CN112162849A - Cloud and edge computing cooperative equipment intelligent control system and method - Google Patents
Cloud and edge computing cooperative equipment intelligent control system and method Download PDFInfo
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Abstract
The invention provides an intelligent management and control system and method for equipment with cloud and edge computing cooperation, which comprises a data acquisition subsystem, a cooperation strategy subsystem, an edge computing subsystem and a cloud computing subsystem, wherein the data acquisition subsystem is used for acquiring data in the equipment manufacturing process; the cooperation strategy subsystem is used for setting and adjusting cooperation strategies of the cloud computing subsystem and the edge computing subsystem, and the cooperation strategies comprise: distributing the computing task to a cloud computing subsystem or an edge computing subsystem according to the management requirement or the technical requirement; the edge computing subsystem and the cloud computing subsystem perform data analysis and processing according to computing tasks distributed by the cooperative strategy subsystem, and send control instructions to the actuator subsystem, and the actuator subsystem controls equipment to operate according to the control instructions. The system and the method combine the application of cloud computing and edge computing technologies.
Description
Technical Field
The invention relates to the technical field of intelligent manufacturing, in particular to an intelligent management and control system and method for equipment with cooperation of cloud and edge computing.
Background
Cloud computing provides data storage, computing and application services to users in an internet manner. The data storage and computing power may be provided by a data center located at a remote location, or may be provided jointly by a plurality of nodes distributed over various locations in a distributed storage and distributed computing manner.
Unlike cloud computing, edge computing runs applications as close as possible to where data is generated. When an application requires fast data sampling and the results are calculated with as little delay as possible; or edge computing is a better choice when there is not enough network bandwidth or reliable network transport to send the data to the cloud.
In an intelligent manufacturing process, real-time performance is very important for management and control of equipment, so edge calculation is a key technology for intelligent equipment management and control. A typical application scenario is that after sensors and data transmission devices are installed on industrial equipment and a production line, edge calculation is used for processing and analyzing data in real time to form judgment or prediction of equipment working conditions, so that equipment parameter adjustment suggestions are given. Compared with the traditional expert system for intelligent control of equipment, the expert system usually solidifies the expert knowledge into rules, carries out simple judgment based on the rules and lacks self-learning capability. While edge computing systems analyze based on models that can learn themselves, constantly optimizing based on historical data and historical decisions.
For edge calculations, the more data the model learns, the better its performance will generally become. However, the storage and computing power of edge computing hardware is limited, and it is often not economical to expand the hardware capacity impractically for large data storage and computation.
Disclosure of Invention
The present invention is made to solve the above technical problems in the prior art, and an object of the present invention is to provide a cloud and edge computing cooperative equipment intelligent management and control system and method that combine cloud computing and edge computing technologies.
According to one aspect of the invention, the intelligent management and control system for the cloud and edge computing cooperative equipment comprises a data acquisition subsystem, a cooperative strategy subsystem, an edge computing subsystem and a cloud computing subsystem, wherein the data acquisition subsystem is used for acquiring data of the equipment manufacturing process; the cooperation strategy subsystem is used for setting and adjusting cooperation strategies of the cloud computing subsystem and the edge computing subsystem, and the cooperation strategies comprise: distributing the computing task to a cloud computing subsystem or an edge computing subsystem according to the management requirement or the technical requirement; the edge computing subsystem and the cloud computing subsystem perform data analysis and processing according to computing tasks distributed by the cooperative strategy subsystem, and send control instructions to the actuator subsystem, and the actuator subsystem controls equipment to operate according to the control instructions.
Preferably, the edge computing subsystem comprises an edge storage module, a data filtering module and a real-time analysis module, the edge storage module is used for storing output data of the data acquisition subsystem in the edge server, and the data filtering module is used for extracting, filtering and converting data of the edge storage module according to a cooperation strategy and sending the data to the cloud computing subsystem; and the real-time analysis module reads required storage data from the edge server, and performs data processing and analysis through a preset algorithm model.
Further, preferably, the edge computing subsystem further includes a control mode module, configured to store a plurality of control models for the equipment, where the control models are stored and used as a current model and a standby model, respectively, and the current model and the standby model are switched according to a program instruction.
