CN115828145B - 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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- CN115828145B CN115828145B CN202310086517.6A CN202310086517A CN115828145B CN 115828145 B CN115828145 B CN 115828145B CN 202310086517 A CN202310086517 A CN 202310086517A CN 115828145 B CN115828145 B CN 115828145B
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Abstract
The invention relates to an on-line monitoring method, a system and a medium of electronic equipment, which belong to the technical field of electronic equipment data monitoring, and the invention obtains a primary diagnosis result of the current equipment based on a historical operation data clustering result and data information within preset time by acquiring dynamic data of a digital twin model within 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. Clustering the descriptive data with the same identity according to a clustering rule by performing secondary clustering on the historical operation data, so that the accuracy of identifying the historical similarity equipment data is improved, and the accuracy of identifying 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 more accuracy on the diagnosis result of the equipment.
Description
Technical Field
The present invention relates to the field of electronic device data monitoring technologies, and in particular, to an online monitoring method, system, and medium for an electronic device.
Background
The automatic technology is widely used in the aspects of industry, agriculture, military, scientific research, transportation, business, medical treatment, service, families and the like, and the adoption of the automatic technology can not only liberate people from heavy physical labor, partial mental labor and severe and dangerous working environments, but also can expand the organ functions of the people, greatly improve the labor productivity and enhance the ability of the people to recognize the world and reform the world. Due to the characteristics of dynamic and mutability of mechanical load, rotational operability 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 also high. In the past, the traditional electrical measurement sensing technology is very difficult or impossible to realize multi-parameter on-line dynamic monitoring; traditional regular and off-line overhauling causes a plurality of potential safety hazards and long downtime, and sudden accidents of the equipment in the running process can not be prevented. Secondly, the online monitoring technology of the electronic equipment still has some problems at present, and the diagnosis accuracy is low when the monitored data of the equipment are diagnosed.
Disclosure of Invention
The invention overcomes the defects of the prior art and provides an online monitoring method, an online monitoring system and an online monitoring medium for electronic equipment.
In order to achieve the above purpose, the invention adopts the following technical scheme:
the first aspect of the invention provides an online monitoring method of electronic equipment, which comprises the following steps:
acquiring various data information of the 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;
acquiring 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 acquiring a primary diagnosis result of the current equipment based on the 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.
Further, in a preferred embodiment of the present invention, a plurality of data information of a current device and environmental 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 environmental data information of the current device, which 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, inputting the three-dimensional model diagram of the current equipment into the virtual scene, and generating an initial digital twin model;
acquiring the position information of a sensor in the three-dimensional model diagram of the current device, and obtaining an integrated digital twin model according to the position information of the sensor in the three-dimensional model diagram of the current device and the initial digital twin model;
acquiring various data information of the 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 a dynamic fused digital twin model, and outputting the dynamic fused digital twin model as a final digital twin model.
Further, in a preferred embodiment of the present invention, historical operation data information of each node of the same device is obtained, and the historical operation data information of each node of the current device is clustered to obtain a historical operation data clustering result, which specifically includes the following steps:
Acquiring descriptive data information of current equipment, acquiring 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;
acquiring historical operation data information of each node of the same equipment, constructing a hash function group, and carrying out hash operation on the historical operation 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 the historical operation data information of each node of the same equipment as a clustering object to calculate an index value, and clustering once 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 between 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 operation data clustering result.
Further, in a preferred embodiment of the present invention, dynamic data of the digital twin model within a preset time is obtained, and a diagnostic result of the current device is obtained based on the clustering result of the historical operation data and the data information within the preset time, which 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;
constructing 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 operation data change curves with curve deviation rate larger than the preset curve deviation rate in a preset monitoring period;
and acquiring a historical diagnosis result of 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, a secondary diagnosis result after secondary screening is obtained according to a primary diagnosis result of the current device, which specifically includes the following steps:
acquiring a historical maintainability strategy of similar equipment corresponding to a historical operation data clustering result with highest similarity in a preset detection period, and judging whether the historical maintainability strategy of similar equipment corresponding to the historical operation data clustering result in the preset detection period is a maintainability strategy of a preset type;
if the historical maintainability strategy of the similar equipment corresponding to the historical operation data clustering result in the preset detection period is a maintainability strategy of a preset type, 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 within the preset detection period, eliminating the historical operation data clustering result corresponding to the historical maintenance strategy of the similar equipment within the preset detection period, and acquiring a primary diagnosis result of the current equipment with the highest next similarity as a screened secondary diagnosis result to be output;
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 period, outputting the historical operation data clustering result with the highest similarity as a screened secondary diagnosis result.
