Industrial monitoring system based on IoT and 5G lightweight terminals

Abstract

The present invention discloses an industrial monitoring system based on the Internet of Things and a 5G lightweight terminal, which belongs to the technical field of the Internet of Things, and specifically includes a sensor module, a communication module, a central processing unit, a visualization module and a graphics processing unit; the robot movement data is collected in real time and transmitted through a 5G network; the central processing unit receives the data, processes and generates a motion trajectory of the robot in a coordinate system; whenever the robot's turning angle accumulates to n360°, the trajectory length and the distance from the initial position are recorded, the ratio value is calculated and a sequence is generated; if the difference between consecutive adjacent ratio values ​​is greater than a preset threshold, it is determined that the robot trajectory is abnormal, and the visualization module prompts an inspection; the graphics processing unit obtains the abnormal motion trajectory during a fault, compares it with the set trajectory, and screens out trajectory segments with a similarity lower than a threshold; the present invention realizes a high-efficiency and low-cost intelligent industrial monitoring method through the collaboration of the Internet of Things and 5G RedCap.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things, and in particular to an industrial monitoring system based on the Internet of Things and a 5G lightweight terminal. Background Art

[0002] In recent years, the development of Internet of Things (IoT) technology and 5G communication technology has provided new solutions for industrial automation. With its high speed, low latency and large number of connections, 5G networks are particularly suitable for data transmission needs in industrial environments. RedCap is a lightweight 5G technology standard that aims to effectively reduce terminal costs and power consumption by simplifying parameters such as bandwidth, number of antennas, modulation order, etc. of 5G terminals, so as to better adapt to IoT application scenarios with medium speed and medium power consumption requirements. Combined with IoT technology, remote monitoring and management of equipment can be achieved, and production flexibility and efficiency can be improved. In the industrial field, industrial robots are needed to transport production materials or products. Real-time collection and processing of robot mobile data is of great significance to improving production efficiency and ensuring production safety.

[0003] As the production environment becomes more complex, robots may encounter various obstacles or signal interference when performing tasks, which may affect the robot's trajectory and cause trajectory deviations. This requires the system to be able to detect and handle these abnormal situations in a timely manner; in industrial production, 5G RedCap can provide stable and efficient connections, enabling factory equipment to achieve real-time data transmission and remote control, thereby improving production efficiency.

[0004] Therefore, a lightweight monitoring method can be constructed through the Internet of Things and 5G networks to achieve high-precision detection of the motion trajectory of the industrial robot while reducing the complexity of the hardware, thereby accurately determining whether the robot has any abnormalities. Based on this, the present invention proposes an industrial monitoring system based on the Internet of Things and 5G lightweight terminals. Summary of the invention

[0005] The purpose of the present invention is to provide an industrial monitoring system based on the Internet of Things and 5G lightweight terminals to solve the following technical problems:

[0006] As the production environment becomes more complicated, robots may encounter various obstacles or signal interference when performing tasks, which may affect the robot's trajectory and cause trajectory deviation. Therefore, how to build a lightweight detection terminal based on the Internet of Things and 5G networks to achieve high-precision detection of the motion trajectory of industrial robots while reducing hardware complexity.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] The industrial monitoring system based on the Internet of Things and 5G lightweight terminals includes sensor modules, communication modules, central processing units, visualization modules and graphics processors, including:

[0009] The sensor module includes a velocity sensor and an inertial sensor, which is used to collect the movement data of the robot in real time, and the movement data includes speed, acceleration and angular velocity;

[0010] A communication module, used to send the mobile data collected by the sensor module to a central processor via a 5G network;

[0011] A central processing unit is used to receive the movement data, and process the acceleration data and the angular velocity data, establish a plane rectangular coordinate system with any point in the factory as the origin, and when the robot is started, mark the initial position coordinates of the robot as (x0, y0), generate the movement trajectory of the robot in the coordinate system according to the processed movement data, and record the steering angle of the robot according to the angular velocity data. Whenever the cumulative steering angle of the robot is n360°, n is a positive integer, obtain the length L of the movement trajectory of the robot in each round of 360-degree steering, and record the distance d from the initial position when the steering angle of the robot is (n-1)360°+x, where 0°<x<360°, calculate the ratio value k=L / d of each round of 360-degree steering, generate a ratio value sequence, and if the difference between consecutive adjacent ratio values ​​is greater than a preset threshold, it is determined that the robot is abnormal;

[0012] A graphics processor is used to select the motion trajectory of the robot when it fails, mark it as an abnormal motion trajectory, compare the abnormal motion trajectory with the set motion trajectory in segments, and select several trajectory segments whose similarity is lower than a preset threshold;

[0013] The visualization module is used to display the robot's movement data and motion trajectory in real time. When the robot has an abnormality, it will prompt and prompt to check the signal distribution of the screened trajectory segment area.

