send the registered simulation data and fault prediction results to the user terminal to instruct the user terminal to display a monitoring picture to the user, the monitoring picture including the operating status of the target real object and the marked fault prediction analyze.
  • an embodiment of the present application provides a server, including a memory, a processor, and a computer program stored in the memory and operable on the processor, and the computer program is implemented when the processor executes the computer program.
  • a server including a memory, a processor, and a computer program stored in the memory and operable on the processor, and the computer program is implemented when the processor executes the computer program. The method as described in the first aspect above.
  • an embodiment of the present application provides a storage medium, the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method as described in the first aspect above is implemented.
  • a digital twin virtual object is generated according to the real-time data, the simulation data in the digital twin virtual model is obtained, and the fault prediction result is obtained according to the simulation data and the pre-trained fault prediction model . It can be seen that the embodiment of the present application can achieve the effect of accurate fault prediction for real objects (such as production lines or workshops, etc.) by combining the digital twin technology with the unsupervised learning characteristics of the pre-trained fault prediction model.
  • FIG. 1 is a schematic flow diagram of a digital twin-based fault prediction method provided in an embodiment of the present application
  • Fig. 2 is a schematic flow chart before step S108 in Fig. 1 of the digital twin-based fault prediction method provided by the embodiment of the present application;
  • Fig. 3 is a specific implementation flowchart of step S206 in Fig. 2 of the digital twin-based fault prediction method provided by the embodiment of the present application;
  • Fig. 4 is a schematic flow chart after step S204 in Fig. 2 of the digital twin-based fault prediction method provided by the embodiment of the present application;
  • FIG. 5 is a schematic structural diagram of a digital twin-based fault prediction device provided by an embodiment of the present application.
  • FIG. 6 is a schematic structural diagram of a server provided by an embodiment of the present application.
  • Fig. 7 is the training process of the fault prediction model of the digital twin-based fault prediction method provided by the embodiment of the present application.
  • the term “if” may be construed, depending on the context, as “when” or “once” or “in response to determining” or “in response to detecting “.
  • the phrase “if determined” or “if [the described condition or event] is detected” may be construed, depending on the context, to mean “once determined” or “in response to the determination” or “once detected [the described condition or event] ]” or “in response to detection of [described condition or event]”.
  • references to "one embodiment” or “some embodiments” or the like in the specification of the present application means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the present application.
  • appearances of the phrases “in one embodiment,” “in some embodiments,” “in other embodiments,” “in other embodiments,” etc. in various places in this specification are not necessarily All refer to the same embodiment, but mean “one or more but not all embodiments” unless specifically stated otherwise.
  • the terms “including”, “comprising”, “having” and variations thereof mean “including but not limited to”, unless specifically stated otherwise.
  • Digital twin is a simulation process that makes full use of data such as physical models, sensor updates, and operating history, integrates multi-disciplinary, multi-physical quantities, multi-scale, and multi-probability, and completes the mapping in the virtual space to reflect the corresponding physical equipment. whole life cycle process.
  • a digital twin is a concept beyond reality that can be viewed as a digital twin of one or more important, interdependent equipment systems.
  • FIG. 1 it is a schematic flow diagram of a digital twin-based fault prediction method provided by the embodiment of the present application.
  • the method can be applied to a server connected to a sensor set on a target real object.
  • the server may be a computing device such as a cloud server, and the method may include the following steps:
  • Step S102 acquiring real-time data of the target real object.
  • the target real object refers to a real-world production line or workshop, etc.
  • real-time data includes static data (such as equipment parameters) and dynamic data (such as equipment operating status).
  • the real-time data of the target real object is collected through sensors installed on various devices in the production line or workshop.
  • Step S104 generating a digital twin virtual object according to the real-time data.
  • the digital twin virtual object refers to the digital twin corresponding to the target real object.
  • an editable logic controller is used to generate digital twin virtual objects based on real-time data.
  • Step S106 acquiring simulation data in the digital twin virtual model.
  • the simulation data refers to the data generated by the digital twin virtual model to dynamically map the operating conditions of the target real object according to the real-time data.
  • Step S108 according to the simulation data and the pre-trained fault prediction model, a fault prediction result is obtained.
  • the pre-trained fault prediction model includes a generative network and a discriminative network.
  • the fault prediction result is obtained according to the simulation data and the pre-trained fault prediction model, including: importing the simulation data and the data generated by the generation network based on the noise data into the discriminant network, and outputting the fault prediction result.
  • the fault prediction result is the fault equipment number, fault type, fault cause and fault time.
  • the simulation data and the data generated by the generation network based on the noise data are judged as real data or generated samples according to the discrimination network, so as to determine the fault data in the simulation data as the fault prediction result.
  • the embodiment of the present application provides a schematic flow chart before step S108 in Figure 1 of the digital twin-based fault prediction method, based on simulation data and pre-trained fault prediction
  • the model, before getting the fault prediction results also includes:
