CN117591283A - Cloud cutting equipment management method and system based on cross-platform data fusion - Google Patents
Cloud cutting equipment management method and system based on cross-platform data fusion Download PDFInfo
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Abstract
The invention discloses a cloud cutting equipment management method and a cloud cutting equipment management system based on cross-platform data fusion, and relates to the technical field of data processing, wherein the method comprises the following steps: preprocessing and classifying characteristics of the operation data streams of the multi-platform cloud cutting equipment to obtain the operation characteristic data streams of the multi-platform cloud cutting equipment, performing task matching on the edge computing end set of the cloud cutting equipment, performing interactive analysis on the operation characteristic data streams of the multi-platform cloud cutting equipment based on the matched edge data stream computing task information, determining a cross-platform data mapping path flow network to perform data mapping fusion, obtaining a cross-platform equipment fusion data stream set, performing edge processing on the cross-platform equipment fusion data stream set based on the edge computing end set of the cloud cutting equipment, and determining the control information set of the cloud cutting equipment to perform cloud cutting equipment operation management. The cloud cutting equipment data cross-platform fusion is achieved, the edge processing speed of equipment operation data is improved, and further the technical effects of cloud cutting equipment management efficiency and control accuracy are improved.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a cloud cutting equipment management method and system based on cross-platform data fusion.
Background
With the development of artificial intelligence, the number and types of cloud-cut devices are increased, and device management is also complicated. The cloud cutting equipment is cutting equipment based on cloud computing, is generally used in metal processing and manufacturing industries, can realize automatic cutting, has the characteristics of automation, intellectualization, high precision and the like, and further greatly improves the production efficiency and reduces the production cost. However, the existing cloud cutting device management is only aimed at a specific platform or a single data source, and cross-platform data fusion cannot be achieved, so that the cloud cutting device management efficiency and precision are limited.
Disclosure of Invention
According to the cloud cutting equipment management method and system based on cross-platform data fusion, the technical problem that the cross-platform data fusion cannot be achieved in the prior art, so that the cloud cutting equipment management efficiency and precision are limited is solved, the cross-platform fusion of the cloud cutting equipment data is achieved, the edge processing speed of equipment operation data is improved, and the technical effects of cloud cutting equipment management efficiency and control precision are improved.
In view of the above problems, the invention provides a cloud cutting equipment management method and system based on cross-platform data fusion.
In a first aspect, the present application provides a cloud cut device management method based on cross-platform data fusion, where the method includes: acquiring and obtaining a multi-platform cloud cutting equipment operation data stream; preprocessing and feature classification are carried out on the operation data stream of the multi-platform cloud cutting equipment, and the operation feature data stream of the multi-platform cloud cutting equipment is obtained; connecting and obtaining a cloud cutting equipment edge computing end set, and performing computing task matching on the cloud cutting equipment edge computing end set to obtain edge data stream computing task information; performing data interaction analysis on the operation characteristic data stream of the multi-platform cloud cutting equipment based on the edge end data stream calculation task information, and determining a cross-platform data mapping path flow direction network; performing data mapping fusion on the operation characteristic data streams of the multi-platform cloud cutting equipment according to the cross-platform data mapping path flow network to obtain a cross-platform equipment fusion data stream set; and respectively carrying out edge processing on the cross-platform equipment fusion data stream set based on the cloud cutting equipment edge computing end set, determining a cloud cutting equipment control information set, and carrying out cloud cutting equipment operation management through the cloud cutting equipment control information set.
On the other hand, the application also provides a cloud cut equipment management system based on cross-platform data fusion, which comprises: the shared cloud platform building module is used for acquiring and acquiring operation data streams of the multi-platform cloud cutting equipment; the data characteristic classification module is used for preprocessing and characteristic classifying the operation data stream of the multi-platform cloud cutting equipment to obtain the operation characteristic data stream of the multi-platform cloud cutting equipment; the computing task matching module is used for connecting and acquiring a cloud cutting equipment edge computing end set, and performing computing task matching on the cloud cutting equipment edge computing end set to obtain edge data stream computing task information; the data interaction analysis module is used for carrying out data interaction analysis on the operation characteristic data stream of the multi-platform cloud cutting equipment based on the edge end data stream calculation task information and determining a cross-platform data mapping path flow network; the data mapping fusion module is used for carrying out data mapping fusion on the operation characteristic data streams of the multi-platform cloud cutting equipment according to the cross-platform data mapping path flow network to obtain a cross-platform equipment fusion data stream set; and the cloud cutting equipment operation management module is used for respectively carrying out edge processing on the cross-platform equipment fusion data stream set based on the cloud cutting equipment edge computing end set, determining a cloud cutting equipment control information set and carrying out cloud cutting equipment operation management through the cloud cutting equipment control information set.
In a third aspect, the present application provides an electronic device comprising a bus, a transceiver, a memory, a processor and a computer program stored on the memory and executable on the processor, the transceiver, the memory and the processor being connected by the bus, the computer program implementing the steps of any of the methods described above when executed by the processor.
