Digital twin-based equipment fault monitoring methods, devices, media, and equipment

Abstract

This application discloses a method, apparatus, medium, and device for equipment fault monitoring based on digital twins, relating to the field of data processing technology. This application associates all devices by monitoring data and driving data, and drives the digital twin model by applying actual driving data to the digital twin model. The overall operation of the equipment is simulated on the digital twin model, and the faulty module and the associated modules are identified. Fault monitoring is no longer an independent judgment of the operating status, but a comprehensive consideration of the correlation between operating devices. This allows both abnormal and potential faults to be monitored. Furthermore, in the process of determining faults, the abnormality of the driving module can be screened by whether the proportion of deviation abnormal data exceeds a threshold, thereby avoiding misjudgment of equipment faults and effectively improving the level of equipment fault monitoring.

Description

Technical Field

[0001] This application relates to the field of data processing technology, specifically to a method, apparatus, medium, and equipment for monitoring equipment faults based on digital twins. Background Technology

[0002] Smart and eco-friendly public toilets are a public restroom solution that combines modern technology with environmental protection concepts. They aim to improve hygiene, conserve resources, reduce pollution, and optimize user experience and management efficiency. Equipped with solar panels or wind power, they are self-contained and adaptable to most outdoor environments. With the support of artificial intelligence algorithms, these eco-friendly public toilets also possess the ability to automatically monitor equipment malfunctions. For example, existing equipment malfunction monitoring technologies, such as digital twins, construct digital twins of physical equipment to map their operating status in real time. This data analysis combined with AI algorithms enables malfunction detection.

[0003] The monitoring level under this method still needs to be improved. On the one hand, the constructed digital twin is an independent entity, while the equipment of the entire smart and environmentally friendly public toilet is highly interconnected. This method can only monitor modules that have already malfunctioned. On the other hand, since the smart and environmentally friendly public toilet is powered by its own energy supply, once the power supply and drive part malfunctions, it will cause the operating status of all connected equipment to be abnormal, which will obviously affect the judgment of equipment failure monitoring. Summary of the Invention

[0004] The main objective of this application is to provide a method, apparatus, medium, and equipment for equipment fault monitoring based on digital twins, aiming to solve the problem that the level of equipment fault monitoring using digital twin technology in the prior art is relatively low.

[0005] To achieve the above objectives, the technical solutions adopted in the embodiments of this application are as follows:

[0006] In a first aspect, embodiments of this application provide a device fault monitoring method based on digital twins, comprising the following steps:

[0007] Acquire actual monitoring data and actual drive data of the target device in its current operating state;

[0008] The target equipment's digital twin model is driven by actual data to obtain target monitoring data;

[0009] Determine whether the proportion of abnormal data in the deviation between the target monitoring data and the actual monitoring data exceeds a threshold, and obtain the judgment result;

[0010] Based on the judgment results, identify the faulty module of the target device and the associated modules that cooperate with the faulty module;

[0011] The system generates fault alarms for faulty modules and provides fault warnings for related modules based on the impact of the faulty modules on related modules.

[0012] In one possible implementation of the first aspect, before determining whether the proportion of abnormal data deviating from the target monitoring data and the actual monitoring data exceeds a threshold and obtaining the determination result, the method further includes:

[0013] Abnormal deviation data are obtained when the deviation between the target monitoring data and the actual monitoring data is outside the deviation range.

[0014] The percentage of abnormal deviation data is obtained based on the deviation anomaly data and the actual monitoring data.

[0015] In one possible implementation of the first aspect, the judgment result includes a first judgment result and a second judgment result. The first judgment result is that the proportion of abnormal data in the deviation between the target monitoring data and the actual monitoring data exceeds a threshold, and the second judgment result is that the proportion of abnormal data in the deviation between the target monitoring data and the actual monitoring data does not exceed a threshold.

[0016] In one possible implementation of the first aspect, based on the judgment result, the faulty module of the target device and the associated modules that cooperate with the faulty module are determined, including:

[0017] Based on the first judgment result, the faulty module of the target device is determined to be the drive module, and the associated modules that cooperate with the drive module are identified.

