Automatic interaction control device and method for electromechanical equipment

Abstract

The invention discloses an electromechanical equipment automation interaction control device comprising a sensing module used for collecting work environment information and equipment state parameters; the decision module is constructed on the basis of a deep learning algorithm and can autonomously formulate an optimal control strategy according to the data collected by the sensing module in combination with historical operation records and preset rules; the execution module is used for controlling the electromechanical equipment to complete a specific task according to the instruction output by the decision module; and the feedback module monitors the execution effect in real time and feeds back the result to the decision module to form a closed-loop control system. The working efficiency, the adaptive capacity and the safety of the electromechanical equipment are greatly improved, and new vitality is injected to technical innovation and development in the field of intelligent manufacturing.

Description

Technical Field

[0001] The present invention relates to the technical field of electromechanical equipment, and in particular to an automatic interactive control device and method for electromechanical equipment. Background Art

[0002] With the continuous improvement of industrial automation and intelligence, electromechanical equipment is being used more and more widely in manufacturing. Traditional electromechanical equipment mainly relies on pre-written programs for operation. Although it can complete the given tasks, its flexibility and adaptability are obviously insufficient when facing complex and changing working environments. For example, when the working environment changes or the equipment is abnormal, traditional electromechanical equipment often cannot respond in time, resulting in reduced work efficiency or even failure. In addition, the electromechanical equipment in the prior art has weak fault detection and self-repair capabilities. Once a problem occurs, manual intervention is usually required to restore normal operation, which not only increases maintenance costs, but also affects the continuity and stability of production. Summary of the invention

[0003] 1. Technical Problems Solved

[0004] The technical problems to be solved by the present invention are the various problems mentioned in the above background technology, and provide an automatic interactive control device and method for electromechanical equipment.

[0005] 2. Technical Solution

[0006] In order to solve the above technical problems, the present invention provides a technical solution: an automatic interactive control device for electromechanical equipment, the device comprising:

[0007] Perception module, used to collect working environment information and equipment status parameters;

[0008] The decision-making module is built based on a deep learning algorithm and can autonomously formulate the optimal control strategy based on the data collected by the perception module, combined with historical operation records and preset rules;

[0009] An execution module controls the electromechanical equipment to complete a specific task according to the instructions output by the decision module;

[0010] The feedback module monitors the execution effect in real time and feeds the result back to the decision module to form a closed-loop control system.

[0011] As an improvement, the perception module specifically includes an environmental sensor and a device status sensor:

[0012] Environmental sensors, which detect the physical conditions of the surrounding environment;

[0013] Equipment status sensors are used to monitor the internal working status of electromechanical equipment.

[0014] As an improvement, the decision-making module can analyze and process the collected data through a machine learning model to predict potential problems and take preventive measures in advance.

[0015] As an improvement, the execution module supports connecting various types of robotic arms and motors as execution elements.

[0016] As an improvement, a method for an automated interactive control device for electromechanical equipment includes the following steps:

[0017] S1. Use the perception module to obtain changes in the external environment and internal state;

[0018] S2, analyzing and processing the information obtained through the decision module to generate corresponding control commands;

[0019] S3, the execution module performs corresponding actions according to the received command;

[0020] S4. The feedback module continuously monitors the execution process, evaluates the execution effect and adjusts the control strategy;

[0021] As an improvement, the decision module of step S2 utilizes historical data and preset rules to optimize the control strategy during the decision-making process to improve the response speed and work efficiency of the system.

[0022] As an improvement, when the execution module of step S3 executes the task, the feedback module obtains the execution data in a timely manner, and dynamically adjusts the working parameters of the execution module through the decision module to adapt to different working conditions and task requirements.

[0023] 3. Beneficial Effects

[0024] The advantages of the present invention compared with the prior art are:

[0025] The present invention provides an automatic interactive control device for electromechanical equipment and a control method thereof, which realizes autonomous perception, intelligent decision-making, precise execution and real-time adjustment of electromechanical equipment in complex and changeable environments by integrating four core modules: perception, decision-making, execution and feedback. The perception module can fully capture environmental and equipment status information; the decision-making module is based on a deep learning algorithm, combined with historical data and preset rules, to effectively predict and respond to potential problems; the execution module supports a variety of robotic arms and motors, enhancing the applicability and flexibility of the system; the feedback module ensures closed-loop management of the control process, improves control accuracy and system stability, greatly improves the work efficiency, adaptability and safety of electromechanical equipment, and injects new vitality into the technological innovation and development in the field of intelligent manufacturing. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1It is a structural diagram of an automatic interactive control device for electromechanical equipment of the present invention. DETAILED DESCRIPTION

[0027] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0028] Embodiment 1

[0029] like Figure 1 As shown, an automatic interactive control device for electromechanical equipment is characterized by comprising:

[0030] The perception module is used to collect working environment information and equipment status parameters. The perception module specifically includes an environmental sensor and an equipment status sensor:

[0031] Environmental sensors, which detect the physical conditions of the surrounding environment;

[0032] Equipment status sensors are used to monitor the internal working status of electromechanical equipment. ;

[0033] The decision-making module is built based on a deep learning algorithm and can independently formulate an optimal control strategy based on the data collected by the perception module, combined with historical operation records and preset rules. The decision-making module can analyze and process the collected data through a machine learning model to predict potential problems and take preventive measures in advance;

[0034] An execution module controls the electromechanical equipment to complete a specific task according to the instructions output by the decision module. The execution module supports connecting various types of mechanical arms and motors as execution elements.