  • Step S202 acquiring real-time training data of the target real object.
  • real-time training data is obtained from open source datasets.
  • Step S204 extracting fault data in the real-time training data.
  • the fault data includes fault equipment number, fault type, fault content and fault time.
  • the named entity recognition is directly carried out on the real-time data, and the fault equipment number, fault type, fault content and fault time in the entity data are extracted.
  • the fault data extraction model can be trained on the basis of Bi-LSTM+CRF neural network.
  • the process of training the fault data extraction model on the Bi-LSTM+CRF neural network can be:
  • Step S206 importing the fault data into a preset fault prediction model for training to obtain a pre-trained fault prediction model.
  • the fault data is imported into the preset fault prediction model for training to obtain a pre-trained fault prediction model, including: importing the fault data and the data generated by the generation network based on the noise data to the discriminant network, and outputting the fault prediction result.
  • FIG. 3 it is a specific implementation flow chart of step S206 in FIG. 2 of the digital twin-based fault prediction method provided by the embodiment of the present application.
  • the fault data is imported into the preset fault prediction model for training, and the obtained Pre-trained failure prediction models, including:
  • Step S302 generating noise data.
  • the noise data is randomly generated variables, which are used to train the generation network to generate fake sample data.
  • Step S304 importing the noise data into the generation network to obtain fake sample data.
  • the generating network is preferably a naive Bayesian model.
  • Step S306 using the fault data as the real data, the real data and the fake sample data as the input, the discrimination result as the output, and the discrimination result as the feedback input of the generation network, and training the discrimination network and the generation network according to the preset objective function , to get the pre-trained fault prediction model.
  • the discriminant network is preferably a decision tree.
  • the preset objective function is:
  • D represents the generation network
  • G represents the confrontation network
  • E represents the expected function
  • X represents the fault data
  • x ⁇ pdata(x) represents the fault data obeys the pdata(x) distribution
  • z represents the noise data
  • z ⁇ p (z) indicates that the noise data obeys the p (z) distribution
  • D(x) indicates the output data after the fault data is imported into the generation network
  • G(z) indicates the output data after the noise data is imported into the judgment network.
  • pdata(x) distribution and p (z) distribution may respectively refer to Gaussian distribution, uniform distribution and the like.
  • steps S302 to S306 reference may be made to the training process of the fault prediction model as shown in FIG. 7 .
  • the named entity recognition technology is used to directly extract the fault data to train the fault prediction data, without additional manual data labeling, which reduces the difficulty of training the fault prediction model.
  • FIG. 4 it is a schematic flow chart after step S204 in FIG. 2 of the digital twin-based fault prediction method provided by the embodiment of the present application.
  • the trained fault prediction model after obtaining the fault prediction results, also includes:
  • Step S402 registering the simulation data and the fault prediction result.
  • Step S404 sending the registered simulation data and fault prediction results to the user terminal to instruct the user terminal to generate a monitoring screen according to the registered simulation data and fault prediction results, and display the monitoring screen to the user.
  • the monitoring screen includes the equipment generation of each equipment in the digital twin virtual model, the equipment operation status and the fault prediction results of the faulty equipment.
  • the simulation data and fault prediction results are obtained by combining the digital twin technology with the pre-trained fault prediction model, and a monitoring screen is generated according to the simulation data and fault prediction results for customers to view, so as to realize the user through monitoring.
  • the screen can check the running status of the target real object and the prediction and analysis of the faulty equipment in the target real object in real time, and detect the faulty equipment in time.
  • a digital twin virtual object is generated according to the real-time data, the simulation data in the digital twin virtual model is obtained, and the fault prediction result is obtained according to the simulation data and the pre-trained fault prediction model .
  • the effect of accurate fault prediction on real objects can be achieved by combining the digital twin technology with the unsupervised learning characteristics of the pre-trained fault prediction model.
  • Fig. 5 shows a structural block diagram of the digital twin-based fault prediction device provided by the embodiment of the present application. For the convenience of description, only the parts related to the embodiment of the present application are shown.
  • the digital twin-based fault prediction device includes:
  • the first obtaining module 51 is used to obtain the real-time data of the target real object
  • a generating module 52 configured to generate a digital twin virtual object according to the real-time data
  • the second acquisition module 53 the simulation data in the digital twin virtual model
  • the fault prediction module 54 is configured to obtain a fault prediction result according to the simulation data and the pre-trained fault prediction model.
  • the device further includes:
  • the third obtaining module is used to obtain the real-time training data of the target real object
  • An extraction module configured to extract fault data in the real-time data samples
  • the training module is used to import the fault data into a preset fault prediction model for training to obtain a pre-trained fault prediction model.
  • the extraction module includes:
  • the extraction unit is configured to extract fault data in the real-time training data according to a preset fault data extraction model.
  • the pre-trained fault prediction model includes a generation network and a discrimination network
  • the training modules include:
  • the fault data is used as real data, the real data and the fake sample data are used as input, and the discrimination result is used as the output, and the discrimination result is used as the feedback input of the generation network, and according to the preset objective function
