Real-time industrial process optimization control system based on edge computing

Abstract

The present invention relates to the technical field of industrial process optimization and control, and in particular to a real-time industrial process optimization and control system based on edge computing, comprising: an acquisition module, an execution module, a data transmission module, an edge computing module, a mobile computing module, a cloud collaboration module, a centralized control module and a local storage module; it provides sufficient computing power for edge computing modules that need to perform complex data analysis by flying drones in factories, thereby improving the processing capability of complex data, and the data is not shipped out of the factory, reducing the risk of data leakage during transmission; secondly, a chip with relatively strong computing power is used to provide computing power for multiple edge computing modules in an area, thereby reducing costs and improving maintenance convenience; furthermore, when an edge computing module fails, the mobile computing module can temporarily replace the failed edge computing module, so that the production equipment continues to maintain stable operation and provides sufficient maintenance time for maintenance personnel.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial process optimization control, and in particular to a real-time industrial process optimization control system based on edge computing. Background Art

[0002] Edge computing is a distributed computing framework that moves core capabilities like computing, storage, and networking from the cloud to the edge of the network. Its core purpose is to provide real-time services through edge nodes close to data sources, meeting the low latency, high bandwidth, and security requirements of industrial scenarios. Edge computing has been widely used in industrial production, with examples including the edge computing-based industrial Internet optimization system disclosed in invention patent publication number CN108924228B and the edge computing-based industrial Internet optimization method and system disclosed in invention patent publication number CN116614366B. Both facilitate the continuous optimization and adjustment of equipment operating parameters during industrial production, ensuring production stability.

[0003] However, during use, it was found that the edge computing devices had poor computing power and had difficulty processing complex data. Although the problem of poor computing power of edge computing devices can be solved by improving the computing power of edge computing devices with chips with stronger computing power, there are many edge computing devices used in factories, and the cost of replacing chips with stronger computing power is high, and they are not always required to perform high-intensity data analysis. In addition, due to the complex environment in the factory, edge computing devices are generally sealed structures, and chips with stronger computing power have high temperatures during operation, making them difficult to use in such an environment, resulting in poor practicality. Therefore, there is an urgent need for a real-time industrial process optimization control system based on edge computing to improve the above problems. Summary of the Invention

[0004] In order to solve the above technical problems, the present invention provides a method of using drones to fly in factories to provide sufficient computing power for edge computing modules that need to perform complex data analysis, thereby improving the ability to process complex data, and the data has not left the factory, reducing the risk of data leakage during transmission; secondly, through a chip with strong computing power, computing power is provided for multiple edge computing modules in an area, reducing costs and improving maintenance convenience; and when an edge computing module fails, the mobile computing module can temporarily replace the failed edge computing module, so that the production equipment can continue to maintain stable operation, providing maintenance personnel with sufficient maintenance time based on edge computing real-time industrial process optimization control system.

[0005] The real-time industrial process optimization control system based on edge computing of the present invention includes:

[0006] Collection module: collects the operating data of production equipment;

[0007] Execution module: performs corresponding actions according to the received signals to ensure stable operation of production equipment;

[0008] Data transmission module: transmits the data collected by the acquisition module and provides a data transmission route for the execution module, edge computing module and mobile computing module;

[0009] Edge computing module: Receives and processes data sent by the data transmission module, analyzes the processed data, determines whether the production equipment is running stably and whether the production meets the standards, generates an optimization plan, and transmits the optimization plan to the execution module. In addition, when the edge computing module encounters insufficient computing power for complex data during data processing, it sends a signal to the mobile computing module.

[0010] Mobile computing module: After receiving the signal from the edge computing module, the mobile computing module moves to the vicinity of the edge computing module that sent the signal, so that the data transmission module sends a copy of the same data processed by the edge computing module to the mobile computing module. The mobile computing module analyzes and processes the data, and transmits the analysis results directly to the execution module, and sends an execution signal to the edge computing module indicating that the analysis is complete.

