Flexible manufacturing equipment real-time monitoring method based on multi-source heterogeneous network

Abstract

The invention belongs to the technical field of flexible manufacturing equipment, and discloses a flexible manufacturing equipment real-time monitoring method based on a multi-source heterogeneous network, the method is applied to a flexible manufacturing equipment real-time monitoring system, and the flexible manufacturing equipment real-time monitoring system comprises a plurality of flexible manufacturing equipment to be monitored, a plurality of sensors and edge computing nodes. The sensors comprise a first sensor and a second sensor, and the method comprises the steps that the first sensor sends operation data to an edge computing node, and the second sensor sends environment data to the edge computing node; the edge computing node monitors whether the to-be-monitored flexible manufacturing equipment is abnormal or not based on the operation data, and monitors whether the environment where the to-be-monitored flexible manufacturing equipment is located is abnormal or not based on the environment data; and when the edge computing node monitors that the to-be-monitored flexible manufacturing equipment or environment is abnormal, outputting abnormal prompt information. According to the invention, data processing and analysis are carried out on the site through the edge computing nodes, and the flexible manufacturing equipment can be monitored in real time.

Description

Technical Field

[0001] The present invention belongs to the technical field of flexible manufacturing equipment, and particularly relates to a real-time monitoring method for flexible manufacturing equipment based on a multi-source heterogeneous network. Background Art

[0002] With the rapid development of technology and the continuous change of market demands, the manufacturing industry is moving towards the direction of intelligence, personalization, and high efficiency. As a manufacturing mode that can quickly adapt to market changes and meet diverse production needs, flexible manufacturing equipment has become an important development trend in modern manufacturing. It has become increasingly important to monitor the flexible manufacturing state in real time.

[0003] The flexible manufacturing equipment monitoring methods provided by related technologies are usually as follows: First, various sensors are used to collect operation data during the operation of the flexible manufacturing equipment, such as temperature, pressure, vibration, and displacement; then, the operation data collected by various sensors is transmitted to a data analysis platform in the cloud; the data analysis platform analyzes the operation data to achieve the monitoring of the flexible manufacturing equipment. During the process of transmitting the operation data to the data analysis platform in the cloud, due to factors such as communication bandwidth limitations, data transmission delays may occur, which may lead to the inability to monitor the flexible manufacturing equipment in real time. Summary of the Invention

[0004] The purpose of the present invention is to sink data processing and analysis to the field through edge computing nodes, which can be quickly performed near the data source, so as to solve the problems of slow response and high latency caused by uploading data to the cloud data center for data processing, and thus can monitor the flexible manufacturing equipment in real time.

[0005] In a first aspect, an embodiment of the present invention provides a real-time monitoring method for flexible manufacturing equipment based on a multi-source heterogeneous network. The method is applied to a real-time monitoring system for flexible manufacturing equipment based on a multi-source heterogeneous network. The real-time monitoring system for flexible manufacturing equipment based on a multi-source heterogeneous network includes a plurality of flexible manufacturing equipment to be monitored, a plurality of sensors, and edge computing nodes. The plurality of sensors include a first sensor and a second sensor. The first sensor is used to obtain the operation data of the flexible manufacturing equipment to be monitored, and the second sensor is used to obtain the environmental data of the environment where the flexible manufacturing equipment to be monitored is located. The method includes:

[0006] The first sensor sends the operation data to the edge computing node, and the second sensor sends the environmental data to the edge computing node.

[0007] The edge computing node monitors whether there is an abnormality in the flexible manufacturing equipment to be monitored based on the operation data, and monitors whether there is an abnormality in the environment where the flexible manufacturing equipment to be monitored is located based on the environmental data.

[0008] When the edge computing node detects an abnormality in the flexible manufacturing equipment to be monitored and / or an abnormality in the environment where the flexible manufacturing equipment to be monitored is located, an abnormality prompt message is output.

[0009] Optionally, the edge computing node includes an algorithm model pre-trained.

[0010] The edge computing node monitors whether there is an abnormality in the flexible manufacturing equipment to be monitored based on the operation data, and monitors whether there is an abnormality in the environment where the flexible manufacturing equipment to be monitored is located based on the environment data, including:

[0011] The edge computing node inputs the operation data into the algorithm model, outputs a first monitoring result through the algorithm model, and determines whether there is an abnormality in the flexible manufacturing equipment to be monitored based on the first monitoring result.