  • the discrimination network and the generation network are trained to obtain a pre-trained fault prediction model.
  • the preset objective function is:
  • D represents the generation network
  • G represents the confrontation network
  • E represents the expected function
  • X represents the fault data
  • x ⁇ pdata(x) represents the fault data obeys the pdata(x) distribution
  • z represents the noise data
  • z ⁇ p (z) indicates that the noise data obeys the p (z) distribution
  • D(x) indicates the output data after the fault data is imported into the generation network
  • G(z) indicates the output data after the noise data is imported into the judgment network.
  • the fault prediction module includes:
  • the fault prediction unit is used to import the simulation data and the data generated by the generation network according to the noise data into the discrimination network, and output a fault prediction result.
  • the digital twin-based fault diagnosis equipment further includes:
  • the registration module is used to register simulation data and fault prediction results.
  • a sending module configured to send the registered simulation data and fault prediction results to the user terminal to instruct the user terminal to display a monitoring picture to the user, the monitoring picture including the operating status of the target real object and the marked fault prediction analyze.
  • FIG. 6 is a schematic structural diagram of a server provided by an embodiment of the present application.
  • the server 6 of this embodiment includes: at least one processor 60, a memory 61, and a computer program 62 stored in the memory 61 and operable on the at least one processor 60, so When the processor 60 executes the computer program 62, the steps in any of the foregoing method embodiments are implemented.
  • the server 6 may be a computing device such as a cloud server.
  • the server may include, but not limited to, a processor 60 and a memory 61 .
  • Fig. 6 is only an example of server 6, and does not constitute a limitation to server 6, and may include more or less components than those shown in the figure, or combine some components, or different components, for example It may also include input and output devices, network access devices, etc.
  • the so-called processor 60 can be a central processing unit (Central Processing Unit, CPU), and the processor 60 can also be other general processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit) , ASIC), off-the-shelf programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.
  • a general-purpose processor may be a microprocessor, or the processor may be any conventional processor, or the like.
  • the storage 61 may be an internal storage unit of the server 6 in some embodiments, such as a hard disk or memory of the server 6 .
  • the memory 61 can also be an external storage device of the server 6 in other embodiments, such as a plug-in hard disk equipped on the server 6, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, flash memory card (Flash Card), etc.
  • the storage 61 may also include both an internal storage unit of the server 6 and an external storage device.
  • the memory 61 is used to store operating system, application program, boot loader (BootLoader), data and other programs, such as the program code of the computer program.
  • the memory 61 can also be used to temporarily store data that has been output or will be output.
  • the embodiment of the present application also provides a storage medium, the storage medium is a computer-readable storage medium, and the readable storage medium stores a computer program, and when the computer program is executed by a processor, it can realize the above-mentioned various method embodiments. in the steps.
  • the integrated unit is realized in the form of a software function unit and sold or used as an independent product, it can be stored in a computer-readable storage medium.
  • all or part of the processes in the methods of the above embodiments in the present application can be completed by instructing related hardware through computer programs, and the computer programs can be stored in a computer-readable storage medium.
  • the computer program When executed by a processor, the steps in the above-mentioned various method embodiments can be realized.
  • the computer program includes computer program code, and the computer program code may be in the form of source code, object code, executable file or some intermediate form.
  • the computer-readable medium may at least include: any entity or device capable of carrying computer program code, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random-access memory (RAM, Random Access Memory), electrical carrier signals, telecommunication signals, and software distribution media.
  • ROM read-only memory
  • RAM random-access memory
  • electrical carrier signals telecommunication signals
  • software distribution media Such as U disk, mobile hard disk, magnetic disk or CD, etc.
  • computer readable media may not be electrical carrier signals and telecommunication signals under legislation and patent practice.
  • the disclosed device/network device and method may be implemented in other ways.
  • the device/network device embodiments described above are only illustrative.
  • the division of the modules or units is only a logical function division.
  • the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces, and the indirect coupling or communication connection of devices or units may be in electrical, mechanical or other forms.
  • the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Evolutionary Computation (AREA)
  • General Engineering & Computer Science (AREA)
  • Software Systems (AREA)
  • Business, Economics & Management (AREA)
  • Data Mining & Analysis (AREA)
  • Biophysics (AREA)
  • General Health & Medical Sciences (AREA)
  • Molecular Biology (AREA)
  • Computing Systems (AREA)
  • Biomedical Technology (AREA)
  • Artificial Intelligence (AREA)
  • Mathematical Physics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Computational Linguistics (AREA)
  • Strategic Management (AREA)
  • Economics (AREA)
  • Human Resources & Organizations (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Game Theory and Decision Science (AREA)
  • Development Economics (AREA)
  • Computer Hardware Design (AREA)
  • Geometry (AREA)
  • Marketing (AREA)
  • Operations Research (AREA)
  • Quality & Reliability (AREA)
  • Tourism & Hospitality (AREA)
  • General Business, Economics & Management (AREA)
  • Debugging And Monitoring (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