[0014] As a further solution of the present invention: in the sensor module, the speed sensor calculates the moving speed of the robot by detecting the rotation speed of the robot's wheel axle, and the inertial sensor measures the acceleration and angular velocity of the robot through an accelerometer and a gyroscope.

[0015] As a further solution of the present invention: before the sensor module transmits the mobile data to the communication module, a low-pass filter is used to smooth the original signal of the mobile data, and the data is compressed by Huffman coding.

[0016] As a further solution of the present invention: In the central processor, the process of processing the acceleration data and the angular velocity data is as follows:

[0017] The inertial sensor is used to measure the current acceleration and angular velocity of the robot in real time, and the error compensation of the acceleration and angular velocity is performed. The formula is:

[0018] ;

[0019] ;

[0020] in It is the measurement value of the accelerometer in the inertial sensor, which represents the acceleration measurement of the body system relative to the inertial system under the body. Indicates the acceleration value after removing the error, g b Indicates the gravitational acceleration in the body coordinate system, b a is the bias of the accelerometer, n a is the measurement noise of the accelerometer;

[0021] It is the measurement value of the gyroscope in the inertial sensor, which represents the angular velocity measurement of the body system relative to the inertial system under the body. represents the angular velocity value after removing the error, b g Indicates the bias of the gyroscope, n g Represents the measurement error of the gyroscope;

[0022] By rotating the matrix and Convert to the plane rectangular coordinate system and remove the earth's rotation acceleration error to obtain the acceleration value a i and the angular velocity ω i .

[0023] As a further solution of the present invention: In the central processor, the process of generating the robot motion trajectory is:

[0024] Get the initial moving speed of the robot when it starts and mark it as V0, then the moving speed of the robot at time t is V i for:

[0025] ;

[0026] Then the robot moves a distance l within time t i for;

[0027] ;

[0028] Then the coordinates of the robot after time t (x i ,y i )for:

[0029] ;

[0030] The coordinates of the robot are collected at unit time intervals t0, and the robot coordinates at different times are connected in sequence to generate a motion trajectory.

[0031] As a further solution of the present invention: In the central processor, the specific process of determining whether the robot has a fault is as follows:

[0032] Generate a sequence of proportional values ​​k1, k2, ..., k n , for any ratio value k i , 1<i<n, calculate the difference c1 and c2 between ki and the adjacent ratio value, c1=|k i -k i-1 |,c2=|k i+1 -k i |, if c1 or c2 is greater than the preset threshold, the robot is judged to be faulty.

[0033] As a further solution of the present invention: in the graphics processor, the segmented comparison process is:

[0034] The abnormal motion trajectory and the set motion trajectory are divided into several trajectory segments according to the change of the robot's moving angle, and marked as abnormal segments and set segments. The starting value and ending value of the steering angle of each abnormal segment are the same as those of the corresponding set segment;

[0035] Select m pairs of coordinate points on any abnormal segment and mark them as (X1j, Y1j) and (X2j, Y2j), where m is a preset value, j∈[1,...,m]; select m pairs of coordinate points on the set segment corresponding to the abnormal segment at an angle and mark them as (Z1j, W1j) and (Z2j, W2j);

[0036] By formula Calculate the similarity S and filter the trajectory segments whose similarity is lower than the preset threshold.

[0037] As a further solution of the present invention: the central processor also includes a data storage unit for storing the processed movement data and motion trajectory, and a ratio value sequence.

[0038] Beneficial effects of the present invention:

[0039] (1) Based on the high speed and low cost of 5G lightweight terminals, the present invention integrates several core sensor modules, which can transmit the robot's motion parameters in real time and perform effective data analysis and processing, thereby ensuring that the central processor can receive and process these data in a timely manner. Compared with the traditional wired transmission method, the application of lightweight terminals based on 5G networks has greatly improved the speed and stability of data transmission. The introduction of 5G RedCap technology has greatly reduced the terminal cost and achieved lightweight and high efficiency of sensor modules.

