CN108830261B - Equipment fault diagnosis method and device based on image recognition - Google Patents
Equipment fault diagnosis method and device based on image recognition Download PDFInfo
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Abstract
本发明公开了基于图像识别的设备故障诊断方法和装置。所述方法包括：获取包含目标设备运行时振动的时域波形的采样图片；基于图像识别算法从故障数据库中查找出与所述采样图片相匹配的故障图片；根据所述故障图片对应的故障描述信息确定所述目标设备的故障。该技术方案能够大大减少故障诊断的操作和消耗的资源，并显著提升诊断效率，节约诊断时间，同时能够保证一定的准确度，适于工业实用，应用场景广泛。
The invention discloses a method and device for diagnosing equipment faults based on image recognition. The method includes: acquiring a sampled picture containing a time-domain waveform of the vibration of the target device during operation; finding a faulty picture matching the sampling picture from a fault database based on an image recognition algorithm; according to a fault description corresponding to the faulty picture information to determine the failure of the target device. The technical solution can greatly reduce the operation of fault diagnosis and the resources consumed, and significantly improve the diagnosis efficiency, save the diagnosis time, and at the same time can ensure a certain accuracy, and is suitable for industrial practice and has a wide range of application scenarios.
Description
Technical Field
The invention relates to the field of fault diagnosis, in particular to an equipment fault diagnosis method and device based on image recognition.
Background
At present, the fault diagnosis of the equipment usually comprises that a detection device collects a time domain waveform of vibration, and a fault can be diagnosed after a series of complex processing, so that the fault diagnosis is very troublesome, a large amount of resources are wasted, and a fault diagnosis mode with higher efficiency and less resource consumption is needed.
Disclosure of Invention
In view of the above, the present invention has been made to provide an apparatus fault diagnosis method and apparatus based on image recognition that overcome or at least partially solve the above problems.
According to an aspect of the present invention, there is provided an apparatus fault diagnosis method based on image recognition, including:
acquiring a sampling picture containing a time domain waveform of vibration of target equipment during running;
searching a fault picture matched with the sampling picture from a fault database based on an image recognition algorithm;
and determining the fault of the target equipment according to the fault description information corresponding to the fault picture.
Optionally, the method further comprises:
when a fault picture matched with the sampling picture does not exist in a fault database, analyzing according to a time domain waveform in the sampling picture, determining a fault of the target equipment, and generating corresponding fault description information;
and generating a fault picture corresponding to the determined fault according to the sampling picture, and correspondingly storing the generated fault picture and the generated fault description information in the fault database.
Optionally, the obtaining a sampling picture containing a time-domain waveform of the target device when vibrating includes:
determining sampling time corresponding to the sampling picture according to the fault period of the target device, so that the sampling picture at least comprises time domain waveforms of a plurality of fault periods; the fault period is determined according to parameters of the target device;
the finding out the fault picture matched with the sampling picture from the fault database based on the image recognition algorithm comprises the following steps:
and finding out a fault picture corresponding to the parameter of the target equipment from the fault database as an alternative picture, and finding out a fault picture matched with the sampling picture from the alternative picture.
Optionally, the obtaining a sampling picture of a time-domain waveform containing vibration of the target device includes:
and acquiring a time domain waveform when the target equipment vibrates by the detection equipment for detecting the target equipment, and generating a sampling picture containing the time domain waveform in a shooting and/or screen capturing mode.
According to another aspect of the present invention, there is provided an apparatus for diagnosing device failure based on image recognition, including:
the sampling picture acquisition unit is used for acquiring a sampling picture containing a time domain waveform of vibration of the target equipment during operation;
the identification unit is used for searching out a fault picture matched with the sampling picture from a fault database based on an image identification algorithm;
and the fault diagnosis unit is used for determining the fault of the target equipment according to the fault description information corresponding to the fault picture.
Optionally, the fault diagnosis unit is further configured to, when a fault picture matched with the sampling picture does not exist in the fault database, analyze the time domain waveform in the sampling picture, determine a fault of the target device, and generate corresponding fault description information; and generating a fault picture corresponding to the determined fault according to the sampling picture, and correspondingly storing the generated fault picture and the generated fault description information in the fault database.
Optionally, the sampling picture acquiring unit is configured to determine, according to the fault period of the target device, a sampling time corresponding to the sampling picture, so that the sampling picture at least includes time domain waveforms of a plurality of fault periods; the fault period is determined according to parameters of the target device;
the identification unit is used for finding out a fault picture corresponding to the parameter of the target device from the fault database as an alternative picture, and finding out a fault picture matched with the sampling picture from the alternative picture.