[0012] The edge computing node inputs the environment data into the algorithm model, outputs a second monitoring result through the algorithm model, and determines whether there is an abnormality in the flexible manufacturing equipment to be monitored based on the second monitoring result.

[0013] Optionally, an edge unified gateway is set in the edge computing node.

[0014] The first sensor sends the operation data to the edge computing node, and the second sensor sends the environment data to the edge computing node, including:

[0015] The first sensor sends the operation data to the edge unified gateway of the edge computing node.

[0016] The second sensor sends the operation data to the edge unified gateway of the edge computing node.

[0017] Optionally, there are multiple edge computing nodes.

[0018] The first sensor sends the operation data to the edge computing node, and the second sensor sends the environment data to the edge computing node, including:

[0019] The first sensor sends the operation data of the same data type to the same edge computing node, and the second sensor sends the environment data of the same data type to the same edge computing node; among them, the operation data of different data types are sent to different edge computing nodes, and the environment data of different data types are sent to different edge computing nodes.

[0020] Or,

[0021] The first sensor sends the operation data of the flexible manufacturing equipment in the same area to the same edge computing node, and the second sensor sends the environmental data in the same environment to the same edge computing node; wherein, the operation data of the flexible manufacturing equipment in different areas are sent to different edge computing nodes, and the environmental data in different environments are sent to different edge computing nodes.

[0022] Optionally, the real-time monitoring system for flexible manufacturing equipment based on a multi-source heterogeneous network further includes a data analysis platform in the cloud, and the method further includes:

[0023] The edge computing node sends the operation data and the environmental data to the data analysis platform;

[0024] The data analysis platform stores the operation data and the environmental data, and performs data analysis and data mining on the operation data and the environmental data.

[0025] Optionally, the edge computing node outputs an exception prompt message, including:

[0026] The edge computing node sends the exception prompt message to a preset target terminal, and the exception prompt message includes at least one of the following information: SMS, phone call, and email.

[0027] In a second aspect, an embodiment of the present invention provides a real-time monitoring system for flexible manufacturing equipment based on a multi-source heterogeneous network. The real-time monitoring system for flexible manufacturing equipment based on a multi-source heterogeneous network includes a plurality of flexible manufacturing equipment to be monitored, a plurality of sensors, and an edge computing node. The plurality of sensors include a first sensor and a second sensor. The first sensor is used to obtain the operation data of the flexible manufacturing equipment to be monitored, and the second sensor is used to obtain the environmental data of the environment where the flexible manufacturing equipment to be monitored is located;

[0028] The first sensor sends the operation data to the edge computing node, and the second sensor sends the environmental data to the edge computing node;

[0029] The edge computing node monitors whether there is an abnormality in the flexible manufacturing equipment to be monitored based on the operation data, and monitors whether there is an abnormality in the environment where the flexible manufacturing equipment to be monitored is located based on the environmental data;

[0030] When the edge computing node monitors that there is an abnormality in the flexible manufacturing equipment to be monitored, and / or there is an abnormality in the environment where the flexible manufacturing equipment to be monitored is located, it outputs an exception prompt message.

[0031] In a third aspect, an embodiment of the present invention provides an electronic device, including:

[0032] At least one processor;

[0033] A memory for storing instructions executable by the at least one processor;

[0034] Wherein, the at least one processor is configured to execute the instructions to implement the method described in the first aspect.

[0035] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium. When the instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device can execute the method described in the first aspect.

[0036] In a fifth aspect, an embodiment of the present invention provides a computer program product, including a computer program which implements the method described in the first aspect when executed by a processor.