La présente invention est applicable au domaine technique du mappage de données, et concerne un procédé et un appareil de prédiction de défaillance basés sur un jumeau numérique, un serveur et un support de stockage. Le procédé consiste à : obtenir des données en temps réel d'un objet réel cible ; générer un objet virtuel jumeau numérique selon les données en temps réel ; obtenir des données de simulation dans un modèle virtuel jumeau numérique ; et obtenir un résultat de prédiction de défaillance selon les données de simulation et un modèle de prédiction de défaillance pré-entraîné. Ainsi, dans des modes de réalisation de la présente invention, la prédiction précise des défaillances d'un objet réel (tel qu'une chaîne de production ou un atelier) peut être réalisée par une combinaison de la technologie de jumeau numérique et des caractéristiques d'apprentissage non supervisé du modèle de prédiction de défaillance pré-entraîné.
PCT/CN2022/097396 2021-06-09 2022-06-07 Procédé et appareil de prédiction de défaillance basés sur un jumeau numérique, serveur et support de stockage WO2022257925A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN202110644072.XA CN113379123A (zh) 2021-06-09 2021-06-09 基于数字孪生的故障预测方法、装置、服务器及存储介质
CN202110644072.X 2021-06-09

Publications (1)

Publication Number Publication Date
WO2022257925A1 true WO2022257925A1 (fr) 2022-12-15

Family

ID=77573235

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2022/097396 WO2022257925A1 (fr) 2021-06-09 2022-06-07 Procédé et appareil de prédiction de défaillance basés sur un jumeau numérique, serveur et support de stockage

Country Status (2)

Country Link
CN (1) CN113379123A (fr)
WO (1) WO2022257925A1 (fr)