Optionally, the sampling picture acquiring unit is configured to acquire a time domain waveform of the vibration of the target device by a detection device that detects the target device, and generate a sampling picture including the time domain waveform in a shooting and/or screen capturing manner.
In accordance with still another aspect of the present invention, there is provided an electronic apparatus including: a processor; and a memory arranged to store computer executable instructions that, when executed, cause the processor to perform a method as any one of the above.
According to a further aspect of the invention, there is provided a computer readable storage medium, wherein the computer readable storage medium stores one or more programs which, when executed by a processor, implement a method as any one of the above.
According to the technical scheme, the fault description information and the corresponding fault picture are stored in the fault database in advance, after the sampling picture containing the time domain waveform of the vibration of the target equipment in operation is obtained, the fault picture matched with the sampling picture is found out from the fault database based on the image recognition algorithm, and the fault of the target equipment is determined according to the fault description information corresponding to the fault picture. The technical scheme can greatly reduce the operation of fault diagnosis and the consumed resources, remarkably improve the diagnosis efficiency, save the diagnosis time, ensure certain accuracy, and is suitable for industrial application and wide in application scene.
The foregoing description is only an overview of the technical solutions of the present invention, and the embodiments of the present invention are described below in order to make the technical means of the present invention more clearly understood and to make the above and other objects, features, and advantages of the present invention more clearly understandable.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
FIG. 1 is a flow chart diagram illustrating a method for diagnosing equipment failure based on image recognition according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of an apparatus for diagnosing device failure based on image recognition according to an embodiment of the present invention;
FIG. 3 shows a schematic structural diagram of an electronic device according to one embodiment of the invention;
fig. 4 shows a schematic structural diagram of a computer-readable storage medium according to an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
Fig. 1 is a flowchart illustrating an apparatus fault diagnosis method based on image recognition according to an embodiment of the present invention. As shown in fig. 1, the method includes:
step S110, a sampling picture including a time-domain waveform of the vibration of the target device during operation is obtained. For example, the target device may be a mechanical device, which generates vibration during operation, so that a corresponding time-domain waveform may be acquired by a detection device such as various sensors. Since the vibration of the device at the time of failure is different from the vibration under normal conditions, the time domain waveforms generated are also different.
It should be noted here that the sampled picture may be one or several separately acquired pictures, or may be each frame in a segment of sampled video. That is, the actual capture may be pictures or video.
And step S120, searching a fault picture matched with the sampling picture from the fault database based on an image recognition algorithm.
The image recognition algorithm can be realized by using the prior art, and actually recognizes whether the time domain waveform in the sampling picture is similar to the time domain waveform in the fault picture, and if the similarity reaches a certain threshold value, the sampling picture is considered to be matched. For example, if the similarity between the sampled picture and the failure picture a in the failure database (where the similarity may also be obtained based on the confidence in the image recognition model) reaches 95% and exceeds a preset threshold value of 90%, the sampled picture is considered to be matched with the failure picture a. The preset threshold value here can be set as required and can be related to the parameters of the apparatus, for example, a threshold value of 85% is set for the large bearing and a threshold value of 95% is set for the small bearing.
Step S130, determining the fault of the target device according to the fault description information corresponding to the fault picture.
One failure picture may correspond to one failure, i.e. the target device may be a damaged rolling element of the bearing; it may also correspond to multiple faults, i.e. the corresponding time domain waveforms are generated by vibrations of the target device when multiple faults occur simultaneously, e.g. not only the rolling elements are damaged, but also the inner ring or the outer ring may be damaged. The fault database can store fault pictures corresponding to various faults and is attached with corresponding fault description information, so that after the corresponding fault pictures are matched, the type of the fault can be directly determined according to the fault description information.
As can be seen, in the method shown in fig. 1, by storing the fault description information and the corresponding fault picture in the fault database in advance, after the sampling picture containing the time domain waveform of the vibration of the target device during operation is acquired, the fault picture matched with the sampling picture is found out from the fault database based on the image recognition algorithm, and the fault of the target device is determined according to the fault description information corresponding to the fault picture. The technical scheme can greatly reduce the operation of fault diagnosis and the consumed resources, remarkably improve the diagnosis efficiency, save the diagnosis time, ensure certain accuracy, and is suitable for industrial application and wide in application scene.