[0040] (2) The present invention utilizes 5G RedCap and the Internet of Things to realize a stable and efficient terminal network. The central processor collects data transmitted back by each lightweight terminal and analyzes the data in a centralized manner. The system can accurately determine whether the robot encounters an obstacle or operates abnormally. By compensating and analyzing the errors of the acceleration and angular velocity data, the motion trajectory of the robot can be generated more accurately. When it is found that the difference between consecutive adjacent ratio values ​​is greater than a preset threshold, the system can issue an early warning in time to prompt the robot to be inspected, thereby avoiding possible failures and losses. When the robot is checked and there is no problem, the motion trajectory of the robot when the failure occurs is obtained and compared with the set motion trajectory in segments. The trajectory segments with a similarity lower than the preset threshold are screened out, and the signal distribution of the corresponding areas of these segments is further checked, thereby optimizing the motion trajectory of the robot, realizing a new control architecture and multi-process collaborative control, and realizing the combination of 5G lightweight terminals and traditional industries. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The present invention will be further described below in conjunction with the accompanying drawings.

[0042] Figure 1 It is a schematic diagram of a module of the present invention;

[0043] Figure 2 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0044] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0045] See also Figure 1-2 As shown, the present invention is an industrial monitoring system based on the Internet of Things and 5G lightweight terminals, including a sensor module, a communication module, a central processing unit, a visualization module and a graphics processor, wherein:

[0046] The sensor module includes a velocity sensor and an inertial sensor, which is used to collect the movement data of the robot in real time, and the movement data includes speed, acceleration and angular velocity;

[0047] A communication module, used to send the mobile data collected by the sensor module to a central processor via a 5G network;

[0048] A central processing unit is used to receive the movement data, and process the acceleration data and the angular velocity data, establish a plane rectangular coordinate system with any point in the factory as the origin, and when the robot is started, mark the initial position coordinates of the robot as (x0, y0), generate the movement trajectory of the robot in the coordinate system according to the processed movement data, and record the steering angle of the robot according to the angular velocity data. Whenever the cumulative steering angle of the robot is n360°, n is a positive integer, obtain the length L of the movement trajectory of the robot in each round of 360-degree steering, and record the distance d from the initial position when the steering angle of the robot is (n-1)360°+x, where 0°<x<360°, calculate the ratio value k=L / d of each round of 360-degree steering, generate a ratio value sequence, and if the difference between consecutive adjacent ratio values ​​is greater than a preset threshold, it is determined that the robot is abnormal;

[0049] A graphics processor is used to select the motion trajectory of the robot when it fails, mark it as an abnormal motion trajectory, compare the abnormal motion trajectory with the set motion trajectory in segments, and select several trajectory segments whose similarity is lower than a preset threshold;

[0050] The visualization module is used to display the robot's movement data and motion trajectory in real time. When the robot has an abnormality, it will prompt and prompt to check the signal distribution of the screened trajectory segment area.

[0051] 5G RedCap supports large-scale machine-to-machine communication, further improving the level of factory automation and intelligence. The application of this technology is not limited to a single factory, but can also be extended to the entire industrial park or production line, thereby achieving a wider range of intelligent management and control; the robot's mobile data is collected in real time through speed sensors and acceleration sensors, and the 5G network is used to achieve rapid data transmission, which ensures the timeliness and integrity of the data. Real-time data transmission enables the central processor to receive and process data in a timely manner, providing an accurate data basis for subsequent motion trajectory generation and fault diagnosis.

[0052] The CPU processes the acceleration and angular velocity data and combines them with the initial position coordinates to accurately generate the robot's motion trajectory in the coordinate system. This precise motion trajectory helps to better understand the actual operating status of the robot and provides data support for optimizing the motion path and improving work efficiency. By calculating the proportional value k=L / d of each 360-degree turn and generating a proportional value sequence, when the difference between consecutive adjacent proportional values ​​is greater than the preset threshold, it can be judged in time that the robot is abnormal and issue a warning signal. This fault detection mechanism helps to detect potential problems in a timely manner and avoid or reduce production losses caused by faults.

[0053] When the robot is checked to be fault-free, the graphics processor can obtain the robot's motion trajectory when it fails, and compare it with the set motion trajectory in segments, screening out the trajectory segments with a similarity below the preset threshold. This method helps to identify abnormal parts in the motion trajectory and provides a basis for further optimizing the robot's motion trajectory.

[0054] In another preferred embodiment of the present invention, in the sensor module, the speed sensor calculates the moving speed of the robot by detecting the rotation speed of the robot's wheel axle, and the inertial sensor measures the acceleration and angular velocity of the robot through an accelerometer and a gyroscope.

[0055] In another preferred embodiment of the present invention, before the sensor module transmits the mobile data to the communication module, it uses a low-pass filter to smooth the original signal of the mobile data and compresses the data through Huffman coding.