Further, preferably, the cloud computing subsystem comprises a cloud storage module, a cloud analysis module and a model optimization module, wherein the cloud storage module is used for storing the filtered data from the edge computing subsystem; the cloud analysis module reads data in the cloud storage module, and calculation is carried out according to a preset data analysis model, so that data analysis and data mining functions are realized; the model optimization module performs iterative learning on an algorithm model of the real-time analysis module, a control model of the control mode module and a data analysis model of the cloud analysis module based on the output of the cloud analysis module, and the learned model parameters are respectively fed back to the real-time analysis module, the control mode module and the cloud analysis module for model upgrading.
Preferably, the collaborative policy further comprises: configuring the maximum storage time limit of different data in edge storage and cloud storage according to the setting or adjustment of task allocation; and configuring rules of data filtering.
Preferably, the data acquisition subsystem is installed in an embedded terminal or a server with data reading and communication functions and is directly connected with equipment on a network topology; the actuator subsystem is arranged in a PLC, a DCS or a server with communication and control functions and is directly connected with the equipment on the network topology; the edge computing subsystem is installed in the server, the edge computing subsystem, the physical equipment for installing the data acquisition subsystem and the physical equipment for installing the actuator subsystem are located in the same local area network, the cooperation strategy subsystem and the cloud computing subsystem are deployed in a remote private cloud or a public cloud, and the cloud computing subsystem and the edge computing subsystem are connected through an IP protocol.
Preferably, when the cloud computing subsystem is deployed in a rented public cloud, the connection between the cloud computing subsystem and the edge computing subsystem is in a VPN manner.
Further, preferably, the server is a physical server or/and a virtual server generated by a virtualization technology.
According to another aspect of the invention, an intelligent management and control method for equipment cooperated with cloud and edge computing is provided, which comprises the following steps:
collecting data of a device manufacturing process;
setting a cooperation strategy for carrying out cloud computing or edge computing analysis on the collected data, wherein the cooperation strategy comprises the following steps: distributing the computing task to a cloud computing subsystem or an edge computing subsystem according to the management requirement or the technical requirement;
analyzing the acquired data through cloud computing or edge computing according to a collaborative strategy;
and controlling the equipment to operate according to the result of the cloud computing or edge computing analysis.
Preferably, the step of analyzing the collected data by cloud computing or edge computing according to the collaborative policy includes:
storing output data of the data acquisition subsystem in an edge server;
and extracting, filtering and converting the data in the edge server according to a cooperative strategy, and calculating through a preset data analysis model in cloud computing to realize data analysis and data mining functions.
Cloud computing and edge computing are long, and the combined application of the cloud computing and the edge computing in the intelligent manufacturing process has a good prospect. The cloud and edge computing cooperative equipment intelligent management and control system and method provided by the invention combine and apply cloud computing and edge computing technologies in an intelligent manufacturing process, and carry out division and cooperation on tasks and data of cloud computing and edge computing. In addition, global unified management is carried out from the aspects of data flow, control flow and model optimization so as to realize the economy and the efficiency of intelligent management and control.
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FIG. 1 is a block diagram of an intelligent management and control system for equipment cooperating with cloud and edge computing according to the present invention;
fig. 2 is a flowchart of an intelligent equipment management and control method based on cloud and edge computing cooperation according to the present invention.
Detailed Description
The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. Moreover, the schematic diagrams provided in the embodiments are only for illustrating the basic idea of the invention in a schematic manner, so that only the modules related to the invention are shown in the drawings, rather than all the modules in the actual implementation, for the understanding and reading of those skilled in the art, and the invention is not limited to the limitations of the invention that can be implemented; in the actual implementation process, the number of modules and the transmission relationship between data and control information may be more complicated. The present invention should be considered within the scope of the present disclosure without affecting the efficacy and attainment of the same.
Fig. 1 is a block diagram of a cloud and edge computing cooperative equipment intelligent management and control system according to the present invention, and as shown in fig. 1, the equipment intelligent management and control system includes a data acquisition subsystem 1, a cooperative policy subsystem 2, an edge computing subsystem 3, a cloud computing subsystem 4, and an actuator subsystem 5, where:
the data acquisition subsystem 1 is used for acquiring data of equipment manufacturing process, preferably, the data acquired by the data acquisition subsystem comprises material data, energy data, equipment data and information labels, wherein the material data is data describing the properties of the material (such as quality, size, composition and the like); the energy data is energy (such as electric energy, steam and water) consumption data in the working process of the equipment; the equipment data refers to parameters (such as pressure, frequency, voltage, loudness and the like) describing the operation state of the equipment per se; the information label is a label which is given to materials for production management needs in the manufacturing process, such as material batches;
the cooperation strategy subsystem 2 is used for setting and adjusting cooperation strategies of the cloud computing subsystem and the edge computing subsystem, and the cooperation strategies include: distributing the computing task to a cloud computing subsystem or an edge computing subsystem according to the management requirement or the technical requirement;
the edge computing subsystem 3 and the cloud computing subsystem 4 perform data analysis and processing according to the computing tasks distributed by the cooperative strategy subsystem, and send control instructions to the actuator subsystem, and the actuator subsystem 5 controls equipment to operate according to the control instructions.