In an embodiment of the present invention, the method further includes: when the fault database does not have a fault picture matched with the sampling picture, analyzing according to a time domain waveform in the sampling picture, determining the fault of the target equipment, and generating corresponding fault description information; and generating a fault picture corresponding to the determined fault according to the sampling picture, and correspondingly storing the generated fault picture and the generated fault description information in a fault database.
Generally, a device requiring fault diagnosis does have a certain fault, but may be misjudged by an operator. Thus, the time domain waveform in the picture may actually correspond to some fault or faults, and may also correspond to normal operating conditions. Under the condition of normal working conditions, matched fault pictures can not be found from the fault database obviously; in addition, it may also be the case that a failure of a device is not included in the failure database. Therefore, when the database does not have a fault picture matched with the sampling picture, a series of modes of converting the time domain waveform into the frequency domain waveform, performing Fourier transform and the like are required to be adopted for fault analysis, and the fault analysis can be realized by adopting the mode in the prior art; the result obtained by the analysis may be that the target device has no fault, and other subsequent processing may not be performed at this time; another result may be that the target device does have a failure and that failure is not included in the failure database. At this time, we need to generate a fault picture corresponding to the new fault, for example, directly use the sampling picture as the fault picture; or, because the time domain waveforms included in the sampled picture include too many fault periods, only the time domain waveform corresponding to one fault period is selected to generate the fault picture.
Therefore, the fault database is continuously perfected, iterative updating can be realized, and actual requirements are met.
In an embodiment of the present invention, in the method, acquiring a sampled picture containing a time-domain waveform of the target device when the target device vibrates includes: determining sampling time corresponding to the sampling picture according to the fault period of the target equipment, so that the sampling picture at least comprises time domain waveforms of a plurality of fault periods; the fault period is determined according to the parameters of the target equipment; finding out the fault picture matched with the sampling picture from the fault database based on an image recognition algorithm comprises the following steps: and finding out a fault picture corresponding to the parameter of the target equipment from the fault database as an alternative picture, and finding out a fault picture matched with the sampling picture from the alternative picture.
The parameters of the target device may include a device model, a rotation speed, and the like. In order to ensure the accuracy of image identification, the acquired time domain waveform generally includes at least one fault period, and optionally may include a plurality of fault periods. And the failure picture in the failure database may contain at least one failure cycle, so that if at least a portion of the time-domain waveform in the sampled picture is similar to the time-domain waveform in a failure picture, the two are considered to be a match.
Because the parameters of different devices may be different, the time domain waveforms acquired during the failure are also different, and the comparison of the time domain waveforms of different devices is of little significance, so in this embodiment, the failure picture corresponding to the parameters is selected according to the parameters of the target device, the matching range is reduced, the accuracy of image recognition is improved, and the mismatching rate is reduced.
In an embodiment of the present invention, in the method, acquiring a sampled picture of a time-domain waveform containing vibration of the target device includes: the method comprises the steps that a detection device for detecting target equipment collects time domain waveforms when the target equipment vibrates, and a sampling picture containing the time domain waveforms is generated in a shooting and/or screen capturing mode.
Sometimes, the detection device does not provide a good data processing interface, and cannot directly acquire the time domain waveform to generate the picture, but the detection devices can output the time domain waveform on the display screen, so in this embodiment, the display device outputting the time domain waveform can be shot or screen-captured, and the time domain waveform is not acquired through the interface. It can be seen that, although the embodiment of the present invention essentially identifies whether the time domain waveforms are similar, the embodiment of the present invention is implemented by identifying whether the pictures are similar, rather than directly identifying the time domain waveforms.
Fig. 2 is a schematic structural diagram of an apparatus for diagnosing device failure based on image recognition according to an embodiment of the present invention. As shown in fig. 2, the apparatus 200 for diagnosing device failure based on image recognition includes:
a sampled picture acquiring unit 210, configured to acquire a sampled picture containing a time-domain waveform of the vibration of the target device during operation. For example, the target device may be a mechanical device, which generates vibration during operation, so that a corresponding time-domain waveform may be acquired by a detection device such as various sensors. Since the vibration of the device at the time of failure is different from the vibration under normal conditions, the time domain waveforms generated are also different.
It should be noted here that the sampled picture may be one or several separately acquired pictures, or may be each frame in a segment of sampled video. That is, the actual capture may be pictures or video.
And the identifying unit 220 is used for searching a fault picture matched with the sampling picture from the fault database based on an image identification algorithm.
