CN115203939A - Equipment analysis and prediction method, device and equipment - Google Patents

Equipment analysis and prediction method, device and equipment Download PDF

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Publication number
CN115203939A
CN115203939A CN202210829254.9A CN202210829254A CN115203939A CN 115203939 A CN115203939 A CN 115203939A CN 202210829254 A CN202210829254 A CN 202210829254A CN 115203939 A CN115203939 A CN 115203939A
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equipment
processed
digital twin
predicting
analyzing
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李劼
邬浩
王琇玲
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Beijing Datamesh Technology Co ltd
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Beijing Datamesh Technology Co ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
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    • G06F16/2228—Indexing structures
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Abstract

The invention discloses a method, a device and equipment for analyzing and predicting equipment, wherein the method comprises the following steps: acquiring equipment to be processed; generating a digital twin according to the equipment to be processed; simulating the operation of the equipment to be processed through the digital twin organism to generate and store telemetering data; and calling a machine model method to analyze and predict the telemetering data to obtain a processing result. By the method, the analysis and prediction method of the universal type state equipment applicable to most application scenes is provided, and waste of resources is effectively reduced.

Description

Equipment analysis and prediction method, device and equipment
Technical Field
The invention relates to the technical field of data processing, in particular to a method, a device and equipment for analyzing and predicting equipment.
Background
When analyzing and predicting the equipment state, a digital twin value scene is usually explored, then equipment to be processed and related data are accessed based on the scene, and then the data are analyzed and predicted, but the exploration of the digital twin value scene has a large amount of requirements which need to be carried out in a virtual and real set scene, and different equipment analysis and prediction also need different scenes.
The existing method for analyzing and predicting the state of the device usually needs to be customized and developed in each scene, which results in a great deal of resource waste.
Therefore, the problem to be solved by the person skilled in the art is how to provide an analysis and prediction method for a general-purpose state device that can be applied to most application scenarios.
Disclosure of Invention
In order to solve the above problems, the present invention provides a method, an apparatus, and a device for analyzing and predicting a device.
According to an aspect of an embodiment of the present invention, there is provided an apparatus analysis and prediction method, including:
acquiring equipment to be processed;
generating a digital twin according to the device to be processed;
simulating the operation of the equipment to be processed through the digital twin organism to generate and store telemetering data;
and calling a machine model method to analyze and predict the telemetering data to obtain a processing result.
Optionally, after generating the digital twin, further comprising:
defining the digital twins and metadata of the digital twins.
Optionally, the defining the digital twins includes:
and defining the association relationship among the digital twins through a parent-child structure based on the equipment to be processed.
Optionally, defining metadata of the digital twin includes:
acquiring current data of the equipment to be processed;
defining metadata of the digital twin according to current data of the device to be processed.
Optionally, the current data includes at least one of:
string, value, time series.
Optionally, after defining the digital twin and the metadata of the digital twin, the method further includes:
generating an interface of the digital twin.
Optionally, storing telemetry data comprises:
storing the telemetry data in a time series database manner.
According to another aspect of the embodiments of the present invention, there is provided an apparatus for analyzing and predicting a device, the apparatus including:
the acquisition module is used for acquiring the equipment to be processed;
the processing module is used for generating a digital twin according to the equipment to be processed; simulating the operation of the equipment to be processed through the digital twin body, and generating and storing telemetering data;
and the generating module is used for calling a machine model method to analyze and predict the telemetering data to obtain a processing result.
According to still another aspect of an embodiment of the present invention, there is provided a computing device including: the system comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface complete mutual communication through the communication bus;
the memory is used for storing at least one executable instruction, and the executable instruction enables the processor to execute the operation corresponding to the analysis and prediction method of the equipment.
According to a further aspect of the embodiments of the present invention, there is provided a computer storage medium having at least one executable instruction stored therein, the executable instruction causing a processor to perform operations corresponding to the analysis and prediction method of the apparatus.
According to the scheme provided by the embodiment of the invention, the equipment to be processed is obtained; generating a digital twin according to the equipment to be processed; simulating the operation of the equipment to be processed through the digital twin body, and generating and storing telemetering data; and calling a machine model method to analyze and predict the telemetering data to obtain a processing result, so that the invention provides an analysis and prediction method of the universal type state equipment which can be suitable for most application scenes, and effectively reduces the waste of resources.
