CN115828145A - Online monitoring method, system and medium for electronic equipment - Google Patents
Online monitoring method, system and medium for electronic equipment Download PDFInfo
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- CN115828145A CN115828145A CN202310086517.6A CN202310086517A CN115828145A CN 115828145 A CN115828145 A CN 115828145A CN 202310086517 A CN202310086517 A CN 202310086517A CN 115828145 A CN115828145 A CN 115828145A
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Abstract
The invention relates to an online monitoring method, a system and a medium of electronic equipment, belonging to the technical field of data monitoring of electronic equipment.A primary diagnosis result of the current equipment is obtained by acquiring dynamic data of a digital twin model within preset time and based on a historical operation data clustering result and data information within the preset time; and finally, obtaining a screened secondary diagnosis result according to the primary diagnosis result of the current equipment, and determining a maintenance strategy according to the screened secondary diagnosis result. The historical operating data are clustered for the second time, and the same descriptive data are clustered according to a clustering rule, so that the identification precision of historical similarity equipment data is improved, and the identification accuracy of the similarity equipment data is improved; on the other hand, the diagnosis process of the current equipment is optimized, so that compared with the prior art, the method has higher accuracy on the diagnosis result of the equipment.
Description
Technical Field
The present invention relates to the field of data monitoring technologies for electronic devices, and in particular, to an online monitoring method, system and medium for an electronic device.
Background
The automation technology is widely applied to the aspects of industry, agriculture, military affairs, scientific research, transportation, commerce, medical treatment, service, families and the like, and the automation technology not only can liberate people from heavy physical labor, partial mental labor and severe and dangerous working environments, but also can expand the functions of human organs, greatly improve the labor productivity and enhance the ability of human beings to know the world and reform the world. Due to the characteristics of dynamic and mutation of mechanical load, rotation operation and structural complexity of mechanical parts, diversity of measured parameters and the like, the requirement for monitoring the mechanical load is high, and the difficulty is high. In the past, the traditional electrical measurement sensing technology is adopted in a large quantity, so that multi-parameter online dynamic monitoring is difficult to realize or cannot be realized at all; the traditional regular and offline maintenance results in many potential safety hazards and long downtime, and sudden accidents of the equipment in the operation process cannot be prevented. Secondly, the on-line monitoring technology of the electronic equipment still has some problems at present, and when the monitored data of the equipment is diagnosed, the diagnosis precision is low.
Disclosure of Invention
The invention overcomes the defects of the prior art and provides an online monitoring method, a system and a medium of electronic equipment.
In order to achieve the purpose, the invention adopts the technical scheme that:
the invention provides an online monitoring method of electronic equipment in a first aspect, which comprises the following steps:
acquiring various data information of current equipment and environmental data information of the current equipment through a sensor, and constructing a digital twin model based on the various data information of the current equipment and the environmental data information of the current equipment;
obtaining historical operation data information of each node of the same equipment, and clustering the historical operation data information of each node of the current equipment to obtain a historical operation data clustering result;
acquiring dynamic data of the digital twin model within preset time, and obtaining a primary diagnosis result of the current equipment based on the historical operation data clustering result and the data information within the preset time;
and obtaining a screened secondary diagnosis result according to the primary diagnosis result of the current equipment, and determining a maintenance strategy according to the screened secondary diagnosis result.
Further, in a preferred embodiment of the present invention, the acquiring, by a sensor, a plurality of data information of a current device and environment data information of the current device, and constructing the digital twin model based on the plurality of data information of the current device and the environment data information of the current device specifically includes the following steps:
constructing a virtual scene, acquiring geometric characteristic data information of current equipment, constructing a three-dimensional model diagram of the current equipment according to the geometric characteristic data information of the current equipment, and inputting the three-dimensional model diagram of the current equipment into the virtual scene to generate an initial digital twin model;
acquiring the position information of a sensor in the three-dimensional model diagram of the current equipment, and obtaining an integrated digital twin model according to the position information of the sensor in the three-dimensional model diagram of the current equipment and the initial digital twin model;
acquiring various data information of current equipment and environmental data information of the current equipment through a sensor, and performing data interaction according to the various data information of the current equipment and the environmental data information of the current equipment so as to dynamically fuse the integrated digital twin model;
and acquiring the dynamically fused digital twin model, and outputting the dynamically fused digital twin model as a final digital twin model.