[0011] Cloud collaboration module: updates and optimizes models in the edge computing module and mobile computing module;

[0012] Centralized control module: Centrally controls and manages the acquisition module, execution module, data transmission module, edge computing module, mobile computing module, and local storage module, verifies the identity information of login personnel, and records the system's operating data;

[0013] Local storage module: Classifies the received data and stores it within a limited time, providing sufficient data for the continuously optimized edge computing module and mobile computing module;

[0014] By flying drones in the factory, sufficient computing power is provided for edge computing modules that need to perform complex data analysis, thereby improving the ability to process complex data. Moreover, the data has not left the factory, reducing the risk of data leakage during transmission. Secondly, a chip with strong computing power can provide computing power for multiple edge computing modules in an area, reducing costs and improving maintenance convenience. Finally, when an edge computing module fails, the mobile computing module can temporarily replace the failed edge computing module, so that the production equipment can continue to maintain stable operation and provide maintenance personnel with sufficient maintenance time.

[0015] Preferably, the acquisition module includes:

[0016] Power management unit: provides stable power to the signal conditioning unit and acquisition unit;

[0017] Signal conditioning unit: adjusts the signal of the acquisition unit;

[0018] Acquisition unit: acquires the operating data of the production equipment and transmits the acquired data to the data transmission module.

[0019] Preferably, the execution module includes:

[0020] Power supply unit: provides power to the drive unit;

[0021] Drive unit: provides power to the actuator;

[0022] Actuator: After receiving the optimization plan, it installs the parameters in the optimization plan to adjust the operating conditions of the production equipment.

[0023] Preferably, the edge computing module includes:

[0024] Real-time data engine: collects device-level data in milliseconds and aligns the data’s timestamps;

[0025] Dynamic optimization unit: uses a lightweight learning model to analyze processed data and derive optimization solutions to provide stable operating parameters for production equipment;

[0026] Fault-tolerant control unit: Sends a signal to the mobile computing module when the network is abnormal or data analysis is stuck.

[0027] Preferably, the mobile computing module includes:

[0028] Flying mechanism: Flying in the air with a wire, close to the edge computing module that sends the signal;

[0029] Support mechanism: supports the wires on the flight mechanism to ensure smooth flight of the flight mechanism;

[0030] Digital twin model: The data factory scene and the distribution of supporting structures and edge computing modules enable repeated flight simulations, optimal route planning, and flight stability.

[0031] Convolutional neural network model: During flight, it analyzes images acquired by the flight mechanism to enable it to avoid obstacles.

[0032] Wireless communication model: wirelessly receives data transmitted by the data transmission module or sends the analyzed optimization solution to the data transmission module;

[0033] Recurrent neural network model: Analyzes and processes the received data and comes up with an optimization solution.

[0034] Preferably, the centralized control module includes:

[0035] Central control unit: Centrally controls and manages the acquisition module, execution module, data transmission module, edge computing module, mobile computing module, and local storage module;

[0036] Identity verification unit: Verify the identity information of the login personnel;

[0037] Operation data recording unit: records the system's operation data and transmits the recorded data to the local storage module.

[0038] Preferably, the local storage module includes:

[0039] Data classification unit: classify data according to the data sending source;

[0040] Data management unit: sets a storage period for the classified data based on the time of receipt, and deletes data that exceeds the storage period to ensure that the data storage unit has sufficient storage space;

[0041] Data storage unit: stores data.

[0042] Preferably, the flight mechanism includes a drone, a millimeter-wave radar, a high-definition camera, a rotating shaft, a power supply line and a servo motor. The millimeter-wave radar, the high-definition camera and the servo motor are all installed on the drone. The rotating shaft is rotatably installed on the drone. The power supply line is wound on the drone, and one end of the power supply line is connected to the inside of the drone, and the other end of the power supply line is connected to the power supply. The servo motor provides power for the power supply line. The drone moves in the air, and at the same time obtains a radar image in front of the drone through the millimeter-wave radar, and obtains a high-definition image in front of the drone through the high-definition camera. After analysis by the convolutional neural network, the drone avoids obstacles on the way. In the process of flight of the drone, the servo motor drives the rotating shaft to rotate, releases the power supply line, and allows the power supply line to continuously supply power to the drone, thereby ensuring the endurance performance of the drone.