The foregoing description is only an overview of the technical solutions of the embodiments of the present invention, and the embodiments of the present invention can be implemented according to the content of the description in order to make the technical means of the embodiments of the present invention more clearly understood, and the detailed description of the embodiments of the present invention is provided below in order to make the foregoing and other objects, features, and advantages of the embodiments of the present invention more clearly understandable.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the embodiments of the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
FIG. 1 illustrates a method flow diagram of an analysis and prediction method of an apparatus provided by an embodiment of the present invention;
FIG. 2 is a diagram illustrating a specific content editing scheme provided by an embodiment of the present invention;
FIG. 3 is a block diagram illustrating an architecture of a specific device condition monitoring analysis prediction based on digital twin according to an embodiment of the present invention;
FIG. 4 is a schematic structural diagram of an analyzing and predicting apparatus of a device according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a computing device provided by an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
Fig. 1 is a flowchart illustrating a method of analyzing and predicting a device according to an embodiment of the present invention. As shown in fig. 1, the method comprises the steps of:
step 11, acquiring equipment to be processed;
step 12, generating a digital twin body according to the equipment to be processed;
step 13, simulating the operation of the equipment to be processed through the digital twin body, and generating and storing telemetering data;
and step 14, calling a machine model method to analyze and predict the telemetering data to obtain a processing result.
In the embodiment, the equipment to be processed is obtained; generating a digital twin according to the device to be processed; simulating the operation of the equipment to be processed through the digital twin organism to generate and store telemetering data; and calling a machine model method to analyze and predict the telemetering data to obtain a processing result, so that the invention provides an analysis and prediction method of the universal type state equipment which can be suitable for most application scenes, and effectively reduces the waste of resources.
In an optional embodiment of the present invention, after step 12, the method may further include:
step 121, defining the digital twin and metadata of the digital twin.
Specifically, the definition of the digital twin includes:
and 1211, defining the association relationship between the digital twin bodies through a parent-child structure based on the device to be processed.
In this embodiment, as shown in fig. 2, when generating a digital twin, a user can edit a virtual model of a business scene such as a building and a factory by itself through an XR content editing tool, and define relationships between buildings and devices and relationships between devices in the scene through the digital twin.
In yet another optional embodiment of the present invention, in step 121, defining metadata of the digital twin includes:
step 1212, acquiring current data of the device to be processed;
step 1213, defining metadata of the digital twin according to current data of the device to be processed, wherein the current data includes at least one of:
string, value, time series.
In this embodiment, the data analysis chart may be self-configured based on the metadata of the digital twin.
In another optional embodiment of the present invention, after step 121, the method may further include:
and step 122, generating an interface of the digital twin body.
In this embodiment, the interface includes an internet of things data access interface such as HTTP, MQTT, coAP, etc., but is not limited to the above.
In a further alternative embodiment of the present invention, step 13, storing telemetry data may include:
and step 131, storing the telemetering data in a time sequence database mode.
In this embodiment, analysis may be performed based on the metadata and uploaded telemetry data, and a system default machine learning algorithm may be selected for prediction.
Fig. 3 is a schematic diagram illustrating an architecture of a specific device status monitoring analysis prediction based on a digital twin according to an embodiment of the present invention, where, as shown in fig. 3, the architecture includes:
the definition module is used for defining the digital twin and metadata of the digital twin;
the interface module is used for providing an interface for the digital twin, wherein the framework can automatically generate the interface after the digital twin is defined;
the storage module is used for storing the telemetering data of the equipment in a K-V key value mode through a time sequence database;
and the analysis and prediction module is used for analyzing and predicting according to the uploaded historical telemetering data by calling a built-in machine model algorithm of the system.
The framework realizes the functions of data access and association, data analysis insight and prediction of digital twin scene equipment in a user code-free configuration mode through a productized platform design.
In the above embodiment of the present invention, the device to be processed is obtained; generating a digital twin according to the device to be processed; simulating the operation of the equipment to be processed through the digital twin body, and generating and storing telemetering data; and calling a machine model method to analyze and predict the telemetering data to obtain a processing result, so that the invention provides an analysis and prediction method of the universal type state equipment which can be suitable for most application scenes, and effectively reduces the waste of resources.
Fig. 4 is a schematic structural diagram of an analysis and prediction apparatus 40 of the device according to the embodiment of the present invention. As shown in fig. 4, the apparatus includes:
an obtaining module 41, configured to obtain a device to be processed;
a processing module 42, configured to generate a digital twin according to the device to be processed; simulating the operation of the equipment to be processed through the digital twin body, and generating and storing telemetering data;
and the generating module 43 is configured to invoke a machine model method to analyze and predict the telemetry data, so as to obtain a processing result.