Further, in a preferred embodiment of the present invention, the method for obtaining historical operating data information of each node of the same device and clustering the historical operating data information of each node of the current device to obtain a historical operating data clustering result specifically includes the following steps:
obtaining descriptive data information of current equipment, obtaining same descriptive data information corresponding to the descriptive data information through a big data network, and constructing a clustering rule according to the descriptive data information of the current equipment and the same descriptive data information corresponding to the descriptive data information;
acquiring historical operating data information of each node of the same equipment, constructing a hash function group, and performing hash operation on the historical operating data information of each node of the same equipment based on the hash function group to acquire a hash group corresponding to the hash function group;
randomly extracting a data set from historical operating data information of each node of the same equipment as a clustering object to calculate an index value, and carrying out primary clustering according to the index value and a hash group corresponding to the hash function group to obtain a plurality of candidate data sets;
and searching the candidate data sets one by one according to the clustering rule to obtain the similarity among the candidate data sets, and performing secondary clustering on the candidate data sets with the similarity larger than the preset similarity to obtain a historical operating data clustering result.
Further, in a preferred embodiment of the present invention, the acquiring dynamic data of the digital twin model within a preset time, and obtaining a primary diagnosis result of the current device based on the historical operating data clustering result and the data information within the preset time specifically includes the following steps:
acquiring dynamic data of the digital twin model within preset time, and constructing an operation data change curve of each monitoring node in the equipment according to the dynamic data of the digital twin model within the preset time;
establishing a historical operation data change curve of each monitoring node in the equipment according to the historical operation data clustering result, and comparing the operation data change curve of each monitoring node in the equipment with the historical operation data change curve of each monitoring node in the equipment to obtain a curve deviation rate of each monitoring period;
acquiring one or more historical operating data change curves of a preset monitoring period, wherein the curve deviation rate is greater than the preset curve deviation rate;
and acquiring a historical diagnosis result of the similar equipment in a preset detection period according to the historical operation data change curve of the preset monitoring period, and outputting the historical diagnosis result of the similar equipment in the preset detection period as a primary diagnosis result of the current equipment.
Further, in a preferred embodiment of the present invention, the obtaining of the secondary diagnosis result after the secondary screening according to the primary diagnosis result of the current device specifically includes the following steps:
acquiring a historical maintainability strategy of the similar equipment in a preset detection period corresponding to a historical operating data clustering result with the highest similarity, and judging whether the historical maintainability strategy of the similar equipment in the preset detection period corresponding to the historical operating data clustering result is a preset type maintainability strategy or not;
if the historical maintenance strategy of the similar equipment in a preset detection period corresponding to the historical operation data clustering result is a preset type maintenance strategy, acquiring the historical maintenance strategy of the current equipment within preset time;
if the historical maintenance strategy of the current equipment within the preset time is different from the historical maintenance strategy of the similar equipment within the preset detection time period, removing the historical operation data clustering result corresponding to the historical maintenance strategy of the similar equipment within the preset detection time period, and obtaining a primary diagnosis result of the current equipment with the highest similarity as a secondary diagnosis result after screening and outputting the secondary diagnosis result;
and if the historical maintenance strategy of the current equipment within the preset time is the same as the historical maintainability strategy of the similar equipment in the preset detection period, outputting the historical operation data clustering result with the highest similarity as a secondary diagnosis result after screening.
Further, in a preferred embodiment of the present invention, the determining the maintenance strategy according to the screened secondary diagnosis result specifically includes the following steps:
acquiring historical fault trends of the secondary diagnosis results, and determining the state level of the current equipment according to the historical fault trends;
determining the current maintenance task content according to the state level of the current equipment, if the current maintenance task content is in a state that the equipment needs to be stopped to work for maintenance, sending a work stopping instruction, and determining a maintenance strategy according to the current maintenance task content;
if the maintenance task content is in a state that the working quality of the equipment is reduced due to the fact that the abnormality of the equipment part is determined, a work stopping instruction is sent out, and a maintenance strategy is determined according to the current maintenance task content;
and if the maintenance task content is in a state that the current equipment does not have faults within the current working time limit, determining a maintenance strategy according to the current maintenance task content.
A second aspect of the present invention provides an online monitoring system for an electronic device, where the system includes a memory and a processor, the memory includes an online monitoring method program for the electronic device, and when the online monitoring method program for the electronic device is executed by the processor, the method includes the following steps:
acquiring various data information of current equipment and environmental data information of the current equipment through a sensor, and constructing a digital twin model based on the various data information of the current equipment and the environmental data information of the current equipment;
obtaining historical operation data information of each node of the same equipment, and clustering the historical operation data information of each node of the current equipment to obtain a historical operation data clustering result;
acquiring dynamic data of the digital twin model within preset time, and obtaining a primary diagnosis result of the current equipment based on the historical operation data clustering result and the data information within the preset time;
and obtaining a screened secondary diagnosis result according to the primary diagnosis result of the current equipment, and determining a maintenance strategy according to the screened secondary diagnosis result.