[0018] In one possible implementation of the first aspect, based on the judgment result, the faulty module of the target device and the associated modules that cooperate with the faulty module are determined, including:

[0019] Based on the second judgment result and the deviation anomaly data, the faulty module of the target device is identified, and the associated module that cooperates with the drive module is also identified.

[0020] In one possible implementation of the first aspect, before issuing a fault alarm for the faulty module and, based on the impact of the faulty module on related modules, issuing a fault warning for the related modules, the method further includes:

[0021] Based on the digital twin model, predict the continued driving results of the actual driving data to obtain predictive monitoring data;

[0022] Based on the predictive monitoring data, the impact of the faulty module on related modules is obtained.

[0023] In one possible implementation of the first aspect, before obtaining target monitoring data by driving the digital twin model of the target device with actual driving data, the method further includes:

[0024] Based on the geometric model, simulation model, and data-driven model of the target device under different working conditions, a digital twin model of the target device is constructed; the digital twin model includes multiple driving modes, and each driving mode corresponds to a working state.

[0025] Secondly, embodiments of this application provide a device for monitoring equipment faults based on digital twins, comprising:

[0026] The acquisition module is used to acquire the actual monitoring data and actual drive data of the target device in its current working state;

[0027] The drive module is used to drive the digital twin model of the target device with actual drive data to obtain target monitoring data;

[0028] The judgment module is used to determine whether the proportion of abnormal data in the deviation between the target monitoring data and the actual monitoring data exceeds the threshold, and to obtain the judgment result.

[0029] The determination module is used to identify the faulty module of the target device and the associated modules that cooperate with the faulty module based on the judgment result.

[0030] The monitoring module is used to issue fault alarms for faulty modules and to provide fault warnings for related modules based on the impact of faulty modules on related modules.

[0031] Thirdly, embodiments of this application provide a computer-readable storage medium storing a computer program, which, when loaded and executed by a processor, implements the device fault monitoring method based on digital twins as provided in any of the first aspects above.

[0032] Fourthly, embodiments of this application provide an electronic device, including a processor and a memory, wherein,

[0033] Memory is used to store computer programs;

[0034] The processor is used to load and execute computer programs to cause electronic devices to perform the digital twin-based device fault monitoring method provided in any of the first aspects above.

[0035] Compared with the prior art, the beneficial effects of this application are:

[0036] This application proposes a method, apparatus, medium, and device for equipment fault monitoring based on digital twins. The method includes: acquiring actual monitoring data and actual driving data of a target device in its current operating state; driving the digital twin model of the target device with the actual driving data to obtain target monitoring data; determining whether the proportion of abnormal data deviating from the target monitoring data and the actual monitoring data exceeds a threshold, and obtaining a judgment result; determining the faulty module of the target device and the associated modules cooperating with the faulty module based on the judgment result; issuing a fault alarm for the faulty module, and issuing a fault warning for the associated modules based on the impact of the faulty module on the associated modules. This application associates all devices through monitoring data and driving data, and drives the digital twin model by applying the actual driving data, simulating the overall operation of the equipment on the digital twin model, and determining the faulty module and the associated modules. This allows fault monitoring to no longer be an independent judgment of the operating state, but rather a comprehensive consideration of the relationships between operating devices, enabling both abnormal and potential faults to be monitored. Furthermore, in the process of determining faults, the proportion of abnormal data deviating from the threshold can be used to filter whether the driving module is abnormal, thereby avoiding misjudgment of equipment faults and effectively improving the level of equipment fault monitoring. Attached Figure Description

[0037] Figure 1 This is a schematic diagram of the electronic device structure of the hardware operating environment involved in the embodiments of this application;

[0038] Figure 2 A flowchart illustrating the device fault monitoring method based on digital twin provided in this application embodiment;

[0039] Figure 3 A schematic diagram of a digital twin-based equipment fault monitoring device provided in an embodiment of this application;

[0040] The diagram is labeled as follows: 101-Processor, 102-Communication bus, 103-Network interface, 104-User interface, 105-Memory. Detailed Implementation

[0041] It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application.