In a fourth aspect, the present application also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of any of the methods described above.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
the method comprises the steps of preprocessing and classifying the collected operation data streams of the multi-platform cloud cutting equipment to obtain operation characteristic data streams of the multi-platform cloud cutting equipment, matching calculation tasks of an edge calculation end set of the cloud cutting equipment, carrying out data interaction analysis based on the matched calculation task information of the edge data streams, and determining a cross-platform data mapping path flow network to carry out data mapping fusion on the operation characteristic data streams of the multi-platform cloud cutting equipment to obtain a cross-platform equipment fusion data stream set; and respectively carrying out edge processing on the cross-platform equipment fusion data stream set based on the cloud cutting equipment edge computing end set, determining a cloud cutting equipment control information set, and carrying out cloud cutting equipment operation management through the cloud cutting equipment control information set. And the cross-platform fusion of cloud cutting equipment data is realized, the edge processing speed of equipment operation data is improved, and the technical effects of improving the management efficiency and the control precision of the cloud cutting equipment are further achieved.
The foregoing description is only an overview of the technical solutions of the present application, and may be implemented according to the content of the specification in order to make the technical means of the present application more clearly understood, and in order to make the above-mentioned and other objects, features and advantages of the present application more clearly understood, the following detailed description of the present application will be given.
Drawings
Fig. 1 is a flow chart of a cloud cutting device management method based on cross-platform data fusion;
fig. 2 is a schematic flow chart of acquiring a multi-platform cloud cutting device operation characteristic data flow in the cloud cutting device management method based on cross-platform data fusion;
fig. 3 is a schematic structural diagram of a cloud cutting device management system based on cross-platform data fusion;
fig. 4 is a schematic structural diagram of an exemplary electronic device of the present application.
Reference numerals illustrate: the system comprises a shared cloud platform building module 11, a data feature classification module 12, a computing task matching module 13, a data interaction analysis module 14, a data mapping fusion module 15, a cloud device operation management module 16, a bus 1110, a processor 1120, a transceiver 1130, a bus interface 1140, a memory 1150, an operating system 1151, application programs 1152 and a user interface 1160.
Detailed Description
In the description of the present application, those skilled in the art will appreciate that the present application may be embodied as methods, apparatuses, electronic devices, and computer-readable storage media. Accordingly, the present application may be embodied in the following forms: complete hardware, complete software (including firmware, resident software, micro-code, etc.), a combination of hardware and software. Furthermore, in some embodiments, the present application may also be embodied in the form of a computer program product in one or more computer-readable storage media, which contain computer program code.
Any combination of one or more computer-readable storage media may be employed by the computer-readable storage media described above. The computer-readable storage medium includes: an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of the computer readable storage medium include the following: portable computer magnetic disks, hard disks, random access memories, read-only memories, erasable programmable read-only memories, flash memories, optical fibers, optical disk read-only memories, optical storage devices, magnetic storage devices, or any combination thereof. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, device.
The technical scheme of the application is that the acquisition, storage, use, processing and the like of the data meet the relevant regulations of national laws.
The present application describes methods, apparatus, and electronic devices provided by the flowchart and/or block diagram.
It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions. These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.
These computer readable program instructions may also be stored in a computer readable storage medium that can cause a computer or other programmable data processing apparatus to function in a particular manner. Thus, instructions stored in a computer-readable storage medium produce an instruction means which implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.
The present application is described below with reference to the drawings in the present application.
Example 1
As shown in fig. 1, the present application provides a cloud cut device management method based on cross-platform data fusion, where the method includes:
step S1: acquiring and obtaining a multi-platform cloud cutting equipment operation data stream;
specifically, in order to realize intelligent and automatic management of the cloud cutting equipment, the operation data streams of the cloud cutting equipment with multiple platforms are acquired in real time through the internet of things equipment such as sensors and actuators deployed on the multiple platforms, and the informationized systems such as a platform data recording system and a manufacturing execution system, wherein the operation data streams of the cloud cutting equipment with multiple platforms are various data sources related to the operation of the cloud cutting equipment, and the operation data streams comprise equipment operation temperature, cutting pressure, operation current, cutting energy consumption, cutting patterns and the like. The cloud cutting equipment operation data acquisition multi-platform source is realized, the equipment operation data acquisition comprehensiveness is improved, and a data base is provided for subsequent equipment operation management analysis.
Step S2: preprocessing and feature classification are carried out on the operation data stream of the multi-platform cloud cutting equipment, and the operation feature data stream of the multi-platform cloud cutting equipment is obtained;
as shown in fig. 2, further, the step of obtaining the operation feature data stream of the multi-platform cloud cutting device further includes:
constructing a multi-platform data preprocessing strategy, wherein the multi-platform data preprocessing strategy comprises normalization processing, data cleaning and format standardization;
preprocessing the operation data stream of the multi-platform cloud cutting equipment based on the multi-platform data preprocessing strategy to obtain a standard multi-platform cloud cutting equipment operation data stream;
acquiring a data characteristic attribute classifier, wherein the data characteristic attribute classifier comprises a data type, a generation time and a storage format;
and classifying and identifying the standard multi-platform cloud cutting equipment operation data stream according to the data characteristic attribute classifier to obtain the multi-platform cloud cutting equipment operation characteristic data stream.
Specifically, in order to improve the data processing efficiency of equipment operation, a multi-platform data preprocessing strategy is firstly constructed, wherein the multi-platform data preprocessing strategy is a data preprocessing step and comprises normalization processing, so that the unification of data dimension units is ensured; data cleaning, namely checking data consistency, and cleaning error values, missing values, invalid values and the like in the data; format normalization, i.e. unifying the data display formats, e.g. determining the data standard format as decimal, ensures that the data storage format is standardized. And carrying out preprocessing steps on the operation data stream of the multi-platform cloud cutting equipment in sequence based on the multi-platform data preprocessing strategy to obtain a preprocessed standard multi-platform cloud cutting equipment operation data stream, so as to ensure standardized data processing.