[0037] The technical solution provided by the embodiment of the present invention, the real-time monitoring method for flexible manufacturing equipment based on a multi-source heterogeneous network is applied to a real-time monitoring system for flexible manufacturing equipment based on a multi-source heterogeneous network. The real-time monitoring system for flexible manufacturing equipment includes edge computing nodes. By sinking data processing and analysis to the field through the edge computing nodes, it can be quickly carried out near the data source, thus solving the problems of slow response and high latency caused by uploading data to the cloud data center for data processing, and thus the flexible manufacturing equipment can be monitored in real time. Moreover, in the related art, when a large amount of data is transmitted to the data analysis platform in the cloud, there are problems of data security and privacy protection. Data processing at the edge computing nodes can implement technologies such as desensitization of sensitive data and enhanced privacy, improving the security of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is an overall technical solution architecture diagram provided by an embodiment of the present invention;

[0039] Figure 2 It is a flowchart of the real-time monitoring method for flexible manufacturing equipment based on a multi-source heterogeneous network provided by an embodiment of the present invention;

[0040] Figure 3 It is a schematic structural diagram of the real-time monitoring system for flexible manufacturing equipment based on a multi-source heterogeneous network provided by an embodiment of the present invention;

[0041] Figure 4 It is a schematic structural diagram of the electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0042] The present invention will be described in detail below through embodiments.

[0043] With the rapid development of technology and the continuous change of market demand, the manufacturing industry is moving towards the direction of intelligentization, personalization, and high efficiency. As a manufacturing mode that can quickly adapt to market changes and meet diverse production needs, flexible manufacturing equipment has become an important development trend in modern manufacturing. It has become increasingly important to monitor the flexible manufacturing status in real time.

[0044] The monitoring methods for flexible manufacturing equipment provided by related technologies are usually as follows: First, various sensors are used to collect operation data during the operation of the flexible manufacturing equipment, such as temperature, pressure, vibration, and displacement, etc.

[0045] Then, the operation data collected by various sensors is transmitted to the data analysis platform in the cloud. The data analysis platform analyzes the operation data to achieve the monitoring of the flexible manufacturing equipment. During the process of transmitting the operation data to the data analysis platform in the cloud, due to factors such as communication bandwidth limitations, data transmission delays may occur, which may further lead to the inability to monitor the flexible manufacturing equipment in real time, seriously restricting the performance and efficiency improvement of the flexible manufacturing equipment. That is to say, in related technologies, the monitoring efficiency of flexible manufacturing equipment is relatively low. If abnormalities or failures occur in the flexible manufacturing equipment during the period when real-time monitoring is not carried out, huge losses will be caused.

[0046] To solve the above technical problems existing in related technologies, the present invention designs a multi-source heterogeneous network method based on edge computing and combined with the OPC UA unified architecture technology. Among them, the full name of OPC UA is OPC Unified Architecture, which is an industrial software interface specification aiming to provide cross-platform, secure, and extensible data interaction. By designing the multi-source heterogeneous network method, it mainly provides functions such as real-time status monitoring, intelligent diagnosis of flexible manufacturing equipment, and monitoring of the surrounding environment of flexible manufacturing equipment, to realize an intelligent flexible manufacturing production line.

[0047] Specifically, a one-stop multi-source heterogeneous network system is constructed for the flexible manufacturing equipment production line. This multi-source heterogeneous network system is implemented using a hierarchical architecture, including a layer of edge computing layer. Single-point or distributed edge computing nodes are established according to the scale and data domain of the flexible manufacturing equipment production line. The operation data generated by the flexible manufacturing equipment on the flexible manufacturing equipment production line or the environmental data collected by sensors (such as cameras, radars, etc.) are uniformly accessed to the affiliated edge computing nodes without directly interacting with the cloud data center. At the same time, with the help of the AI computing power of the edge computing nodes, the governance of algorithm models is introduced to achieve target monitoring and tracking, etc. For example, it is possible to monitor people or instruments in the environment where the flexible manufacturing equipment is located (such as a laboratory) to monitor whether there are foreign personnel entering the laboratory or other instruments entering.

[0048] Moreover, the cloud data center and edge computing nodes can adopt a two-level cloud-edge collaboration solution to incorporate functions such as system management, real-time status monitoring, predictive maintenance, and monitoring of the surrounding environment of flexible manufacturing equipment or sensors.