[0042] See attached document Figure 1 , attached Figure 1This is a schematic diagram of the electronic device structure of the hardware operating environment involved in the embodiments of this application. The electronic device may include: a processor 101, such as a central processing unit (CPU), a communication bus 102, a user interface 104, a network interface 103, and a memory 105. The communication bus 102 is used to realize the connection and communication between these components. The user interface 104 may include a display screen and an input unit such as a keyboard. Optionally, the user interface 104 may also include a standard wired interface and a wireless interface. The network interface 103 may optionally include a standard wired interface and a wireless interface (such as a Wi-Fi interface). The memory 105 may be a storage device independent of the aforementioned processor 101. The memory 105 may be a high-speed random access memory (RAM) or a stable non-volatile memory (NVM), such as at least one disk storage device. The processor 101 may be a general-purpose processor, including a central processing unit, a network processor, etc., or it may be a digital signal processor, an application-specific integrated circuit, a field-programmable gate array or other programmable logic device, discrete gate or transistor logic device, or discrete hardware component.

[0043] Those skilled in the art will understand that the appendix Figure 1 The structure shown does not constitute a limitation on the electronic device and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0044] As attached Figure 1 As shown, the memory 105, which serves as a storage medium, may include an operating system, a network communication module, a user interface module, and a device fault monitoring device based on digital twins.

[0045] In the appendix Figure 1 In the electronic device shown, the network interface 103 is mainly used for data communication with the network server; the user interface 104 is mainly used for data interaction with the user; the processor 101 and the memory 105 in this application can be set in the electronic device. The electronic device calls the device fault monitoring device based on digital twin stored in the memory 105 through the processor 101 and executes the device fault monitoring method based on digital twin provided in the embodiment of this application.

[0046] See attached document Figure 2 Based on the hardware device of the foregoing embodiments, embodiments of this application provide a device fault monitoring method based on digital twins, including the following steps:

[0047] S10: Obtain the actual monitoring data and actual drive data of the target device in its current working state.

[0048] In the specific implementation process, the target equipment, that is, the equipment that needs fault monitoring, in the context of smart and environmentally friendly public toilets, includes solar or wind power generation equipment, lighting equipment, ventilation fans, toilets, water pumps, motors, and other equipment within the toilet. Whether the equipment is in use or not, operational data can be collected. This operational data can be collected by setting up sensors; for example, vibration sensors can collect vibration data for water pumps, vibration sensors can collect shaft vibration data for motors, or temperature sensors can collect operating temperature data for motors, and light sensors can output monitoring data for lighting equipment. Alternatively, actual monitoring data can be output through converters, such as outputting the operating voltage data of the motor.

[0049] Drive data refers to the energy input data that maintains the operation of these devices and enables the smart eco-friendly public toilet to function. For example, in standby mode, the solar power module's power output bus will generate data, which can be characterized by current and voltage data—this is the actual drive data. Each device operates through this energy drive, resulting in operational data. Using the acquisition methods described above, the actual monitoring data representing the device's operation can be obtained. Different operating states will generate different drive and monitoring data. For instance, the required drive data will differ depending on whether the smart eco-friendly public toilet is in use, standby, ventilation, or flushing mode. Similarly, the different devices required for different states will lead to different corresponding actual monitoring data.

[0050] S20: Use actual driving data to drive the digital twin model of the target device to obtain target monitoring data.

[0051] In practical implementation, to locate the faulty module, a digital twin model of the target equipment is used for comparative judgment. A digital twin fully utilizes physical models, sensors, operational history, and other data, integrating multi-disciplinary, multi-physical quantity, multi-scale, and multi-probability simulation processes to complete mapping in virtual space, thereby reflecting the entire lifecycle of the corresponding physical equipment. A digital twin is a concept that transcends reality; it can be viewed as a digital mapping system of one or more important, interdependent equipment systems. By driving the digital twin model with actual data, the operation of the target equipment is simulated. Ideally, all equipment is fault-free, and the target monitoring data mapped on the digital twin model should be consistent with the actual monitoring data.

[0052] In one embodiment, before obtaining target monitoring data by driving a digital twin model of the target device with actual driving data, the method further includes:

[0053] Based on the geometric model, simulation model, and data-driven model of the target device under different working conditions, a digital twin model of the target device is constructed; the digital twin model includes multiple driving modes, and each driving mode corresponds to a working state.

