Power plant equipment operation state detection method and device, equipment and storage medium

Abstract

The invention relates to the technical field of intelligent power plants, in particular to a power plant equipment operation state detection method and device, equipment and a storage medium, and aims to improve the reliability of operation data of power equipment by performing noise removal on multi-mode operation data of the power plant equipment in the operation process. And fusing the data of the at least two modals through the data fusion model corresponding to the power plant equipment, improving the accuracy of mapping of different modal features to the equipment operation state, performing time sequence analysis on the fused data to obtain the operation state change of the power equipment in the time sequence, thereby realizing the operation state detection of the power plant equipment. The technical problem that in the prior art, the precision of state monitoring of the power equipment is low is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of smart power plants, and in particular to methods, devices, equipment, and storage media for detecting the operating status of power plant equipment. Background Art

[0002] During the operation of a thermal power plant, in order to ensure the stability of thermal power generation and power transportation, a large number of power equipment need to cooperate with each other. It is very necessary to perform status detection on these power equipment. However, there are many types and quantities of power equipment that affect power generation efficiency or transportation efficiency. Manual regular maintenance is inefficient and overly dependent on the personal experience of staff. In order to improve the efficiency of power equipment monitoring, equipment status detection can be achieved by electrically connecting to these power equipment. However, the detection indicators of different equipment are different, and the accuracy is low.

[0003] The above content is only used to assist in understanding the technical solution of the present invention and does not constitute an admission that the above content is prior art. Summary of the Invention

[0004] The main purpose of the present invention is to provide a method, device, equipment and storage medium for detecting the operating status of power plant equipment, aiming to solve the technical problem of low accuracy in status monitoring of power equipment in the prior art.

[0005] To achieve the above object, the present invention provides a method for detecting the operating status of power plant equipment, the method comprising the following steps:

[0006] Obtain multi-modal operating data of power plant equipment during operation;

[0007] removing noise from the operating data to obtain multimodal target operating data;

[0008] fusing the multimodal target operating data in at least two modes using a data fusion model corresponding to the power plant equipment to obtain fused data;

[0009] The equipment operation status is detected on the fused data through a time series analysis model.

[0010] Optionally, fusing the multimodal target operation data into at least two modalities using a data fusion model corresponding to the power plant equipment to obtain fused data includes:

[0011] extracting historical maintenance data of the power plant equipment;

[0012] Determining a target data modality representing an operating status of the power plant equipment based on the historical equipment maintenance data;

[0013] Extracting target operation data to be fused corresponding to the target data modality from the multimodal target operation data;

[0014] The target operation data to be fused is fused through a data fusion model to obtain fused data.

[0015] Optionally, extracting the target operation data to be fused corresponding to the target data modality from the multimodal target operation data includes:

[0016] Time-aligning the multimodal target operation data to obtain multimodal target operation data to be fused;

[0017] The target operation data to be fused corresponding to the target data modality is extracted from the multimodal target operation data to be fused.

[0018] Optionally, fusing the target operating data to be fused through a data fusion model to obtain fused data includes:

[0019] Extracting the modal feature representation corresponding to the target operation data to be fused;

[0020] Determining the support between each modal feature representation, and calculating the support mean and support variance based on the support;

[0021] Based on the support matrix and the support variance, weighting is performed on the target operation data to be fused by using a support weighting model to obtain the weight of the target operation data to be fused of each modality;

[0022] Data fusion is performed according to the weights of the target operating data to be fused of each modality through a data fusion model to obtain fused data.

[0023] Optionally, the operating data includes at least: temperature data, image data, acoustic wave data and electrical data;

[0024] The step of removing noise from the operating data to obtain multi-modal target operating data includes:

[0025] Performing an outlier test on the temperature data to obtain target temperature data;

[0026] Performing grayscale processing on the image data to obtain target image data;

[0027] removing environmental noise from the sound wave data to obtain target image data;

[0028] Performing wavelet transform on the electrical data to obtain target electrical data.

[0029] Optionally, performing device operation status detection on the fused data using a time series analysis model includes:

[0030] Building a time series analysis model based on historical operating data of the power plant equipment;

[0031] The current operating status of the power plant equipment is predicted according to the fused data and the historical operating data through the time series analysis model.