Further, in a preferred embodiment of the present invention, the maintenance strategy is determined according to the screened secondary diagnosis result, which specifically includes the following steps:
acquiring a historical fault trend of the secondary diagnosis result, and determining a state level of current equipment according to the historical fault trend;
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 of needing to stop the equipment to work for maintenance, sending a stop work 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 equipment component is abnormal, a working 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 is in a current working time limit and cannot fail, determining a maintenance strategy according to the current maintenance task content.
The second aspect of the present invention provides an online monitoring system for an electronic device, where the system includes a memory and a processor, where 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 following steps are implemented:
acquiring various data information of the 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;
acquiring 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 acquiring a primary diagnosis result of the current equipment based on the 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, historical operation data information of each node of the same device is obtained, and the historical operation data information of each node of the current device is clustered to obtain a historical operation data clustering result, which specifically includes the following steps:
Acquiring descriptive data information of current equipment, acquiring 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;
acquiring historical operation data information of each node of the same equipment, constructing a hash function group, and carrying out hash operation on the historical operation 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 the historical operation data information of each node of the same equipment as a clustering object to calculate an index value, and clustering once 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 between 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 operation data clustering result.
In this embodiment, a secondary diagnosis result after secondary screening is obtained according to the primary diagnosis result of the current device, which specifically includes the following steps:
acquiring a historical maintainability strategy of similar equipment corresponding to a historical operation data clustering result with highest similarity in a preset detection period, and judging whether the historical maintainability strategy of similar equipment corresponding to the historical operation data clustering result in the preset detection period is a maintainability strategy of a preset type;
if the historical maintainability strategy of the similar equipment corresponding to the historical operation data clustering result in the preset detection period is a maintainability strategy of a preset type, 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 within the preset detection period, eliminating the historical operation data clustering result corresponding to the historical maintenance strategy of the similar equipment within the preset detection period, and acquiring a primary diagnosis result of the current equipment with the highest next similarity as a screened secondary diagnosis result to be output;
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 period, outputting the historical operation data clustering result with the highest similarity as a screened secondary diagnosis result.
A third aspect of the present invention provides a computer-readable storage medium comprising an on-line monitoring method program of an electronic device, which when executed by a processor, implements the steps of the on-line monitoring method of an electronic device as claimed in any one of the preceding claims.
The invention solves the defects existing in the background technology, and has the following beneficial effects:
according to the invention, various data information of the current equipment and environmental data information of the current equipment are obtained through the sensor, a digital twin model is constructed based on the various data information of the current equipment and the environmental data information of the current equipment, historical operation data information of each node of the same equipment is further obtained, and clustering processing is carried out on the historical operation data information of each node of the current equipment so as to obtain a historical operation data clustering result; acquiring dynamic data of the digital twin model within preset time, and acquiring a primary diagnosis result of the current equipment based on the 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. Clustering the descriptive data with the same identity according to a clustering rule by performing secondary clustering on the historical operation data, so that the accuracy of identifying the historical similarity equipment data is improved, and the accuracy of identifying 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 more accuracy on the diagnosis result of the equipment.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other embodiments of the drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 shows an overall method flow diagram of an online monitoring method for an electronic device;
FIG. 2 shows a first method flow diagram of an online monitoring method of an electronic device;
FIG. 3 shows a second method flow diagram of an online monitoring method of an electronic device;
fig. 4 shows a system block diagram of an online monitoring system of an electronic device.