[0054] In the specific implementation process, the geometric model is obtained through 3D modeling using CAD, BIM (Building Information Modeling), or point cloud scanning; the simulation model, also known as the physical model, is based on physical laws, such as ANSYS or finite element simulation; the data-driven model can be obtained based on machine learning, such as LSTM prediction, or digital threads, linking data throughout its entire lifecycle. A multi-dimensional digital twin model is constructed using these three models. To match different working states in application scenarios, multiple driving modes are designed for the digital twin model, distinguishing and constructing each model according to its working state. Ultimately, multiple models are displayed based on a single model but with different driving modes.

[0055] S30: Determine whether the proportion of abnormal data in the deviation between the target monitoring data and the actual monitoring data exceeds the threshold, and obtain the judgment result.

[0056] In practical implementation, since the smart eco-friendly toilet is self-supplied with energy, any abnormalities in the functional drive system need to be investigated independently, rather than being quickly determined through the supplier. For example, if the smart eco-friendly toilet is powered by solar energy, all equipment needs to be connected to solar power generation equipment to operate. An abnormality in the solar power generation equipment will directly cause abnormalities in the operating data of all connected monitoring equipment, resulting in a significant deviation between the target monitoring data and the actual monitoring data. Therefore, this embodiment determines the proportion of abnormal data with deviation. If it exceeds a threshold, it is determined as the first judgment result; otherwise, it is determined as the second judgment result. In other words, the judgment results include both the first and second judgment results. The first judgment result is that the proportion of abnormal data with deviation between the target monitoring data and the actual monitoring data exceeds the threshold; the second judgment result is that the proportion of abnormal data with deviation between the target monitoring data and the actual monitoring data does not exceed the threshold.

[0057] In one embodiment, before determining whether the proportion of abnormal data deviating from the target monitoring data and the actual monitoring data exceeds a threshold, the method further includes:

[0058] Abnormal deviation data are obtained when the deviation between the target monitoring data and the actual monitoring data is outside the deviation range.

[0059] The percentage of abnormal deviation data is obtained based on the deviation anomaly data and the actual monitoring data.

[0060] In the specific implementation process, there will be a certain deviation between the actual monitored data and the theoretical monitored data, that is, the target monitored data. In other words, under normal circumstances, even without faults or anomalies, the actual monitored data will fluctuate within a certain range relative to the target monitored data. Therefore, the condition for determining the deviation and abnormal data is that the deviation between the target monitored data and the actual monitored data is outside the deviation range, that is, the data fluctuation has exceeded the normal fluctuation range.

[0061] S40: Based on the judgment result, determine the faulty module of the target device and the associated modules that cooperate with the faulty module.

[0062] In the specific implementation process, since the modeling process involves all devices, it is possible not only to directly identify the faulty module during fault determination, but also to identify the associated modules that cooperate with the faulty module, thereby predicting the fault.

[0063] Specifically, in the event of a driver module malfunction, based on the assessment results, the faulty module of the target device and its associated modules that work in conjunction with the faulty module are identified, including:

[0064] Based on the first judgment result, the faulty module of the target device is determined to be the drive module, and the associated modules that cooperate with the drive module are identified.

[0065] If the driver module is functioning normally, based on the assessment results, identify the faulty module of the target device and its associated modules, including:

[0066] Based on the second judgment result and the deviation anomaly data, the faulty module of the target device is identified, and the associated module that cooperates with the drive module is also identified.

[0067] In the specific implementation process, since the aforementioned judgment process is to screen out whether the drive module is abnormal, if the drive module is abnormal, a fault alarm will be triggered, and after the repair is completed, the fault will be monitored again when the drive module is normal; if the drive module is normal, then the faulty module can be determined based on the data where the deviation between the actual monitoring data and the target monitoring data is outside the deviation range, i.e., the deviation abnormal data.

[0068] S50: Issue a fault alarm for the faulty module and provide a fault warning for the associated modules based on the impact of the faulty module on the associated modules.