[0049] To describe the solution clearly, the overall technical architecture provided by the embodiments of the present invention will be elaborated below. As Figure 1 shown, from Figure 1 it can be seen that:

[0050] 1. The bottom layer is the flexible equipment (i.e., flexible manufacturing equipment) and sensor layer. It is the data source of the multi-source heterogeneous network system and the object to be managed. Among them, the flexible manufacturing equipment includes CNC machining equipment, industrial robots, and AGV automated guided vehicles in the flexible manufacturing equipment laboratory; various data acquisition sensors (for example, it can include force sensors and displacement sensors, etc.) are used to collect the operation data of the flexible manufacturing equipment; cameras and radars can also be called sensors and are used to collect the environmental data of the flexible manufacturing equipment laboratory.

[0051] 2. The second layer is the edge layer. The edge processing and analysis service closest to the data source can be horizontally composed of one or more edge computing nodes ( Figure 1 shown as edge computing node 1, edge computing node 2,..., edge computing node n in the figure), mainly providing services such as data acquisition, data conversion, data transmission, decision analysis, model inference, and application management, and providing the edge unified gateway OPCUA. For example, by adding an edge gateway to the edge computing node and using the OPCUA unified architecture (multi-source heterogeneous) to solve the problem that various Internet of Things devices, flexible manufacturing equipment of different brands and models cannot communicate and interconnect effectively. And by sinking data processing and analysis to the site through the edge computing node, it can be carried out quickly near the data source, thus solving the problems of slow response and high latency caused by uploading data to the cloud data center for data processing. Moreover, in the related technology, when transmitting a large amount of data to the data analysis platform in the cloud, there are problems of data security and privacy protection. By performing data processing in the edge computing node, technologies such as sensitive data desensitization and privacy enhancement can be realized to improve the security of the system.

[0052] 3. The cloud data center collaborates with the edge computing node side, mainly providing services such as resource management, remote monitoring, application governance, model training, deployment and upgrade, and providing corresponding interfaces externally.

[0053] 4. Application layer. The edge computing node can provide an interface to the user side, and the cloud data center can also provide an interface to the user side, thereby providing applications such as real-time status monitoring, fault prediction, and production process optimization, and can be extended through the interface according to the scenario requirements.

[0054] After elaborating on the embodiments of the present invention in detail, a real-time monitoring method for a flexible manufacturing equipment based on a multi-source heterogeneous network provided by the embodiments of the present invention will be elaborated in detail below.

[0055] The embodiments of the present invention provide a real-time monitoring method for a flexible manufacturing equipment based on a multi-source heterogeneous network. This method is applied to a real-time monitoring system for a flexible manufacturing equipment based on a multi-source heterogeneous network. The real-time monitoring system for a flexible manufacturing equipment based on a multi-source heterogeneous network includes multiple flexible manufacturing equipments to be monitored, multiple sensors, and an edge computing node. The multiple sensors include a first sensor and a second sensor. The first sensor is used to obtain the operation data of the flexible manufacturing equipment to be monitored, and the second sensor is used to obtain the environmental data of the environment where the flexible manufacturing equipment to be monitored is located.

[0056] Among them, the flexible manufacturing equipment to be monitored can be the flexible equipment 1, flexible equipment 2, ……, other flexible equipments mentioned above. Figure 1 The first sensor is used to obtain the operation data of the flexible manufacturing equipment to be monitored, which can be the force sensor, displacement sensor, ……, other sensors mentioned above. Figure 1 The second sensor is used to obtain the environmental data of the environment where the flexible manufacturing equipment to be monitored is located, which is not shown in the above. Figure 1 For example, the second sensor can collect the temperature data and humidity data in the flexible manufacturing equipment laboratory; the second sensor can also be a camera or a radar, etc., so that in subsequent steps, it can be monitored whether there are abnormal personnel or abnormal instruments in the flexible manufacturing equipment laboratory.

[0057] As Figure 2 shown, the real-time monitoring method for a flexible manufacturing equipment based on a multi-source heterogeneous network can include the following steps:

[0058] S210, the first sensor sends the operation data to the edge computing node, and the second sensor sends the environmental data to the edge computing node.

[0059] Specifically, after the first sensor obtains the operation data of the flexible manufacturing equipment, it can transmit the operation data to the edge computing node; after the second sensor obtains the environmental data of the environment where the flexible manufacturing device is located, it can transmit the environmental data to the edge computing node.

