Mechanical operation intelligent system based on Internet of Things

Abstract

The invention discloses a mechanical operation intelligent system based on the Internet of Things. Comprising a data processing decision-making system, an edge calculation module, a multi-source data fusion module, a fault prediction maintenance management module, a man-machine interaction module, an energy management module and a cross-industry adaptation extension module. The data processing decision-making system comprises a local data preprocessing layer, a cloud big data processing layer and an artificial intelligence decision-making layer. And the edge calculation module is used for transmitting, storing and scheduling the data respectively, and is used for locally processing the real-time data and making a real-time decision according to an analysis result. The invention relates to the technical field of mechanical automatic regulation and control, and the system solves the defects of an existing mechanical operation system in efficiency, energy consumption, equipment maintenance and intelligent decision through integrating the Internet of Things, edge calculation, big data analysis and artificial intelligence, effectively improves the efficiency of mechanical operation, and reduces the energy consumption.

Description

Technical Field

[0001] The present invention belongs to the technical field of mechanical automation control, and specifically relates to an intelligent mechanical operation system based on the Internet of Things. Background Art

[0002] With the development of Industry 4.0 and intelligent technologies, mechanical operations are playing a vital role in various fields. However, existing mechanical operation systems rely heavily on manual control and lack real-time monitoring and intelligent scheduling capabilities, resulting in low efficiency, high energy consumption, and unpredictable equipment failures. To address these issues, intelligent operation systems based on the Internet of Things (IoT) have emerged. While existing IoT technology enables remote monitoring of equipment, it cannot meet the demands of mechanical equipment in complex operating environments due to poor real-time performance, insufficient processing power, and a lack of intelligent decision-making.

[0003] Therefore, it is very necessary to develop an intelligent mechanical operation system that integrates multi-sensors, edge computing, artificial intelligence and big data analysis technologies. Summary of the Invention

[0004] In response to the above situation, in order to make up for the above-mentioned existing defects, the present invention provides an intelligent mechanical operation system based on the Internet of Things. This system solves the shortcomings of existing mechanical operation systems in efficiency, energy consumption, equipment maintenance and intelligent decision-making by integrating the Internet of Things, edge computing, big data analysis and artificial intelligence, effectively improving the efficiency of mechanical operations and reducing energy consumption.

[0005] The present invention proposes an intelligent mechanical operation system based on the Internet of Things, including a data processing and decision-making system, an edge computing module, a multi-source data fusion module, a fault prediction and maintenance management module, a human-computer interaction module, an energy management module and a cross-industry adaptation and extension module: the data processing and decision-making system includes a local data preprocessing layer, a cloud big data processing layer and an artificial intelligence decision-making layer, which are used to transmit, store and schedule data respectively; the edge computing module is used to locally process real-time data and make real-time decisions based on the analysis results; the multi-source data fusion module is used to integrate equipment sensor data, external environment data and historical operation data to provide data support for the data processing and decision-making system; the fault prediction and maintenance management module is used to monitor the equipment status in real time and predict and manage equipment failures; the human-computer interaction module is used to provide operators with real-time equipment status and operation information through mobile devices; the energy management module is used to dynamically adjust the energy consumption of the equipment according to the equipment load and external environment changes; the cross-industry adaptation and extension module is used to customize the equipment according to the needs of different fields.

[0006] Furthermore, the local data preprocessing layer is used to collect, preprocess and filter the raw data collected by cameras, thermostats and various data sensors installed on on-site mechanical equipment to ensure the transmission of useful data; the cloud-based big data processing layer uses a distributed computing framework to analyze and store large-scale data in real time, and optimize the scheduling of mechanical operations; the artificial intelligence decision-making layer uses deep learning and machine learning algorithms to predict equipment status, optimize operation paths, and perform intelligent scheduling according to environmental changes.

[0007] Furthermore, the edge computing module is used for real-time data processing, reducing data transmission delays and improving response speed; the multi-source data fusion module fuses equipment sensor data, external environment data, and historical operation data, and combines meteorological, soil moisture, and operation site information to optimize operation parameters and decisions.