The method comprises the steps of reproducing and obtaining a data characteristic attribute classifier, wherein the data characteristic attribute classifier is used for carrying out multi-characteristic marking on equipment operation data attributes and comprises data types such as cutting pressure types, cutting energy consumption types and the like; the generation time, i.e., the data generation time period; storage formats such as JSON format, text format, etc. And sequentially classifying and identifying the standard multi-platform cloud cutting equipment operation data streams according to the data characteristic attribute classifier, and integrating the data streams with the same attribute characteristics into one type to obtain integrated multi-platform cloud cutting equipment operation characteristic data streams. The data standardized preprocessing and the data attribute characteristic identification are realized, the subsequent analysis quality of the data is ensured, and the data processing efficiency is further improved.
Step S3: connecting and obtaining a cloud cutting equipment edge computing end set, and performing computing task matching on the cloud cutting equipment edge computing end set to obtain edge data stream computing task information;
further, the step of obtaining the task information of the edge data stream calculation further includes:
based on the historical calculation task information of the cloud cutting equipment edge calculation end set, constructing a cloud cutting equipment control task feature library;
Performing association division on the cloud computing end set of the cloud computing equipment according to the platform perception positions to determine an edge computing end association platform set;
carrying out data processing requirement analysis on the edge computing end association platform set to acquire processing requirement characteristic information of an edge computing end platform;
and performing task feature matching on the processing demand feature information of the edge end platform and the cloud cutting equipment control task feature library to obtain the edge end data stream calculation task information.
Specifically, the cloud cutting equipment edge computing end set is obtained through connection with the cloud scheduling center of the cloud cutting equipment, and is edge side terminal processing equipment with computing resources in a cloud cutting system, cloud processing is not needed, equipment computing service can be provided nearby, and the cloud cutting equipment edge computing end set has the advantages of being low in time delay and high in response speed. And performing calculation task matching on the cloud cutting equipment edge calculation end set, and firstly, constructing a cloud cutting equipment control task feature library based on historical calculation task information of the cloud cutting equipment edge calculation end set, wherein the cloud cutting equipment control task feature library is task feature data about cloud cutting equipment control in an edge calculation end historical calculation task, such as task features of equipment fault early warning, operation performance analysis, energy consumption assessment and the like. And performing association division on the cloud cutting equipment edge computing end sets according to the platform perception positions, namely performing calculation task distribution according to the perception distance between each cloud cutting equipment data source platform and the edge computing end, so as to determine an edge computing end association platform set, wherein the edge computing end association platform set is the cloud cutting equipment data source platform set which needs to be calculated and processed by each edge computing end.
And further, carrying out data processing requirement analysis on the edge computing end association platform set, namely carrying out cloud cutting equipment data processing requirement determination on multiple association platforms to be processed by each edge computing end, wherein the cloud cutting energy platform is used for processing requirement characteristics such as equipment energy consumption metering, energy consumption utilization and the like, so as to acquire corresponding edge end platform processing requirement characteristic information of each platform. And performing task feature matching on the processing demand feature information of the edge end platform and the cloud cutting equipment control task feature library to obtain edge end data stream calculation task information, wherein the edge end data stream calculation task information is cloud cutting equipment calculation task information to be processed by each edge calculation end. The rapid task feature matching of the edge computing end is realized, the task response speed of cloud cutting equipment computing is improved, and further the data edge computing and shunting accuracy and the computing processing efficiency are improved.
Step S4: performing data interaction analysis on the operation characteristic data stream of the multi-platform cloud cutting equipment based on the edge end data stream calculation task information, and determining a cross-platform data mapping path flow direction network;
further, the determining a cross-platform data mapping path flows to the network, and the steps of the application further include:
Respectively carrying out associated interactive mapping on the operation characteristic data streams of the multi-platform cloud cutting equipment based on the calculation task information of the edge data streams to obtain an operation characteristic data stream set of the edge equipment;
sequencing the edge end equipment operation characteristic data stream set according to the cloud cutting equipment platform processing node, and determining the upstream and downstream sequences of the cloud cutting equipment operation data streams;
and carrying out data interaction flow direction marking according to the upstream and downstream sequences of the cloud cutting equipment operation data flow, and determining the cross-platform data mapping path flow direction network.
Specifically, based on the edge end data stream calculation task information, data interaction analysis is performed on the operation characteristic data stream of the multi-platform cloud cutting equipment, namely, the data interaction flow direction of each platform cloud cutting equipment is analyzed. And firstly, respectively carrying out associated interactive mapping on the operation characteristic data streams of the multi-platform cloud cutting equipment based on the edge data stream calculation task information, namely mapping the associated cloud cutting equipment data streams of each edge calculation end, and obtaining an edge equipment operation characteristic data stream set matched with the edge data stream calculation task mapping. And sequencing the edge equipment operation characteristic data stream set according to cloud cutting equipment platform processing nodes, wherein the cloud cutting equipment processing nodes are monitoring processing sequence nodes of data sources of all the platform equipment, and determining edge equipment operation data streams according to the platform node sequence so as to determine the upstream and downstream sequence of the cloud cutting equipment operation data streams, for example, cloud cutting equipment order platform nodes are upstream of cloud cutting equipment cutting platform nodes, and therefore data streams such as cloud cutting patterns related to the cloud cutting equipment order platform nodes are upstream of data streams such as cutting speed, cutting pressure and the like related to the cloud cutting equipment cutting platform nodes.