In this embodiment, obtaining historical operating data information of each node of the same device, and performing clustering processing on the historical operating data information of each node of the current device to obtain a historical operating data clustering result specifically includes the following steps:
obtaining descriptive data information of current equipment, obtaining same descriptive data information corresponding to the descriptive data information through a big data network, and constructing a clustering rule according to the descriptive data information of the current equipment and the same descriptive data information corresponding to the descriptive data information;
acquiring historical operating data information of each node of the same equipment, constructing a hash function group, and performing hash operation on the historical operating data information of each node of the same equipment based on the hash function group to acquire a hash group corresponding to the hash function group;
randomly extracting a data set from historical operating data information of each node of the same equipment as a clustering object to calculate an index value, and carrying out primary clustering according to the index value and a hash group corresponding to the hash function group to obtain a plurality of candidate data sets;
and searching the candidate data sets one by one according to the clustering rule to obtain the similarity among the candidate data sets, and performing secondary clustering on the candidate data sets with the similarity larger than the preset similarity to obtain a historical operating data clustering result.
In this embodiment, obtaining a secondary diagnosis result after secondary screening according to the primary diagnosis result of the current device specifically includes the following steps:
acquiring a historical maintainability strategy of the similar equipment in a preset detection period corresponding to a historical operating data clustering result with the highest similarity, and judging whether the historical maintainability strategy of the similar equipment in the preset detection period corresponding to the historical operating data clustering result is a preset type maintainability strategy or not;
if the historical maintenance strategy of the similar equipment in a preset detection period corresponding to the historical operation data clustering result is a preset type maintenance strategy, acquiring the historical maintenance strategy of the current equipment within preset time;
if the historical maintenance strategy of the current equipment within the preset time is different from the historical maintenance strategy of the similar equipment within the preset detection time period, removing the historical operation data clustering result corresponding to the historical maintenance strategy of the similar equipment within the preset detection time period, and obtaining a primary diagnosis result of the current equipment with the highest similarity as a secondary diagnosis result after screening and outputting the secondary diagnosis result;
and if the historical maintenance strategy of the current equipment within the preset time is the same as the historical maintenance strategy of the similar equipment within the preset detection time period, outputting the historical operation data clustering result with the highest similarity as a secondary diagnosis result after screening.
A third aspect of the present invention provides a computer-readable storage medium containing a program of an online monitoring method of an electronic device, which when executed by a processor, implements the steps of the online monitoring method of the electronic device according to any one of the above.
The invention solves the defects in the background art, and has the following beneficial effects:
the method comprises the steps of obtaining various data information of current equipment and environmental data information of the current equipment through a sensor, constructing a digital twin model based on the various data information of the current equipment and the environmental data information of the current equipment, further obtaining historical operation data information of each node of the same equipment, and clustering the historical operation data information of each node of the current equipment to obtain a historical operation data clustering result; acquiring dynamic data of the digital twin model within preset time, and obtaining a primary diagnosis result of the current equipment based on a historical operation data clustering result and data information within the preset time; and finally, obtaining a screened secondary diagnosis result according to the primary diagnosis result of the current equipment, and determining a maintenance strategy according to the screened secondary diagnosis result. The historical operating data are clustered for the second time, and the same descriptive data are clustered according to a clustering rule, so that the identification precision of historical similarity equipment data is improved, and the identification accuracy of the similarity equipment data is improved; on the other hand, the diagnosis process of the current equipment is optimized, so that compared with the prior art, the method has higher accuracy on the diagnosis result of the equipment.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the prior art descriptions will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that drawings of other embodiments can be obtained according to the drawings without creative efforts.
FIG. 1 illustrates an overall method flow diagram of a method for online monitoring of an electronic device;
FIG. 2 illustrates a first method flow diagram of a method for online monitoring of an electronic device;
FIG. 3 illustrates a second method flow diagram of a method of online monitoring of an electronic device;
FIG. 4 shows a system block diagram of an online monitoring system for an electronic device.
Detailed description of the preferred embodiments
In order that the above objects, features and advantages of the present invention can be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings. It should be noted that the embodiments and features of the embodiments of the present application may be combined with each other without conflict.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those specifically described herein, and therefore the scope of the present invention is not limited by the specific embodiments disclosed below.