[0032] In addition, to achieve the above-mentioned purpose, the present invention further provides a device for detecting the operating status of power plant equipment, the device comprising:

[0033] An acquisition module is used to obtain multi-modal operating data of power plant equipment during operation;

[0034] a denoising module, configured to remove noise from the operating data to obtain multimodal target operating data;

[0035] a fusion module, configured to fuse the multimodal target operation data in at least two modes through a data fusion model corresponding to the power plant equipment to obtain fused data;

[0036] The detection module is used to detect the device operation status of the fused data through a time series analysis model.

[0037] In addition, to achieve the above-mentioned purpose, the present invention also proposes a power plant equipment operating status detection device, which includes: a memory, a processor, and a power plant equipment operating status detection program stored on the memory and executable on the processor, and the power plant equipment operating status detection program is configured to implement the steps of the power plant equipment operating status detection method described above.

[0038] In addition, to achieve the above-mentioned purpose, the present invention also proposes a storage medium, on which a power plant equipment operation status detection program is stored. When the power plant equipment operation status detection program is executed by a processor, the steps of the power plant equipment operation status detection method described above are implemented.

[0039] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the steps of the power plant equipment operating status detection method as described above.

[0040] The present invention discloses a method for detecting the operating status of power plant equipment, which comprises: acquiring multimodal operating data of power plant equipment during operation; performing noise removal on the operating data to obtain multimodal target operating data; performing data fusion of at least two modalities on the multimodal target operating data through a data fusion model corresponding to the power plant equipment to obtain fused data; and performing equipment operating status detection on the fused data through a time series analysis model. Compared with the prior art, the present invention improves the reliability of the operating data of the power equipment by performing noise removal on the multimodal operating data of the power plant equipment during operation, and improves the accuracy of mapping different modal features to the equipment operating status by fusing at least two modalities through a data fusion model corresponding to the power plant equipment, and performs time series analysis on the fused data to obtain the operating status changes of the power equipment in time series, thereby realizing power plant equipment operating status detection and avoiding the technical problem of low accuracy of status monitoring of power equipment in the prior art. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0042] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0043] Figure 1 This is a flow chart of a first embodiment of a method for detecting the operating status of power plant equipment according to the present invention;

[0044] Figure 2 This is a flow chart of a second embodiment of a method for detecting the operating status of power plant equipment according to the present invention;

[0045] Figure 3 This is a structural block diagram of a first embodiment of a device for detecting the operating status of power plant equipment according to the present invention;

[0046] Figure 4 It is a structural diagram of a power plant equipment operating status detection device in a hardware operating environment involved in an embodiment of the present invention.

[0047] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0048] It should be understood that the specific embodiments described herein are merely used to explain the technical solutions of the present application and are not intended to limit the present application.

[0049] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0050] Based on this, the embodiment of the present invention provides a method for detecting the operating status of power plant equipment, referring to Figure 1 , Figure 1 The figure is a flow chart of a first embodiment of a method for detecting the operating status of power plant equipment according to the present invention.

[0051] In this embodiment, the power plant equipment operating status detection method includes:

[0052] Step S10: Acquire multi-modal operating data of power plant equipment during operation.

[0053] It should be noted that the execution subject of this embodiment may be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, mobile phone, etc., or an electronic device capable of implementing the above functions, a control computer, etc. The following describes this embodiment and the following embodiments using a control computer as an example.

[0054] It should be noted that in this embodiment and subsequent embodiments, the multimodal operating data includes at least: temperature data, image data, acoustic wave data, electrical data and other multiple modal operating data, among which the image data includes at least: visible light, infrared images, video signals, light intensity signals at local sensitive points of the pipeline and other data, the electrical data includes at least vibration frequency signals, gas concentration signals, current and voltage signals and other data, and the acoustic wave data includes at least lidar detection and abnormal sound monitoring, etc.

[0055] It should be understood that due to the differences in the external manifestations of the operating status of different equipment, for example: for a boiler, the higher the vibration level, the more unstable the operating status, and the more stable the flame color during combustion, the more stable the operating status. In traditional technology, generally only one dimension of the operating data is analyzed to determine the operating status of the equipment, but this method is often affected by environmental factors and has low accuracy. For example: during the operation of the above-mentioned boiler, if the combustion condition is judged only by the flame color of the combustion chamber, if the flue gas exhaust flow input into the boiler combustion chamber is too small, it will also lead to incomplete combustion in the combustion chamber. This condition is not caused by the power equipment itself. When judging the specific status of the equipment, it is easy to be misjudged as insufficient oxygen, affecting the operating efficiency of the power equipment.