And carrying out data interaction flow direction marking according to the upstream and downstream sequences of the running data flow of the cloud cutting equipment, wherein the upstream data flow is required to flow to the downstream data flow so as to carry out data convergence and fusion. And sequentially marking the data flow direction of the operation characteristic data flow set of the edge terminal equipment through the upstream and downstream sequences of the data flow, so as to further determine a cross-platform data mapping path flow direction network, wherein the cross-platform data mapping path flow direction network is a converging flow direction relation network of the multi-platform data flow to be calculated by each edge computing terminal. The method realizes comprehensive and clear representation of the mapping flow direction relation of the multi-platform data flow, and further improves the accuracy and the efficiency of cross-platform data fusion.
Step S5: performing data mapping fusion on the operation characteristic data streams of the multi-platform cloud cutting equipment according to the cross-platform data mapping path flow network to obtain a cross-platform equipment fusion data stream set;
further, the step of obtaining the cross-platform device fusion data stream set further includes:
determining a cross-platform data mapping path according to the cross-platform data mapping path flow network;
acquiring a cross-platform data fusion rule, wherein the cross-platform data fusion rule comprises format conversion and data alignment;
Performing format conversion on the multi-platform cloud cutting equipment operation characteristic data stream based on the cross-platform data mapping path to obtain an equipment operation characteristic format mapping data stream;
and carrying out data alignment on the equipment operation characteristic format mapping data stream, and fusing to obtain the cross-platform equipment fusion data stream set.
Specifically, the data mapping fusion is performed on the operation characteristic data streams of the multi-platform cloud cutting equipment according to the cross-platform data mapping path flow direction network, and firstly, a cross-platform data mapping path is determined according to the cross-platform data mapping path flow direction network, wherein the cross-platform data mapping path is a mapping flow direction path of the platform data streams. And then formulating a cross-platform data fusion rule, wherein the cross-platform data fusion rule is a cross-platform data fusion processing step comprising format conversion and data alignment. And carrying out format conversion on the operation characteristic data stream of the multi-platform cloud cutting device based on the cross-platform data mapping path, converting the format of an upstream data stream into the format of a downstream data stream of the mapping path, thereby obtaining the operation characteristic format mapping data stream of the device after format conversion, unifying the standard data format, and facilitating data fusion. And performing data alignment on the equipment operation characteristic format mapping data stream, wherein the data alignment refers to alignment and matching of data of different platforms, and alignment fusion is performed on the data streams of the same time period and characteristic types to obtain a cross-platform equipment fusion data stream set after multi-platform data fusion. The cross-platform fusion of cloud cutting equipment data is realized, and the accuracy and consistency of data fusion are ensured so as to facilitate the subsequent data analysis and utilization.
Step S6: and respectively carrying out edge processing on the cross-platform equipment fusion data stream set based on the cloud cutting equipment edge computing end set, determining a cloud cutting equipment control information set, and carrying out cloud cutting equipment operation management through the cloud cutting equipment control information set.
Further, the determining the cloud cutting device control information set further includes:
performing analysis model convergence training based on the cloud cutting equipment control task feature library, and constructing a cloud cutting equipment control feature analysis model library;
performing processing model configuration on the cloud cutting equipment edge computing end set according to the cloud cutting equipment control characteristic analysis model library, and determining an edge computing end control analysis model set;
and respectively carrying out control analysis on the cross-platform equipment fusion data stream set through the edge computing end control analysis model set to obtain the cloud cutting equipment control information set.
Further, the steps of the present application further include:
acquiring equipment operation control feedback parameter information through a cloud cutting equipment monitoring module;
carrying out data loss analysis on the equipment operation control feedback parameter information by using a mean square error loss function to obtain cloud cutting equipment operation control loss data;
And optimizing and updating the edge computing end control analysis model set based on the cloud cutting equipment operation control loss data to obtain an edge computing end control optimization analysis model set.
Specifically, edge processing is respectively performed on the cross-platform device fusion data stream sets based on the cloud cut device edge computing end sets. The cloud cutting equipment control task feature library is firstly used for carrying out analysis model convergence training, a historical cloud cutting equipment control feature data set can be obtained through a data mining technology, then the historical cloud cutting equipment control feature data set is subjected to neural network model, and model training is sequentially carried out according to each control task feature in the cloud cutting equipment control task feature library until the model accuracy reaches a convergence state. The cloud cutting equipment energy consumption data extraction is carried out on the historical cloud cutting equipment control characteristic data set, and then training is carried out on the cloud cutting equipment energy consumption data through the neural network model, so that a cloud cutting equipment energy consumption utilization control analysis model is obtained. And constructing a cloud cutting equipment control characteristic analysis model library by constructing control analysis models of all cloud cutting equipment correspondingly trained by the cloud cutting equipment control task characteristic library.