Description of the embodiments
In order that the above-recited objects, features and advantages of the present invention will be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description. It should be noted that, in the case of no conflict, the embodiments of the present application and the features in the embodiments may be combined with each other.
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 described herein, and therefore the scope of the present invention is not limited to the specific embodiments disclosed below.
FIG. 1 shows an overall method flow diagram of an online monitoring method for an electronic device;
the first aspect of the invention provides an online monitoring method of electronic equipment, which comprises the following steps:
s102, acquiring various data information of the 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 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;
s106, acquiring dynamic data of the digital twin model within preset time, and acquiring a primary diagnosis result of the current equipment based on the historical operation data clustering result and data information within the preset time;
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.
In this embodiment, the historical operation data is clustered for the second time, and the descriptive data with the same identity is clustered according to a clustering rule, so that the accuracy of identifying the historical similarity equipment data is improved, and the accuracy of identifying 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 more accuracy on the diagnosis result of the equipment.
Further, in a preferred embodiment of the present invention, a plurality of data information of a current device and environmental 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 environmental data information of the current device, which 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, inputting the three-dimensional model diagram of the current equipment into the virtual scene, and generating an initial digital twin model;
acquiring the position information of a three-dimensional model diagram of the sensor in the current equipment, and acquiring 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 the 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 obtaining the dynamic fused digital twin model, and outputting the dynamic fused digital twin model as a final digital twin model.
In this embodiment, the geometric feature data information of the current device may be external dimension information of each part of the device, and the three-dimensional model diagram of the current device may be constructed by three-dimensional software such as maya software and SolidWorks software.
FIG. 2 shows a first method flow diagram of an online monitoring method of an electronic device;
further, in a preferred embodiment of the present invention, historical operation data information of each node of the same device is obtained, and the historical operation data information of each node of the current device is clustered to obtain a historical operation data clustering result, which specifically includes the following steps:
s202, acquiring descriptive data information of current equipment, acquiring 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 operation data information of each node of the same equipment, constructing a hash function set, and carrying out hash operation on the historical operation data information of each node of the same equipment based on the Ha Xihan set to acquire a hash set corresponding to the hash function set;
s206, randomly extracting a data set from the historical operation data information of each node of the same equipment as a clustering object to calculate an index value, and clustering once 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, searching the candidate data sets one by one according to a clustering rule to obtain the similarity between 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 operation data clustering result.
In this embodiment, the descriptive data information of the device may be a period, an operation speed, an operation time, an operation speed of a rotating part, and the like, where the same descriptive data information corresponding to the descriptive data information may be understood as a relationship between a period and a frequency, because the system cannot know the relationship between the period and the frequency, because the period and the frequency have a meaning of a certain similarity, it is known that the reciprocal of the period is a frequency, and because other data in the data uploaded by the user are the same, only one of the period or the frequency is uploaded, and at this time, the data of the two are actually the same, but the system cannot identify. Therefore, the invention constructs the clustering rule according to the conversion relation of the period and the frequency, the construction process can be realized by a machine learning technology, a neural network technology and the like, the data can be identified by training the data, the application is not limited by the two realization modes, and the realization can be realized by other modes by a person skilled in the art. 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 device may be data in the form of histogram data, table data, or the like.
Further, in a preferred embodiment of the present invention, dynamic data of a digital twin model within a preset time is obtained, and a diagnostic result of a current device is obtained based on a clustering result of historical operation data and data information within the preset time, which 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;
constructing 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 operation data change curves with curve deviation rate larger than the preset curve deviation rate in a preset monitoring period;
according to the historical operation data change curve of the preset monitoring period, the historical diagnosis result of the similar equipment in the preset detection period is obtained, and the historical diagnosis result of the similar equipment in the preset detection period is used as one-time diagnosis result of the current equipment to be output.
In this embodiment, the historical operation data clustering result is compared with the operation data of the current device, so that the historical diagnosis result of the similar device in the preset detection period is screened out, and the diagnosis result is output as a diagnosis result.