Cited By (14)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116026367A (zh) * 2023-03-29 2023-04-28 中国人民解放军火箭军工程大学 基于数字孪生技术的激光惯组故障诊断方法、系统及设备
CN116319260A (zh) * 2023-05-09 2023-06-23 新华三技术有限公司 一种网络故障诊断方法、装置、设备及存储介质
CN116776744A (zh) * 2023-08-15 2023-09-19 工业云制造(四川)创新中心有限公司 一种基于增强现实的装备制造控制方法及电子设备
CN116861204A (zh) * 2023-09-05 2023-10-10 山东山森数控技术有限公司 基于数字孪生的智能制造设备数据管理系统
CN116885858A (zh) * 2023-09-08 2023-10-13 中国标准化研究院 一种基于数字孪生技术的配电网故障处理方法及系统
CN116956720A (zh) * 2023-07-19 2023-10-27 安徽斯维尔信息科技有限公司 一种工业数字孪生的仿真运维系统
CN116977122A (zh) * 2023-07-06 2023-10-31 双龙软创(深圳)科技有限公司 基于数字孪生技术的远程自动化监测危房的方法
CN116881672B (zh) * 2023-09-05 2023-11-21 江西南昌济生制药有限责任公司 一种故障检测模型训练方法、装置、电子设备及存储介质
CN117130351A (zh) * 2023-09-18 2023-11-28 上海勘测设计研究院有限公司 基于数字孪生技术的新能源场站区域联合控制保护系统
CN117148048A (zh) * 2023-10-30 2023-12-01 国网江苏省电力有限公司南通供电分公司 基于数字孪生技术的配电网故障预测方法及系统
CN117318033A (zh) * 2023-09-27 2023-12-29 国网江苏省电力有限公司南通供电分公司 一种结合数据孪生的电网数据管理方法及系统
CN117349102A (zh) * 2023-12-05 2024-01-05 网思科技股份有限公司 数字孪生运维数据质量检验方法、系统和介质
CN117669388A (zh) * 2024-01-30 2024-03-08 武汉理工大学 故障样本生成方法、装置、计算机介质
CN117669388B (zh) * 2024-01-30 2024-05-31 武汉理工大学 故障样本生成方法、装置、计算机介质

Families Citing this family (17)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113379123A (zh) * 2021-06-09 2021-09-10 中德（珠海）人工智能研究院有限公司 基于数字孪生的故障预测方法、装置、服务器及存储介质
CN113923000B (zh) * 2021-09-29 2023-11-03 卡奥斯数字科技(青岛)有限公司 一种安全处理方法、装置、电子设备、及存储介质
CN113844507B (zh) * 2021-10-22 2023-10-10 暨南大学 一种基于数字孪生的列车模拟运行系统构建方法
CN113886973B (zh) * 2021-10-25 2023-04-18 江苏远望仪器集团有限公司 基于虚实映射的船舶航速处理方法、装置及处理设备
CN114184883A (zh) * 2021-11-22 2022-03-15 国网河南省电力公司漯河供电公司 一种基于配电网故障仿真的配网故障检测精度计算方法
CN114218754A (zh) * 2021-11-23 2022-03-22 中国电子产品可靠性与环境试验研究所（（工业和信息化部电子第五研究所）（中国赛宝实验室）） 无人机数字孪生方法、系统、计算机设备和存储介质
CN114373047B (zh) * 2021-12-29 2023-05-12 达闼机器人股份有限公司 一种基于数字孪生监控物理世界的方法、装置及存储介质
CN114826440B (zh) * 2022-03-31 2023-11-24 文山电视调频骨干转播台 基于数字孪生的发射机监测故障定位方法
CN114897196B (zh) * 2022-05-11 2023-01-13 山东大卫国际建筑设计有限公司 一种办公建筑供水网络的运行管理方法、设备及介质
CN114707428B (zh) * 2022-06-01 2022-09-02 中科航迈数控软件(深圳)有限公司 数控机床不可观环节仿真方法、装置、终端及存储介质
CN115356639B (zh) * 2022-09-29 2023-04-18 深圳先进技术研究院 一种双向锂离子电池智能健康监控方法及系统
CN116071039B (zh) * 2022-12-21 2024-01-19 广州辰创科技发展有限公司 一种基于故障树的故障诊断方法
CN116011991B (zh) * 2022-12-30 2023-12-19 中国电子科技集团公司第三十八研究所 基于代理和备份技术的多人协同任务保障方法
CN116108717B (zh) * 2023-01-17 2023-09-26 中山大学 一种基于数字孪生的交通运输设备运行预测方法及装置
CN116184293B (zh) * 2023-03-01 2024-02-13 深圳市中科恒辉科技有限公司 一种基于数字孪生锂电池系统的故障诊断方法及报警系统
CN116451878A (zh) * 2023-06-16 2023-07-18 山东捷瑞数字科技股份有限公司 基于数字孪生技术的故障预测方法、装置、设备及介质
CN117289685B (zh) * 2023-11-27 2024-02-02 青岛创新奇智科技集团股份有限公司 一种基于人工智能的生产线故障预测与自愈方法及系统