[0056] Step S20: removing noise from the operation data to obtain multi-modal target operation data.

[0057] Since there are a large number of interference factors during the operation of power equipment, in order to improve the efficiency of subsequent data fusion and improve the accuracy of equipment operation status detection, this embodiment removes noise from the operation data of each mode, thereby improving the reliability of the operation data of each mode.

[0058] Optionally, removing noise from the operating data to obtain multimodal target operating data includes:

[0059] Performing an outlier test on the temperature data to obtain target temperature data;

[0060] Performing grayscale processing on the image data to obtain target image data;

[0061] removing environmental noise from the sound wave data to obtain target image data;

[0062] Performing wavelet transform on the electrical data to obtain target electrical data.

[0063] In specific implementation, outlier testing of temperature data refers to performing a time-series continuity analysis on historical temperature data within a historical period and eliminating temperature data with higher deviations to obtain target temperature data; color images usually have three color channels (red, green, and blue), each channel has an independent pixel value, while grayscale images have only one channel, and the amount of data is significantly reduced. Grayscale processing of image data can convert color images into grayscale images, thereby reducing computational complexity and storage requirements.

[0064] Environmental noise removal of acoustic wave data is mainly used to improve signal quality, enhance voice clarity, reduce interference, and adapt to complex environments. Since there are a large number of electronic components in power equipment, when an electronic component is damaged, it will cause distortion of electrical data. This embodiment improves the operating efficiency and reliability of the power system by performing wavelet transform on various types of electrical data.

[0065] Step S30: fusing the multi-modal target operating data into at least two modalities using a data fusion model corresponding to the power plant equipment to obtain fused data.

[0066] In the specific implementation, since the operating data of different modes have no direct correlation, when analyzing the equipment operating status, in order to improve the correlation between the data and the accuracy of the subsequent equipment operating status through data analysis, this embodiment can now perform feature fusion on the data of each mode to obtain fused data representing multiple modes.

[0067] Step S40: performing device operation status detection on the fused data using a time series analysis model.

[0068] The time series analysis model uses fused data to detect the equipment operating status. This means combining the changing trends of the historical operating data of power plant equipment with time series analysis of the current fused data of the equipment to determine whether there is data distortion during the operation of the power plant equipment.

[0069] Optionally, performing device operation status detection on the fused data using a time series analysis model includes:

[0070] Building a time series analysis model based on historical operating data of the power plant equipment;

[0071] The current operating status of the power plant equipment is predicted according to the fused data and the historical operating data through the time series analysis model.

[0072] In this embodiment, the timing analysis model can be a timing analysis model established based on the LSTM algorithm, which is mainly used to perform timing change analysis on the historical operating data of the power plant equipment and the fused data in the current cycle, so as to predict the current operating status and equipment operating conditions of the power plant equipment.

[0073] This embodiment improves the reliability of the operating data of the power equipment by removing noise from the multi-modal operating data of the power plant equipment during operation, and fuses the data of at least two modes through the data fusion model corresponding to the power plant equipment to improve the accuracy of mapping different modal features to the operating status of the equipment. The fused data is used for time series analysis to obtain the operating status changes of the power equipment in time series, thereby realizing the operating status detection of the power plant equipment and avoiding the technical problem of low accuracy of status monitoring of power equipment in the existing technology.

[0074] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above embodiment 1 can be referred to the above introduction and will not be described in detail later. Figure 2 Step S30 includes:

[0075] Step S301: extracting historical maintenance data of the power plant equipment.

[0076] Step S302: determining a target data modality representing the operating status of the power plant equipment based on the historical equipment maintenance data.

[0077] Step S303: extracting the target operation data to be fused corresponding to the target data modality from the multimodal target operation data.

[0078] Step S304: fusing the target operation data to be fused through a data fusion model to obtain fused data.

[0079] It should be noted that the historical maintenance data of power plant equipment includes at least historical operating data, historical operating conditions, historical maintenance records, etc.