FIG. 1 illustrates an overall method flow diagram of a method for online monitoring of an electronic device;
the invention provides an online monitoring method of electronic equipment in a first aspect, which comprises the following steps:
s102, acquiring various data information of current equipment and environmental data information of the current equipment through a sensor, and constructing a digital twin model based on the various data information of the current equipment and the environmental data information of the current equipment;
s104, acquiring historical operating data information of each node of the same equipment, and clustering the historical operating data information of each node of the current equipment to obtain a historical operating data clustering result;
s106, acquiring dynamic data of the digital twin model within preset time, and obtaining a primary diagnosis result of the current equipment based on a historical operation data clustering result and data information within the preset time;
and S108, obtaining a screened secondary diagnosis result according to the primary diagnosis result of the current equipment, and determining a maintenance strategy according to the screened secondary diagnosis result.
It should be noted that, in this embodiment, the historical operating data is clustered twice, and the same descriptive data is clustered according to the clustering rule, so as to improve the accuracy of identifying the historical similarity device data, and thus improve the accuracy of identifying the similarity device data; on the other hand, the diagnosis process of the current equipment is optimized, so that compared with the prior art, the method has higher accuracy on the diagnosis result of the equipment.
Further, in a preferred embodiment of the present invention, the acquiring, by the sensor, a plurality of data information of the current device and environment data information of the current device, and constructing the digital twin model based on the plurality of data information of the current device and the environment data information of the current device specifically includes the following steps:
constructing a virtual scene, acquiring geometric feature data information of current equipment, constructing a three-dimensional model diagram of the current equipment according to the geometric feature data information of the current equipment, and inputting the three-dimensional model diagram of the current equipment into the virtual scene to generate an initial digital twin model;
acquiring the position information of a three-dimensional model diagram of the sensor in the current equipment, and obtaining an integrated digital twin model according to the position information of the three-dimensional model diagram of the sensor in the current equipment and the initial digital twin model;
acquiring various data information of current equipment and environmental data information of the current equipment through a sensor, and performing data interaction according to the various data information of the current equipment and the environmental data information of the current equipment so as to dynamically fuse the integrated digital twin model;
and acquiring the dynamically fused digital twin model, and outputting the dynamically fused digital twin model as a final digital twin model.
It should be noted that, in this embodiment, the geometric feature data information of the current device may be external dimension information of each component of the device, the three-dimensional model map of the current device may be constructed by three-dimensional software such as maya software and SolidWorks software, and the method may virtualize the running state of the device, so that a user may obtain the dynamics of the device through a digital twin model, visualize the dynamics of the device, and provide a better visual effect for the user or the device.
FIG. 2 illustrates a first method flow diagram of a method for online monitoring of an electronic device;
further, in a preferred embodiment of the present invention, the method for obtaining historical operating data information of each node of the same device and clustering the historical operating data information of each node of the current device to obtain a historical operating data clustering result specifically includes the following steps:
s202, obtaining descriptive data information of the current equipment, obtaining the same descriptive data information corresponding to the descriptive data information through a big data network, and constructing a clustering rule according to the descriptive data information of the current equipment and the same descriptive data information corresponding to the descriptive data information;
s204, acquiring historical operating data information of each node of the same equipment, constructing a hash function group, and performing hash operation on the historical operating data information of each node of the same equipment based on the hash function group to acquire a hash group corresponding to the hash function group;
s206, randomly extracting a data set from historical operation data information of each node of the same equipment as a clustering object to calculate an index value, and carrying out primary clustering according to the index value and a hash group corresponding to the hash function group to obtain a plurality of candidate data sets;
and S208, retrieving the candidate data sets one by one according to the clustering rule to obtain the similarity among the candidate data sets, and performing secondary clustering on the candidate data sets with the similarity larger than the preset similarity to obtain a historical operating data clustering result.
In this embodiment, the descriptive data information of the device may be a period, an operating speed, an operating time, an operating speed of a rotating component, and the like, where the same descriptive data information corresponding to the descriptive data information may be understood as, for example, a relationship between the period and the frequency, because the system cannot know the relationship between the period and the frequency, and because the period and the frequency have a certain similarity, as is well known, a reciprocal of the period is the frequency, and because other data in the data uploaded by the user are the same, only one of the period and the frequency is uploaded, at this time, the data of the two are actually the same, but the system cannot recognize the same. Therefore, the clustering rule is constructed according to the conversion relation of the period and the frequency, the construction process can be realized through a machine learning technology, a neural network technology and the like, and the data can be identified through training the data. By the method, the retrieval precision of the historical operation data information of each node of the same equipment can be improved. Wherein, the more similar the data, the higher the probability that the index values are the same. The historical operation data information of each node of the same equipment can be data in the forms of histogram data, table data and the like.