FIG. 3 shows a second method flow diagram of an online monitoring method of an electronic device;
further, in a preferred embodiment of the present invention, a secondary diagnosis result after secondary screening is obtained according to a primary diagnosis result of a current device, which specifically includes the following steps:
s302, acquiring a historical maintainability strategy of the similar equipment corresponding to the historical operation data clustering result with the highest similarity in a preset detection period, and judging whether the historical maintainability strategy of the similar equipment corresponding to the historical operation data clustering result in the preset detection period is a maintainability strategy of a preset type;
s304, if the historical maintainability strategy of the similar equipment corresponding to the clustering result of the historical operation data is a maintainability strategy of a preset type in a preset detection period, acquiring the historical maintainability strategy of the current equipment within a preset time;
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 period, eliminating the historical operation data clustering result corresponding to the historical maintenance strategy of the similar equipment within the preset detection period, and acquiring the primary diagnosis result of the current equipment with the highest next similarity as the screened secondary diagnosis result to be output;
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 period, outputting the historical operation data clustering result with the highest similarity as a screened secondary diagnosis result.
It should be noted that, in this embodiment, the predetermined type of maintainability policy is a maintainability policy that is not subjected to overhaul, if parts of different product types are subjected to replacement, such data is not output as a clustering result of historical operation data, but only when the historical maintainability policy of the similar device corresponding to the clustering result of the historical operation data in the predetermined detection period is the predetermined type of maintainability policy, the data is further screened, and only when the historical maintainability policy of the current device in the predetermined time is the same as the historical maintainability policy of the similar device in the predetermined detection period, it is indicated that the maintainability conditions of the 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 maintenance strategy is determined according to the screened secondary diagnosis result, and specifically includes the following steps:
Acquiring a historical fault trend of the secondary diagnosis result, and determining the state level of the current equipment according to the historical fault trend;
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 of needing to stop the equipment to work for maintenance, sending a stop work 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 equipment component is abnormal, a working 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 is in a current working time limit and cannot fail, determining a maintenance strategy according to the current maintenance task content.
It should be noted that, by the method, the maintenance strategy can be determined according to the actual equipment condition, so that the maintenance strategy of the method is more reasonable.
In addition, the method can further comprise the following steps:
acquiring data information of a current historical operation data clustering result, and judging whether the data information has data missing or data error;
If the data information has data missing or data error, acquiring a node where the data information is located, and acquiring the data information of the data missing or data error;
generating a search tag according to the data information of the data missing or the data error, and searching according to the search tag from the current historical operation data clustering result to obtain correct text data corresponding to the data information of the data missing or the data error;
and replacing the position node where the data information is in the data missing or the data error according to the correct text data corresponding to the data information of the data missing or the data error, and generating a final historical operation data clustering result.
It should be noted that, when the user uploads data in the data information of the current historical operation data clustering result, some situations of data missing or data error may exist, and the data in the situations can be corrected by the method, so that the effectiveness of the historical operation data clustering result is ensured, and the robustness of the diagnosis system is improved.
Fig. 4 shows a system block diagram of an online monitoring system of an electronic device.
The second aspect of the present invention provides an on-line monitoring system for an electronic device, the system comprising a memory 41 and a processor 62, the memory 41 containing an on-line monitoring method program for the electronic device, the on-line monitoring method program for the electronic device, when executed by the processor 62, implementing the steps of:
acquiring various data information of the 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;
acquiring 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 a digital twin model within a preset time, and acquiring 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, historical operation data information of each node of the same device is obtained, and clustering processing is performed on the historical operation data information of each node of the current device to obtain a historical operation data clustering result, which specifically includes the following steps:
Acquiring descriptive data information of current equipment, acquiring 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;
acquiring historical operation data information of each node of the same equipment, constructing a hash function set, and carrying out hash operation on the historical operation data information of each node of the same equipment based on the Ha Xihan array to acquire a hash group corresponding to the hash function set;
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 performing 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 between 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 operation data clustering result.
