Gantry crane cloud edge collaborative intelligent management and control system

Abstract

The application relates to the technical field of intelligent management and control of gantry cranes, in particular to a cloud-edge collaborative intelligent management and control system for a gantry crane, which comprises a cloud server, an edge computing device and a gantry crane body, the cloud server is used for storing and processing data generated in the operation process of the gantry crane, the edge computing device is located at the site of the gantry crane and is used for processing the operation data of the gantry crane in real time and interacting with the cloud server, the gantry crane body comprises a driver, a sensor and a controller, the driver is used for realizing the movement of the crane, the sensor is used for collecting the operation data of the gantry crane in real time, and the controller controls the operation of the driver according to the instructions of the cloud server and the edge computing device. The application can realize real-time intelligent monitoring and remote control of the gantry crane, improve the operation efficiency and reduce the safety risk, and can also improve the efficiency and stability of the system through cloud-edge collaboration.

Description

Technical Field

[0001] This invention relates to the field of intelligent control technology for gantry cranes, and more particularly to a cloud-edge collaborative intelligent control system for gantry cranes. Background Technology

[0002] Gantry cranes are heavy equipment widely used in ports, factories, freight yards and other places. Their main function is to carry out large-scale cargo handling and loading and unloading operations. Due to the complexity of operation and safety requirements of gantry cranes, traditional manual or semi-automatic control methods can no longer meet the needs of modern logistics and manufacturing.

[0003] Existing gantry crane control systems mainly rely on manual operation, which is inefficient and prone to safety accidents caused by operational errors. In addition, although existing intelligent control systems can achieve a certain degree of automation and remote control, they still have problems such as data transmission delay and low system stability. Summary of the Invention

[0004] To achieve the above objectives, the present invention provides a cloud-edge collaborative intelligent management and control system for gantry cranes.

[0005] The cloud-edge collaborative intelligent control system for gantry cranes includes a cloud server, edge computing devices, the gantry crane itself, and a user interface module.

[0006] The cloud server is used to store and process data generated during the operation of the gantry crane, and also has intelligent scheduling and optimization functions;

[0007] The edge computing device is located at the gantry crane site and is used to process the gantry crane's operating data in real time and to interact with the cloud server.

[0008] The gantry crane body includes a drive, sensors, and a controller; the drive is used to realize the movement of the crane; the sensors are used to collect the operating data of the gantry crane in real time and transmit the data to the edge computing device; the controller controls the operation of the drive according to the instructions of the cloud server and the edge computing device. Through this system, real-time intelligent monitoring and remote control of the gantry crane can be realized, improving work efficiency and reducing safety risks.

[0009] The user interface module is used to display the operating status information of the gantry crane and to receive user operation commands.

[0010] Furthermore, the cloud server possesses real-time data analysis, historical data mining, predictive maintenance, intelligent scheduling, and remote control capabilities.

[0011] The real-time data analysis involves analyzing the real-time data of the gantry crane's operating status, operating efficiency, and energy consumption to obtain its operating characteristics.

[0012] The historical data mining refers to the in-depth mining of historical operational data through big data technology and machine learning algorithms.

[0013] The predictive maintenance: Based on historical and real-time data, predict potential failures and maintenance needs of the gantry crane;

[0014] The intelligent scheduling refers to the intelligent scheduling of operations based on the actual operating status and task requirements of the gantry crane.

[0015] The remote control refers to the remote monitoring and operation of the gantry crane via the network, and the provision of optimized operation strategies for the gantry crane through data interaction with edge computing devices.

[0016] Furthermore, the edge computing device has the ability to process data in real time and make rapid decisions, and it can interact with the cloud server to achieve intelligent collaboration between local and cloud environments, thereby further improving the efficiency and stability of the system.

[0017] Furthermore, the sensors include a position sensor, a speed sensor, an acceleration sensor, a temperature sensor, and a pressure sensor.

[0018] The position sensor is used to obtain the crane's operating position in real time;

[0019] The speed sensor is used to monitor the operating speed of the crane;

[0020] The acceleration sensor is used to acquire the crane's motion acceleration and further monitor its motion status;

[0021] The temperature sensor is used to detect the temperature of various key parts of the crane in order to prevent overheating and other malfunctions.

[0022] The pressure sensors are used to monitor various pressure changes that may occur during the operation of the crane, thereby ensuring the stable operation of the crane. Together, these sensors provide the system with comprehensive, real-time, and accurate operating data.