[0060] Moreover, in order to solve the problem that various Internet of Things devices, flexible manufacturing equipments of different brands and different models cannot communicate and interconnect effectively. An edge unified gateway is set in the edge computing node in the embodiments of the present invention.

[0061] At this time, the first sensor sends the operation data to the edge computing node, and the second sensor sends the environmental data to the edge computing node, which can include the following two steps, namely step a1 and step a2:

[0062] Step a1, the first sensor sends operation data to the edge unified gateway of the edge computing node.

[0063] Step a2, the second sensor sends operation data to the edge unified gateway of the edge computing node.

[0064] In this embodiment, by using the OPC UA unified architecture to access data sources (multi-source heterogeneous), the problem that various IoT devices, flexible manufacturing equipment of different brands and models cannot communicate and interconnect effectively is solved.

[0065] In practical applications, there are usually multiple edge computing nodes. At this time, the specific implementation manners of S210, where the first sensor sends operation data to the edge computing node and the second sensor sends environment data to the edge computing node, can include the following two types.

[0066] The first implementation manner can be: the first sensor sends operation data of the same data type to the same edge computing node, and the second sensor sends environment data of the same data type to the same edge computing node.

[0067] Among them, operation data of different data types are sent to different edge computing nodes, and environment data of different data types are sent to different edge computing nodes.

[0068] Specifically, before the first sensor sends operation data to the edge computing node, the data type of the operation data can be determined, and then according to the data type, the operation data belonging to the same data type are transmitted to the same edge computing node. In this way, operation data of different data types are transmitted to different edge computing nodes, which helps to improve the data processing efficiency of the edge computing node and thus better monitor the flexible manufacturing device in real time.

[0069] Similarly, before the second sensor sends environment data to the edge computing node, the data type of the environment data can be determined, and then according to the data type, the environment data belonging to the same data type are transmitted to the same edge computing node. In this way, environment data of different data types are transmitted to different edge computing nodes, which helps to improve the data processing efficiency of the edge computing node and thus better monitor the flexible manufacturing device in real time.

[0070] The second implementation manner can be: the first sensor sends operation data of flexible manufacturing equipment in the same area to the same edge computing node, and the second sensor sends environment data in the same environment to the same edge computing node.

[0071] Among them, the operation data of flexible manufacturing equipment in different regions is sent to different edge computing nodes, and the environmental data in different environments is sent to different edge computing nodes.

[0072] Specifically, in this embodiment, the operation data can be sent according to the area where the flexible manufacturing equipment is located. The first sensor can send the operation data of the flexible manufacturing equipment belonging to the same area (area) to the same edge computing node. In this way, it helps the edge computing node to uniformly process the operation data of the flexible manufacturing equipment in the same area after receiving the operation data. Among them, how to divide the area can be determined according to the actual situation and is not specifically limited here.

[0073] Similarly, the second sensor can send the environmental data in the same environment (environmental area) to the same edge computing node. In this way, it helps the edge computing node to uniformly process the environmental data of the flexible manufacturing equipment in the same area after receiving the environmental data. Among them, how to divide the environmental area can also be determined according to the actual situation and is not specifically limited here.

[0074] Of course, the first sensor can send the operation data to the edge computing node with a relatively small current load, and the second sensor can also send the load data to the edge computing node with a relatively small current load. These are all reasonable and are not specifically limited in the embodiments of the present invention. In addition, in practical applications, multiple edge computing nodes can work together to process data.

[0075] S220, the edge computing node monitors whether there is an abnormality in the flexible manufacturing equipment to be monitored based on the operation data, and monitors whether there is an abnormality in the environment where the flexible manufacturing equipment to be monitored is located based on the environmental data.

[0076] Specifically, after receiving the operation data, the edge computing node can quickly and efficiently analyze the operation data to determine whether there is an abnormality in the flexible manufacturing equipment. For example, if the operation data includes the equipment operation temperature, the edge computing node can compare the equipment operation temperature with the preset temperature. If the equipment operation temperature is higher than the preset temperature, then it can be determined that there is an abnormality in the operation of the flexible manufacturing equipment to be monitored.