[0080] Different types of power plant equipment have different external representations. For example, a boiler can use the flame color and combustion vibration of the combustion chamber to represent its operating status, and electronic components can use data such as the current and voltage output by the electronic components to represent its operating status. Therefore, in order to reduce the workload of data fusion and improve the efficiency of subsequent equipment operating status detection, this embodiment can first determine the target data modality that represents the operating status of the power plant equipment, and then perform data fusion on the equipment operating data of the target data modality.

[0081] Optionally, extracting the target operation data to be fused corresponding to the target data modality from the multimodal target operation data includes:

[0082] Time-aligning the multimodal target operation data to obtain multimodal target operation data to be fused;

[0083] The target operation data to be fused corresponding to the target data modality is extracted from the multimodal target operation data to be fused.

[0084] In the specific implementation, since there are differences in the collection standards when collecting the operating data of multiple modes of the device, in order to align the external representation data formed by the changes in the operating status of the device at the same time and improve the accuracy of the device operation changes, this embodiment first time-aligns the multi-modal target operating data, thereby reducing the characteristic error in the timing. After time alignment, the aligned multi-modal target operating data to be fused can also be encoded, and the target operating data to be fused can be fused in a fixed encoding form to generate a complete message data, which is convenient for subsequent data compilation and decompilation.

[0085] Among them, time alignment refers to using a unified time axis and supplementing data with shorter lengths and deleting data with longer lengths through interpolation or fitting, so that the data lengths of the running data of each target to be fused remain consistent.

[0086] Optionally, fusing the target operating data to be fused through a data fusion model to obtain fused data includes:

[0087] Extracting the modal feature representation corresponding to the target operation data to be fused;

[0088] Determining the support between each modal feature representation, and calculating the support mean and support variance based on the support;

[0089] Based on the support matrix and the support variance, weighting is performed on the target operation data to be fused by using a support weighting model to obtain the weight of the target operation data to be fused of each modality;

[0090] Data fusion is performed according to the weights of the target operating data to be fused of each modality through a data fusion model to obtain fused data.

[0091] In the specific implementation, the modal feature representation records the characteristic parameters of the target operating data to be fused, including but not limited to data values, units, etc. Depending on the modality, it may include current value, RGB grayscale value, frequency, amplitude and other parameters.

[0092] Among them, the calculation formula for calculating the support between each modal feature representation is:

[0093]

[0094] Among them, r(t) is the support between modal feature representations, α is the correction coefficient corresponding to the power plant equipment type, and z i (t) and z j (t) is the modal feature representation of participation support, and n is the total number of target operation data to be fused.

[0095] It should be understood that there may be multiple types of data that characterize the operating status of the equipment, but the degree of characterization of different data varies. For example, with respect to the combustion performance of the boiler combustion chamber alone, the flame color, the temperature of the inner wall of the combustion chamber, etc. can better characterize the combustion performance of the combustion chamber, while the vibration amplitude of the outer wall of the boiler has a smaller weight. Therefore, when fusing data, this embodiment improves the credibility of the fused data in judging the operating status of the equipment by assigning weights to the target operating data to be fused of each modality.

[0096] This embodiment extracts historical maintenance data of the power plant equipment; determines the target data modality that affects the operating status of the power plant equipment based on the historical equipment maintenance data; extracts the target operating data to be fused corresponding to the target data modality from the multi-modal target operating data; and fuses the target operating data to be fused through a data fusion model to obtain fused data, which combines the operating data of multiple modalities and improves the credibility of subsequent equipment operating status detection.

[0097] This application also provides a power plant equipment operating status detection device, please refer to Figure 3 , the power plant equipment operating status detection device includes:

[0098] The acquisition module 10 is used to acquire multi-modal operating data of power plant equipment during operation.

[0099] The denoising module 20 is configured to remove noise from the operation data to obtain multi-modal target operation data.

[0100] The fusion module 30 is configured to perform data fusion of at least two modalities on the multi-modal target operation data through a data fusion model corresponding to the power plant equipment to obtain fused data.

[0101] The detection module 40 is configured to detect the device operating status of the fused data using a time series analysis model.

[0102] This embodiment improves the reliability of the operating data of the power equipment by removing noise from the multi-modal operating data of the power plant equipment during operation, and fuses the data of at least two modes through the data fusion model corresponding to the power plant equipment to improve the accuracy of mapping different modal features to the operating status of the equipment. The fused data is used for time series analysis to obtain the operating status changes of the power equipment in time series, thereby realizing the operating status detection of the power plant equipment and avoiding the technical problem of low accuracy of status monitoring of power equipment in the existing technology.