[0069] In practical implementation, the digital twin model simulates the overall operation of the equipment, identifies faulty modules and their associated modules, and transforms fault monitoring from an independent assessment of operational status to a comprehensive consideration of the relationships between operating devices, enabling the monitoring of both abnormal and potential faults. For potential faults in associated modules, prediction can be made based on the impact of the faulty module on it. That is, before issuing fault alarms for the faulty module and providing early warnings for the associated modules based on their impact, the method also includes:

[0070] Based on the digital twin model, predict the continued driving results of the actual driving data to obtain predictive monitoring data;

[0071] Based on the predictive monitoring data, the impact of the faulty module on related modules is obtained.

[0072] In the actual implementation process, if a certain device malfunctions, it will inevitably affect the related modules that work with it. The result of this impact can be predicted by a digital twin model. Because the digital twin model has learned from a large amount of historical data, it can predict deviation and abnormal data, predict the future trend of the data, and predict how the data that has not yet shown any abnormalities in the actual monitoring data will change in the future as it continues to operate. In other words, the trend of the data that is currently operating normally will change in the future. Then, based on the magnitude of the predicted data, the impact of the faulty module on the related modules can be determined.

[0073] For example, a potential malfunction in the biodegradation tank of a smart public toilet, such as a heating rod experiencing performance degradation, directly affects the temperature sensor data within the tank. The impact of the faulty module (heating system) on related modules (microbial community) can be categorized into two types: physical impacts (e.g., the heating rod burning out due to overheating of its internal resistance wire from prolonged inefficient operation, or the controller being damaged by prolonged high-load operation) and non-physical impacts (e.g., the temperature within the biodegradation tank failing to reach the optimal fermentation threshold, or reduced microbial activity). Based on these different impact categories, different levels of fault warnings can be issued to the related modules. In the above embodiment, the prediction using a digital twin model indicates that the microbial decomposition efficiency of the biodegradation tank will be significantly reduced during future low-temperature periods, which falls under the category of non-physical impacts. Smart public toilets can be monitored in real-time via a 5G smart cloud platform, which can also transmit and issue maintenance work orders for alarms and warnings, providing a foundation for intelligent management.

[0074] For example, if a power outage occurs in a smart public toilet, the data directly affected will be from equipment requiring power, such as motors and gas processing equipment. The impact of the faulty module on related modules can be categorized into two types: physical impacts, such as motor jamming, water pump failure, and shaft breakage; and non-physical impacts, such as increased lighting temperature and insufficient water pump pressure. Based on these different impact categories, different levels of fault warnings can be issued to the related modules. In the above embodiment, prediction using a digital twin model indicates that the concentration of the mixture in the degradation tank will be affected in the future, resulting in lower decomposition efficiency; this falls under the category of non-physical impacts. Smart public toilets can be systematically connected and managed through a 5G smart cloud platform. Real-time monitoring and control commands can be transmitted and issued via the cloud platform for alarms and warnings, providing a foundation for intelligent management.

[0075] In this embodiment, all devices are associated with monitoring data and driving data. By applying actual driving data to a digital twin model, the overall operation of the devices is simulated on the digital twin model. The faulty modules and their associated modules are identified, so that fault monitoring is no longer an independent judgment of the operating status, but a comprehensive consideration of the correlation between operating devices. This allows both abnormal and potential faults to be monitored. Furthermore, in the process of fault identification, the abnormality of the driving module can be screened by whether the proportion of deviation abnormal data exceeds a threshold, thereby avoiding misjudgment of device faults and effectively improving the level of device fault monitoring.

[0076] See attached document Figure 3 Based on the same inventive concept as in the foregoing embodiments, this application also provides a device fault monitoring device based on digital twins, comprising:

[0077] The acquisition module is used to acquire the actual monitoring data and actual drive data of the target device in its current working state;

[0078] The drive module is used to drive the digital twin model of the target device with actual drive data to obtain target monitoring data;

[0079] The judgment module is used to determine whether the proportion of abnormal data in the deviation between the target monitoring data and the actual monitoring data exceeds the threshold, and to obtain the judgment result.

[0080] The determination module is used to identify the faulty module of the target device and the associated modules that cooperate with the faulty module based on the judgment result.

[0081] The monitoring module is used to issue fault alarms for faulty modules and to provide fault warnings for related modules based on the impact of faulty modules on related modules.