[0043] Preferably, the support mechanism includes a bracket and a support shaft, the bracket is installed at a high place in the factory, and the support shaft is installed on the bracket; the support shaft supports the power supply line by passing the drone over the support shaft.

[0044] Preferably, the surface of the support shaft has an anti-slip structure, which improves the supporting effect of the support shaft on the power supply line.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] 1. By flying drones in factories, sufficient computing power is provided for edge computing modules that need to perform complex data analysis, improving the ability to process complex data. In addition, real-time data is not sent out of the factory, reducing the risk of data leakage during transmission.

[0047] 2. A single chip with high computing power can provide computing power for multiple edge computing modules in an area, reducing costs and improving maintenance convenience.

[0048] 3. When an edge computing module fails, the mobile computing module can temporarily replace the failed edge computing module, allowing the production equipment to continue to operate stably and providing maintenance personnel with sufficient maintenance time. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a structural diagram of the real-time industrial process optimization control system based on edge computing of the present invention;

[0050] Figure 2 It is a structural diagram of the acquisition module of the present invention;

[0051] Figure 3 It is a structural diagram of the execution module of the present invention;

[0052] Figure 4 It is a structural diagram of the edge computing module of the present invention;

[0053] Figure 5 It is a structural diagram of the mobile computing module of the present invention;

[0054] Figure 6 It is a structural diagram of the centralized control module of the present invention;

[0055] Figure 7 It is a structural diagram of the local storage module of the present invention;

[0056] Figure 8 It is a first isometric structural diagram of the flight mechanism and the support mechanism of the present invention;

[0057] Figure 9 It is a second isometric structural diagram of the flight mechanism and the support mechanism of the present invention;

[0058] Figure 10 It is a front view structural schematic diagram of the flight mechanism and the support mechanism of the present invention.

[0059] Markings in the attached figure: 1. UAV; 2. Millimeter-wave radar; 3. High-definition camera; 4. Rotating shaft; 5. Power supply line; 6. Servo motor; 7. Bracket; 8. Support shaft. DETAILED DESCRIPTION

[0060] To facilitate understanding of the present invention, the present invention will be described more fully below with reference to the accompanying drawings. The present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive.

[0061] like Figures 1 to 10 As shown in the figure, the real-time industrial process optimization control system based on edge computing includes:

[0062] Acquisition module: collects the operating data of production equipment (temperature, pressure and flow of production equipment, etc.);

[0063] Execution module: performs corresponding actions according to the received signals to ensure stable operation of production equipment (such as motors, valves and other electrical components);

[0064] Data transmission module: transmits the data collected by the acquisition module and provides a data transmission route for the execution module, edge computing module and mobile computing module;

[0065] Edge computing module: Receives and processes data sent by the data transmission module, analyzes the processed data, determines whether the production equipment is running stably and whether the production meets the standards, generates an optimization plan, and transmits the optimization plan to the execution module. In addition, when the edge computing module encounters insufficient computing power for complex data during data processing, it sends a signal to the mobile computing module.

[0066] Mobile computing module: After receiving the signal from the edge computing module, the mobile computing module moves to the vicinity of the edge computing module that sent the signal, so that the data transmission module sends a copy of the same data processed by the edge computing module to the mobile computing module. The mobile computing module analyzes and processes the data, and transmits the analysis results directly to the execution module, and sends an execution signal to the edge computing module indicating that the analysis is complete.

[0067] Cloud collaboration module: updates and optimizes models in the edge computing module and mobile computing module;

[0068] Centralized control module: Centrally controls and manages the acquisition module, execution module, data transmission module, edge computing module, mobile computing module, and local storage module, verifies the identity information of login personnel, and records the system's operating data;

[0069] Local storage module: Classifies the received data and stores it within a limited time, providing sufficient data for the continuously optimized edge computing module and mobile computing module;

[0070] The acquisition module includes:

[0071] Power management unit: provides stable power to the signal conditioning unit and acquisition unit;

[0072] Signal conditioning unit: adjusts the signal of the acquisition unit;

[0073] Acquisition unit; acquires the operating data of the production equipment and transmits the acquired data to the data transmission module;

[0074] The execution module includes:

[0075] Power supply unit: provides power to the drive unit;