Further, in a preferred embodiment of the present invention, the obtaining of dynamic data of the digital twin model within a preset time and the obtaining of a primary diagnosis result of the current device based on the historical operating data clustering result and the data information within the preset time specifically include the following steps:
acquiring dynamic data of the digital twin model within preset time, and constructing an operation data change curve of each monitoring node in the equipment according to the dynamic data of the digital twin model within the preset time;
the method comprises the steps of constructing historical operation data change curves of all monitoring nodes in the equipment according to historical operation data clustering results, and comparing the operation data change curves of all monitoring nodes in the equipment with the historical operation data change curves of all monitoring nodes in the equipment to obtain a curve deviation rate of each monitoring period;
acquiring one or more historical operating data change curves of a preset monitoring period, wherein the curve deviation rate is greater than the preset curve deviation rate;
and obtaining a historical diagnosis result of the similar equipment in a preset detection period according to a historical operation data change curve of the preset monitoring period, and outputting the historical diagnosis result of the similar equipment in the preset detection period as a primary diagnosis result of the current equipment.
It should be noted that, in this embodiment, the historical operation data clustering result is compared with the operation data of the current device, so as to screen out the historical diagnosis result of the similar device in the preset detection period, and thus the diagnosis result is output as a primary diagnosis result.
FIG. 3 illustrates a second method flow diagram of a method of online monitoring of an electronic device;
further, in a preferred embodiment of the present invention, the obtaining of the secondary diagnosis result after the secondary screening according to the primary diagnosis result of the current device specifically includes the following steps:
s302, acquiring a historical maintainability strategy of the historical operation data clustering result with the highest similarity corresponding to the similar equipment in a preset detection time period, and judging whether the historical maintainability strategy of the historical operation data clustering result corresponding to the similar equipment in the preset detection time period is a preset type maintainability strategy or not;
s304, if the historical operating data clustering result corresponds to a maintenance strategy of the similar equipment in a preset detection time period, the historical maintenance strategy of the current equipment in a preset time is obtained;
s306, if the historical maintenance strategy of the current equipment within the preset time is different from the historical maintenance strategy of the similar equipment within the preset detection time, removing the historical operation data clustering result corresponding to the historical maintenance strategy of the similar equipment within the preset detection time, and obtaining a primary diagnosis result of the current equipment with the highest similarity as a secondary diagnosis result after screening and outputting the secondary diagnosis result;
and S308, if the historical maintenance strategy of the current equipment within the preset time is the same as the historical maintenance strategy of the similar equipment within the preset detection time period, outputting the historical operation data clustering result with the highest similarity as a secondary diagnosis result after screening.
It should be noted that, in this embodiment, the preset type of maintainability policy is a maintainability policy that has not been overhauled, for example, when parts of different product models are replaced, such data is not output as a historical operating data clustering result, and only when the historical maintainability policy of the historical operating data clustering result corresponding to the similar device in the preset detection period is the preset type of maintainability policy, such data is further screened, and only when the historical maintainability policy of the current device in the preset time is the same as the historical maintainability policy of the similar device in the preset detection period, it is indicated that the maintenance conditions of the current device and the current device are highly consistent, and the accuracy of the diagnosis result can be effectively improved by the method.
Further, in a preferred embodiment of the present invention, the determining the maintenance strategy according to the screened secondary diagnosis result specifically includes the following steps:
acquiring historical fault trends of secondary diagnosis results, and determining the state level of the current equipment according to the historical fault trends;
determining the current maintenance task content according to the state level of the current equipment, if the current maintenance task content is in a state that the equipment needs to be stopped to work for maintenance, sending a work stopping instruction, and determining a maintenance strategy according to the current maintenance task content;
if the maintenance task content is in a state that the working quality of the equipment is reduced due to the fact that the abnormality of the equipment part is determined, a work stopping instruction is sent out, and a maintenance strategy is determined according to the current maintenance task content;
and if the maintenance task content is in a state that the current equipment does not have faults within the current working time limit, determining a maintenance strategy according to the current maintenance task content.
It should be noted that, the method can determine the maintenance strategy according to the actual equipment condition, so that the maintenance strategy of the method is more reasonable.
In addition, the method can also comprise the following steps:
acquiring data information of a current historical operating data clustering result, and judging whether the data information has data loss or data error;
if the data information has data loss or data error, acquiring a position node where the data information has data loss or data error, and acquiring data information of the data loss or the data error;
generating a retrieval tag according to the data information with data missing or data error, and retrieving from the current historical operating data clustering result according to the retrieval tag to obtain correct text data corresponding to the data information with data missing or data error;
and replacing the position node where the data information data is missing or the data error is located again according to the correct text data corresponding to the data information with the data missing or the data error, and generating a final historical operating data clustering result.
It should be noted that some situations of data missing or data error may exist in the data information of the current historical operating data clustering result when the user uploads data, and the data in the situations can be corrected by the method, so that the effectiveness of the historical operating data clustering result is ensured, and the robustness of the diagnostic system is improved.