[0023] Furthermore, the controller precisely controls the drive according to instructions from the cloud server and edge computing devices, including start-up, stop-up, speed adjustment, and direction change operations, ensuring the smooth, safe, and efficient operation of the gantry crane. The controller uses advanced control algorithms to parse and execute instructions from the cloud server and edge computing devices, precisely controlling the drive. During start-up and stop, the controller starts and stops based on real-time operating status and equipment parameters. During speed adjustment, the controller precisely adjusts the speed according to operational requirements and real-time operating status to meet different operational needs. During direction change, the controller precisely controls the drive's steering based on real-time operating status and instructions to achieve smooth and accurate direction changes. The design and implementation of the controller ensures the smooth, safe, and efficient operation of the gantry crane under various working conditions, improving operational efficiency and reducing safety risks.

[0024] Furthermore, the user interface module can display the crane's operating parameters, such as position, speed, acceleration, temperature, and pressure, in real time, as well as the status information of the cloud server and edge computing devices. The user interface module can also display system warnings and alarms, allowing users to promptly understand and address potential problems. It also receives user operation commands, such as start, stop, speed adjustment, and direction change commands, and transmits these commands to the cloud server and edge computing devices, thereby enabling remote control of the crane. The user interface module is designed with an easy-to-understand and easy-to-operate interface layout and operation method, providing a user-friendly interactive experience, allowing users to conveniently and quickly operate and manage the gantry crane cloud-edge collaborative intelligent management and control system.

[0025] Furthermore, the cloud server uses machine learning and artificial intelligence technologies to deeply mine and learn from historical data, thereby generating optimized operating strategies.

[0026] Furthermore, the real-time data processing and rapid decision-making capabilities enable the independent completion of some data processing and decision-making tasks even when the cloud server is unavailable or experiences excessive latency, ensuring the continuous and stable operation of the gantry crane. The edge computing device also features efficient edge computing algorithms such as stream computing and fog computing, enabling rapid processing and analysis of received data, and generating control commands for the drive based on the processing results. The edge computing device also possesses data preprocessing capabilities, capable of cleaning, transforming, and compressing large amounts of received raw data to reduce network transmission pressure and improve data processing efficiency. After network connectivity is restored, the edge computing device can feed back the processing results to the cloud server, achieving intelligent management through cloud-edge collaboration.

[0027] Furthermore, the sensor is designed with high precision and high sensitivity to ensure the accuracy and real-time performance of data acquisition.

[0028] Furthermore, the controller employs advanced algorithms including fuzzy control. This fuzzy control calculates the matching degree of each fuzzy rule based on the fuzzification of the system state and the fuzzy rules in the rule base, and determines the control signal of the driver based on these matching degrees. The specific formula can be expressed as: U = ∑(μi*Ui) / ∑μi, where U is the control signal, μi is the matching degree of the i-th fuzzy rule, and Ui is the control signal corresponding to the i-th fuzzy rule.

[0029] The beneficial effects of this invention are:

[0030] This invention achieves real-time intelligent monitoring and remote control of gantry cranes through the collaborative work of cloud servers, edge computing devices, and the gantry crane itself. By adopting a cloud-edge collaborative approach, it can fully utilize the powerful computing and big data processing capabilities of the cloud while leveraging the real-time data processing and rapid decision-making capabilities of edge computing devices, thereby further improving the system's efficiency and stability. The system employs multiple sensors to comprehensively monitor the gantry crane's operating status, providing accurate data support, enabling the system to make more intelligent and optimized decisions, thus significantly improving operational efficiency and reducing safety risks. Attached Figure Description

[0031] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only for this invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0032] Figure 1 This is a schematic diagram of the intelligent system logic according to an embodiment of the present invention;

[0033] Figure 2 This is a schematic diagram of the gantry crane body according to an embodiment of the present invention;

[0034] Figure 3 This is a schematic diagram of a cloud server according to an embodiment of the present invention. Detailed Implementation

[0035] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0036] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0037] like Figure 1-3 As shown, the cloud-edge collaborative intelligent control system for gantry cranes includes a cloud server, edge computing devices, the gantry crane itself, and a user interface module.

[0038] The cloud server is used to store and process the data generated during the operation of the gantry crane, and also has intelligent scheduling and optimization functions;

[0039] Edge computing devices are located at the gantry crane site to process the gantry crane's operating data in real time and to interact with cloud servers.

[0040] The gantry crane body includes a drive, sensors, and a controller. The drive is used to realize the movement of the crane. The sensors are used to collect the operating data of the gantry crane in real time and transmit the data to the edge computing device. The controller controls the operation of the drive according to the instructions of the cloud server and the edge computing device. Through this system, real-time intelligent monitoring and remote control of the gantry crane can be realized, improving operation efficiency and reducing safety risks.