And performing processing model configuration on the cloud cutting equipment edge computing end set according to the cloud cutting equipment control feature analysis model library, namely performing feature matching on edge end data stream computing task information of the cloud cutting equipment edge computing end set and the cloud cutting equipment control feature analysis model library, performing control feature analysis model configuration according to task features, and determining a corresponding edge computing end control analysis model set. And respectively performing control analysis on the cross-platform equipment fusion data stream set through the edge computing end control analysis model set, namely performing intelligent analysis on the cross-platform equipment fusion data stream by utilizing a cloud cutting equipment management learning model, and outputting to obtain a cloud cutting equipment control information set, wherein the cloud cutting equipment control information set is cloud cutting equipment control analysis information matched with edge computing end data stream computing task information, such as cutting process parameter control information, cutting energy consumption control information, equipment fault operation and maintenance control information and the like. And cloud cutting equipment operation management is carried out through the cloud cutting equipment control information set, so that cloud cutting equipment automatic management is realized, the equipment operation data edge processing speed is improved, and further, the cloud cutting equipment management efficiency and control precision are improved.
In order to ensure the control accuracy of the cloud cutting equipment, the cloud cutting equipment monitoring module is used for carrying out feedback monitoring on the equipment operation control process, wherein the cloud cutting equipment monitoring module is in communication connection with the monitoring equipment and the monitoring sensor and is used for carrying out information monitoring on the equipment operation control process, equipment operation control feedback parameter information is acquired and acquired, and the equipment operation control feedback parameter information is actual equipment operation condition parameters after equipment operation management is carried out through a cloud cutting equipment control information set, and comprises a cutting state, cutting energy consumption and the like. If the error between the model control analysis output result and the actual equipment running condition parameter is out of the preset range, the output accuracy of the edge computing end control analysis model is insufficient. And therefore, carrying out data loss analysis on the equipment operation control feedback parameter information by using a mean square error loss function, wherein the mean square error loss function is used for calculating the mean value of the squares of the difference value between the predicted value and the true value, and taking the loss calculation result as cloud cutting equipment operation control loss data.
And optimizing and updating the edge computing end control analysis model set based on the cloud cutting equipment operation control loss data, calculating a model gradient through the loss data, updating model parameters by using a gradient descent algorithm to minimize a loss function, and obtaining an optimized edge computing end control optimization analysis model set through continuously iterating and updating the model parameters, so as to ensure the application performance and generalization capability of the model. By means of feedback adjustment of the model, accuracy and adaptability of model output are improved, timely and accurate adjustment and control of control technological parameters of the cloud cutting equipment are achieved, and production efficiency and production performance of the cloud cutting equipment are improved.
In summary, the cloud cutting equipment management method and system based on cross-platform data fusion provided by the application have the following technical effects:
the method comprises the steps of preprocessing and classifying the collected operation data streams of the multi-platform cloud cutting equipment to obtain operation characteristic data streams of the multi-platform cloud cutting equipment, matching calculation tasks of an edge calculation end set of the cloud cutting equipment, carrying out data interaction analysis based on the matched calculation task information of the edge data streams, and determining a cross-platform data mapping path flow network to carry out data mapping fusion on the operation characteristic data streams of the multi-platform cloud cutting equipment to obtain a cross-platform equipment fusion data stream set; and respectively carrying out edge processing on the cross-platform equipment fusion data stream set based on the cloud cutting equipment edge computing end set, determining a cloud cutting equipment control information set, and carrying out cloud cutting equipment operation management through the cloud cutting equipment control information set. And the cross-platform fusion of cloud cutting equipment data is realized, the edge processing speed of equipment operation data is improved, and the technical effects of improving the management efficiency and the control precision of the cloud cutting equipment are further achieved.
Example two
Based on the same inventive concept as the cloud cutting device management method based on cross-platform data fusion in the foregoing embodiment, the present invention further provides a cloud cutting device management system based on cross-platform data fusion, as shown in fig. 3, where the system includes:
The shared cloud platform building module 11 is used for acquiring and acquiring operation data streams of the multi-platform cloud cutting equipment;
the data feature classification module 12 is configured to perform preprocessing and feature classification on the operation data stream of the multi-platform cloud cutting device, and obtain an operation feature data stream of the multi-platform cloud cutting device;
the computing task matching module 13 is used for connecting and acquiring a cloud cutting equipment edge computing end set, and performing computing task matching on the cloud cutting equipment edge computing end set to obtain edge data stream computing task information;
the data interaction analysis module 14 is configured to perform data interaction analysis on the operation feature data stream of the multi-platform cloud cutting device based on the edge data stream calculation task information, and determine a cross-platform data mapping path flow direction network;
the data mapping fusion module 15 is configured to perform data mapping fusion on the operation feature data stream of the multi-platform cloud cutting device according to the cross-platform data mapping path flow network, so as to obtain a cross-platform device fusion data stream set;
and the cloud cutting equipment operation management module 16 is used for respectively carrying out edge processing on the cross-platform equipment fusion data stream set based on the cloud cutting equipment edge computing end set, determining a cloud cutting equipment control information set and carrying out cloud cutting equipment operation management through the cloud cutting equipment control information set.