Optionally, the processing module 42 is further configured to define the digital twins and metadata of the digital twins.
Optionally, the processing module 42 is further configured to define, based on the device to be processed, an association relationship between the digital twin bodies through a parent-child structure.
Optionally, the processing module 42 is further configured to obtain current data of the device to be processed;
and defining metadata of the digital twin according to the current data of the device to be processed.
Optionally, the current data includes at least one of:
string, value, time series.
Optionally, the processing module 42 is further configured to generate an interface of the digital twin body.
Optionally, the processing module 42 is further configured to store the telemetry data in a time series database.
It should be understood that the above description of the method embodiments illustrated in fig. 1 to 3 is merely an illustration of the technical solution of the present invention by way of alternative examples, and does not limit the method for analyzing and predicting the device according to the present invention. In other embodiments, the execution steps and the sequence of the analyzing and predicting method of the device according to the present invention may be different from those of the above embodiments, and the embodiments of the present invention are not limited thereto.
It should be noted that this embodiment is an apparatus embodiment corresponding to the above method embodiment, and all the implementations in the above method embodiment are applicable to this apparatus embodiment, and the same technical effects can be achieved.
Embodiments of the present invention provide a non-volatile computer storage medium, where at least one executable instruction is stored in the computer storage medium, and the computer executable instruction may execute the analysis and prediction method of the device in any of the method embodiments described above.
Fig. 5 is a schematic structural diagram of a computing device according to an embodiment of the present invention, and the specific embodiment of the present invention does not limit the specific implementation of the computing device.
As shown in fig. 5, the computing device may include: a processor (processor), a Communications Interface (Communications Interface), a memory (memory), and a Communications bus.
Wherein: the processor, the communication interface, and the memory communicate with each other via a communication bus. A communication interface for communicating with network elements of other devices, such as clients or other servers. And a processor for executing the program, and in particular, for performing the relevant steps in the above embodiments of the method for analyzing and predicting of a device for a computing device.
In particular, the program may include program code comprising computer operating instructions.
The processor may be a central processing unit CPU or an Application Specific Integrated Circuit ASIC or one or more Integrated circuits configured to implement embodiments of the present invention. The computing device includes one or more processors, which may be the same type of processor, such as one or more CPUs; or may be different types of processors such as one or more CPUs and one or more ASICs.
And the memory is used for storing programs. The memory may comprise high-speed RAM memory, and may also include non-volatile memory (non-volatile memory), such as at least one disk memory.
The program may in particular be adapted to cause a processor to perform the method of analyzing and predicting of a device in any of the method embodiments described above. For specific implementation of each step in the program, reference may be made to corresponding steps and corresponding descriptions in units in the analysis and prediction method embodiments of the foregoing device, which are not described herein again. It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described devices and modules may refer to the corresponding process descriptions in the foregoing method embodiments, and are not described herein again.
The algorithms or displays presented herein are not inherently related to any particular computer, virtual machine, or other apparatus. Various general purpose systems may also be used with the teachings herein. The required structure for constructing such a system is apparent from the description above. In addition, embodiments of the present invention are not directed to any particular programming language. It is appreciated that a variety of programming languages may be used to implement the teachings of embodiments of the present invention as described herein, and any descriptions of specific languages are provided above to disclose the best modes of embodiments of the invention.
In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the embodiments of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects.
Those skilled in the art will appreciate that the modules in the devices in an embodiment may be adaptively changed and arranged in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components.
Moreover, those of skill in the art will appreciate that while some embodiments herein include some features included in other embodiments, not others, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments.
The various component embodiments of the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that a microprocessor or Digital Signal Processor (DSP) may be used in practice to implement some or all of the functionality of some or all of the components according to embodiments of the present invention. Embodiments of the invention may also be implemented as apparatus or device programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing embodiments of the present invention may be stored on computer-readable media or may be in the form of one or more signals. Such a signal may be downloaded from an internet website, or provided on a carrier signal, or provided in any other form.
It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that the word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. Embodiments of the invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names. The steps in the above embodiments should not be construed as limiting the order of execution unless specified otherwise.