In this embodiment, a secondary diagnosis result after secondary screening is obtained according to a primary diagnosis result of a current device, which specifically includes the following steps:
Acquiring a historical maintainability strategy of similar equipment corresponding to a historical operation data clustering result with highest similarity in a preset detection period, and judging whether the historical maintainability strategy of similar equipment corresponding to the historical operation data clustering result in the preset detection period is a maintainability strategy of a preset type;
if the historical maintainability strategy of the similar equipment corresponding to the historical operation data clustering result in the preset detection period is a maintainability strategy of a preset type, acquiring the historical maintainability strategy of the current equipment in 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 within the preset detection period, eliminating the historical operation data clustering result corresponding to the historical maintenance strategy of the similar equipment within the preset detection period, and acquiring the primary diagnosis result of the current equipment with the highest next similarity as a screened secondary diagnosis result to be output;
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 period, the historical operation data clustering result with highest similarity corresponds to the historical operation data clustering result of the similar equipment within the preset detection period, and the historical operation data clustering result is output as a screened secondary diagnosis result.
A third aspect of the present invention provides a computer-readable storage medium including an on-line monitoring method program of an electronic device, which when executed by a processor, implements the steps of the on-line monitoring method of an electronic device as in any one of the items.
In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above described device embodiments are only illustrative, e.g. the division of the units is only one logical function division, and there may be other divisions in practice, such as: multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. In addition, the various components shown or discussed may be coupled or directly coupled or communicatively coupled to each other via some interface, whether indirectly coupled or communicatively coupled to devices or units, whether electrically, mechanically, or otherwise.
The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units; can be located in one place or distributed to a plurality of network units; some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present invention may be integrated in one processing unit, or each unit may be separately used as one unit, or two or more units may be integrated in one unit; the integrated units may be implemented in hardware or in hardware plus software functional units.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware related to program instructions, and the foregoing program may be stored in a computer readable storage medium, where the program, when executed, performs steps including the above method embodiments; and the aforementioned storage medium includes: a mobile storage device, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk or an optical disk, or the like, which can store program codes.
Alternatively, the above-described integrated units of the present invention may be stored in a computer-readable storage medium if implemented in the form of software functional modules and sold or used as separate products. Based on such understanding, the technical solutions of the embodiments of the present invention may be embodied in essence or a part contributing to the prior art in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) 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, ROM, RAM, magnetic or optical disk, or other medium capable of storing program code.
The foregoing is merely illustrative 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 think about variations or substitutions within the technical scope of the present invention, and the invention should be covered. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (6)
1. An on-line monitoring method of electronic equipment is characterized by comprising the following steps:
acquiring various data information of current equipment through a sensor, and constructing a digital twin model based on the various data information of the current equipment and environmental data information of the current equipment;
acquiring 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 acquiring a primary diagnosis result of the current equipment based on the historical operation data clustering result and data information within the preset time;
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 method comprises the steps of acquiring 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, and specifically comprises the following steps:
acquiring descriptive data information of current equipment, acquiring 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;
acquiring historical operation data information of each node of the same equipment, constructing a hash function group, and carrying out hash operation on the historical operation 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 the historical operation data information of each node of the same equipment as a clustering object to calculate an index value, and clustering once according to the index value and a hash group corresponding to the hash function group to obtain a plurality of candidate data sets;
searching the candidate data sets one by one according to the clustering rule to obtain the similarity between 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 operation data clustering result;
Obtaining a secondary diagnosis result after secondary screening according to the primary diagnosis result of the current equipment, and specifically comprising the following steps:
acquiring a historical maintainability strategy of similar equipment corresponding to a historical operation data clustering result with highest similarity in a preset detection period, and judging whether the historical maintainability strategy of similar equipment corresponding to the historical operation data clustering result in the preset detection period is a maintainability strategy of a preset type;
if the historical maintainability strategy of the similar equipment corresponding to the historical operation data clustering result in the preset detection period is a maintainability strategy of a preset type, 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 within the preset detection period, eliminating the historical operation data clustering result corresponding to the historical maintenance strategy of the similar equipment within the preset detection period, and acquiring a primary diagnosis result of the current equipment with the highest next similarity as a screened secondary diagnosis result to be output;
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 period, outputting the historical operation data clustering result with the highest similarity as a screened secondary diagnosis result.