[0103] In one embodiment, the fusion module 30 is further used to extract historical maintenance data of the power plant equipment; determine a target data modality representing the operating status of the power plant equipment based on the historical equipment maintenance data; extract target operating data to be fused corresponding to the target data modality from the multi-modal target operating data; and fuse the target operating data to be fused through a data fusion model to obtain fused data.

[0104] In one embodiment, the fusion module 30 is further used to time-align the multimodal target operation data to obtain multimodal target operation data to be fused; and extract the target operation data to be fused corresponding to the target data modality from the multimodal target operation data to be fused.

[0105] In one embodiment, the fusion module 30 is further used to extract the modal feature representations corresponding to the target operating data to be fused; determine the support between each modal feature representation, and calculate the support mean and support variance based on the support; weight the target operating data to be fused through a support weighting model based on the support matrix and the support variance to obtain the weights of the target operating data to be fused of each modality; perform data fusion through a data fusion model according to the weights of the target operating data to be fused of each modality to obtain fused data.

[0106] In one embodiment, the denoising module 20 is further used to perform an outlier test on the temperature data to obtain target temperature data; perform grayscale processing on the image data to obtain target image data; perform environmental noise removal on the sound wave data to obtain target image data; and perform wavelet transform on the electrical data to obtain target electrical data.

[0107] In one embodiment, the detection module 40 is further configured to construct a time series analysis model based on the historical operation data of the power plant equipment; and predict the current operation status of the power plant equipment through the time series analysis model according to the fusion data and the historical operation data.

[0108] The present application provides a power plant equipment operating status detection device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the power plant equipment operating status detection method in the above-mentioned embodiment one.

[0109] Reference below Figure 4 , which shows a schematic structural diagram of a power plant equipment operating status detection device suitable for implementing an embodiment of the present application. The power plant equipment operating status detection device in the embodiment of the present application may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 4 The power plant equipment operating status detection device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0110] like Figure 4As shown, the power plant equipment operating status detection device may include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes based on a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM) 1004. RAM 1004 also stores various programs and data required for the operation of the power plant equipment operating status detection device. Processing device 1001, ROM 1002, and RAM 1004 are connected to each other via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touch screen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, a magnetic tape, hard disk, etc.; and communication devices 1009. The communication device 1009 can allow the power plant equipment operating status detection device to communicate wirelessly or wired with other devices to exchange data. Although the figure shows a power plant equipment operating status detection device with various systems, it should be understood that it is not required to implement or have all of the systems shown. More or fewer systems may be implemented or have instead.

[0111] In particular, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program comprising program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0112] The power plant equipment operating status detection device provided in this application utilizes the power plant equipment operating status detection method described in the aforementioned embodiment, thereby resolving the technical issues surrounding power plant equipment operating status detection. Compared to the prior art, the power plant equipment operating status detection device provided in this application achieves the same beneficial effects as the power plant equipment operating status detection method described in the aforementioned embodiment. Other technical features of the power plant equipment operating status detection device are the same as those disclosed in the aforementioned embodiment and are not further detailed here.

[0113] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0114] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0115] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, a computer program) stored thereon, wherein the computer-readable program instructions are used to execute the power plant equipment operating status detection method in the above-mentioned embodiment.

[0116] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0117] The computer-readable storage medium may be included in the power plant equipment operating status detection device; or may exist independently without being assembled into the power plant equipment operating status detection device.

[0118] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the power plant equipment operation status detection device, the power plant equipment operation status detection device can detect the power plant equipment operation status.

[0119] Computer program code for performing the operations of the present application may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages such as Java, Smalltalk, C++, and conventional procedural programming languages such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0120] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, program segment or a part of code, and the module, program segment or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.

[0121] The modules described in the embodiments of the present application may be implemented in software or hardware, wherein the name of a module does not necessarily limit the unit itself.

[0122] The computer-readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the aforementioned method for detecting the operating status of power plant equipment. This computer-readable storage medium can address the technical issues of detecting the operating status of power plant equipment. Compared to the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the method for detecting the operating status of power plant equipment provided in the aforementioned embodiment, and are not further elaborated here.