[0076] Drive unit: provides power to the actuator;

[0077] Actuator: After receiving the optimization plan, it installs the parameters in the optimization plan to adjust the operating conditions of the production equipment;

[0078] The edge computing module includes:

[0079] Real-time data engine: collects device-level data in milliseconds and aligns the data’s timestamps;

[0080] Dynamic optimization unit: uses a lightweight learning model to analyze processed data and derive optimization solutions to provide stable operating parameters for production equipment;

[0081] Fault-tolerant control unit: Sends a signal to the mobile computing module when the network is abnormal or data analysis is stuck;

[0082] The mobile computing module includes:

[0083] Flying mechanism: Flying in the air with a wire, close to the edge computing module that sends the signal;

[0084] Support mechanism: supports the wires on the flight mechanism to ensure smooth flight of the flight mechanism;

[0085] Digital twin model: The data factory scene and the distribution of supporting structures and edge computing modules enable repeated flight simulations, optimal route planning, and flight stability.

[0086] Convolutional neural network model: During flight, it analyzes images acquired by the flight mechanism to enable it to avoid obstacles.

[0087] Wireless communication model: wirelessly receives data transmitted by the data transmission module or sends the analyzed optimization solution to the data transmission module;

[0088] Recurrent neural network model: analyzes and processes the received data and comes up with an optimization solution;

[0089] The centralized control module includes:

[0090] Central control unit: Centrally controls and manages the acquisition module, execution module, data transmission module, edge computing module, mobile computing module, and local storage module;

[0091] Identity verification unit: Verify the identity information of the login personnel;

[0092] Operation data recording unit: records the system's operation data and transmits the recorded data to the local storage module;

[0093] The local storage module includes:

[0094] Data classification unit: classify data according to the data sending source;

[0095] Data management unit: sets a storage period for the classified data based on the time of receipt, and deletes data that exceeds the storage period to ensure that the data storage unit has sufficient storage space;

[0096] Data storage unit: stores data;

[0097] The flight mechanism includes a drone 1, a millimeter-wave radar 2, a high-definition camera 3, a rotating shaft 4, a power supply line 5, and a servo motor 6. The millimeter-wave radar 2, the high-definition camera 3, and the servo motor 6 are all installed on the drone 1. The rotating shaft 4 is rotatably installed on the drone 1. The power supply line 5 is wound on the drone 1, and one end of the power supply line 5 is connected to the inside of the drone 1, and the other end of the power supply line 5 is connected to a power source. The servo motor 6 provides power for the power supply line 5.

[0098] The support mechanism includes a bracket 7 and a support shaft 8. The bracket 7 is installed at a high place in the factory building, and the support shaft 8 is installed on the bracket 7;

[0099] The surface of the support shaft 8 is an anti-slip structure.

[0100] The lightweight learning model adopted by the dynamic optimization unit can be adjusted according to the type of lightweight learning model in actual use; the real-time industrial process optimization control system based on edge computing of the present invention, its installation method, connection method or setting method are all common mechanical methods, as long as it can achieve its beneficial effects, it can be implemented; the support mechanism is multiple groups, and is distributed according to the actual factory situation; the drone 1, millimeter wave radar 2, high-definition camera 3, power supply line 5 and servo motor 6 of the real-time industrial process optimization control system based on edge computing of the present invention are purchased on the market, and technicians in this industry only need to install and operate them according to the accompanying instruction manual, without the need for technical personnel in this field to pay creative labor.