[0077] Similarly, after receiving the environmental data, the edge computing node can quickly and efficiently analyze the environmental data to determine whether there is an abnormality in the environment where the flexible manufacturing equipment is located. For example, if the environmental data includes the environmental temperature, the edge computing node can compare the environmental temperature with the preset temperature. If the environmental temperature is higher than the preset temperature, then it can be determined whether there is an abnormality in the environment where the flexible manufacturing equipment to be monitored is located.

[0078] By sinking data processing and analysis to the site through edge computing nodes, it can be quickly carried out near the data source, thus solving the problems of slow response and high latency caused by uploading data to the cloud data center for data processing, and realizing the real-time monitoring of flexible manufacturing equipment.

[0079] As an implementation manner of the embodiment of the present invention, the edge computing node includes an algorithm model pre-trained. Among them, the algorithm model is pre-trained on the cloud server.

[0080] At this time, the edge computing node monitors whether there is an abnormality in the flexible manufacturing equipment to be monitored based on the operation data, and monitors whether there is an abnormality in the environment where the flexible manufacturing equipment to be monitored is located based on the environment data, which may include the following two steps, namely step b1 and step b2:

[0081] Step b1, the edge computing node inputs the operation data into the algorithm model, outputs the first monitoring result through the algorithm model, and judges whether there is an abnormality in the flexible manufacturing equipment to be monitored based on the first monitoring result.

[0082] Step b2, the edge computing node inputs the environment data into the algorithm model, outputs the second monitoring result through the algorithm model, and judges whether there is an abnormality in the flexible manufacturing equipment to be monitored based on the second monitoring result.

[0083] Specifically, after receiving the operation data, the edge computing node can input the operation data into the algorithm model. The algorithm model can obtain the monitoring result through data reasoning on the operation data. For the sake of clear description of the solution, it can be called the first monitoring result. The first monitoring result can be various operation indicators of the current operation of the flexible manufacturing equipment. The edge computing node can judge whether the operation of the flexible manufacturing equipment is abnormal based on the various operation indicators.

[0084] Similarly, after receiving the environment data, the edge computing node inputs the environment data into the algorithm model, outputs the monitoring result through the algorithm model. For the sake of clear description of the solution, it can be called the second monitoring result, and judges whether there is an abnormality in the environment where the flexible manufacturing equipment to be monitored is located based on the second monitoring result. For example, if the algorithm model identifies that there are abnormal personnel or abnormal instruments entering the flexible manufacturing equipment laboratory, the edge computing node can judge that there is an abnormality in the flexible manufacturing equipment laboratory currently.

[0085] S230, when the edge computing node monitors that there is an abnormality in the flexible manufacturing equipment to be monitored, and / or, there is an abnormality in the environment where the flexible manufacturing equipment to be monitored is located, an abnormality prompt message is output.

[0086] Specifically, if the edge computing node monitors that there is an abnormality in the flexible manufacturing equipment, or the edge computing node monitors that there is an abnormality in the environment where the flexible manufacturing equipment is located, in order to enable professionals to perform abnormality handling as soon as possible, an abnormality prompt message can be output to the application layer.

[0087] As an implementation manner of the embodiment of the present invention, the edge computing node outputs an abnormality prompt message, which may include the following steps:

[0088] The edge computing node sends an abnormality prompt message to a preset target terminal, and the abnormality prompt message includes at least one of the following information: SMS, phone call, and email.

[0089] Specifically, the edge computing node can provide an application interface. Therefore, the edge computing node can send an SMS or an email to the user terminal through the application interface, and can also send an abnormality prompt message in the form of a phone call. Of course, in actual applications, there can be many forms of the abnormality prompt message, which will not be listed here. And the content of the abnormality prompt message is not specifically displayed either.

[0090] The technical solution provided by the embodiment of the present invention, the real-time monitoring method of the flexible manufacturing equipment based on the multi-source heterogeneous network is applied to the real-time monitoring system of the flexible manufacturing equipment based on the multi-source heterogeneous network. The real-time monitoring system of the flexible manufacturing equipment includes an edge computing node. By sinking data processing and analysis to the field through the edge computing node, it can be quickly carried out near the data source, thus solving the problems of slow response and high latency caused by uploading data to the cloud data center for data processing, so that the flexible manufacturing equipment can be monitored in real time. And, in the related art, there are problems of data security and privacy protection when transmitting a large amount of data to the data analysis platform in the cloud. By performing data processing at the edge computing node, technologies such as sensitive data desensitization and privacy enhancement can be realized, improving the security of the system.