Claims (10)

1. A method for analyzing and predicting a device, the method comprising:
acquiring equipment to be processed;
generating a digital twin according to the equipment to be processed;
simulating the operation of the equipment to be processed through the digital twin body, and generating and storing telemetering data;
and calling a machine model method to analyze and predict the telemetering data to obtain a processing result.
2. The method of analyzing and predicting a device according to claim 1, further comprising, after generating the digital twin:
defining the digital twins and metadata of the digital twins.
3. The method of analyzing and predicting a device according to claim 2, wherein defining the digital twin includes:
and defining the association relationship among the digital twin bodies through a parent-child structure based on the equipment to be processed.
4. The method of analyzing and predicting a device according to claim 2, wherein defining metadata of the digital twin includes:
acquiring current data of the equipment to be processed;
defining metadata of the digital twin according to current data of the device to be processed.
5. The device analysis and prediction method of claim 4, wherein the current data comprises at least one of:
string, value, time series.
6. The method of analyzing and predicting of an apparatus according to claim 2, further comprising, after defining the digital twins and the metadata of the digital twins:
generating an interface of the digital twin.
7. The method of analyzing and predicting device of claim 1, wherein storing telemetry data comprises:
storing the telemetry data in a time series database manner.
8. An apparatus for analyzing and predicting a device, the apparatus comprising:
the acquisition module is used for acquiring the equipment to be processed;
the processing module is used for generating a digital twin body according to the equipment to be processed; simulating the operation of the equipment to be processed through the digital twin organism to generate and store telemetering data;
and the generating module is used for calling a machine model method to analyze and predict the telemetering data to obtain a processing result.
9. A computing device, comprising: the system comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface complete mutual communication through the communication bus;
the memory is configured to store at least one executable instruction that when executed causes the processor to perform a method of analysis and prediction for an apparatus as claimed in any of claims 1 to 7.
10. A computer storage medium having stored therein at least one executable instruction that when executed causes a computing device to perform the method of analyzing and predicting according to any one of claims 1-7.
CN202210829254.9A 2022-07-15 2022-07-15 Equipment analysis and prediction method, device and equipment Pending CN115203939A (en)

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Application Number Priority Date Filing Date Title
CN202210829254.9A CN115203939A (en) 2022-07-15 2022-07-15 Equipment analysis and prediction method, device and equipment

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210829254.9A CN115203939A (en) 2022-07-15 2022-07-15 Equipment analysis and prediction method, device and equipment

Publications (1)

Publication Number Publication Date
CN115203939A true CN115203939A (en) 2022-10-18

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CN202210829254.9A Pending CN115203939A (en) 2022-07-15 2022-07-15 Equipment analysis and prediction method, device and equipment

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