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20190325572A1 (en) * 2018-04-20 2019-10-24 Siemens Healthcare Gmbh Real-time and accurate soft tissue deformation prediction
CN111008502A (zh) * 2019-11-25 2020-04-14 北京航空航天大学 一种数字孪生驱动的复杂装备故障预测方法
CN111400930A (zh) * 2020-04-09 2020-07-10 武汉大学 基于虚实孪生空间的电力设备小样本故障诊断方法及系统
CN111458142A (zh) * 2020-04-02 2020-07-28 苏州智传新自动化科技有限公司 基于生成对抗网络和卷积神经网络的滑动轴承故障诊断方法
CN111596604A (zh) * 2020-06-12 2020-08-28 中国科学院重庆绿色智能技术研究院 一种基于数字孪生的工程装备故障智能诊断与自愈控制系统及方法
CN112418455A (zh) * 2020-11-28 2021-02-26 北京三维天地科技股份有限公司 一种设备故障预测与健康管理系统
CN113379123A (zh) * 2021-06-09 2021-09-10 中德（珠海）人工智能研究院有限公司 基于数字孪生的故障预测方法、装置、服务器及存储介质

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111461338A (zh) * 2020-03-06 2020-07-28 北京仿真中心 基于数字孪生的智能系统更新方法、装置
CN112382064B (zh) * 2020-11-12 2023-01-20 广东电网有限责任公司 一种基于数字孪生技术的电力物联网故障预警方法及系统

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20190325572A1 (en) * 2018-04-20 2019-10-24 Siemens Healthcare Gmbh Real-time and accurate soft tissue deformation prediction
CN111008502A (zh) * 2019-11-25 2020-04-14 北京航空航天大学 一种数字孪生驱动的复杂装备故障预测方法
CN111458142A (zh) * 2020-04-02 2020-07-28 苏州智传新自动化科技有限公司 基于生成对抗网络和卷积神经网络的滑动轴承故障诊断方法
CN111400930A (zh) * 2020-04-09 2020-07-10 武汉大学 基于虚实孪生空间的电力设备小样本故障诊断方法及系统
CN111596604A (zh) * 2020-06-12 2020-08-28 中国科学院重庆绿色智能技术研究院 一种基于数字孪生的工程装备故障智能诊断与自愈控制系统及方法
CN112418455A (zh) * 2020-11-28 2021-02-26 北京三维天地科技股份有限公司 一种设备故障预测与健康管理系统
CN113379123A (zh) * 2021-06-09 2021-09-10 中德（珠海）人工智能研究院有限公司 基于数字孪生的故障预测方法、装置、服务器及存储介质