[0041] The user interface module is used to display the operating status information of the gantry crane and to receive user operation commands.

[0042] Cloud servers are equipped with real-time data analysis, historical data mining, predictive maintenance, intelligent scheduling, and remote control.

[0043] Real-time data analysis: Analyze the real-time data of the gantry crane's operating status, operating efficiency, and energy consumption to obtain its operating characteristics;

[0044] Historical data mining: Deeply mining historical operational data through big data technology and machine learning algorithms;

[0045] Predictive maintenance: Based on historical and real-time data, predict potential failures and maintenance needs of gantry cranes;

[0046] Intelligent scheduling: Intelligent operation scheduling is performed based on the actual operating status and task requirements of the gantry crane;

[0047] Remote control: Remotely monitor and operate the gantry crane via the network, and provide optimized operation strategies for the gantry crane through data interaction with edge computing devices.

[0048] Edge computing devices have the ability to process data in real time and make rapid decisions. They can also interact with cloud servers to achieve intelligent collaboration between local and cloud environments, thereby further improving the efficiency and stability of the system.

[0049] Sensors include position sensors, speed sensors, acceleration sensors, temperature sensors, and pressure sensors.

[0050] Position sensors are used to obtain the crane's operating position in real time;

[0051] Speed ​​sensors are used to monitor the operating speed of cranes;

[0052] Accelerometers are used to acquire the motion acceleration of the crane and to further monitor its motion status;

[0053] Temperature sensors are used to detect the temperature of critical parts of the crane in order to prevent overheating and other malfunctions.

[0054] Pressure sensors are used to monitor various pressure changes that may occur during the operation of the crane, thereby ensuring the stable operation of the crane. These sensors together provide the system with comprehensive, real-time, and accurate operating data.

[0055] The controller precisely controls the drive unit according to instructions from the cloud server and edge computing devices, including start-up, stop, speed adjustment, and direction change operations, ensuring the smooth, safe, and efficient operation of the gantry crane. Through advanced control algorithms, the controller parses and executes instructions from the cloud server and edge computing devices, precisely controlling the drive unit. During start-up and stop, the controller initiates and stops operations based on real-time operating status and equipment parameters. During speed adjustment, the controller precisely adjusts the speed according to operational requirements and real-time operating status to meet different operational needs. During direction change, the controller precisely controls the drive unit's steering based on real-time operating status and instructions to achieve smooth and accurate direction changes. The design and implementation of the controller ensures the smooth, safe, and efficient operation of the gantry crane under various working conditions, improving operational efficiency and reducing safety risks.

[0056] The user interface module can display the crane's operating parameters, such as position, speed, acceleration, temperature, and pressure, in real time, as well as the status information of the cloud server and edge computing devices. It can also display system warnings and alarms, allowing users to promptly understand and address potential problems. Furthermore, the user interface module receives user commands, such as start, stop, speed adjustment, and direction change commands, and transmits these commands to the cloud server and edge computing devices, thereby enabling remote control of the crane. The user interface module is designed with an easy-to-understand and user-friendly layout and operation method, providing a friendly user experience that allows users to conveniently and quickly operate and manage the gantry crane's cloud-edge collaborative intelligent management system.

[0057] Cloud servers use machine learning and artificial intelligence technologies to deeply mine and learn from historical data, thereby generating optimized operating strategies.

[0058] Real-time data processing and rapid decision-making enable the device to independently complete some data processing and decision-making tasks when the cloud server is unavailable or has excessive latency, ensuring the continuous and stable operation of the gantry crane. Edge computing devices also feature efficient edge computing algorithms such as stream computing and fog computing, rapidly processing and analyzing received data and generating control commands for the drives based on the processing results. Furthermore, edge computing devices possess data preprocessing capabilities, cleaning, transforming, and compressing large amounts of raw data to reduce network transmission pressure and improve data processing efficiency. Once the network connection is restored, the edge computing device can feed back the processing results to the cloud server, enabling intelligent cloud-edge collaborative management.

[0059] The sensor is designed with high precision and high sensitivity to ensure the accuracy and real-time performance of data acquisition.

[0060] The controller employs advanced algorithms, including fuzzy control. Fuzzy control calculates the matching degree of each fuzzy rule based on the fuzzification of the system state and the fuzzy rules in the rule base, and determines the control signal of the driver based on these matching degrees. The specific formula can be expressed as: U = ∑(μi*Ui) / ∑μi, where U is the control signal, μi is the matching degree of the i-th fuzzy rule, and Ui is the control signal corresponding to the i-th fuzzy rule.