The image recognition algorithm herein can be implemented by using the prior art, and actually recognizes whether the time domain waveform in the sampled picture is similar to the time domain waveform in the fault picture, and if the similarity (here, the similarity may also be obtained based on the confidence in the image recognition model) reaches a certain threshold, it is considered as a match. For example, if the similarity between the sampled picture and the failure picture a in the failure database reaches 95% and exceeds a preset threshold value of 90%, the sampled picture is considered to be matched with the failure picture a. The preset threshold value here can be set as required and can be related to the parameters of the apparatus, for example, a threshold value of 85% is set for the large bearing and a threshold value of 95% is set for the small bearing.
And the fault diagnosis unit 230 is configured to determine a fault of the target device according to the fault description information corresponding to the fault picture.
One failure picture may correspond to one failure, i.e. the target device may be a damaged rolling element of the bearing; it may also correspond to multiple faults, i.e. the corresponding time domain waveforms are generated by vibrations of the target device when multiple faults occur simultaneously, e.g. not only the rolling elements are damaged, but also the inner ring or the outer ring may be damaged. The fault database can store fault pictures corresponding to various faults and is attached with corresponding fault description information, so that after the corresponding fault pictures are matched, the type of the fault can be directly determined according to the fault description information.
It can be seen that, in the apparatus shown in fig. 2, through mutual cooperation of the units, the fault description information and the corresponding fault picture are stored in the fault database in advance, after the sample picture containing the time domain waveform of the vibration of the target device during operation is acquired, the fault picture matched with the sample picture is found out from the fault database based on the image recognition algorithm, and the fault of the target device is determined according to the fault description information corresponding to the fault picture. The technical scheme can greatly reduce the operation of fault diagnosis and the consumed resources, remarkably improve the diagnosis efficiency, save the diagnosis time, ensure certain accuracy, and is suitable for industrial application and wide in application scene.
In an embodiment of the present invention, in the above apparatus, the fault diagnosing unit 230 is further configured to, when a fault picture matching the sampling picture does not exist in the fault database, analyze according to a time domain waveform in the sampling picture, determine a fault of the target device, and generate corresponding fault description information; and generating a fault picture corresponding to the determined fault according to the sampling picture, and correspondingly storing the generated fault picture and the generated fault description information in a fault database.
Generally, a device requiring fault diagnosis does have a certain fault, but may be misjudged by an operator. Thus, the time domain waveform in the picture may actually correspond to some fault or faults, and may also correspond to normal operating conditions. Under the condition of normal working conditions, matched fault pictures can not be found from the fault database obviously; in addition, it may also be the case that a failure of a device is not included in the failure database. Therefore, when the database does not have a fault picture matched with the sampling picture, a series of modes of converting the time domain waveform into the frequency domain waveform, performing Fourier transform and the like are required to be adopted for fault analysis, and the fault analysis can be realized by adopting the mode in the prior art; the result obtained by the analysis may be that the target device has no fault, and other subsequent processing may not be performed at this time; another result may be that the target device does have a failure and that failure is not included in the failure database. At this time, we need to generate a fault picture corresponding to the new fault, for example, directly use the sampling picture as the fault picture; or, because the time domain waveforms included in the sampled picture include too many fault periods, only the time domain waveform corresponding to one fault period is selected to generate the fault picture.
Therefore, the fault database is continuously perfected, iterative updating can be realized, and actual requirements are met.
In an embodiment of the present invention, in the above apparatus, the sampling picture obtaining unit 210 is configured to determine, according to a fault period of the target device, a sampling time corresponding to the sampling picture, so that the sampling picture at least includes time-domain waveforms of a plurality of fault periods; the fault period is determined according to the parameters of the target equipment; the identifying unit 220 is configured to find out a fault picture corresponding to the parameter of the target device from the fault database as an alternative picture, and find out a fault picture matching the sampling picture from the alternative picture.
The parameters of the target device may include a device model, a rotation speed, and the like. In order to ensure the accuracy of image identification, the acquired time domain waveform generally includes at least one fault period, and optionally may include a plurality of fault periods. And the failure picture in the failure database may contain at least one failure cycle, so that if at least a portion of the time-domain waveform in the sampled picture is similar to the time-domain waveform in a failure picture, the two are considered to be a match.
Because the parameters of different devices may be different, the time domain waveforms acquired during the failure are also different, and the comparison of the time domain waveforms of different devices is of little significance, so in this embodiment, the failure picture corresponding to the parameters is selected according to the parameters of the target device, the matching range is reduced, the accuracy of image recognition is improved, and the mismatching rate is reduced.
In an embodiment of the present invention, in the above apparatus, the sampled picture acquiring unit 210 is configured to acquire, by a detection device that detects a target device, a time-domain waveform of the vibration of the target device, and generate a sampled picture including the time-domain waveform by shooting and/or screen-capturing.