[0008] Furthermore, the fault prediction and maintenance management module generates fault warnings based on the health of the equipment, allowing for pre-arranged repairs and maintenance to reduce downtime caused by unexpected failures. By monitoring equipment status in real time, the system can also automatically adjust operating parameters to optimize equipment efficiency.

[0009] Furthermore, the human-computer interaction module provides real-time equipment status information through mobile devices, allowing operators to intuitively view equipment operating data, environmental parameters and other information, make adjustments quickly, and display equipment maintenance steps, operation processes, etc. to operators, thereby improving operator work efficiency and safety.

[0010] Furthermore, the energy management module can dynamically adjust energy consumption according to changes in equipment load and external environment. When the load is low, the system will automatically adjust equipment power to reduce energy consumption and ensure efficient use of energy.

[0011] The beneficial effects achieved by the present invention using the above structure are as follows: The advantages of the mechanical operation intelligent system based on the Internet of Things of the present invention are:

[0012] 1. Through the local data pre-processing layer, cloud-based big data processing layer, and artificial intelligence decision-making layer in the data processing and decision-making system, large amounts of data can be analyzed and processed in real time, thereby dynamically optimizing and scheduling mechanical operations. The operating paths of agricultural machinery and the operating sequence of industrial production lines can be accurately optimized through artificial intelligence algorithms to improve operating efficiency. Through the edge computing module, the rapid response and real-time data change speed can be further improved, the delay in data transmission can be reduced, and the immediate adjustment and precise execution of various operations can be ensured.

[0013] 2. Through the fault prediction maintenance management module, the health status of the equipment can be monitored in real time, potential faults can be predicted and early warning information can be automatically generated, and equipment repair and maintenance can be arranged in advance, thereby effectively reducing the downtime caused by sudden faults and avoiding production interruptions due to equipment failures.

[0014] 3. The energy management module can dynamically adjust the energy consumption of the equipment according to the equipment load and external environment such as temperature and humidity, avoid energy waste, thereby reducing operating costs and improving energy utilization efficiency.

[0015] 4. Through the multi-source data fusion module, sensor data, external environment data and historical operation data can be integrated and combined with external information to further optimize operation parameters and decisions, thereby improving the intelligence level and adaptability of the system.

[0016] 5. Through mobile devices, operators can easily view equipment status, operation progress, environmental parameters and other information in real time, make adjustments quickly, provide operators with equipment maintenance steps and operation processes, and effectively improve operators' work efficiency.

[0017] 6. Through cross-industry adaptation and expansion modules, it can be flexibly customized according to the needs of different industries and fields, ensuring that it can meet the specific requirements of different operating systems and achieve optimal configuration and operating results in agriculture, industry and other fields. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0019] Figure 1 This is a schematic diagram of the process of the mechanical operation intelligent system based on the Internet of Things of the present invention Figure 1 ;

[0020] Figure 2 This is a schematic diagram of the process of the mechanical operation intelligent system based on the Internet of Things of the present invention Figure 2 . DETAILED DESCRIPTION

[0021] 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.

[0022] It should be noted that the words "front", "rear", "left", "right", "up" and "down" used in the following description refer to directions in the accompanying drawings, and the words "inside" and "outside" refer to directions toward or away from the geometric center of a specific component, respectively.

[0023] like Figure 1 and Figure 2 As shown, the technical solution adopted by the present invention is as follows: The present invention proposes an Internet of Things-based intelligent mechanical operation system, including a data processing and decision-making system, an edge computing module, a multi-source data fusion module, a fault prediction and maintenance management module, a human-computer interaction module, an energy management module, and a cross-industry adaptation and expansion module:

[0024] The data processing and decision-making system includes a local data pre-processing layer, a cloud-based big data processing layer, and an artificial intelligence decision-making layer. The local data pre-processing layer is used to collect, pre-process, and filter raw data collected by cameras, thermostats, and various data sensors installed on on-site mechanical equipment to ensure the transmission of useful data. The cloud-based big data processing layer uses a distributed computing framework to analyze and store large-scale data in real time and optimize the scheduling of mechanical operations. The artificial intelligence decision-making layer uses deep learning and machine learning algorithms to predict equipment status, optimize operation paths, and perform intelligent scheduling based on environmental changes. The edge computing module is used to locally process real-time data and make real-time decisions based on the analysis results, reducing data transmission delays and improving response speed.