FIG. 4 shows a system block diagram of an online monitoring system for an electronic device.
The second aspect of the present invention provides an online monitoring system for an electronic device, the system includes a memory 41 and a processor 62, the memory 41 contains an online monitoring method program for the electronic device, and when the online monitoring method program for the electronic device is executed by the processor 62, the following steps are implemented:
acquiring various data information of current equipment and environmental data information of the current equipment through a sensor, and constructing a digital twin model based on the various data information of the current equipment and the environmental data information of the current equipment;
obtaining historical operation data information of each node of the same equipment, and clustering the historical operation data information of each node of the current equipment to obtain a historical operation data clustering result;
acquiring dynamic data of the digital twin model within preset time, and obtaining a primary diagnosis result of the current equipment based on a historical operation data clustering result and data information within the preset time;
and obtaining a screened secondary diagnosis result according to the primary diagnosis result of the current equipment, and determining a maintenance strategy according to the screened secondary diagnosis result.
In this embodiment, obtaining historical operating data information of each node of the same device, and performing clustering processing on the historical operating data information of each node of the current device to obtain a historical operating data clustering result specifically includes the following steps:
obtaining descriptive data information of current equipment, obtaining same descriptive data information corresponding to the descriptive data information through a big data network, and constructing a clustering rule according to the descriptive data information of the current equipment and the same descriptive data information corresponding to the descriptive data information;
acquiring historical operating data information of each node of the same equipment, constructing a hash function group, and performing hash operation on the historical operating data information of each node of the same equipment based on the hash function group to acquire a hash group corresponding to the hash function group;
randomly extracting a data set from historical operating data information of each node of the same equipment as a clustering object to calculate an index value, and carrying out primary clustering according to the index value and a hash group corresponding to the hash function group to obtain a plurality of candidate data sets;
and searching the candidate data sets one by one according to the clustering rule to obtain the similarity among the candidate data sets, and performing secondary clustering on the candidate data sets with the similarity larger than the preset similarity to obtain a historical operating data clustering result.
In this embodiment, obtaining a secondary diagnosis result after secondary screening according to a primary diagnosis result of the current device specifically includes the following steps:
acquiring a historical maintainability strategy of the similar equipment in a preset detection period corresponding to a historical operating data clustering result with the highest similarity, and judging whether the historical maintainability strategy of the similar equipment in the preset detection period corresponding to the historical operating data clustering result is a preset type maintainability strategy or not;
if the historical maintenance strategy of the similar equipment in the preset detection period corresponding to the historical operation data clustering result is a preset type maintenance strategy, acquiring the historical maintenance strategy of the current equipment within the preset time;
if the historical maintenance strategy of the current equipment within the preset time is different from the historical maintenance strategy of the similar equipment in the preset detection time, removing the historical operation data clustering result corresponding to the historical maintenance strategy of the similar equipment in the preset detection time, and obtaining a primary diagnosis result of the current equipment with the highest next similarity as a secondary diagnosis result after screening and outputting the primary diagnosis result;
and if the historical maintenance strategy of the current equipment within the preset time is the same as the historical maintenance strategy of the similar equipment in the preset detection period, outputting the historical operation data clustering result corresponding to the historical maintenance strategy of the similar equipment in the preset detection period and having the highest similarity as the secondary diagnosis result after screening.
A third aspect of the present invention provides a computer-readable storage medium, which includes an online monitoring method program of an electronic device, and when the online monitoring method program of the electronic device is executed by a processor, the steps of the online monitoring method of the electronic device as in any one of the above are implemented.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. The above-described device embodiments are merely illustrative, for example, the division of the unit is only a logical functional division, and there may be other division ways in actual implementation, such as: multiple units or components may be combined, or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the coupling, direct coupling or communication connection between the components shown or discussed may be through some interfaces, and the indirect coupling or communication connection between the devices or units may be electrical, mechanical or other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units; can be located in one place or distributed on a plurality of network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, all the functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately regarded as one unit, or two or more units may be integrated into one unit; the integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.