[0082] Those skilled in the art should understand that the division of the various modules in the embodiments is merely a logical functional division. In actual applications, they can be fully or partially integrated into one or more actual carriers. These modules can be implemented entirely in software through processing unit calls, entirely in hardware, or a combination of software and hardware. It should be noted that each module in the digital twin-based equipment fault monitoring device in this embodiment corresponds one-to-one with each step in the digital twin-based equipment fault monitoring method in the aforementioned embodiments. Therefore, the specific implementation of this embodiment can refer to the implementation of the aforementioned digital twin-based equipment fault monitoring method, which will not be repeated here.

[0083] Based on the same inventive concept as in the foregoing embodiments, embodiments of this application also provide a computer-readable storage medium storing a computer program, which, when loaded and executed by a processor, implements the device fault monitoring method based on digital twins as provided in the embodiments of this application.

[0084] Based on the same inventive concept as in the foregoing embodiments, embodiments of this application also provide an electronic device, including a processor and a memory, wherein,

[0085] Memory is used to store computer programs;

[0086] The processor is used to load and execute computer programs to cause the electronic device to perform the device fault monitoring method based on digital twins provided in the embodiments of this application.

[0087] In some embodiments, the computer-readable storage medium may be a memory such as FRAM, ROM, PROM, EPROM, EEPROM, flash memory, magnetic surface memory, optical disk, or CD-ROM; or it may be a device including one or any combination of the above-mentioned memories. The computer may be a variety of computing devices, including smart terminals and servers.

[0088] In some embodiments, executable instructions may take the form of a program, software, software module, script, or code, written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and may be deployed in any form, including as a standalone program or as a module, component, subroutine, or other unit suitable for use in a computing environment.

[0089] As an example, executable instructions may, but do not necessarily, correspond to files in the file system. They may be stored as part of a file that holds other programs or data, for example, in one or more scripts in a Hyper Text Markup Language (HTML) document, in a single file dedicated to the program in question, or in multiple collaborative files (e.g., a file that stores one or more modules, subroutines, or code sections).

[0090] As an example, executable instructions can be deployed to execute on a single computing device, or on multiple computing devices located in one location, or on multiple computing devices distributed across multiple locations and interconnected via a communication network.

[0091] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0092] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0093] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as read-only memory / random access memory, magnetic disk, optical disk) and includes several instructions to cause a multimedia terminal device (which may be a mobile phone, computer, television receiver, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0094] In summary, this application provides a method, apparatus, medium, and device for equipment fault monitoring based on digital twins. The method includes: acquiring actual monitoring data and actual driving data of a target device in its current operating state; driving the digital twin model of the target device with the actual driving data to obtain target monitoring data; determining whether the proportion of abnormal data deviating from the target monitoring data and the actual monitoring data exceeds a threshold, and obtaining a judgment result; determining the faulty module of the target device and the associated modules cooperating with the faulty module based on the judgment result; issuing a fault alarm for the faulty module, and issuing a fault warning for the associated modules based on the impact of the faulty module on the associated modules. This application associates all devices through monitoring data and driving data, and drives the digital twin model by applying the actual driving data, simulating the overall operation of the equipment on the digital twin model, and determining the faulty module and the associated modules. This allows fault monitoring to no longer be an independent judgment of the operating state, but rather a comprehensive consideration of the relationships between operating devices, enabling both abnormal and potential faults to be monitored. Furthermore, in the process of determining faults, the proportion of abnormal data deviating from the threshold can be used to filter whether the driving module is abnormal, thereby avoiding misjudgment of equipment faults and effectively improving the level of equipment fault monitoring.