Sometimes, the detection device does not provide a good data processing interface, and cannot directly acquire the time domain waveform to generate the picture, but the detection devices can output the time domain waveform on the display screen, so in this embodiment, the display device outputting the time domain waveform can be shot or screen-captured, and the time domain waveform is not acquired through the interface. It can be seen that, although the embodiment of the present invention essentially identifies whether the time domain waveforms are similar, the embodiment of the present invention is implemented by identifying whether the pictures are similar, rather than directly identifying the time domain waveforms.
In summary, according to the technical scheme of the present invention, the fault description information and the corresponding fault picture are stored in the fault database in advance, after the sampling picture containing the time domain waveform of the vibration of the target device during operation is acquired, the fault picture matched with the sampling picture is found out from the fault database based on the image recognition algorithm, and the fault of the target device is determined according to the fault description information corresponding to the fault picture. The technical scheme can greatly reduce the operation of fault diagnosis and the consumed resources, remarkably improve the diagnosis efficiency, save the diagnosis time, ensure certain accuracy, and is suitable for industrial application and wide in application scene.
It should be noted that:
the algorithms and displays presented herein are not inherently related to any particular computer, virtual machine, or other apparatus. Various general purpose devices may be used with the teachings herein. The required structure for constructing such a device will be apparent from the description above. Moreover, the present invention is not directed to any particular programming language. It is appreciated that a variety of programming languages may be used to implement the teachings of the present invention as described herein, and any descriptions of specific languages are provided above to disclose the best mode of the invention.
In the description provided herein, numerous specific details are set forth. It is understood, however, that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: that the invention as claimed requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.
Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments described herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments may be used in any combination.
The various component embodiments of the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that a microprocessor or Digital Signal Processor (DSP) may be used in practice to implement some or all of the functions of some or all of the components in the image recognition based device failure diagnosis apparatus according to the embodiments of the present invention. The present invention may also be embodied as apparatus or device programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present invention may be stored on computer-readable media or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.
For example, fig. 3 shows a schematic structural diagram of an electronic device according to an embodiment of the invention. The electronic device comprises a processor 310 and a memory 320 arranged to store computer executable instructions (computer readable program code). The memory 320 may be an electronic memory such as a flash memory, an EEPROM (electrically erasable programmable read only memory), an EPROM, a hard disk, or a ROM. The memory 320 has a storage space 330 storing computer readable program code 331 for performing any of the method steps described above. For example, the storage space 330 for storing the computer readable program code may comprise respective computer readable program codes 331 for respectively implementing various steps in the above method. The computer readable program code 331 may be read from or written to one or more computer program products. These computer program products comprise a program code carrier such as a hard disk, a Compact Disc (CD), a memory card or a floppy disk. Such a computer program product is typically a computer readable storage medium such as described in fig. 4. Fig. 4 shows a schematic structural diagram of a computer-readable storage medium according to an embodiment of the present invention. The computer readable storage medium 400 has stored thereon a computer readable program code 331 for performing the steps of the method according to the invention, readable by a processor 310 of the electronic device 300, which computer readable program code 331, when executed by the electronic device 300, causes the electronic device 300 to perform the steps of the method described above, in particular the computer readable program code 331 stored on the computer readable storage medium may perform the method shown in any of the embodiments described above. The computer readable program code 331 may be compressed in a suitable form.
It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.
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| CN109584232B (en) * | 2018-11-28 | 2021-02-09 | 成都天衡智造科技有限公司 | Equipment use state online monitoring method, system and terminal based on image recognition |
| CN110443316A (en) * | 2019-08-07 | 2019-11-12 | 上海金智晟东电力科技有限公司 | Board turning recognition methods, device and the distribution network failure of fault detector monitor system |
| CN111160420B (en) * | 2019-12-13 | 2023-10-10 | 北京三快在线科技有限公司 | Map-based fault diagnosis method, map-based fault diagnosis device, electronic equipment and storage medium |
| CN111324637B (en) * | 2020-02-05 | 2021-01-15 | 北京工业大数据创新中心有限公司 | Fault symptom searching method and system for industrial time sequence data |
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Denomination of invention: Equipment fault diagnosis method and device based on image recognition Granted publication date: 20211130 Pledgee: Zhongguancun Branch of Bank of Beijing Co.,Ltd. Pledgor: BEIJING HAN ENERGY TECHNOLOGY CO.,LTD. Registration number: Y2024980046966 |