[0056] In another preferred embodiment of the present invention, in the central processor, the process of processing the acceleration data and the angular velocity data is:

[0057] The inertial sensor is used to measure the current acceleration and angular velocity of the robot in real time, and the error compensation of the acceleration and angular velocity is performed. The formula is:

[0058] ;

[0059] ;

[0060] in It is the measurement value of the accelerometer in the inertial sensor, which represents the acceleration measurement of the body system relative to the inertial system under the body. Indicates the acceleration value after removing the error, g b Indicates the gravitational acceleration in the body coordinate system, b a is the bias of the accelerometer, n a is the measurement noise of the accelerometer;

[0061] It is the measurement value of the gyroscope in the inertial sensor, which represents the angular velocity measurement of the body system relative to the inertial system under the body. represents the angular velocity value after removing the error, b g Indicates the bias of the gyroscope, n g Represents the measurement error of the gyroscope;

[0062] By rotating the matrix and Convert to the plane rectangular coordinate system and remove the earth's rotation acceleration error to obtain the acceleration value a i and the angular velocity ωi .

[0063] By performing error compensation on acceleration data and angular velocity data, the errors caused by sensor bias and measurement noise can be effectively reduced, thereby obtaining more accurate robot motion data. This precise data is the basis for generating reliable motion trajectories and performing accurate fault diagnosis.

[0064] In another preferred embodiment of the present invention, in the central processor, the process of generating the robot motion trajectory is:

[0065] Get the initial moving speed of the robot when it starts and mark it as V0, then the moving speed of the robot at time t is V i for:

[0066] ;

[0067] Then the robot moves a distance l within time t i for;

[0068] ;

[0069] Then the coordinates of the robot after time t (x i ,y i )for:

[0070] ;

[0071] The coordinates of the robot are collected at unit time intervals t0, and the robot coordinates at different times are connected in sequence to generate a motion trajectory.

[0072] In another preferred embodiment of the present invention, in the central processor, the specific process of determining whether the robot has failed is as follows:

[0073] Generate a sequence of proportional values ​​k1, k2, ..., k n , for any ratio value k i , 1<i<n, calculate the difference c1 and c2 between ki and the adjacent ratio value, c1=|k i -k i-1 |,c2=|k i+1 -k i |, if c1 or c2 is greater than the preset threshold, the robot is judged to be faulty.

[0074] By using the acceleration and angular velocity values ​​after removing the errors, the robot's moving speed and moving distance can be calculated more accurately, thereby obtaining more accurate robot coordinates. By continuously collecting coordinates and connecting them, a smooth motion trajectory that reflects the actual motion situation can be generated, which helps to analyze the robot's motion state and optimize its motion path.

[0075] Accurate motion trajectories can help detect abnormalities or instability that may occur during the robot's motion, such as deviations from the predetermined path, inconsistent speed, etc. By analyzing these data, the robot's motion control strategy can be adjusted and optimized to improve the robot's motion efficiency and stability.

[0076] By monitoring the robot's motion trajectory and speed changes, the system can promptly detect potential fault signs, such as abnormal fluctuations in motion trajectory or sudden changes in speed. This makes it possible to predict and maintain faults in advance, thereby reducing downtime and repair costs caused by faults.

[0077] In another preferred embodiment of the present invention, in the system optimization module, the process of segmented comparison is:

[0078] The abnormal motion trajectory and the set motion trajectory are divided into several trajectory segments according to the change of the robot's moving angle, and marked as abnormal segments and set segments. The starting value and ending value of the steering angle of each abnormal segment are the same as those of the corresponding set segment;

[0079] Select m pairs of coordinate points on any abnormal segment and mark them as (X1j, Y1j) and (X2j, Y2j), where m is a preset value, j∈[1,...,m]; select m pairs of coordinate points on the set segment corresponding to the abnormal segment at an angle and mark them as (Z1j, W1j) and (Z2j, W2j);

[0080] By formula Calculate the similarity S and filter the trajectory segments whose similarity is lower than the preset threshold.

[0081] In another preferred embodiment of the present invention, the central processor further comprises a data storage unit for storing processed movement data and movement trajectory, and a ratio value sequence.