[0061] This invention is intended to cover all such substitutions, modifications, and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A cloud-edge collaborative intelligent control system for gantry cranes, characterized in that, Includes cloud servers, edge computing devices, the gantry crane body, and user interface modules; The cloud server is used to store and process data generated during the operation of the gantry crane, and also has intelligent scheduling and optimization functions; The edge computing device is located at the gantry crane site and is used to process the gantry crane's operating data in real time and to interact with the cloud server. The gantry crane body includes a drive, sensors, and a controller; the drive is used to realize the movement of the crane; the sensors are used to collect the operating data of the gantry crane in real time and transmit the data to the edge computing device; the controller controls the operation of the drive according to the instructions of the cloud server and the edge computing device. The user interface module is used to display the operating status information of the gantry crane and to receive user operation commands; The edge computing device has the ability to process data in real time and make rapid decisions, and it can also interact with cloud servers. The controller precisely controls the drive according to instructions from the cloud server and edge computing devices, including start-up, stop-up, speed adjustment, and direction change operations, ensuring the smooth, safe, and efficient operation of the gantry crane. Through advanced control algorithms, the controller parses and executes instructions from the cloud server and edge computing devices to precisely control the drive. During start-up and stop, the controller starts and stops based on real-time operating status and equipment parameters. During speed adjustment, the controller precisely adjusts the speed according to operational requirements and real-time operating status. During direction change, the controller precisely controls the drive's direction based on real-time operating status and instructions. The real-time data processing and rapid decision-making capabilities enable the independent completion of some data processing and decision-making tasks even when the cloud server is unavailable or has excessive latency. The edge computing device also features efficient edge computing algorithms such as stream computing and fog computing to rapidly process and analyze received data and generate control commands for the driver based on the processing results. The edge computing device also has data preprocessing capabilities, enabling it to clean, transform, and compress large amounts of received raw data to reduce network transmission pressure. Once the network connection is restored, the edge computing device can feed back the processing results to the cloud server.

2. The cloud-edge collaborative intelligent control system for gantry cranes according to claim 1, characterized in that, The cloud server is equipped with real-time data analysis, historical data mining, predictive maintenance, intelligent scheduling, and remote control. The real-time data analysis involves analyzing the real-time data of the gantry crane's operating status, operating efficiency, and energy consumption to obtain its operating characteristics. The historical data mining refers to the in-depth mining of historical operational data through big data technology and machine learning algorithms. The predictive maintenance: Based on historical and real-time data, predict potential failures and maintenance needs of the gantry crane; The intelligent scheduling refers to the intelligent scheduling of operations based on the actual operating status and task requirements of the gantry crane. The remote control refers to the remote monitoring and operation of the gantry crane via the network, and the provision of optimized operation strategies for the gantry crane through data interaction with edge computing devices.

3. The cloud-edge collaborative intelligent control system for gantry cranes according to claim 1, characterized in that, The sensors include a position sensor, a velocity sensor, an acceleration sensor, a temperature sensor, and a pressure sensor. The position sensor is used to obtain the crane's operating position in real time; The speed sensor is used to monitor the operating speed of the crane; The acceleration sensor is used to acquire the crane's motion acceleration and further monitor its motion status; The temperature sensor is used to detect the temperature of various key parts of the crane in order to prevent overheating and other malfunctions. The pressure sensor is used to monitor various pressure changes that may occur during the operation of the crane, thereby ensuring the stable operation of the crane.

4. The cloud-edge collaborative intelligent control system for gantry cranes according to claim 1, characterized in that, The user interface module can display the crane's position, speed, acceleration, temperature, and pressure operating parameters in real time, as well as the status information of the cloud server and edge computing devices. The user interface module can also display the system's early warning and alarm information. The user interface module can also receive user operation commands and transmit these commands to the cloud server and edge computing devices.

5. The cloud-edge collaborative intelligent control system for gantry cranes according to claim 2, characterized in that, The cloud server uses machine learning and artificial intelligence technologies to deeply mine and learn from historical data.

6. The cloud-edge collaborative intelligent control system for gantry cranes according to claim 1, characterized in that, The sensor is designed with high precision and high sensitivity to ensure the accuracy and real-time performance of data acquisition.

7. The cloud-edge collaborative intelligent control system for gantry cranes according to claim 1, characterized in that, The controller employs advanced algorithms, including fuzzy control. The fuzzy control calculates the matching degree of each fuzzy rule based on the fuzzification of the system state and the fuzzy rules in the rule base, and determines the control signal of the driver based on these matching degrees. The specific formula can be expressed as: U = ∑(μi * Ui) / ∑μi, where U is the control signal, μi is the matching degree of the i-th fuzzy rule, and Ui is the control signal corresponding to the i-th fuzzy rule.

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