Those of ordinary skill in the art will understand that: all or part of the steps for realizing the method embodiments can be completed by hardware related to program instructions, the program can be stored in a computer readable storage medium, and the program executes the steps comprising the method embodiments when executed; and the aforementioned storage medium includes: a mobile storage device, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
Alternatively, the integrated unit of the present invention may be stored in a computer-readable storage medium if it is implemented in the form of a software functional module and sold or used as a separate product. Based on such understanding, the technical solutions of the embodiments of the present invention may be essentially implemented or a part contributing to the prior art may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the methods of the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, a ROM, a RAM, a magnetic or optical disk, or various other media that can store program code.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. An online monitoring method of electronic equipment is characterized by comprising the following steps:
acquiring various data information of current equipment and environmental data information of the current equipment through a sensor, and constructing a digital twin model based on the various data information of the current equipment and the environmental data information of the current equipment;
obtaining historical operation data information of each node of the same equipment, and clustering the historical operation data information of each node of the current equipment to obtain a historical operation data clustering result;
acquiring dynamic data of the digital twin model within preset time, and obtaining a primary diagnosis result of the current equipment based on the historical operation data clustering result and the data information within the preset time;
and obtaining a screened secondary diagnosis result according to the primary diagnosis result of the current equipment, and determining a maintenance strategy according to the screened secondary diagnosis result.
2. The on-line monitoring method of the electronic device according to claim 1, wherein a plurality of data information of the current device and environment data information of the current device are obtained through a sensor, and a digital twin model is constructed based on the plurality of data information of the current device and the environment data information of the current device, specifically comprising the following steps:
constructing a virtual scene, acquiring geometric characteristic data information of current equipment, constructing a three-dimensional model diagram of the current equipment according to the geometric characteristic data information of the current equipment, and inputting the three-dimensional model diagram of the current equipment into the virtual scene to generate an initial digital twin model;
acquiring the position information of a sensor in the three-dimensional model diagram of the current equipment, and obtaining an integrated digital twin model according to the position information of the sensor in the three-dimensional model diagram of the current equipment and the initial digital twin model;
acquiring various data information of current equipment and environmental data information of the current equipment through a sensor, and performing data interaction according to the various data information of the current equipment and the environmental data information of the current equipment so as to dynamically fuse the integrated digital twin model;
and acquiring the dynamically fused digital twin model, and outputting the dynamically fused digital twin model as a final digital twin model.
3. The online monitoring method of the electronic device according to claim 1, wherein historical operating data information of each node of the same device is obtained, and the historical operating data information of each node of the current device is clustered to obtain a historical operating data clustering result, specifically comprising the following steps:
obtaining descriptive data information of current equipment, obtaining same descriptive data information corresponding to the descriptive data information through a big data network, and constructing a clustering rule according to the descriptive data information of the current equipment and the same descriptive data information corresponding to the descriptive data information;
acquiring historical operating data information of each node of the same equipment, constructing a hash function group, and performing hash operation on the historical operating data information of each node of the same equipment based on the hash function group to acquire a hash group corresponding to the hash function group;
randomly extracting a data set from historical operating data information of each node of the same equipment as a clustering object to calculate an index value, and carrying out primary clustering according to the index value and a hash group corresponding to the hash function group to obtain a plurality of candidate data sets;
and searching the candidate data sets one by one according to the clustering rule to obtain the similarity among the candidate data sets, and performing secondary clustering on the candidate data sets with the similarity larger than the preset similarity to obtain a historical operating data clustering result.
4. The on-line monitoring method of electronic equipment according to claim 1, wherein dynamic data of the digital twin model within a preset time is obtained, and a diagnosis result of current equipment is obtained based on the historical operating data clustering result and data information within the preset time, and specifically comprises the following steps:
acquiring dynamic data of the digital twin model within preset time, and constructing an operation data change curve of each monitoring node in the equipment according to the dynamic data of the digital twin model within the preset time;
establishing a historical operation data change curve of each monitoring node in the equipment according to the historical operation data clustering result, and comparing the operation data change curve of each monitoring node in the equipment with the historical operation data change curve of each monitoring node in the equipment to obtain a curve deviation rate of each monitoring period;
acquiring one or more historical operating data change curves of a preset monitoring period, wherein the curve deviation rate is greater than the preset curve deviation rate;
and acquiring a historical diagnosis result of the similar equipment in a preset detection period according to the historical operation data change curve of the preset monitoring period, and outputting the historical diagnosis result of the similar equipment in the preset detection period as a primary diagnosis result of the current equipment.
5. The online monitoring method of electronic equipment according to claim 1, wherein a secondary diagnosis result after secondary screening is obtained according to the primary diagnosis result of the current equipment, specifically comprising the following steps:
acquiring a historical maintainability strategy of the similar equipment in a preset detection period corresponding to a historical operating data clustering result with the highest similarity, and judging whether the historical maintainability strategy of the similar equipment in the preset detection period corresponding to the historical operating data clustering result is a preset type maintainability strategy or not;
if the historical maintenance strategy of the similar equipment in a preset detection period corresponding to the historical operation data clustering result is a preset type maintenance strategy, acquiring the historical maintenance strategy of the current equipment within preset time;
if the historical maintenance strategy of the current equipment within the preset time is different from the historical maintenance strategy of the similar equipment within the preset detection time period, removing the historical operation data clustering result corresponding to the historical maintenance strategy of the similar equipment within the preset detection time period, and obtaining a primary diagnosis result of the current equipment with the highest similarity as a secondary diagnosis result after screening and outputting the secondary diagnosis result;
and if the historical maintenance strategy of the current equipment within the preset time is the same as the historical maintenance strategy of the similar equipment within the preset detection time period, outputting the historical operation data clustering result with the highest similarity as a secondary diagnosis result after screening.