Further, the system further comprises:
the preprocessing strategy construction unit is used for constructing a multi-platform data preprocessing strategy, wherein the multi-platform data preprocessing strategy comprises normalization processing, data cleaning and format standardization;
the data stream preprocessing unit is used for preprocessing the operation data stream of the multi-platform cloud cutting equipment based on the multi-platform data preprocessing strategy to acquire the operation data stream of the standard multi-platform cloud cutting equipment;
the attribute classifier acquisition unit is used for acquiring a data characteristic attribute classifier, wherein the data characteristic attribute classifier comprises a data type, generation time and a storage format;
and the data flow classification identification unit is used for classifying and identifying the standard multi-platform cloud cutting equipment operation data flow according to the data characteristic attribute classifier to obtain the multi-platform cloud cutting equipment operation characteristic data flow.
Further, the system further comprises:
the task feature library construction unit is used for constructing a cloud cutting equipment control task feature library based on historical calculation task information of the cloud cutting equipment edge calculation end set;
the association dividing unit is used for carrying out association division on the cloud cutting equipment edge computing end set according to the platform perception positions and determining an edge computing end association platform set;
The processing demand analysis unit is used for carrying out data processing demand analysis on the edge computing end association platform set and obtaining processing demand characteristic information of the edge computing end platform;
and the task feature matching unit is used for matching the task feature of the processing requirement feature information of the edge end platform with the cloud cutting equipment control task feature library to obtain the edge end data stream calculation task information.
Further, the system further comprises:
the associated interaction mapping unit is used for respectively carrying out associated interaction mapping on the operation characteristic data streams of the multi-platform cloud cutting equipment based on the calculation task information of the edge end data streams to obtain an operation characteristic data stream set of the edge end equipment;
the data stream ordering unit is used for ordering the edge end equipment operation characteristic data stream set according to the cloud cutting equipment platform processing node and determining the upstream and downstream sequence of the cloud cutting equipment operation data stream;
and the interactive flow direction marking unit is used for marking the data interactive flow direction according to the upstream and downstream sequences of the cloud cutting equipment operation data flow and determining the cross-platform data mapping path flow direction network.
Further, the system further comprises:
the mapping path determining unit is used for determining a cross-platform data mapping path according to the cross-platform data mapping path flow network;
The fusion rule acquisition unit is used for acquiring a cross-platform data fusion rule, wherein the cross-platform data fusion rule comprises format conversion and data alignment;
the format conversion unit is used for carrying out format conversion on the multi-platform cloud cutting equipment operation characteristic data stream based on the cross-platform data mapping path to obtain an equipment operation characteristic format mapping data stream;
and the data alignment unit is used for carrying out data alignment on the equipment operation characteristic format mapping data streams, and fusing to obtain the cross-platform equipment fusion data stream set.
Further, the system further comprises:
the model convergence training unit is used for carrying out analysis model convergence training based on the cloud cutting equipment control task feature library and constructing a cloud cutting equipment control feature analysis model library;
the processing model configuration unit is used for carrying out processing model configuration on the cloud cutting equipment edge computing end set according to the cloud cutting equipment control characteristic analysis model library, and determining an edge computing end control analysis model set;
and the control analysis unit is used for respectively carrying out control analysis on the cross-platform equipment fusion data stream set through the edge computing end control analysis model set to obtain the cloud cutting equipment control information set.
Further, the system further comprises:
the feedback parameter information acquisition unit is used for acquiring equipment operation control feedback parameter information through the cloud cutting equipment monitoring module;
the loss analysis unit is used for carrying out data loss analysis on the equipment operation control feedback parameter information by utilizing a mean square error loss function to obtain cloud cutting equipment operation control loss data;
and the model optimization updating unit is used for optimizing and updating the edge computing end control analysis model set based on the cloud cutting equipment operation control loss data to obtain the edge computing end control optimization analysis model set.
The foregoing various modifications and specific examples of the cloud computing device management method based on cross-platform data fusion in the first embodiment of fig. 1 are applicable to the cloud computing device management system based on cross-platform data fusion in this embodiment, and by the foregoing detailed description of the cloud computing device management method based on cross-platform data fusion, those skilled in the art can clearly know the implementation method of the cloud computing device management system based on cross-platform data fusion in this embodiment, so for brevity of description, it will not be described in detail herein.
In addition, the application further provides an electronic device, which comprises a bus, a transceiver, a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the transceiver, the memory and the processor are respectively connected through the bus, and when the computer program is executed by the processor, the processes of the method embodiment for controlling output data are realized, and the same technical effects can be achieved, so that repetition is avoided and redundant description is omitted.
Exemplary electronic device
In particular, referring to FIG. 4, the present application also provides an electronic device comprising a bus 1110, a processor 1120, a transceiver 1130, a bus interface 1140, a memory 1150, and a user interface 1160.
In this application, the electronic device further includes: computer programs stored on the memory 1150 and executable on the processor 1120, which when executed by the processor 1120, implement the various processes of the method embodiments described above for controlling output data.
A transceiver 1130 for receiving and transmitting data under the control of the processor 1120.
In this application, a bus architecture (represented by bus 1110), the bus 1110 may include any number of interconnected buses and bridges, with the bus 1110 connecting various circuits, including one or more processors, represented by the processor 1120, and memory, represented by the memory 1150.
Bus 1110 represents one or more of any of several types of bus structures, including a memory bus and memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures. By way of example, and not limitation, such an architecture includes: industry standard architecture buses, micro-channel architecture buses, expansion buses, video electronics standards association, and peripheral component interconnect buses.