[0101] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. Real-time industrial process optimization control system based on edge computing, characterized by: include: Collection module: collects the operating data of production equipment; Execution module: performs corresponding actions according to the received signals to ensure stable operation of production equipment; Data transmission module: transmits the data collected by the acquisition module and provides a data transmission route for the execution module, edge computing module and mobile computing module; Edge computing module: Receives and processes data sent by the data transmission module, analyzes the processed data, determines whether the production equipment is running stably and whether the production meets the standards, generates an optimization plan, and transmits the optimization plan to the execution module. In addition, when the edge computing module encounters insufficient computing power for complex data during data processing, it sends a signal to the mobile computing module. Mobile computing module: After receiving the signal from the edge computing module, the mobile computing module moves to the vicinity of the edge computing module that sent the signal, so that the data transmission module sends a copy of the same data processed by the edge computing module to the mobile computing module. The mobile computing module analyzes and processes the data, and transmits the analysis results directly to the execution module, and sends an execution signal to the edge computing module indicating that the analysis is complete. Cloud collaboration module: updates and optimizes models in the edge computing module and mobile computing module; Centralized control module: Centrally controls and manages the acquisition module, execution module, data transmission module, edge computing module, mobile computing module, and local storage module, verifies the identity information of login personnel, and records the system's operating data; Local storage module: Classifies the received data and stores it within a limited time, providing sufficient data for the continuously optimized edge computing module and mobile computing module; The mobile computing module includes: Flying mechanism: Flying in the air with a wire, close to the edge computing module that sends the signal; Support mechanism: supports the wires on the flight mechanism to ensure smooth flight of the flight mechanism; Digital twin model: The data factory scene and the distribution of supporting structures and edge computing modules enable repeated flight simulations, optimal route planning, and flight stability. Convolutional neural network model: During flight, it analyzes images acquired by the flight mechanism to enable it to avoid obstacles. Wireless communication model: wirelessly receives data transmitted by the data transmission module or sends the analyzed optimization solution to the data transmission module; Recurrent neural network model: analyzes and processes the received data and comes up with an optimization solution; The flight mechanism comprises a drone (1), a millimeter-wave radar (2), a high-definition camera (3), a rotating shaft (4), a power supply line (5) and a servo motor (6); the millimeter-wave radar (2), the high-definition camera (3) and the servo motor (6) are all mounted on the drone (1); the rotating shaft (4) is rotatably mounted on the drone (1); the power supply line (5) is wound on the drone (1); one end of the power supply line (5) is connected to the inside of the drone (1); the other end of the power supply line (5) is connected to a power source; and the servo motor (6) provides power for the power supply line (5).

2. The real-time industrial process optimization control system based on edge computing according to claim 1, characterized in that: The acquisition module includes: Power management unit: provides stable power to the signal conditioning unit and acquisition unit; Signal conditioning unit: adjusts the signal of the acquisition unit; Acquisition unit: acquires the operating data of the production equipment and transmits the acquired data to the data transmission module.

3. The real-time industrial process optimization control system based on edge computing according to claim 1, characterized in that: The execution module includes: Power supply unit: provides power to the drive unit; Drive unit: provides power to the actuator; Actuator: After receiving the optimization plan, it installs the parameters in the optimization plan to adjust the operating conditions of the production equipment.

4. The real-time industrial process optimization control system based on edge computing according to claim 1, characterized in that: The edge computing module includes: Real-time data engine: collects device-level data in milliseconds and aligns the data’s timestamps; Dynamic optimization unit: uses a lightweight learning model to analyze processed data and derive optimization solutions to provide stable operating parameters for production equipment; Fault-tolerant control unit: Sends a signal to the mobile computing module when the network is abnormal or data analysis is stuck.

5. The real-time industrial process optimization control system based on edge computing according to claim 1, characterized in that: The centralized control module includes: Central control unit: Centrally controls and manages the acquisition module, execution module, data transmission module, edge computing module, mobile computing module, and local storage module; Identity verification unit: Verify the identity information of the login personnel; Operation data recording unit: records the system's operation data and transmits the recorded data to the local storage module.

6. The real-time industrial process optimization control system based on edge computing according to claim 1, characterized in that: The local storage module includes: Data classification unit: classify data according to the data sending source; Data management unit: sets a storage period for the classified data based on the time of receipt, and deletes data that exceeds the storage period to ensure that the data storage unit has sufficient storage space; Data storage unit: stores data.

7. The real-time industrial process optimization control system based on edge computing according to claim 1, characterized in that: The support mechanism comprises a bracket (7) and a support shaft (8); the bracket (7) is installed at a high place in the factory building, and the support shaft (8) is installed on the bracket (7).

8. The real-time industrial process optimization control system based on edge computing according to claim 7, characterized in that: The surface of the support shaft (8) has an anti-slip structure.

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