[0095] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A digital-twin-based device failure monitoring method, characterized by, The method comprises the following steps: obtaining actual monitoring data and actual driving data of a target device in a current working state; the actual monitoring data is the running state data of a functional device, and the actual driving data is the energy output data of a power generation device; driving a digital twin model of the target device with the actual driving data to obtain target monitoring data; judging whether the proportion of deviation abnormal data of the target monitoring data and the actual monitoring data exceeds a threshold value to obtain a judgment result; before the judgment whether the proportion of deviation abnormal data of the target monitoring data and the actual monitoring data exceeds a threshold value to obtain a judgment result, the method further comprises: obtaining deviation abnormal data according to the deviation of the target monitoring data and the actual monitoring data being outside a deviation range; obtaining the proportion of deviation abnormal data according to the deviation abnormal data and the actual monitoring data; determining a fault module of the target device and an associated module cooperating with the fault module according to the judgment result; the judgment result comprises a first judgment result and a second judgment result, the first judgment result is that the proportion of deviation abnormal data of the target monitoring data and the actual monitoring data exceeds a threshold value, and the second judgment result is that the proportion of deviation abnormal data of the target monitoring data and the actual monitoring data does not exceed a threshold value; determining the fault module of the target device and the associated module cooperating with the fault module according to the first judgment result; determining the fault module of the target device and the associated module cooperating with the fault module according to the second judgment result and the deviation abnormal data; performing fault alarm on the fault module, and performing fault early warning on the associated module according to the influence result of the fault module on the associated module. before the fault alarm on the fault module and the fault early warning on the associated module according to the influence result of the fault module on the associated module, the method further comprises: predicting a continuous driving result of the actual driving data according to the digital twin model to obtain predicted monitoring data; 2. The digital-twin-based device failure monitoring method according to claim 1, characterized in that, obtaining the influence result of the fault module on the associated module according to the predicted monitoring data. before the driving of the digital twin model of the target device with the actual driving data to obtain target monitoring data, the method further comprises: constructing the digital twin model of the target device according to the geometric model, the simulation model and the data-driven model of the target device under different working states; wherein the digital twin model comprises a plurality of driving modes, and one driving mode corresponds to one working state. 3.The digital-twin-based device failure monitoring method of claim 1, wherein, comprise: an acquisition module configured to acquire actual monitoring data and actual driving data of a target device in a current working state; 4. A device failure monitoring apparatus based on digital twinning, characterized by, ​ ​ The actual monitoring data is operation state data of the functional equipment, and the actual driving data is energy output data of the power generation equipment; The driving module is configured to drive the digital twin model of the target equipment with the actual driving data to obtain target monitoring data; The judging module is configured to judge whether a proportion of deviation abnormal data of the target monitoring data and the actual monitoring data exceeds a threshold to obtain a judgment result; Before the judging whether the proportion of deviation abnormal data of the target monitoring data and the actual monitoring data exceeds the threshold to obtain the judgment result, the method further comprises: obtaining deviation abnormal data according to the deviation of the target monitoring data and the actual monitoring data being outside a deviation range; obtaining a proportion of the deviation abnormal data according to the deviation abnormal data and the actual monitoring data; The determining module is configured to determine a fault module of the target equipment and an associated module cooperating with the fault module according to the judgment result; the judgment result comprises a first judgment result and a second judgment result; the first judgment result is that the proportion of deviation abnormal data of the target monitoring data and the actual monitoring data exceeds the threshold, and the second judgment result is that the proportion of deviation abnormal data of the target monitoring data and the actual monitoring data does not exceed the threshold; The determining module is configured to determine a fault module of the target equipment and an associated module cooperating with the fault module according to the judgment result; the judgment result comprises a first judgment result and a second judgment result; the first judgment result is that the proportion of deviation abnormal data of the target monitoring data and the actual monitoring data exceeds the threshold, and the second judgment result is that the proportion of deviation abnormal data of the target monitoring data and the actual monitoring data does not exceed the threshold; The determining module is configured to determine a fault module of the target equipment and an associated module cooperating with the fault module according to the judgment result; the judgment result comprises a first judgment result and a second judgment result; the first judgment result is that the proportion of deviation abnormal data of the target monitoring data and the actual monitoring data exceeds the threshold, and the second judgment result is that the proportion of deviation abnormal data of the target monitoring data and the actual monitoring data does not exceed the threshold; The monitoring module is configured to perform fault alarm on the fault module and perform fault early warning on the associated module according to an influence result of the fault module on the associated module. The computer program is loaded and executed by the processor to implement the device fault monitoring method based on digital twinning according to any one of claims 1-3. The electronic device comprises a processor and a memory, wherein 5. A computer-readable storage medium storing a computer program, characterized in that, The memory is configured to store a computer program; 6. An electronic device, comprising: The processor is configured to load and execute the computer program to enable the electronic device to perform the device fault monitoring method based on digital twinning according to any one of claims 1-3. ​ ​

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