[0091] On the basis of the above embodiment, in an implementation manner, the real-time monitoring system of the flexible manufacturing equipment based on the multi-source heterogeneous network further includes a data analysis platform in the cloud, and the method may further include the following steps, namely step c1 and step c2:

[0092] Step c1, the edge computing node sends operation data and environment data to the data analysis platform.

[0093] Step c2, the data analysis platform stores the operation data and environment data, and performs data analysis and data mining on the operation data and environment data.

[0094] Specifically, the edge computing node can not only analyze the operation data and environmental data, but also send the operation data and environmental data to the data analysis platform in the cloud. The data analysis platform in the cloud can store the operation data and environmental data, and perform data analysis and data mining on the operation data and environmental data. In this way, the data analysis platform in the cloud can provide services such as resource management, remote monitoring, application governance, model training, deployment and upgrade, and provide corresponding application interfaces for the application layer. Since the data analysis platform in the cloud does not have such high requirements for the real-time performance of data transmission, it will not be affected by network problems such as narrow network bandwidth.

[0095] In a second aspect, an embodiment of the present invention provides a real-time monitoring system 30 for a flexible manufacturing equipment based on a multi-source heterogeneous network, as Figure 3 shown. The real-time monitoring system for the flexible manufacturing equipment based on the multi-source heterogeneous network includes a plurality of flexible manufacturing equipments 310 to be monitored, a plurality of sensors 320, and an edge computing node 330 (usually there are multiple, so multiple are shown in Figure 3 ). The plurality of sensors 320 include a first sensor 321 and a second sensor 322. The first sensor 321 is used to obtain the operation data of the flexible manufacturing equipment to be monitored, and the second sensor 322 is used to obtain the environmental data of the environment where the flexible manufacturing equipment to be monitored is located;

[0096] The first sensor 321 sends the operation data to the edge computing node 330, and the second sensor 322 sends the environmental data to the edge computing node 330;

[0097] The edge computing node 330 monitors whether the flexible manufacturing equipment to be monitored is abnormal based on the operation data, and monitors whether the environment where the flexible manufacturing equipment to be monitored is located is abnormal based on the environmental data;

[0098] When the edge computing node 330 monitors that the flexible manufacturing equipment to be monitored is abnormal and / or the environment where the flexible manufacturing equipment to be monitored is located is abnormal, it outputs an abnormal prompt message.

[0099] In a third aspect, an embodiment of the present invention provides an electronic device 400, as Figure 4 shown, including:

[0100] At least one processor 401;

[0101] A memory 402 for storing instructions executable by the at least one processor;

[0102] Wherein, the at least one processor is configured to execute the instructions to implement the method described in the first aspect.

[0103] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium. When instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the method described in the first aspect.

[0104] In a fifth aspect, an embodiment of the present invention provides a computer program product, including a computer program which, when executed by a processor, implements the method described in the first aspect.

[0105] Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention without departing from the principles and spirit of the present invention.

Claims

1. A real-time monitoring method for flexible manufacturing equipment based on multi-source heterogeneous networks, characterized in that: The method is applied to a flexible manufacturing equipment real-time monitoring system based on a multi-source heterogeneous network, the flexible manufacturing equipment real-time monitoring system based on a multi-source heterogeneous network includes a plurality of flexible manufacturing equipment to be monitored, a plurality of sensors and an edge computing node, the plurality of sensors include a first sensor and a second sensor, the first sensor is used to obtain operation data of the flexible manufacturing equipment to be monitored, and the second sensor is used to obtain environmental data of the environment in which the flexible manufacturing equipment to be monitored is located; the method includes: The first sensor sends the operation data to the edge computing node, and the second sensor sends the environmental data to the edge computing node; The edge computing node monitors whether the flexible manufacturing equipment to be monitored has an abnormality based on the operation data, and monitors whether the environment in which the flexible manufacturing equipment to be monitored is located has an abnormality based on the environmental data; When the edge computing node detects that the flexible manufacturing equipment to be monitored has an abnormality and / or that the environment in which the flexible manufacturing equipment to be monitored is located has an abnormality, the edge computing node outputs abnormality prompt information.