[0123] The present application also provides a computer program product, comprising a computer program, which implements the steps of the above-mentioned method for detecting the operating status of power plant equipment when executed by a processor.

[0124] The computer program product provided in this application can solve the technical problem of detecting the operating status of power plant equipment. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as the beneficial effects of the power plant equipment operating status detection method provided in the above embodiment, and will not be repeated here.

[0125] The above description is only part of the embodiments of the present application and does not limit the patent scope of the present application. All equivalent structural transformations made by using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect application in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A method for detecting the operating status of power plant equipment, characterized in that: The power plant equipment operating status detection method comprises: Obtain multi-modal operating data of power plant equipment during operation; removing noise from the operating data to obtain multimodal target operating data; fusing the multimodal target operating data in at least two modes using a data fusion model corresponding to the power plant equipment to obtain fused data; The equipment operation status is detected on the fused data through a time series analysis model.

2. The method for detecting the operating status of power plant equipment according to claim 1, wherein: The step of fusing the multi-modal target operation data in at least two modes through a data fusion model corresponding to the power plant equipment to obtain fused data includes: extracting historical maintenance data of the power plant equipment; Determining a target data modality representing an operating status of the power plant equipment based on the historical equipment maintenance data; Extracting target operation data to be fused corresponding to the target data modality from the multimodal target operation data; The target operation data to be fused is fused through a data fusion model to obtain fused data.

3. The method for detecting the operating status of power plant equipment according to claim 2, wherein: The extracting the target operation data to be fused corresponding to the target data modality from the multimodal target operation data includes: Time-aligning the multimodal target operation data to obtain multimodal target operation data to be fused; The target operation data to be fused corresponding to the target data modality is extracted from the multimodal target operation data to be fused.

4. The method for detecting the operating status of power plant equipment according to claim 2, wherein: The step of fusing the target operation data to be fused through a data fusion model to obtain fused data includes: Extracting the modal feature representation corresponding to the target operation data to be fused; Determining the support between each modal feature representation, and calculating the support mean and support variance based on the support; Based on the support matrix and the support variance, weighting is performed on the target operation data to be fused by using a support weighting model to obtain the weight of the target operation data to be fused of each modality; Data fusion is performed according to the weights of the target operating data to be fused of each modality through a data fusion model to obtain fused data.

5. The method for detecting the operating status of power plant equipment according to any one of claims 1 to 4, characterized in that: The operating data includes at least: temperature data, image data, sound wave data and electrical data; The step of removing noise from the operating data to obtain multi-modal target operating data includes: Performing an outlier test on the temperature data to obtain target temperature data; Performing grayscale processing on the image data to obtain target image data; removing environmental noise from the sound wave data to obtain target image data; Performing wavelet transform on the electrical data to obtain target electrical data.

6. The method for detecting the operating status of power plant equipment according to any one of claims 1 to 4, characterized in that: The detecting of the device operation status of the fused data by using a time series analysis model includes: Building a time series analysis model based on historical operating data of the power plant equipment; The current operating status of the power plant equipment is predicted according to the fused data and the historical operating data through the time series analysis model.

7. A device for detecting the operating status of power plant equipment, characterized in that: The power plant equipment operating status detection device includes: An acquisition module is used to obtain multi-modal operating data of power plant equipment during operation; a denoising module, configured to remove noise from the operating data to obtain multimodal target operating data; a fusion module, configured to fuse the multimodal target operation data in at least two modes through a data fusion model corresponding to the power plant equipment to obtain fused data; The detection module is used to detect the device operation status of the fused data through a time series analysis model.

8. A power plant equipment operating status detection device, characterized in that: The power plant equipment operation status detection device includes: a memory, a processor, and a power plant equipment operation status detection program stored in the memory and executable on the processor. The power plant equipment operation status detection program is configured to implement the power plant equipment operation status detection method according to any one of claims 1 to 6.

9. A storage medium, characterized in that: The storage medium stores a power plant equipment operating status detection program, which, when executed by a processor, implements the power plant equipment operating status detection method according to any one of claims 1 to 6.

10. A computer program product, characterized in that The computer program product includes a computer program, and when the computer program is executed by a processor, the steps of the method for detecting the operating status of power plant equipment according to any one of claims 1 to 6 are implemented.

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