Preferably, the adjustment of the synergy policy is made on a time period, such as daily, every 12 hours, every 6 hours, or other period; meanwhile, the adjustment of the cooperative strategy is temporarily carried out according to the actual requirement of the production process.
Preferably, the collaborative policy further comprises: calculating the quantity and the updating frequency of data generated in the edge server and the cloud server according to the task allocation condition, and configuring or adjusting the longest storage time limit of the data in the edge storage and the cloud storage; configuring a rule of data filtering, optionally having high real-time requirement, and completing a task of calculation by depending on current data or short-term data to distribute the task to an edge calculation subsystem; and a computing task with low real-time requirement is distributed to the cloud computing subsystem.
In one embodiment, the edge computation subsystem 3 includes an edge storage module 31, a data filtering module 32, and a real-time analysis module 33, wherein:
the edge storage module is used for storing the output data of the data acquisition subsystem in the edge server, and has the function of automatically deleting the overdue data due to the capacity limit of the edge server, so that the cyclic utilization of the storage space is realized;
the data filtering module is used for extracting, filtering and converting the data of the edge storage module according to a cooperation strategy and sending the data to the cloud computing subsystem, specifically, extracting the data of the edge storage module according to the cooperation strategy, filtering the extracted data, removing error data, repeated data and incomplete data, sequentially mapping numerical values of the filtered data according to a function relation, performing precision conversion according to an output precision requirement, performing data coding according to a coding rule, and finally sending the data to the cloud computing subsystem;
the real-time analysis module is used for reading required data from the edge server and processing and analyzing the data through a preset algorithm model.
Preferably, the edge calculation subsystem further includes a control mode module 34 for saving a control model for the equipment. The control model specifies the algorithm or logic with which the actuator is equipped to drive the execution system in the face of the output information of the real-time analysis module. Further, preferably, the control model may hold N, i.e., 1 current model and (N-1) standby models (N is a positive integer). The current model and the standby model can be switched according to program instructions.
In addition, preferably, the actuator subsystem receives an instruction of the control mode module, selects a more optimal model in the current state from the standby control models, and replaces the more optimal model with the current model; the original current model is returned to the standby model. And the actuator subsystem receives the output of the real-time analysis module and adjusts the equipment operation parameters in the current mode.
In one embodiment, the cloud computing subsystem 4 includes a cloud storage module 41, a cloud analysis module 42, and a model optimization module 43, the cloud storage module is configured to store filtered data from the edge computing subsystem; the cloud analysis module reads data in the cloud storage module, and calculation is carried out according to a preset data analysis model, so that data analysis and data mining functions are realized; the model optimization module performs iterative learning on an algorithm model of the real-time analysis module, a control model of the control mode module and a data analysis model of the cloud analysis module based on the output of the cloud analysis module, and the learned model parameters are respectively fed back to the real-time analysis module, the control mode module and the cloud analysis module for model upgrading.
Compared with the edge computing subsystem, the learning sample of the cloud computing subsystem comes from a longer life cycle, so that the model after cloud learning is more accurate; for time series sample data, the cloud learning can take account of historical working conditions of the equipment and track changes of the current working conditions of the equipment, and the learned model is more suitable for the current working conditions of the equipment than a model adopted by a current edge computing subsystem.
In one embodiment, the data acquisition subsystem is installed in an embedded terminal or a server with data reading and communication functions and is directly connected with equipment on a network topology; the actuator subsystem is arranged in a PLC, a DCS or a server with communication and control functions and is directly connected with the equipment on the network topology; the edge computing subsystem is installed in the server, the edge computing subsystem, the physical equipment for installing the data acquisition subsystem and the physical equipment for installing the actuator subsystem are located in the same local area network, the cooperation strategy subsystem and the cloud computing subsystem are deployed in a remote private cloud or a rented public cloud, and the cloud computing subsystem is connected with the edge computing subsystem through an IP protocol.
Preferably, when the cloud computing subsystem is deployed in a rented public cloud, the cloud computing subsystem and the edge computing subsystem are connected in a VPN mode, the storage and computing capacity is built or rented at the cloud end, the unit cost is lower than that of edge computing, the resource elasticity capable of stretching out and drawing back as required is achieved, and the requirements for learning and optimizing the edge computing model are well met.
Preferably, the server is a physical server or/and a virtual server generated by a virtualization technique. Further, the server may be a cluster of multiple virtual servers or physical servers.
Fig. 2 is a flowchart of the cloud and edge computing cooperative equipment intelligent control method according to the present invention, and as shown in fig. 2, the cloud and edge computing cooperative equipment intelligent control method includes:
step S1, collecting data of the equipment manufacturing process;
step S2, setting a cooperation policy for performing cloud computing or edge computing analysis on the collected data, where the cooperation policy includes: distributing the computing task to a cloud computing subsystem or an edge computing subsystem according to the management requirement or the technical requirement;
step S3, analyzing the collected data through cloud computing or edge computing according to the cooperation strategy;
and step S4, controlling equipment to operate according to the result of cloud computing or edge computing analysis.
Preferably, the step of analyzing the collected data by cloud computing or edge computing according to the collaborative policy includes:
storing output data of the data acquisition subsystem in an edge server;
and extracting, filtering and converting the data in the edge server according to a cooperative strategy, and calculating through a preset data analysis model in cloud computing to realize data analysis and data mining functions.
Preferably, the method further comprises the following steps: and extracting the data in the edge server according to a cooperative strategy, and processing and analyzing the data through an algorithm model preset in the edge calculation.
Further, preferably, the step of analyzing the collected data through cloud computing or edge computing according to the collaborative strategy includes:
and setting a control model of the equipment, and controlling the operation of the equipment through the output of a preset data analysis model in the cloud computing.
In addition, preferably, iterative learning is performed on an algorithm model and a control model preset in the edge computing and a data analysis model preset in the cloud computing based on an output of the data analysis model preset in the cloud computing, and the model is upgraded.
The cloud and edge computing cooperative equipment intelligent control system and method provided by the invention are used for combining the cloud computing and edge computing technologies according to the intelligent control requirements of industrial equipment, and provide a method for dividing work and cooperating tasks and data of the cloud computing and the edge computing, so that global unified management is performed from the aspects of data flow, control flow and model optimization, and the economical efficiency and high efficiency of intelligent control are realized. Meanwhile, the intelligent equipment management and control system based on the cooperation of cloud computing and edge computing is designed, and the intelligent equipment management and control system has universality in process intelligent manufacturing and discrete intelligent manufacturing.
While the foregoing disclosure shows illustrative embodiments of the invention, it should be noted that various changes and modifications could be made herein without departing from the scope of the invention as defined by the appended claims. Furthermore, although elements of the invention may be described or claimed in the singular, the plural is contemplated unless limitation to a single element is explicitly stated.
Claims (10)
1. The intelligent management and control system for the equipment with the cooperation of cloud and edge computing is characterized by comprising a data acquisition subsystem, a cooperation strategy subsystem, an edge computing subsystem and a cloud computing subsystem, wherein the data acquisition subsystem is used for acquiring data in the manufacturing process of the equipment; the cooperation strategy subsystem is used for setting and adjusting cooperation strategies of the cloud computing subsystem and the edge computing subsystem, and the cooperation strategies comprise: distributing the computing task to a cloud computing subsystem or an edge computing subsystem according to the management requirement or the technical requirement; the edge computing subsystem and the cloud computing subsystem perform data analysis and processing according to computing tasks distributed by the cooperative strategy subsystem, and send control instructions to the actuator subsystem, and the actuator subsystem controls equipment to operate according to the control instructions.
2. The intelligent cloud and edge computing cooperative equipment management and control system according to claim 1, wherein the edge computing subsystem comprises an edge storage module, a data filtering module and a real-time analysis module, the edge storage module is used for storing output data of the data acquisition subsystem in an edge server, and the data filtering module is used for extracting, filtering and converting data of the edge storage module according to a cooperative strategy and sending the data to the cloud computing subsystem; and the real-time analysis module reads required storage data from the edge server, and performs data processing and analysis through a preset algorithm model.
3. The intelligent cloud and edge computing collaborative equipment management and control system according to claim 2, wherein the edge computing subsystem further includes a control mode module for storing a plurality of control models for equipment, the control models being stored as a current model and a standby model, and the current model and the standby model being switched according to a program instruction.
4. The cloud and edge computing collaborative equipment intelligent management and control system according to claim 3, wherein the cloud computing subsystem comprises a cloud storage module, a cloud analysis module and a model optimization module, and the cloud storage module is used for storing filtered data from the edge computing subsystem; the cloud analysis module reads data in the cloud storage module, and calculation is carried out according to a preset data analysis model, so that data analysis and data mining functions are realized; the model optimization module performs iterative learning on an algorithm model of the real-time analysis module, a control model of the control mode module and a data analysis model of the cloud analysis module based on the output of the cloud analysis module, and the learned model parameters are respectively fed back to the real-time analysis module, the control mode module and the cloud analysis module for model upgrading.
5. The cloud and edge computing collaborative equipment intelligent management and control system according to claim 2, wherein the collaborative policy further includes: configuring the maximum storage time limit of different data in edge storage and cloud storage according to the setting or adjustment of task allocation; and configuring rules of data filtering.
6. The intelligent management and control system for equipment with cloud and edge computing cooperation according to claim 1, wherein the data acquisition subsystem is installed in an embedded terminal or a server with data reading and communication functions and is directly connected with the equipment on a network topology; the actuator subsystem is arranged in a PLC, a DCS or a server with communication and control functions and is directly connected with the equipment on the network topology; the edge computing subsystem is installed in the server, the edge computing subsystem, the physical equipment for installing the data acquisition subsystem and the physical equipment for installing the actuator subsystem are located in the same local area network, the cooperation strategy subsystem and the cloud computing subsystem are deployed in a remote private cloud or a public cloud, and the cloud computing subsystem and the edge computing subsystem are connected through an IP protocol.
7. The cloud and edge computing cooperative equipment intelligent management and control system according to claim 6, wherein when a cloud computing subsystem is deployed in a rented public cloud, the connection of the cloud computing subsystem and the edge computing subsystem is in a VPN manner.
8. The cloud and edge computing collaborative equipment intelligent management and control system according to claim 6, wherein the server is a physical server or/and a virtual server generated through a virtualization technology.
9. The intelligent management and control method for equipment cooperated with cloud and edge computing is characterized by comprising the following steps:
collecting data of a device manufacturing process;
setting a cooperation strategy for carrying out cloud computing or edge computing analysis on the collected data, wherein the cooperation strategy comprises the following steps: distributing the computing task to a cloud computing subsystem or an edge computing subsystem according to the management requirement or the technical requirement;
analyzing the acquired data through cloud computing or edge computing according to a collaborative strategy;
and controlling the equipment to operate according to the result of the cloud computing or edge computing analysis.
10. The intelligent cloud and edge computing collaborative equipment management and control method according to claim 9, wherein the step of analyzing the collected data through cloud computing or edge computing according to the collaborative policy includes:
storing output data of the data acquisition subsystem in an edge server;
and extracting, filtering and converting the data in the edge server according to a cooperative strategy, and calculating through a preset data analysis model in cloud computing to realize data analysis and data mining functions.
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| CN113064731B (en) * | 2021-06-03 | 2021-11-02 | 明品云(北京)数据科技有限公司 | Cloud-edge-architecture-based big data processing terminal device, processing method and medium |
| CN113641491A (en) * | 2021-08-10 | 2021-11-12 | 微喂苍穹(上海)健康科技有限公司 | 5G air disinfection edge calculation method and system |
| CN113835884A (en) * | 2021-09-08 | 2021-12-24 | 深圳供电局有限公司 | Cloud edge cooperative management and control system and method |
| CN113572857A (en) * | 2021-09-23 | 2021-10-29 | 环球数科集团有限公司 | Resource management system applied to Internet of things edge cloud collaborative computing environment |
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| CN113810792A (en) * | 2021-11-19 | 2021-12-17 | 南京绛门信息科技股份有限公司 | Edge data acquisition and analysis system based on cloud computing |
| CN114343617A (en) * | 2021-12-10 | 2022-04-15 | 中国科学院深圳先进技术研究院 | Patient gait real-time prediction method based on edge cloud cooperation |
| CN114363330A (en) * | 2021-12-20 | 2022-04-15 | 安尼梅森(北京)数码科技有限公司 | Edge server management system and method |
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