[0035] Working principle of the present invention: First, the device includes four main components: perception module, decision module, execution module and feedback module. The perception module is composed of environmental sensors and equipment status sensors. The environmental sensors are responsible for detecting physical conditions such as temperature, humidity, light intensity, etc. in the working area, while the equipment status sensors are used to monitor the internal operating status of electromechanical equipment, such as motor speed, current size, vibration frequency, etc. The data collected by these sensors provides the basis for subsequent intelligent decision-making.

[0036] Secondly, the decision-making module is built based on a deep learning algorithm, which can process the data collected by the perception module and combine historical operation records and preset rules to autonomously generate the optimal control strategy. This module can not only respond immediately based on the current environment and equipment status, but also predict potential problems through machine learning models and take preventive measures in advance to avoid failures.

[0037] Thirdly, the execution module receives control instructions from the decision module and controls the electromechanical equipment to complete specific tasks. This module supports the connection of various types of actuators, such as robotic arms, motors, etc., to adapt to different application scenarios. The design of the execution module takes into account flexibility and scalability, and actuators can be easily added or replaced according to actual needs.

[0038] Finally, the feedback module plays a vital role in the entire control process. It monitors the action effect of the execution module in real time and feeds the monitoring results back to the decision module to form a closed-loop control system. This real-time feedback mechanism not only ensures the accuracy and stability of the control, but also dynamically adjusts the output of the decision module according to the actual execution effect to achieve the best control effect.

[0039] In the specific operation process, the perception module is first used to obtain changes in the external environment and internal state (step S1), and then the decision module analyzes and processes the obtained information to generate corresponding control commands (step S2). In this process, the decision module uses historical data and preset rules to optimize the control strategy and improve the response speed and work efficiency of the system. Next, the execution module performs corresponding actions according to the received commands (step S3), while the feedback module continuously monitors the execution process, evaluates the execution effect and adjusts the control strategy (step S4). If an abnormal situation is found in any link, the system will automatically start the emergency plan to ensure the safe and stable operation of the system.

[0040] Through the above implementation, the electromechanical equipment automatic interactive control device and control method provided by the present invention not only overcome the limitations of traditional electromechanical equipment when facing complex and changeable environments, but also greatly improve the working efficiency, adaptability and safety of electromechanical equipment.

[0041] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0042] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

[0043] The present invention and its embodiments are described above, and such description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. In short, if ordinary technicians in the field are inspired by it, without departing from the purpose of the invention, they can design a structure and embodiment similar to the technical solution without creativity, which should belong to the protection scope of the present invention.

Claims

1. An automatic interactive control device for electromechanical equipment, characterized in that: The device comprises: Perception module, used to collect working environment information and equipment status parameters; The decision-making module is built based on a deep learning algorithm and can autonomously formulate the optimal control strategy based on the data collected by the perception module, combined with historical operation records and preset rules; An execution module controls the electromechanical equipment to complete a specific task according to the instructions output by the decision module; The feedback module monitors the execution effect in real time and feeds the result back to the decision module to form a closed-loop control system.

2. The electromechanical equipment automatic interactive control device according to claim 1, characterized in that: The perception module specifically includes an environmental sensor and a device status sensor: Environmental sensors, which detect the physical conditions of the surrounding environment; Equipment status sensors are used to monitor the internal working status of electromechanical equipment.

3. The electromechanical equipment automatic interactive control device according to claim 1, characterized in that: The decision-making module can analyze and process the collected data through a machine learning model to predict potential problems and take preventive measures in advance.

4. The electromechanical equipment automatic interactive control device according to claim 1, characterized in that: The execution module supports connecting various types of robotic arms and motors as execution elements.

5. The method of an automatic interactive control device for electromechanical equipment according to claim 1, characterized in that: The following steps are involved: S1. Use the perception module to obtain changes in the external environment and internal state; S2, analyzing and processing the information obtained through the decision module to generate corresponding control commands; S3, the execution module performs corresponding actions according to the received command; S4, the feedback module continuously monitors the execution process, evaluates the execution effect and adjusts the control strategy.

6. The method of the electromechanical equipment automatic interactive control device according to claim 5, characterized in that: The decision module of step S2 utilizes historical data and preset rules to optimize the control strategy during the decision-making process to improve the response speed and work efficiency of the system.

7. The method of an automatic interactive control device for electromechanical equipment according to claim 5, characterized in that: When the execution module of step S3 executes the task, the feedback module obtains the execution data in a timely manner, and dynamically adjusts the working parameters of the execution module through the decision module to adapt to different working conditions and task requirements.

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