[0025] The multi-source data fusion module is used to integrate equipment sensor data, external environment data, and historical operation data. It also performs data fusion on equipment sensor data, external environment data, and historical operation data, and combines them with meteorological, soil moisture, and operation site information to optimize operation parameters and make decisions, providing data support for the data processing and decision-making system.

[0026] The fault prediction and maintenance management module is used to monitor equipment status in real time, predict and manage equipment failures, generate fault warning information based on the health status of the equipment, arrange repair and maintenance work in advance, and reduce downtime caused by sudden failures. By monitoring equipment status in real time, the system can also automatically adjust operating parameters to optimize equipment operating efficiency;

[0027] The human-machine interaction module provides operators with real-time equipment status and operation information through mobile devices. Operators can intuitively view equipment operating data, environmental parameters and other information, making quick adjustments. The module also displays equipment maintenance steps and operation processes to operators, improving their work efficiency and safety.

[0028] The energy management module is used to dynamically adjust the energy consumption of the equipment according to the equipment load and external environment changes. When the load is low, the system will automatically adjust the equipment power to reduce energy consumption and ensure efficient use of energy;

[0029] Cross-industry adaptation expansion modules are used to customize the equipment according to the needs of different fields.

[0030] Example 1

[0031] Application of agricultural machinery operation systems: Agricultural machinery equipment, such as tractors and seeders, are equipped with cameras, thermostats, and humidity sensors. The local data preprocessing layer of the data processing and decision-making system preprocesses the raw data collected from the equipment, and selects valid data to be transmitted to the cloud big data processing layer for storage and real-time analysis. The artificial intelligence decision-making layer optimizes the operation path and operation plan based on the analysis results. During the operation of agricultural machinery, the system uses the edge computing module to process the real-time data of the machinery and reduce the delay of data transmission. When soil moisture and meteorological data change, the edge computing module instantly adjusts the working status of irrigation equipment to ensure efficient use of water resources. The fault prediction and maintenance management module monitors the operating status of agricultural machinery in real time and analyzes the health status of the equipment. When the system detects potential faults in mechanical equipment, such as abnormal engine temperature or excessive wear of mechanical parts, it automatically generates fault warning information and arranges repair or maintenance work in advance, thereby reducing mechanical downtime and production losses. Operators can view the real-time status and operation progress of agricultural machinery through mobile devices such as smartphones or tablets. When the system detects an abnormal situation, it issues a real-time alert to the operator. The energy management module can dynamically adjust the energy consumption of the equipment according to the equipment load and external environment, such as temperature changes. When the load of agricultural machinery is low, the power consumption of the machinery is automatically reduced to achieve energy saving and reduce fuel waste and operating costs. The cross-industry adaptation extension module is set to customize the configuration according to the characteristics of different agricultural operations, and automatically adjust the operating parameters of the machinery, such as driving speed and sowing depth, to achieve the best operation effect.

[0032] Example 2

[0033] For the application of industrial production line operation system: the data collected by various acquisition devices such as temperature sensors, pressure sensors and vibration sensors installed on various mechanical equipment on the industrial production line are preliminarily screened and processed by the local data preprocessing layer to ensure that valid data can be transmitted to the cloud data. The cloud big data processing layer stores and analyzes the data in real time, and optimizes the equipment's operation path and operation sequence through the artificial intelligence decision-making layer to improve the efficiency of the production line. During the industrial production process, the edge computing module processes the real-time data on the production line to reduce data transmission delays. When the robot welding equipment has an excessive workload, the system adjusts the welding parameters or reminds the operator to check in real time through the edge computing module to reduce production line downtime; the equipment's operating status is communicated through the edge computing module. Real-time monitoring is carried out through the fault prediction maintenance management module. When the current fluctuation of the welding equipment exceeds the normal range, the system will automatically generate a fault warning to remind the operator to check the equipment and arrange a maintenance plan in advance. Through fault prediction and preventive maintenance, the sudden shutdown of the production line can be reduced and the service life of the equipment can be extended. The operator can view the status information of each device in real time through mobile devices such as smartphones or tablets, obtain key data such as the health status of the equipment, production progress, energy consumption, and make operational adjustments based on the optimization suggestions provided by the system. The energy management module can dynamically adjust the energy consumption of the equipment according to the load of the production line. Under low load conditions, the power output of the equipment is automatically reduced to avoid energy waste. During idle time periods, the equipment enters standby mode to reduce power consumption and ensure efficient use of energy.

[0034] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, material, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, material, or apparatus.

[0035] While 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 these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent mechanical operation system based on the Internet of Things, characterized by: It includes a data processing decision system, an edge computing module, a multi-source data fusion module, a fault prediction and maintenance management module, a human-computer interaction module, an energy management module and a cross-industry adaptation and extension module: the data processing decision system includes a local data preprocessing layer, a cloud big data processing layer and an artificial intelligence decision layer, which are used to transmit, store and schedule data respectively; the edge computing module is used to locally process real-time data and make real-time decisions based on the analysis results; the multi-source data fusion module is used to integrate equipment sensor data, external environment data and historical operation data to provide data support for the data processing decision system; the fault prediction and maintenance management module is used to monitor the equipment status in real time and predict and manage equipment failures; the human-computer interaction module is used to provide operators with real-time equipment status and operation information through mobile devices; the energy management module is used to dynamically adjust the energy consumption of the equipment according to the equipment load and external environment changes; the cross-industry adaptation and extension module is used to customize the equipment according to the needs of different fields.

2. The IoT-based intelligent mechanical operation system according to claim 1, characterized in that: The local data preprocessing layer is used to collect, preprocess and filter the raw data collected by cameras, temperature controllers and various data sensors installed on on-site mechanical equipment to ensure the transmission of useful data.

3. The intelligent mechanical operation system based on the Internet of Things according to claim 1, characterized in that: The cloud-based big data processing layer uses a distributed computing framework to perform real-time analysis and storage of large-scale data, and optimizes the scheduling of mechanical operations; the artificial intelligence decision-making layer uses deep learning and machine learning algorithms to predict equipment status, optimize operation paths, and perform intelligent scheduling based on environmental changes.

4. The intelligent mechanical operation system based on the Internet of Things according to claim 1, characterized in that: The edge computing module is used for real-time data processing, reducing data transmission delays and improving response speed.

5. The intelligent mechanical operation system based on the Internet of Things according to claim 1, characterized in that: The multi-source data fusion module fuses equipment sensor data, external environment data, and historical operation data, and optimizes operation parameters and decisions by combining meteorological, soil moisture, and operation site information.

6. The intelligent mechanical operation system based on the Internet of Things according to claim 1, characterized in that: The Fault Prediction and Maintenance Management module generates fault warnings based on the health of the equipment, allowing for pre-arranged repairs and maintenance to reduce downtime caused by unexpected failures. By monitoring equipment status in real time, the system can also automatically adjust operating parameters to optimize equipment efficiency.

7. The intelligent mechanical operation system based on the Internet of Things according to claim 1, characterized in that: The human-computer interaction module provides real-time equipment status information through mobile devices, allowing operators to intuitively view equipment operating data, environmental parameters and other information, make quick adjustments, and display equipment maintenance steps, operation processes, etc. to operators, thereby improving operator work efficiency and safety.

8. The intelligent mechanical operation system based on the Internet of Things according to claim 1, characterized in that: The energy management module can dynamically adjust energy consumption according to equipment load and external environment changes. When the load is low, the system will automatically adjust the equipment power to reduce energy consumption and ensure efficient use of energy.

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