6. The online monitoring method of electronic equipment according to claim 1, wherein determining a maintenance strategy according to the screened secondary diagnosis result specifically includes the following steps:
acquiring historical fault trends of the secondary diagnosis results, and determining the state level of the current equipment according to the historical fault trends;
determining the current maintenance task content according to the state level of the current equipment, if the current maintenance task content is in a state that the equipment needs to be stopped to work for maintenance, sending a work stopping instruction, and determining a maintenance strategy according to the current maintenance task content;
if the maintenance task content is in a state that the working quality of the equipment is reduced due to the fact that the abnormality of the equipment part is determined, a work stopping instruction is sent out, and a maintenance strategy is determined according to the current maintenance task content;
and if the maintenance task content is in a state that the current equipment does not have faults within the current working time limit, determining a maintenance strategy according to the current maintenance task content.
7. An online monitoring system of an electronic device, the system comprising a memory and a processor, the memory containing an online monitoring method program of the electronic device, the online monitoring method program of the electronic device when executed by the processor implementing the steps of:
acquiring various data information of current equipment and environmental data information of the current equipment through a sensor, and constructing a digital twin model based on the various data information of the current equipment and the environmental data information of the current equipment;
obtaining historical operation data information of each node of the same equipment, and clustering the historical operation data information of each node of the current equipment to obtain a historical operation data clustering result;
acquiring dynamic data of the digital twin model within preset time, and obtaining a primary diagnosis result of the current equipment based on the historical operation data clustering result and the data information within the preset time;
and obtaining a screened secondary diagnosis result according to the primary diagnosis result of the current equipment, and determining a maintenance strategy according to the screened secondary diagnosis result.
8. The on-line monitoring system of electronic equipment according to claim 7, wherein historical operating data information of each node of the same equipment is obtained, and the historical operating data information of each node of the current equipment is clustered to obtain a historical operating data clustering result, specifically comprising the following steps:
obtaining descriptive data information of current equipment, obtaining same descriptive data information corresponding to the descriptive data information through a big data network, and constructing a clustering rule according to the descriptive data information of the current equipment and the same descriptive data information corresponding to the descriptive data information;
acquiring historical operating data information of each node of the same equipment, constructing a hash function group, and performing hash operation on the historical operating data information of each node of the same equipment based on the hash function group to acquire a hash group corresponding to the hash function group;
randomly extracting a data set from historical operating data information of each node of the same equipment as a clustering object to calculate an index value, and carrying out primary clustering according to the index value and a hash group corresponding to the hash function group to obtain a plurality of candidate data sets;
and searching the candidate data sets one by one according to the clustering rule to obtain the similarity among the candidate data sets, and performing secondary clustering on the candidate data sets with the similarity larger than the preset similarity to obtain a historical operating data clustering result.
9. The on-line monitoring system of the electronic device according to claim 7, wherein the secondary diagnosis result after the secondary screening is obtained according to the primary diagnosis result of the current device, specifically comprising the following steps:
acquiring a historical maintainability strategy of the similar equipment in a preset detection period corresponding to a historical operating data clustering result with the highest similarity, and judging whether the historical maintainability strategy of the similar equipment in the preset detection period corresponding to the historical operating data clustering result is a preset type maintainability strategy or not;
if the historical maintenance strategy of the similar equipment in a preset detection period corresponding to the historical operation data clustering result is a preset type maintenance strategy, acquiring the historical maintenance strategy of the current equipment within preset time;
if the historical maintenance strategy of the current equipment within the preset time is different from the historical maintenance strategy of the similar equipment within the preset detection time period, removing the historical operation data clustering result corresponding to the historical maintenance strategy of the similar equipment within the preset detection time period, and obtaining a primary diagnosis result of the current equipment with the highest similarity as a secondary diagnosis result after screening and outputting the secondary diagnosis result;
and if the historical maintenance strategy of the current equipment within the preset time is the same as the historical maintenance strategy of the similar equipment within the preset detection time period, outputting the historical operation data clustering result with the highest similarity as a secondary diagnosis result after screening.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium comprises an online monitoring method program of an electronic device, which when executed by a processor, implements the steps of the online monitoring method of an electronic device according to any one of claims 1-6.
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