Cited By (24)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116026367A (zh) * 2023-03-29 2023-04-28 中国人民解放军火箭军工程大学 基于数字孪生技术的激光惯组故障诊断方法、系统及设备
CN116319260A (zh) * 2023-05-09 2023-06-23 新华三技术有限公司 一种网络故障诊断方法、装置、设备及存储介质
CN116319260B (zh) * 2023-05-09 2023-08-18 新华三技术有限公司 一种网络故障诊断方法、装置、设备及存储介质
CN116977122B (zh) * 2023-07-06 2024-04-19 双龙软创(深圳)科技有限公司 基于数字孪生技术的远程自动化监测危房的方法
CN116977122A (zh) * 2023-07-06 2023-10-31 双龙软创(深圳)科技有限公司 基于数字孪生技术的远程自动化监测危房的方法
CN116956720A (zh) * 2023-07-19 2023-10-27 安徽斯维尔信息科技有限公司 一种工业数字孪生的仿真运维系统
CN116956720B (zh) * 2023-07-19 2024-01-30 安徽斯维尔信息科技有限公司 一种工业数字孪生的仿真运维系统
CN116776744A (zh) * 2023-08-15 2023-09-19 工业云制造(四川)创新中心有限公司 一种基于增强现实的装备制造控制方法及电子设备
CN116776744B (zh) * 2023-08-15 2023-10-31 工业云制造(四川)创新中心有限公司 一种基于增强现实的装备制造控制方法及电子设备
CN116861204A (zh) * 2023-09-05 2023-10-10 山东山森数控技术有限公司 基于数字孪生的智能制造设备数据管理系统
CN116881672B (zh) * 2023-09-05 2023-11-21 江西南昌济生制药有限责任公司 一种故障检测模型训练方法、装置、电子设备及存储介质
CN116861204B (zh) * 2023-09-05 2023-12-08 山东山森数控技术有限公司 基于数字孪生的智能制造设备数据管理系统
CN116885858B (zh) * 2023-09-08 2023-12-08 中国标准化研究院 一种基于数字孪生技术的配电网故障处理方法及系统
CN116885858A (zh) * 2023-09-08 2023-10-13 中国标准化研究院 一种基于数字孪生技术的配电网故障处理方法及系统
CN117130351A (zh) * 2023-09-18 2023-11-28 上海勘测设计研究院有限公司 基于数字孪生技术的新能源场站区域联合控制保护系统
CN117130351B (zh) * 2023-09-18 2024-03-19 上海勘测设计研究院有限公司 基于数字孪生技术的新能源场站区域联合控制保护系统
CN117318033A (zh) * 2023-09-27 2023-12-29 国网江苏省电力有限公司南通供电分公司 一种结合数据孪生的电网数据管理方法及系统
CN117318033B (zh) * 2023-09-27 2024-05-24 国网江苏省电力有限公司南通供电分公司 一种结合数据孪生的电网数据管理方法及系统
CN117148048A (zh) * 2023-10-30 2023-12-01 国网江苏省电力有限公司南通供电分公司 基于数字孪生技术的配电网故障预测方法及系统
CN117148048B (zh) * 2023-10-30 2024-01-12 国网江苏省电力有限公司南通供电分公司 基于数字孪生技术的配电网故障预测方法及系统
CN117349102A (zh) * 2023-12-05 2024-01-05 网思科技股份有限公司 数字孪生运维数据质量检验方法、系统和介质
CN117349102B (zh) * 2023-12-05 2024-03-15 网思科技股份有限公司 数字孪生运维数据质量检验方法、系统和介质
CN117669388A (zh) * 2024-01-30 2024-03-08 武汉理工大学 故障样本生成方法、装置、计算机介质
CN117669388B (zh) * 2024-01-30 2024-05-31 武汉理工大学 故障样本生成方法、装置、计算机介质

Also Published As

Publication number Publication date
CN113379123A (zh) 2021-09-10

Similar Documents

Publication Publication Date Title
WO2022257925A1 (fr) Procédé et appareil de prédiction de défaillance basés sur un jumeau numérique, serveur et support de stockage
CN110363220B (zh) 行为类别检测方法、装置、电子设备和计算机可读介质
CN112990294B (zh) 行为判别模型的训练方法、装置、电子设备及存储介质
CN112036187A (zh) 一种结合上下文语境的视频弹幕文本审核方法及系统
CN112883990A (zh) 数据分类方法及装置、计算机存储介质、电子设备
CN114584377A (zh) 流量异常检测方法、模型的训练方法、装置、设备及介质
CN101414352A (zh) 信息处理设备、信息处理方法和程序
CN115774784A (zh) 一种文本对象的识别方法及装置
CN116977249A (zh) 缺陷检测的方法、模型训练的方法和装置
CN113987188A (zh) 一种短文本分类方法、装置及电子设备
CN113591881A (zh) 基于模型融合的意图识别方法、装置、电子设备及介质
CN111611981A (zh) 信息识别方法和装置及信息识别神经网络训练方法和装置
CN110929033A (zh) 长文本分类方法、装置、计算机设备及存储介质
CN110715799A (zh) 断路器机械状态检测方法、装置及终端设备
CN116911883B (zh) 基于ai鉴真技术和溯源量化的农产防伪溯源方法及云平台
CN114005005B (zh) 双重批标准化的零实例图像分类方法
CN113723524B (zh) 基于预测模型的数据处理方法、相关设备及介质
CN117591813B (zh) 基于多维特征的复杂装备故障诊断方法及系统
CN113111713B (zh) 一种图像检测方法、装置、电子设备及存储介质
CN117011213A (zh) 一种缺陷检测模型的训练方法和相关装置
CN117271674A (zh) 字段类型识别方法、装置、电子设备及存储介质
CN117216618A (zh) 基于ai模型的反欺诈识别方法、装置、设备及存储介质
TWM629361U (zh) 匯款即時檢核系統
CN111915437A (zh) 基于rnn的反洗钱模型的训练方法、装置、设备及介质
CN116702014A (zh) 人口识别方法、装置、终端设备及存储介质

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 22819523

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE