CN114513542A - Production equipment control method and device, computer equipment and storage medium - Google Patents
Production equipment control method and device, computer equipment and storage medium Download PDFInfo
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Abstract
The application relates to a production equipment control method, a production equipment control device, a computer device, a storage medium and a computer program product. The method comprises the following steps: acquiring device data from at least two plant management systems; performing edge calculation on the equipment data to obtain edge calculation data; sending the edge computing data to a cloud server so as to perform data processing on the edge computing data through the cloud server to obtain cloud computing data; generating control instructions based on the cloud computing data; and sending the control instruction to target production equipment so as to control the target production equipment according to the control instruction. The method can improve the control efficiency of the production equipment.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a method and an apparatus for controlling a production device, a computer device, a storage medium, and a computer program product.
Background
With the development of computer technology, a plurality of systems for managing a factory exist in a digital factory, each system forms a data isolated island, data sharing and interconnection are difficult to perform, the value of data is low, a decision maker is difficult to obtain effective information from each system, and the efficiency of controlling production equipment is low. How to control production equipment according to large-scale data in a digital factory becomes a key problem of optimizing productivity and improving production efficiency.
Disclosure of Invention
In view of the above, it is necessary to provide a production equipment control method, apparatus, computer equipment, computer-readable storage medium, and computer program product capable of improving production efficiency.
In a first aspect, the present application provides a production facility control method. The method comprises the following steps:
acquiring device data from at least two plant management systems;
performing edge calculation on the equipment data to obtain edge calculation data;
sending the edge computing data to a cloud server so as to perform data processing on the edge computing data through the cloud server to obtain cloud computing data;
generating control instructions based on the cloud computing data;
and sending the control instruction to target production equipment so as to control the target production equipment according to the control instruction.
In a second aspect, the application also provides a production equipment control device. The device comprises:
an acquisition module for acquiring device data from at least two plant management systems;
the computing module is used for carrying out edge computing on the equipment data to obtain edge computing data;
the data processing module is used for sending the edge computing data to a cloud server so as to perform data processing on the edge computing data through the cloud server to obtain cloud computing data;
a generation module for generating a control instruction based on the cloud computing data;
and the sending module is used for sending the control instruction to target production equipment so as to control the target production equipment according to the control instruction.
In one embodiment, the edge calculation data is training data samples; the data processing module is further configured to:
sending the training data samples to a cloud server so that the cloud server trains a pre-trained machine learning model according to the training data samples to obtain a trained machine learning model, and taking the trained machine learning model as cloud computing data;
the generation module is further configured to:
and generating a control instruction based on the trained machine learning model.
In one embodiment, the generating module is further configured to:
receiving the cloud computing data through an application program interface of the cloud server;
and feeding back the cloud computing data to a client so as to enable the client to generate a control instruction based on the cloud computing data.
In one embodiment, the generating module is further configured to:
dividing the cloud computing data into first authority data and second authority data;
feeding back the first authority data to a client with group authority; and (c) a second step of,
and feeding back the second authority data to the client with the factory authority.
In one embodiment, the obtaining module is further configured to:
acquiring equipment data from at least two factory management systems through an intelligent gateway;
carrying out format conversion on the equipment data to obtain the converted equipment data;
the computing module is further configured to:
and performing edge calculation on the converted equipment data through the micro service cluster to obtain edge calculation data.
In one embodiment, the apparatus further comprises:
the modeling module is used for modeling the production factory based on the equipment data to obtain a digital twin model;
the loading module is used for loading the edge computing data and the cloud computing data into the digital twin model to obtain the loaded digital twin model;
the sending module is further configured to send the loaded digital twin model to a client, so that the client displays the loaded digital twin model.
In a third aspect, the present application also provides a computer device. The computer device comprises a memory storing a computer program and a processor implementing the following steps when executing the computer program:
acquiring device data from at least two plant management systems;
performing edge calculation on the equipment data to obtain edge calculation data;
sending the edge computing data to a cloud server so as to perform data processing on the edge computing data through the cloud server to obtain cloud computing data;
generating control instructions based on the cloud computing data;
and sending the control instruction to target production equipment so as to control the target production equipment according to the control instruction.
In a fourth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of:
acquiring device data from at least two plant management systems;
performing edge calculation on the equipment data to obtain edge calculation data;
sending the edge computing data to a cloud server so as to perform data processing on the edge computing data through the cloud server to obtain cloud computing data;
generating control instructions based on the cloud computing data;
and sending the control instruction to target production equipment so as to control the target production equipment according to the control instruction.
In a fifth aspect, the present application further provides a computer program product. The computer program product comprising a computer program which when executed by a processor performs the steps of:
acquiring device data from at least two plant management systems;
performing edge calculation on the equipment data to obtain edge calculation data;
sending the edge computing data to a cloud server so as to perform data processing on the edge computing data through the cloud server to obtain cloud computing data;
generating control instructions based on the cloud computing data;
and sending the control instruction to target production equipment so as to control the target production equipment according to the control instruction.
The production equipment control method, the production equipment control device, the computer equipment, the storage medium and the computer program product acquire the equipment data from at least two factory management systems and perform edge calculation on the equipment data to obtain edge calculation data, so that the equipment data in a plurality of factory management systems can be centralized and unified to perform edge calculation, and interconnection and intercommunication of data in each factory management system are realized. And then sending the edge computing data in each edge node to a cloud server, and performing data processing on the edge computing data in each edge node through the cloud server to obtain cloud computing data, so that the edge computing data can be further analyzed and calculated through the cloud server, and effective information is extracted from the edge computing data and used by a decision maker for controlling target production equipment. And finally, generating a control instruction based on the cloud computing data and sending the control instruction to the target production equipment so as to control the target production equipment according to the control instruction. Therefore, the production equipment can be controlled through the mass data from each factory management system, the control efficiency of the production equipment is improved, the production efficiency of a factory can be further improved, and the productivity is optimized.
Drawings
FIG. 1 is a diagram of an example of an application environment of a method for controlling a production facility;
FIG. 2 is a schematic flow chart of a method for controlling a production facility in one embodiment;
FIG. 3 is a flow diagram illustrating a method for generating control instructions based on a machine learning model, according to one embodiment;
FIG. 4 is a schematic flow diagram illustrating a method for generating control instructions based on cloud computing data in one embodiment;
FIG. 5 is a schematic diagram of an intelligent gateway in one embodiment;
FIG. 6 is an architecture diagram of a microservice cluster, under an embodiment;
FIG. 7 is a logical schematic of a production equipment control system in one embodiment;
FIG. 8 is a schematic diagram of a field management layer in one embodiment;
FIG. 9 is a schematic diagram of the Paas layer in one embodiment;
FIG. 10 is a schematic diagram of an application layer in one embodiment;
FIG. 11 is a schematic flow chart showing a production apparatus control method in another embodiment;
FIG. 12 is a block diagram showing the construction of a production apparatus control device in one embodiment;
FIG. 13 is a block diagram showing the construction of a production apparatus control device in another embodiment;
FIG. 14 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The production equipment control method provided by the embodiment of the application can be applied to the application environment shown in fig. 1. Where the edge node 102 communicates with the cloud server 104 over a network. The data storage system may store data that cloud server 104 needs to process. The data storage system may be integrated on the cloud server 104 or may be placed on another network server. The edge node 102 obtains device data from at least two plant management systems; performing edge calculation on the equipment data to obtain edge calculation data; sending the edge computing data to the cloud server 104, so as to perform data processing on the edge computing data through the cloud server 104 to obtain cloud computing data; generating a control instruction based on the cloud computing data; and sending the control instruction to the target production equipment so as to control the target production equipment according to the control instruction. The edge node 102 may be an independent physical server, or may be a server cluster formed by a plurality of service nodes in a block chain system, a point-To-point (P2P, Peer To Peer) network is formed among the service nodes, and the P2P Protocol is an application layer Protocol operating on a Transmission Control Protocol (TCP). The cloud server 104 may be a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, web services, cloud communication, middleware services, domain name services, security services, Content Delivery Networks (CDNs), and big data and artificial intelligence platforms.
In one embodiment, as shown in fig. 2, a method for controlling a production facility is provided, which is described by taking the method as an example for being applied to an edge node in fig. 1, and includes the following steps:
s202, acquiring equipment data from at least two factory management systems.
The factory Management System is a System for managing production activities in a factory, and includes an MES (Manufacturing Execution System), an SPC (Statistical Process Control) System, an SCM (Supply Chain Management) System, an ERP (Enterprise Resource Planning) System, a WMS (Warehouse Management) System, a PLM (Product Lifecycle Management) System, and the like. The equipment data is data related to production equipment, including temperature, rotating speed, voltage, current, fault, alarm information and the like of the production equipment in the production process, and service life, maintenance record, capacity and the like of the production equipment.
In one embodiment, the sensors transmit the collected device data to the edge nodes over various industrial buses. Industrial buses include CAN (Controller Area Network) buses, Profibus (Field Bus) buses, WishBone buses, and the like.
And S204, performing edge calculation on the equipment data to obtain edge calculation data.
The edge calculation is a calculation mode for deploying the workload at the edge node, and includes data format conversion, data cleaning, effective information extraction, fault analysis, data statistics, real-time or offline data analysis with a small calculation amount, and other data calculation. The data format conversion converts, for example, device data acquired from heterogeneous plant management systems into a unified data format. The data cleaning is a process of cleaning dirty data in the equipment data, and comprises the steps of correcting inconsistent data and error data in the equipment data, removing repeated data, supplementing missing data and the like. The edge calculation data is data obtained by performing edge calculation on the device data through an edge node, and may be data obtained by data cleaning, data obtained by format conversion, effective information extracted from the device data, or an analysis result obtained by real-time or offline data analysis, and the like.
Because if all calculations are put at the cloud, the cloud generates large calculation pressure, and the calculation capacity requirement on massive device data is difficult to meet, so that some data processing processes with small calculation amount are put at edge nodes, the calculation pressure of the cloud can be reduced, the cost can be reduced, and the processing efficiency on the device data can be improved.
And S206, sending the edge computing data to a cloud server so as to perform data processing on the edge computing data through the cloud server to obtain cloud computing data.
The cloud server is a cloud server providing basic cloud computing services such as cloud services, a cloud database, cloud computing, a cloud function, cloud storage, Network services, cloud communication, middleware services, domain name services, security services, a Content Delivery Network (CDN), a big data and artificial intelligence platform and the like. The cloud server can perform a data processing process with a large calculation amount, such as performing finer-grained data splitting on edge calculation data, training a machine learning model, and the like. For example, the cloud server performs data splitting on a product order to obtain a plurality of fine-grained data such as formula material data, delivery time, an order supplier and the like. For example, the cloud server performs data splitting on the equipment fault alarm information to obtain a plurality of fine-grained data such as fault occurrence time and fault codes. The cloud computing data is obtained by further processing the edge computing data by the cloud server, for example, fine-grained data obtained by splitting the data or a trained machine learning model.
The cloud server provides an open API (Application Programming Interface) Interface for the terminal, so that the terminal can multiplex cloud computing data provided by the cloud server through the API Interface, thereby realizing data multiplexing. For example, the terminal may reuse fine-grained data provided by a cloud server or a machine learning model, etc. through the API interface.
In one embodiment, the edge nodes send the edge computing data to the cloud server, the cloud server stores the edge computing data acquired from each edge node in the database, and then the data in the database is processed through the data center to obtain the cloud computing data. The database may be a Kafka database, a time sequence database, a memory database, or a relational database. The cloud server gathers the edge computing data acquired from each edge node in the database, and then performs data processing, so that interconnection, intercommunication and sharing of data are realized, and the processing efficiency of large-scale data is improved.
And S208, generating a control command based on the cloud computing data.
The control instruction is an instruction for controlling the production equipment, such as a power-on instruction, a power-off instruction, a fault handling instruction, a production scheduling instruction, an order handling instruction, and the like. The edge node may generate a control instruction based on cloud computing data, for example, the device data is device alarm information, the cloud computing data is a fault analysis report obtained by analyzing according to the alarm information, and the edge node generates a control instruction for performing fault repair based on the fault analysis report.
And S210, sending the control instruction to the target production equipment so as to control the target production equipment according to the control instruction.
And the edge node sends the control instruction to the target production equipment so as to control the target production equipment according to the control instruction. For example, the control instruction is a start instruction, the edge node sends the start instruction to the granulation equipment, and the granulation equipment is started when receiving the start instruction.
In the above embodiment, the device data is obtained from at least two factory management systems, and the edge calculation is performed on the device data to obtain the edge calculation data, so that the device data in a plurality of factory management systems can be collected and unified for edge calculation, and interconnection and intercommunication of data in each factory management system are realized. And then sending the edge computing data in each edge node to a cloud server, and performing data processing on the edge computing data in each edge node through the cloud server to obtain cloud computing data, so that the edge computing data can be further analyzed and calculated through the cloud server, and effective information is extracted from the edge computing data and used by a decision maker for controlling target production equipment. And finally, generating a control instruction based on the cloud computing data and sending the control instruction to the target production equipment so as to control the target production equipment according to the control instruction. Therefore, the production equipment can be controlled through the mass data from each factory management system, the control efficiency of the production equipment is improved, the production efficiency of a factory can be further improved, and the productivity is optimized.
In one embodiment, the edge calculation data is training data samples; as shown in fig. 3, S206 specifically includes S302, and S208 specifically includes S304:
s302, the training data samples are sent to a cloud server, so that the cloud server trains the pre-trained machine learning model according to the training data samples to obtain a trained machine learning model, and the trained machine learning model is used as cloud computing data.
The training data samples are data samples used for machine learning model training. For example, the training data samples may be energy consumption data samples, capacity data samples, fault information data samples, etc. of the production equipment over a historical period of time. The machine learning model may be a decision tree model, a neural network model, a naive bayes model, or a support vector machine model, etc. The decision tree model is a tree structure consisting of root nodes and leaf nodes and can be trained through a recursive learning algorithm. The neural network model comprises a feedforward neural network model, a convolution neural network model, a residual convolution neural network model, a deep learning model and the like. A naive bayes model is a model that classifies a data set according to a naive bayes criterion. The support vector machine model is a generalized linear classifier that performs binary classification on data according to a supervised learning manner.
And the cloud server trains the pre-trained machine learning model according to the training data samples, and the parameters of the machine learning model are adjusted through training to obtain the trained machine learning model. For example, the cloud server trains the pre-trained machine learning model according to the energy consumption data samples to obtain a machine learning model for predicting the energy consumption of the production equipment. For example, the cloud server trains the pre-trained machine learning model according to the productivity data samples to obtain a machine learning model for estimating the productivity of the production equipment.
And S304, generating a control command based on the trained machine learning model.
The cloud server sends the trained machine learning model to the edge node, and the edge node generates a control instruction based on the trained machine learning model. For example, the trained machine learning model is used for predicting the energy consumption of the production equipment, the edge node predicts the energy consumption of the production equipment according to the machine learning model, and a shutdown instruction is generated according to a prediction result. For example, the trained machine learning model is a machine learning model for estimating the capacity of the production equipment in each production process, the edge node estimates the capacity of the production equipment in each production process according to the machine learning model, and performs production scheduling on the product order according to the estimation result to generate the scheduling instruction.
In the above embodiment, the edge node sends the training data sample to the cloud server, so that the cloud server trains the pre-trained machine learning model according to the training data sample to obtain the trained machine learning model. And the edge node generates a control instruction based on the trained machine learning model. Therefore, the production equipment can be controlled through the machine learning model, and the efficiency of controlling the production equipment is improved.
In one embodiment, as shown in fig. 4, S208 specifically includes the following steps:
s402, receiving cloud computing data through an application program interface of the cloud server.
The application program interface is an interface used for data interaction with the client, and data in the cloud server can be accessed through the application program interface. The cloud server provides a uniform application program interface for the client, so that various clients in an application layer can multiplex basic data provided by the cloud server, and data sharing is realized.
S404, feeding back the cloud computing data to the client so that the client generates a control instruction based on the cloud computing data.
The client may be various clients related to the production activity, such as a client of a warehouse system, a client of an ingredient system, or a client of a production management system.
In an embodiment, S404 specifically includes: dividing cloud computing data into first authority data and second authority data; feeding back the first authority data to the client with the group authority; and feeding back the second authority data to the client with the factory authority.
The first authority data is data which can be acquired by clients with group authority. The second authority data is data that can be acquired by a client having the factory authority. The range of the first right data may be larger than the range of the second right data. The edge node divides the client into different authorities, so that part of the clients have higher group authorities, can acquire first authority data and control the production equipment according to the first authority data, and the other part of the clients only have factory authorities, can acquire second authority data and control the production equipment according to the second authority data. Therefore, the cloud computing data can be managed in a grading mode, and the safety of the data is guaranteed.
In the above embodiment, the cloud computing data is received through the application program interface of the cloud server, and the cloud computing data is fed back to the client, so that the client generates the control instruction based on the cloud computing data. The cloud server provides a uniform application program interface for the client, so that various clients in an application layer can multiplex basic data provided by the cloud server, the data multiplexing of the clients is facilitated, and the data sharing efficiency is improved.
In one embodiment, S202 specifically includes: acquiring equipment data from at least two factory management systems through an intelligent gateway; carrying out format conversion on the equipment data to obtain converted equipment data; s204 specifically comprises: and performing edge calculation on the converted equipment data through the micro service cluster to obtain edge calculation data.
The intelligent gateway is a network device for realizing network interconnection in a network layer. As shown in fig. 5, the intelligent gateway may obtain the device data from the plant management system through a Message Queue (MQ), a TCP (Transmission Control Protocol), a REST (Representational State Transfer) Protocol, a gRPC (Remote Procedure Calls) Protocol, or the like, or obtain the device data from a Database of the plant management system through a JDBC (Java Database Connectivity).
The micro-service cluster is a cluster formed by a plurality of micro-service terminals. The micro-service terminals in the micro-service cluster comprise micro-services for performing report statistics, micro-services for performing fault alarm, micro-services for performing offline data analysis and micro-services for performing real-time data analysis, and each micro-service terminal can independently run in a corresponding process. As shown in fig. 6, a plurality of micro-services form a micro-service cluster, and the micro-service cluster obtains data from a business database, a local cache, or an API interface provided by a cloud service. The micro-servers communicate with each other through a gRPC protocol, and a Nacos (Dynamic Naming and Configuration Service, registry) cluster configures and registers and manages the micro-services. The Sentinel component controls the flow of the microservice cluster, and the RoundRobin realizes load balance among microservices. And after load balancing, the network request sent by the client is sent to the micro-service cluster.
And after the intelligent gateway acquires the equipment data, the intelligent gateway performs format conversion on the equipment data. For example, the device data is converted into data in JSON (JSON Object Notation) format or the like to reduce the amount of data or to facilitate storage of the data. And then, performing edge calculation on the converted equipment data through the micro service cluster to obtain edge calculation data.
In the above embodiment, the intelligent gateway obtains the device data from the at least two plant management systems, so that the device data of the at least two plant management systems can be collected together by the intelligent gateway to implement data sharing. And then, format conversion is carried out on the equipment data to obtain the converted equipment data so as to be convenient for storing the equipment data. In addition, each micro server is an independent unit, so that development, testing, operation and maintenance can be independently performed, and the whole micro service cluster has high expandability.
In one embodiment, the following steps are further included after S206: modeling a production factory based on the equipment data to obtain a digital twin model; loading the edge computing data and the cloud computing data into a digital twin model to obtain a loaded digital twin model; and sending the loaded digital twin model to the client so that the client can display the loaded digital twin model.
Wherein the digital twin model is a visualized digital model obtained by modeling the production plant. The digital twin model may include various components of the production plant, such as various production lines, various production sections in the production line, various production equipment in the production sections, and the like. The edge node loads the edge computing data and the cloud computing data into the digital twin model in real time, so that the edge computing data and the cloud computing data are displayed in the loaded digital twin model in real time, the data calculated by the edge node and the cloud server are provided for a user in a visual mode, and the user can conveniently check the data.
In the above embodiment, the edge node models the production plant according to the device data acquired from each plant management system, and maps the production devices controlled by each plant management system into the digital twin model, so that the operation conditions of each production device can be displayed in real time. The edge node loads the edge computing data and the cloud computing data into the digital twin model and sends the loaded digital twin model to the client, so that the edge computing data and the cloud computing data can be displayed in the digital twin model, a user can conveniently and visually check the edge computing data and the cloud computing data, a control instruction is issued according to the edge computing data and the cloud computing data, and the efficiency of controlling production equipment is improved.
In one embodiment, as shown in fig. 7, the production device control system includes a sensing layer, a field management layer, a Paas (Platform as a Service) layer, an application layer, and a presentation layer, where the sensing layer collects device data via a sensor and transmits the data collected by the sensor to a plant management system in the field management layer via an industrial bus. The edge node in the field management layer acquires the device data from the factory management system and performs edge calculation on the device data, for example, format conversion, data cleaning, real-time or offline data analysis and the like on the device data, and the edge node can send the calculated edge calculation data to the client side so that the client side displays the edge calculation data on the data display screen. As shown in fig. 8, an edge node in the field management layer obtains device data from a factory management system or a factory management system database through an intelligent gateway, stores the device data in a data storage unit, and provides the device data in the storage unit to digital factory microservices, MES microservices, and AI microservices, and each microservice performs edge calculation on the device data to obtain edge calculation data. The edge node sends the edge computing data to the cloud server through the cloud edge coordination unit so as to perform data processing on the edge computing data through the cloud server to obtain cloud computing data. The intelligent gateway acquires cloud computing data from the cloud server and stores the cloud computing data in the data storage unit. The edge node sends the edge computing data and the cloud computing data to the digital twin unit so as to load the edge computing data and the cloud computing data into the digital twin model, and sends the loaded digital twin model to the client for display.
As shown in fig. 9, in the Paas layer, the container orchestration engine manages and controls the edge nodes through the cloud edge coordination unit. The data routing acquires edge computing data from the edge nodes through the cloud edge coordination unit, then stores the edge computing data into a data storage system of the cloud server, and gathers the data in each edge node together through the data storage system to perform interconnection, intercommunication, development and sharing of the data, so that the use value of the data is improved. And the data center station performs data processing on the data of the data storage system, including model training, offline analysis, time sequence analysis, real-time analysis and the like. And the data center station provides the processed cloud computing data to the basic service unit for data sharing. The data center station and the basic service unit provide an open API interface for the application, and the application layer can call the data provided by the basic service unit and the data center station through the API interface. The Paas layer provides computing service for mass data by using the cloud server, so that the clients of the application layer can share the cloud computing data through the API (application program interface) of the cloud server, the cloud server can be prevented from processing the data aiming at each client repeatedly, and the utilization efficiency of the data is improved.
As shown in fig. 10, the clients in the application layer include a factory authority client and a group authority client, the group authority client has a higher authority, and can acquire all the device data, edge computing data and cloud computing data, and control the production devices from the group level according to the acquired data, so as to perform order management on the product orders of the whole group, and perform process coordination and material balance among the production factories. In order to ensure data security and avoid data leakage, a client of a factory authority can only acquire equipment data, edge calculation data and cloud calculation data related to production activities of a factory, and issue a control instruction to production equipment according to the acquired data so as to perform production task management, equipment energy consumption management and inventory management.
In one embodiment, as shown in fig. 11, the production apparatus control method includes the steps of:
s1102, acquiring device data from at least two factory management systems through an intelligent gateway.
And S1104, performing format conversion on the device data to obtain the converted device data.
And S1106, performing edge calculation on the converted equipment data through the micro service cluster to obtain edge calculation data.
And S1108, sending the edge computing data to a cloud server so as to perform data processing on the edge computing data through the cloud server to obtain cloud computing data.
And S1110, receiving cloud computing data through an application program interface of the cloud server.
S1112, dividing the cloud computing data into first right data and second right data; feeding back the first authority data to the client with the group authority; and feeding back the second authority data to the client with the factory authority.
S1114, receiving a control instruction generated by the client based on the cloud computing data, and forwarding the control instruction to the target production device, so as to control the target production device according to the control instruction.
S1116, modeling the production factory based on the equipment data to obtain a digital twin model.
S1118, loading the edge computing data and the cloud computing data into a digital twin model to obtain the loaded digital twin model.
And S1120, sending the loaded digital twin model to the client so that the client can display the loaded digital twin model.
The specific contents of S1102 to S1120 may refer to the above specific implementation process.
It should be understood that, although the steps in the flowcharts related to the embodiments as described above are sequentially displayed as indicated by arrows, the steps are not necessarily performed sequentially as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a part of the steps in the flowcharts related to the embodiments described above may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the execution order of the steps or stages is not necessarily sequential, but may be rotated or alternated with other steps or at least a part of the steps or stages in other steps.
Based on the same inventive concept, the embodiment of the application also provides a production equipment control device for realizing the production equipment control method. The implementation scheme for solving the problem provided by the apparatus is similar to the implementation scheme described in the above method, so specific limitations in one or more embodiments of the production equipment control apparatus provided below can be referred to the limitations in the above production equipment control method, and are not described herein again.
In one embodiment, as shown in fig. 12, there is provided a production apparatus control device including: an obtaining module 1202, a calculating module 1204, a data processing module 1206, a generating module 1208, and a sending module 1210, wherein:
an acquisition module 1202 that acquires device data from at least two plant management systems;
a calculation module 1204, configured to perform edge calculation on the device data to obtain edge calculation data;
the data processing module 1206 is configured to send the edge computing data to a cloud server, so that the edge computing data is subjected to data processing by the cloud server to obtain cloud computing data;
a generating module 1208, configured to generate a control instruction based on the cloud computing data;
the sending module 1210 is configured to send the control instruction to the target production device, so as to control the target production device according to the control instruction.
In the above embodiment, the device data is obtained from at least two plant management systems, and the edge calculation data is obtained by performing the edge calculation on the device data, so that the device data in a plurality of plant management systems can be centralized and unified for performing the edge calculation, thereby implementing interconnection and intercommunication of data in each plant management system. And then sending the edge computing data in each edge node to a cloud server, and performing data processing on the edge computing data in each edge node through the cloud server to obtain cloud computing data, so that the edge computing data can be further analyzed and calculated through the cloud server, and effective information is extracted from the edge computing data and used by a decision maker for controlling target production equipment. And finally, generating a control instruction based on the cloud computing data and sending the control instruction to the target production equipment so as to control the target production equipment according to the control instruction. Therefore, the production equipment can be controlled through the mass data from each factory management system, the control efficiency of the production equipment is improved, the production efficiency of a factory can be further improved, and the productivity is optimized.
In one embodiment, the edge calculation data is training data samples; the data processing module 1206:
sending the training data samples to a cloud server so that the cloud server trains a pre-trained machine learning model according to the training data samples to obtain a trained machine learning model, and taking the trained machine learning model as cloud computing data;
the generation module 1208:
and generating a control instruction based on the trained machine learning model.
In one embodiment, the generating module 1208 is further configured to:
receiving cloud computing data through an application program interface of a cloud server;
and feeding back the cloud computing data to the client so that the client generates a control instruction based on the cloud computing data.
In one embodiment, the generating module 1208 is further configured to:
dividing cloud computing data into first authority data and second authority data;
feeding back the first authority data to a client with group authority; and the number of the first and second groups,
and feeding back the second authority data to the client with the factory authority.
In one embodiment, the obtaining module 1202 is further configured to:
acquiring equipment data from at least two factory management systems through an intelligent gateway;
carrying out format conversion on the equipment data to obtain converted equipment data;
the calculation module 1204 is further configured to:
and performing edge calculation on the converted equipment data through the micro service cluster to obtain edge calculation data.
In one embodiment, as shown in fig. 13, the apparatus further comprises:
the modeling module 1212 is configured to model the production plant based on the device data to obtain a digital twin model;
a loading module 1214, configured to load the edge computing data and the cloud computing data into the digital twin model, so as to obtain a loaded digital twin model;
the sending module 1210 is further configured to send the loaded digital twin model to the client, so that the client displays the loaded digital twin model.
The respective modules in the production apparatus control device described above may be realized in whole or in part by software, hardware, and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, and the computer device may be an edge node, and the edge node may be a server or a server cluster composed of servers, and its internal structure diagram may be as shown in fig. 14. The computer device includes a processor, a memory, an Input/Output interface (I/O for short), and a communication interface. The processor, the memory and the input/output interface are connected through a system bus, and the communication interface is connected to the system bus through the input/output interface. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used for storing production device control data. The input/output interface of the computer device is used for exchanging information between the processor and an external device. The communication interface of the computer device is used for connecting and communicating with an external terminal through a network. The computer program is executed by a processor to implement a production apparatus control method.
It will be appreciated by those skilled in the art that the configuration shown in fig. 14 is a block diagram of only a portion of the configuration associated with the present application, and is not intended to limit the computing device to which the present application may be applied, and that a particular computing device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory and a processor, the memory having a computer program stored therein, the processor implementing the following steps when executing the computer program: acquiring device data from at least two plant management systems; performing edge calculation on the equipment data to obtain edge calculation data; sending the edge computing data to a cloud server so as to perform data processing on the edge computing data through the cloud server to obtain cloud computing data; generating control instructions based on the cloud computing data; and sending the control instruction to target production equipment so as to control the target production equipment according to the control instruction.
In one embodiment, the edge calculation data is training data samples; the processor, when executing the computer program, further performs the steps of: sending the training data samples to a cloud server so that the cloud server trains a pre-trained machine learning model according to the training data samples to obtain a trained machine learning model, and taking the trained machine learning model as cloud computing data; and generating a control instruction based on the trained machine learning model.
In one embodiment, the processor, when executing the computer program, further performs the steps of: receiving the cloud computing data through an application program interface of the cloud server; and feeding back the cloud computing data to a client so as to enable the client to generate a control instruction based on the cloud computing data.
In one embodiment, the processor, when executing the computer program, further performs the steps of: dividing the cloud computing data into first authority data and second authority data; feeding back the first authority data to a client with group authority; and feeding back the second authority data to the client with the factory authority.
In one embodiment, the processor, when executing the computer program, further performs the steps of: acquiring equipment data from at least two factory management systems through an intelligent gateway; carrying out format conversion on the equipment data to obtain the converted equipment data; and performing edge calculation on the converted equipment data through the micro service cluster to obtain edge calculation data.
In one embodiment, the processor, when executing the computer program, further performs the steps of: modeling a production plant based on the equipment data to obtain a digital twin model; loading the edge computing data and the cloud computing data into the digital twin model to obtain the loaded digital twin model; and sending the loaded digital twin model to a client so that the client can display the loaded digital twin model.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of: acquiring device data from at least two plant management systems; performing edge calculation on the equipment data to obtain edge calculation data; sending the edge computing data to a cloud server so as to perform data processing on the edge computing data through the cloud server to obtain cloud computing data; generating control instructions based on the cloud computing data; and sending the control instruction to target production equipment so as to control the target production equipment according to the control instruction.
In one embodiment, the edge calculation data is training data samples; the computer program when executed by the processor further realizes the steps of: sending the training data samples to a cloud server so that the cloud server trains a pre-trained machine learning model according to the training data samples to obtain a trained machine learning model, and taking the trained machine learning model as cloud computing data; and generating a control instruction based on the trained machine learning model.
In one embodiment, the computer program when executed by the processor further performs the steps of: receiving the cloud computing data through an application program interface of the cloud server; and feeding back the cloud computing data to a client so that the client generates a control instruction based on the cloud computing data.
In one embodiment, the computer program when executed by the processor further performs the steps of: dividing the cloud computing data into first authority data and second authority data; feeding the first authority data back to a client with group authority; and feeding back the second authority data to the client with the factory authority.
In one embodiment, the computer program when executed by the processor further performs the steps of: acquiring equipment data from at least two factory management systems through an intelligent gateway; carrying out format conversion on the equipment data to obtain the converted equipment data; and performing edge calculation on the converted equipment data through the micro service cluster to obtain edge calculation data.
In one embodiment, the computer program when executed by the processor further performs the steps of: modeling a production plant based on the equipment data to obtain a digital twin model; loading the edge computing data and the cloud computing data into the digital twin model to obtain the loaded digital twin model; and sending the loaded digital twin model to a client so that the client can display the loaded digital twin model.
In one embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, performs the steps of:
acquiring device data from at least two plant management systems; performing edge calculation on the equipment data to obtain edge calculation data; sending the edge computing data to a cloud server so as to perform data processing on the edge computing data through the cloud server to obtain cloud computing data; generating control instructions based on the cloud computing data; and sending the control instruction to target production equipment so as to control the target production equipment according to the control instruction.
In one embodiment, the edge calculation data is training data samples; the computer program when executed by the processor further realizes the steps of: sending the training data samples to a cloud server so that the cloud server trains a pre-trained machine learning model according to the training data samples to obtain a trained machine learning model, and taking the trained machine learning model as cloud computing data; and generating a control instruction based on the trained machine learning model.
In one embodiment, the computer program when executed by the processor further performs the steps of: receiving the cloud computing data through an application program interface of the cloud server; and feeding back the cloud computing data to a client so as to enable the client to generate a control instruction based on the cloud computing data.
In one embodiment, the computer program when executed by the processor further performs the steps of: dividing the cloud computing data into first authority data and second authority data; feeding back the first authority data to a client with group authority; and feeding back the second authority data to the client with the factory authority.
In one embodiment, the computer program when executed by the processor further performs the steps of: acquiring equipment data from at least two factory management systems through an intelligent gateway; carrying out format conversion on the equipment data to obtain the converted equipment data; and performing edge calculation on the converted equipment data through the micro service cluster to obtain edge calculation data.
In one embodiment, the computer program when executed by the processor further performs the steps of: modeling a production plant based on the equipment data to obtain a digital twin model; loading the edge computing data and the cloud computing data into the digital twin model to obtain the loaded digital twin model; and sending the loaded digital twin model to a client so that the client can display the loaded digital twin model.
It should be noted that, the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) referred to in the present application are information and data authorized by the user or sufficiently authorized by each party, and the collection, use and processing of the related data need to comply with the relevant laws and regulations and standards of the relevant country and region.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high-density embedded nonvolatile Memory, resistive Random Access Memory (ReRAM), Magnetic Random Access Memory (MRAM), Ferroelectric Random Access Memory (FRAM), Phase Change Memory (PCM), graphene Memory, and the like. Volatile Memory can include Random Access Memory (RAM), external cache Memory, and the like. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others. The databases referred to in various embodiments provided herein may include at least one of relational and non-relational databases. The non-relational database may include, but is not limited to, a block chain based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic devices, quantum computing based data processing logic devices, etc., without limitation.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is specific and detailed, but not construed as limiting the scope of the present application. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.
Claims (10)
1. A production facility control method, characterized by comprising:
acquiring device data from at least two plant management systems;
performing edge calculation on the equipment data to obtain edge calculation data;
sending the edge computing data to a cloud server so as to perform data processing on the edge computing data through the cloud server to obtain cloud computing data;
generating control instructions based on the cloud computing data;
and sending the control instruction to target production equipment so as to control the target production equipment according to the control instruction.
2. The method of claim 1, wherein the edge calculation data is training data samples; the sending the edge computing data to a cloud server to perform data processing on the edge computing data through the cloud server to obtain cloud computing data comprises:
sending the training data samples to a cloud server so that the cloud server trains a pre-trained machine learning model according to the training data samples to obtain a trained machine learning model, and taking the trained machine learning model as cloud computing data;
the generating control instructions based on the cloud computing data comprises:
and generating a control instruction based on the trained machine learning model.
3. The method of claim 1, wherein the generating control instructions based on the cloud computing data comprises:
receiving the cloud computing data through an application program interface of the cloud server;
and feeding back the cloud computing data to a client so as to enable the client to generate a control instruction based on the cloud computing data.
4. The method of claim 3, further comprising:
dividing the cloud computing data into first authority data and second authority data;
the feeding back the cloud computing data to the client comprises:
feeding back the first authority data to a client with group authority; and the number of the first and second groups,
and feeding back the second authority data to the client with the factory authority.
5. The method of claim 1, wherein the obtaining device data from at least two plant management systems comprises:
acquiring equipment data from at least two factory management systems through an intelligent gateway;
carrying out format conversion on the equipment data to obtain the converted equipment data;
the performing edge calculation on the device data to obtain edge calculation data includes:
and performing edge calculation on the converted equipment data through the micro service cluster to obtain edge calculation data.
6. The method of claim 1, further comprising:
modeling a production plant based on the equipment data to obtain a digital twin model;
loading the edge computing data and the cloud computing data into the digital twin model to obtain the loaded digital twin model;
and sending the loaded digital twin model to a client so that the client can display the loaded digital twin model.
7. A production facility control apparatus, characterized in that the apparatus comprises:
an acquisition module for acquiring device data from at least two plant management systems;
the computing module is used for carrying out edge computing on the equipment data to obtain edge computing data;
the data processing module is used for sending the edge computing data to a cloud server so as to perform data processing on the edge computing data through the cloud server to obtain cloud computing data;
a generation module for generating a control instruction based on the cloud computing data;
and the sending module is used for sending the control instruction to target production equipment so as to control the target production equipment according to the control instruction.
8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method of any of claims 1 to 6.
9. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 6.
10. A computer program product comprising a computer program, characterized in that the computer program realizes the steps of the method of any one of claims 1 to 6 when executed by a processor.
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