Processor 1120 may be an integrated circuit chip with signal processing capabilities. In implementation, the steps of the above method embodiments may be implemented by instructions in the form of integrated logic circuits in hardware or software in a processor. The processor includes: general purpose processors, central processing units, network processors, digital signal processors, application specific integrated circuits, field programmable gate arrays, complex programmable logic devices, programmable logic arrays, micro control units or other programmable logic devices, discrete gates, transistor logic devices, discrete hardware components. The methods, steps and logic blocks disclosed in the present application may be implemented or performed. For example, the processor may be a single-core processor or a multi-core processor, and the processor may be integrated on a single chip or located on multiple different chips.
The processor 1120 may be a microprocessor or any conventional processor. The method steps disclosed in connection with the present application may be performed directly by a hardware decoding processor or by a combination of hardware and software modules in a decoding processor. The software modules may be located in random access memory, flash memory, read only memory, programmable read only memory, erasable programmable read only memory, registers, and the like, as known in the art. The readable storage medium is located in a memory, and the processor reads the information in the memory and, in combination with its hardware, performs the steps of the above method.
Bus 1110 may also connect together various other circuits such as peripheral devices, voltage regulators, or power management circuits, bus interface 1140 providing an interface between bus 1110 and transceiver 1130, all of which are well known in the art. Therefore, this application will not be further described.
The transceiver 1130 may be one element or a plurality of elements, such as a plurality of receivers and transmitters, providing a means for communicating with various other apparatus over a transmission medium. For example: the transceiver 1130 receives external data from other devices, and the transceiver 1130 is configured to transmit the data processed by the processor 1120 to the other devices. Depending on the nature of the computer device, a user interface 1160 may also be provided, for example: touch screen, physical keyboard, display, mouse, speaker, microphone, trackball, joystick, stylus.
It should be appreciated that in this application, the memory 1150 may further include memory located remotely from the processor 1120, which may be connected to a server through a network. One or more portions of the above-described networks may be an ad hoc network, an intranet, an extranet, a virtual private network, a local area network, a wireless local area network, a wide area network, a wireless wide area network, a metropolitan area network, an internet, a public switched telephone network, a plain old telephone service network, a cellular telephone network, a wireless fidelity network, and combinations of two or more of the foregoing. For example, the cellular telephone network and wireless network may be global system for mobile communications devices, code division multiple access devices, worldwide interoperability for microwave access devices, general packet radio service devices, wideband code division multiple access devices, long term evolution devices, LTE frequency division duplex devices, LTE time division duplex devices, advanced long term evolution devices, general mobile communications devices, enhanced mobile broadband devices, mass machine class communications devices, ultra-reliable low-latency communications devices, and the like.
It should be appreciated that the memory 1150 in this application may be either volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory. Wherein the nonvolatile memory includes: read-only memory, programmable read-only memory, erasable programmable read-only memory, electrically erasable programmable read-only memory, or flash memory.
The volatile memory includes: random access memory, which serves as an external cache. By way of example, and not limitation, many forms of RAM are available, such as: static random access memory, dynamic random access memory, synchronous dynamic random access memory, double data rate synchronous dynamic random access memory, enhanced synchronous dynamic random access memory, synchronous link dynamic random access memory, and direct memory bus random access memory. The memory 1150 of the electronic device described herein includes, but is not limited to, the memory described above and any other suitable type of memory.
In this application, memory 1150 stores the following elements of operating system 1151 and application programs 1152: an executable module, a data structure, or a subset thereof, or an extended set thereof.
Specifically, the operating system 1151 includes various device programs, such as: a framework layer, a core library layer, a driver layer, etc., for implementing various basic services and processing hardware-based tasks. The applications 1152 include various applications such as: and the media player and the browser are used for realizing various application services. A program for implementing the method of the present application may be included in the application 1152. The application 1152 includes: applets, objects, components, logic, data structures, and other computer apparatus-executable instructions that perform particular tasks or implement particular abstract data types.
In addition, the application further provides a computer readable storage medium, on which a computer program is stored, where the computer program when executed by a processor implements each process of the above-mentioned method embodiment for controlling output data, and the same technical effects can be achieved, and for avoiding repetition, a detailed description is omitted herein.
The foregoing is merely a specific embodiment of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope of the present application, and the changes and substitutions are intended to be covered in the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. The cloud cutting equipment management method based on cross-platform data fusion is characterized by comprising the following steps of:
acquiring and obtaining a multi-platform cloud cutting equipment operation data stream;
preprocessing and feature classification are carried out on the operation data stream of the multi-platform cloud cutting equipment, and the operation feature data stream of the multi-platform cloud cutting equipment is obtained;
connecting and obtaining a cloud cutting equipment edge computing end set, and performing computing task matching on the cloud cutting equipment edge computing end set to obtain edge data stream computing task information;
performing data interaction analysis on the operation characteristic data stream of the multi-platform cloud cutting equipment based on the edge end data stream calculation task information, and determining a cross-platform data mapping path flow direction network;
performing data mapping fusion on the operation characteristic data streams of the multi-platform cloud cutting equipment according to the cross-platform data mapping path flow network to obtain a cross-platform equipment fusion data stream set;
and respectively carrying out edge processing on the cross-platform equipment fusion data stream set based on the cloud cutting equipment edge computing end set, determining a cloud cutting equipment control information set, and carrying out cloud cutting equipment operation management through the cloud cutting equipment control information set.