2. The method according to claim 1, characterized in that The edge computing node includes a pre-trained algorithm model; The edge computing node monitors whether the flexible manufacturing equipment to be monitored has an abnormality based on the operation data, and monitors whether the environment in which the flexible manufacturing equipment to be monitored is located has an abnormality based on the environmental data, including: The edge computing node inputs the operation data into the algorithm model, outputs a first monitoring result through the algorithm model, and determines whether the flexible manufacturing equipment to be monitored is abnormal based on the first monitoring result; The edge computing node inputs the environmental data into the algorithm model, outputs a second monitoring result through the algorithm model, and determines whether there is an abnormality in the flexible manufacturing equipment to be monitored based on the second monitoring result.

3. The method according to claim 1, characterized in that The edge computing node is provided with an edge unified gateway; The first sensor sends the operation data to the edge computing node, and the second sensor sends the environment data to the edge computing node, including: The first sensor sends the operation data to the edge unified gateway of the edge computing node; The second sensor sends the operating data to the edge unified gateway of the edge computing node.

4. The method according to claim 1, characterized in that: There are multiple edge computing nodes; The first sensor sends the operation data to the edge computing node, and the second sensor sends the environment data to the edge computing node, including: The first sensor sends the operating data of the same data type to the same edge computing node, and the second sensor sends the environmental data of the same data type to the same edge computing node; wherein the operating data of different data types are sent to different edge computing nodes, and the environmental data of different data types are sent to different edge computing nodes; or, The first sensor sends the operating data of flexible manufacturing equipment in the same area to the same edge computing node, and the second sensor sends the environmental data in the same environment to the same edge computing node; wherein, the operating data of flexible manufacturing equipment in different areas are sent to different edge computing nodes, and the environmental data in different environments are sent to different edge computing nodes.

5. The method according to any one of claims 1 to 4, characterized in that: The flexible manufacturing equipment real-time monitoring system based on multi-source heterogeneous network also includes a cloud data analysis platform, and the method also includes: The edge computing node sends the operation data and the environment data to the data analysis platform; The data analysis platform stores the operation data and the environment data, and performs data analysis and data mining on the operation data and the environment data.

6. The method according to any one of claims 1 to 4, characterized in that: The edge computing node outputs abnormal prompt information, including: The edge computing node sends abnormal prompt information to a preset target terminal, and the abnormal prompt information includes at least one of the following information: text message, phone call and email.

7. A real-time monitoring system for flexible manufacturing equipment based on multi-source heterogeneous networks, characterized in that: The flexible manufacturing equipment real-time monitoring system based on a multi-source heterogeneous network includes a plurality of flexible manufacturing equipment to be monitored, a plurality of sensors, and an edge computing node, wherein the plurality of sensors include a first sensor and a second sensor, wherein the first sensor is used to obtain operation data of the flexible manufacturing equipment to be monitored, and the second sensor is used to obtain environmental data of an environment in which the flexible manufacturing equipment to be monitored is located; The first sensor sends the operation data to the edge computing node, and the second sensor sends the environmental data to the edge computing node; The edge computing node monitors whether the flexible manufacturing equipment to be monitored has an abnormality based on the operation data, and monitors whether the environment in which the flexible manufacturing equipment to be monitored is located has an abnormality based on the environmental data; When the edge computing node detects that the flexible manufacturing equipment to be monitored has an abnormality and / or that the environment in which the flexible manufacturing equipment to be monitored is located has an abnormality, the edge computing node outputs abnormality prompt information.

8. An electronic device, characterized in that: include: at least one processor; a memory for storing the at least one processor-executable instruction; The at least one processor is configured to execute the instructions to implement the method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that: When the instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the method according to any one of claims 1 to 6.

10. A computer program product, characterized in that The method comprises a computer program, which implements the method according to any one of claims 1 to 6 when being executed by a processor.

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