2. The method for on-line monitoring of electronic equipment according to claim 1, wherein the method comprises the steps of obtaining a plurality of data information of the current equipment through a sensor, and constructing a digital twin model based on the plurality of data information of the current equipment and the environmental data information of the current equipment, and specifically comprises 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, inputting the three-dimensional model diagram of the current equipment into the virtual scene, and generating an initial digital twin model;
acquiring the position information of a sensor in the three-dimensional model diagram of the current device, and obtaining an integrated digital twin model according to the position information of the sensor in the three-dimensional model diagram of the current device and the initial digital twin model;
acquiring various data information of the 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 a dynamic fused digital twin model, and outputting the dynamic fused digital twin model as a final digital twin model.
3. The method for online monitoring of electronic equipment according to claim 1, wherein the method for online monitoring of electronic equipment is characterized by obtaining dynamic data of the digital twin model within a preset time, and obtaining a diagnostic result of the current equipment based on the historical operation 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;
constructing 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 operation data change curves with curve deviation rate larger than the preset curve deviation rate in a preset monitoring period;
And acquiring a historical diagnosis result of 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.
4. The on-line monitoring method of an electronic device according to claim 1, wherein the determining the maintenance strategy according to the screened secondary diagnosis result comprises the following steps:
acquiring a historical fault trend of the secondary diagnosis result, and determining a state level of current equipment according to the historical fault trend;
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 of needing to stop the equipment to work for maintenance, sending a stop work 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 equipment component is abnormal, a working 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 is in a current working time limit and cannot fail, determining a maintenance strategy according to the current maintenance task content.
5. An on-line monitoring system of an electronic device, wherein the system comprises a memory and a processor, the memory contains an on-line monitoring method program of the electronic device, and when the on-line monitoring method program of the electronic device is executed by the processor, the following steps are realized:
acquiring various data information of current equipment through a sensor, and constructing a digital twin model based on the various data information of the current equipment and environmental data information of the current equipment;
acquiring 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 acquiring a primary diagnosis result of the current equipment based on the historical operation data clustering result and data information within the preset time;
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;
acquiring 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, wherein the method specifically comprises the following steps of:
Acquiring descriptive data information of current equipment, acquiring 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;
acquiring historical operation data information of each node of the same equipment, constructing a hash function group, and carrying out hash operation on the historical operation 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 the historical operation data information of each node of the same equipment as a clustering object to calculate an index value, and clustering once according to the index value and a hash group corresponding to the hash function group to obtain a plurality of candidate data sets;
searching the candidate data sets one by one according to the clustering rule to obtain the similarity between 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 operation data clustering result;
Obtaining a secondary diagnosis result after secondary screening according to the primary diagnosis result of the current equipment, and specifically comprising the following steps:
acquiring a historical maintainability strategy of similar equipment corresponding to a historical operation data clustering result with highest similarity in a preset detection period, and judging whether the historical maintainability strategy of similar equipment corresponding to the historical operation data clustering result in the preset detection period is a maintainability strategy of a preset type;
if the historical maintainability strategy of the similar equipment corresponding to the historical operation data clustering result in the preset detection period is a maintainability strategy of a preset type, 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 within the preset detection period, eliminating the historical operation data clustering result corresponding to the historical maintenance strategy of the similar equipment within the preset detection period, and acquiring a primary diagnosis result of the current equipment with the highest next similarity as a screened secondary diagnosis result to be output;
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 period, outputting the historical operation data clustering result with the highest similarity as a screened secondary diagnosis result.
6. A computer readable storage medium, characterized in that the computer readable storage medium comprises an on-line monitoring method program of an electronic device, which, when being executed by a processor, implements the steps of the on-line monitoring method of an electronic device according to any of claims 1-4.
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