2. The method of claim 1, wherein the obtaining a multi-platform cloud cutting device operational feature data stream comprises:
Constructing a multi-platform data preprocessing strategy, wherein the multi-platform data preprocessing strategy comprises normalization processing, data cleaning and format standardization;
preprocessing the operation data stream of the multi-platform cloud cutting equipment based on the multi-platform data preprocessing strategy to obtain a standard multi-platform cloud cutting equipment operation data stream;
acquiring a data characteristic attribute classifier, wherein the data characteristic attribute classifier comprises a data type, a generation time and a storage format;
and classifying and identifying the standard multi-platform cloud cutting equipment operation data stream according to the data characteristic attribute classifier to obtain the multi-platform cloud cutting equipment operation characteristic data stream.
3. The method of claim 1, wherein the obtaining edge data stream computation task information comprises:
based on the historical calculation task information of the cloud cutting equipment edge calculation end set, constructing a cloud cutting equipment control task feature library;
performing association division on the cloud computing end set of the cloud computing equipment according to the platform perception positions to determine an edge computing end association platform set;
carrying out data processing requirement analysis on the edge computing end association platform set to acquire processing requirement characteristic information of an edge computing end platform;
And performing task feature matching on the processing demand feature information of the edge end platform and the cloud cutting equipment control task feature library to obtain the edge end data stream calculation task information.
4. The method of claim 1, wherein the determining a cross-platform data mapping path flow to a network comprises:
respectively carrying out associated interactive mapping on the operation characteristic data streams of the multi-platform cloud cutting equipment based on the calculation task information of the edge data streams to obtain an operation characteristic data stream set of the edge equipment;
sequencing the edge end equipment operation characteristic data stream set according to the cloud cutting equipment platform processing node, and determining the upstream and downstream sequences of the cloud cutting equipment operation data streams;
and carrying out data interaction flow direction marking according to the upstream and downstream sequences of the cloud cutting equipment operation data flow, and determining the cross-platform data mapping path flow direction network.
5. The method of claim 1, wherein the obtaining a set of cross-platform device fusion data streams comprises:
determining a cross-platform data mapping path according to the cross-platform data mapping path flow network;
acquiring a cross-platform data fusion rule, wherein the cross-platform data fusion rule comprises format conversion and data alignment;
Performing format conversion on the multi-platform cloud cutting equipment operation characteristic data stream based on the cross-platform data mapping path to obtain an equipment operation characteristic format mapping data stream;
and carrying out data alignment on the equipment operation characteristic format mapping data stream, and fusing to obtain the cross-platform equipment fusion data stream set.
6. The method of claim 3, wherein the determining a set of cloud cut device control information comprises:
performing analysis model convergence training based on the cloud cutting equipment control task feature library, and constructing a cloud cutting equipment control feature analysis model library;
performing processing model configuration on the cloud cutting equipment edge computing end set according to the cloud cutting equipment control characteristic analysis model library, and determining an edge computing end control analysis model set;
and respectively carrying out control analysis on the cross-platform equipment fusion data stream set through the edge computing end control analysis model set to obtain the cloud cutting equipment control information set.
7. The method of claim 6, wherein the method comprises:
acquiring equipment operation control feedback parameter information through a cloud cutting equipment monitoring module;
carrying out data loss analysis on the equipment operation control feedback parameter information by using a mean square error loss function to obtain cloud cutting equipment operation control loss data;
And optimizing and updating the edge computing end control analysis model set based on the cloud cutting equipment operation control loss data to obtain an edge computing end control optimization analysis model set.
8. Cloud cutting equipment management system based on cross-platform data fusion, which is characterized by comprising:
the shared cloud platform building module is used for acquiring and acquiring operation data streams of the multi-platform cloud cutting equipment;
the data characteristic classification module is used for preprocessing and characteristic classifying the operation data stream of the multi-platform cloud cutting equipment to obtain the operation characteristic data stream of the multi-platform cloud cutting equipment;
the computing task matching module is used for connecting and acquiring a cloud cutting equipment edge computing end set, and performing computing task matching on the cloud cutting equipment edge computing end set to obtain edge data stream computing task information;
the data interaction analysis module is used for carrying out data interaction analysis on the operation characteristic data stream of the multi-platform cloud cutting equipment based on the edge end data stream calculation task information and determining a cross-platform data mapping path flow network;
the data mapping fusion module is used for carrying out data mapping fusion on the operation characteristic data streams of the multi-platform cloud cutting equipment according to the cross-platform data mapping path flow network to obtain a cross-platform equipment fusion data stream set;
And the cloud cutting equipment operation management module is used for respectively carrying out edge processing on the cross-platform equipment fusion data stream set based on the cloud cutting equipment edge computing end set, determining a cloud cutting equipment control information set and carrying out cloud cutting equipment operation management through the cloud cutting equipment control information set.
9. An electronic device comprising a bus, a transceiver, a memory, a processor and a computer program stored on the memory and executable on the processor, the transceiver, the memory and the processor being connected by the bus, characterized in that the computer program when executed by the processor implements the steps of the cross-platform data fusion based cloud cut device management method according to any of claims 1-7.
10. A computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements the steps in the cross-platform data fusion based cloud cut device management method according to any of claims 1-7.
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