[0082] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. The industrial monitoring system based on the Internet of Things and 5G lightweight terminals is characterized by: It includes sensor module, communication module, central processing unit, visualization module and graphics processor, among which: The sensor module includes a velocity sensor and an inertial sensor, which is used to collect the movement data of the robot in real time, and the movement data includes speed, acceleration and angular velocity; A communication module, used to send the mobile data collected by the sensor module to a central processor via a 5G network; A central processing unit is used to receive the movement data, and process the acceleration data and the angular velocity data, establish a plane rectangular coordinate system with any point in the factory as the origin, and when the robot is started, mark the initial position coordinates of the robot as (x0, y0), generate the movement trajectory of the robot in the coordinate system according to the processed movement data, and record the steering angle of the robot according to the angular velocity data. Whenever the cumulative steering angle of the robot is n360°, n is a positive integer, obtain the length L of the movement trajectory of the robot in each round of 360-degree steering, and record the distance d from the initial position when the steering angle of the robot is (n-1)360°+x, where 0°<x<360°, calculate the ratio value k=L / d of each round of 360-degree steering, generate a ratio value sequence, and if the difference between consecutive adjacent ratio values ​​is greater than a preset threshold, it is determined that the robot is abnormal; A graphics processor is used to select the motion trajectory of the robot when it fails, mark it as an abnormal motion trajectory, compare the abnormal motion trajectory with the set motion trajectory in segments, and select several trajectory segments whose similarity is lower than a preset threshold; The visualization module is used to display the robot's movement data and motion trajectory in real time. When the robot has an abnormality, it will give a prompt and prompt to check the signal distribution of the screened trajectory segment area.

2. The industrial monitoring system based on the Internet of Things and 5G lightweight terminals according to claim 1 is characterized in that: In the sensor module, the speed sensor calculates the moving speed of the robot by detecting the rotation speed of the robot's wheel axle, and the inertial sensor measures the acceleration and angular velocity of the robot through an accelerometer and a gyroscope.

3. The industrial monitoring system based on the Internet of Things and 5G lightweight terminals according to claim 1 is characterized in that: Before the sensor module transmits the mobile data to the communication module, it uses a low-pass filter to smooth the original signal of the mobile data and compresses the data through Huffman coding.

4. The industrial monitoring system based on the Internet of Things and 5G lightweight terminals according to claim 2 is characterized in that: In the central processing unit, the process of processing the acceleration data and the angular velocity data is as follows: The inertial sensor is used to measure the current acceleration and angular velocity of the robot in real time, and the error compensation of the acceleration and angular velocity is performed. The formula is: ； ； in It is the measurement value of the accelerometer in the inertial sensor, which represents the acceleration measurement of the body system relative to the inertial system under the body. Indicates the acceleration value after removing the error, g b Indicates the gravitational acceleration in the body coordinate system, b a is the bias of the accelerometer, n a is the measurement noise of the accelerometer; It is the measurement value of the gyroscope in the inertial sensor, which represents the angular velocity measurement of the body system relative to the inertial system under the body. represents the angular velocity value after removing the error, b g Indicates the bias of the gyroscope, n g Represents the measurement error of the gyroscope; By rotating the matrix and Convert to the plane rectangular coordinate system and remove the earth's rotation acceleration error to obtain the acceleration value a i and the angular velocity ω i .

5. The industrial monitoring system based on the Internet of Things and 5G lightweight terminals according to claim 4 is characterized in that: In the central processor, the process of generating the robot motion trajectory is as follows: Get the initial moving speed of the robot when it starts and mark it as V0, then the moving speed of the robot at time t is V i for: ； Then the robot moves a distance l at time t i for; ； Then the coordinates of the robot after time t (x i ,y i )for: ； The coordinates of the robot are collected at unit time intervals t0, and the robot coordinates at different times are connected in sequence to generate a motion trajectory.

6. The industrial monitoring system based on the Internet of Things and 5G lightweight terminals according to claim 1 is characterized in that: In the central processor, the specific process of determining whether the robot has failed is as follows: Generate a sequence of proportional values ​​k1, k2, ..., k n , for any ratio value k i , 1<i<n, calculate the difference c1 and c2 between ki and the adjacent ratio value, c1=|k i -k i-1 |,c2=|k i+1 -k i |, if c1 or c2 is greater than the preset threshold, the robot is judged to be faulty.

7. The industrial monitoring system based on the Internet of Things and 5G lightweight terminals according to claim 1 is characterized in that: In the graphics processor, the segmented comparison process is as follows: The abnormal motion trajectory and the set motion trajectory are divided into several trajectory segments according to the change of the robot's moving angle, and marked as abnormal segments and set segments. The starting value and ending value of the steering angle of each abnormal segment are the same as those of the corresponding set segment; Select m pairs of coordinate points on any abnormal segment and mark them as (X1j, Y1j) and (X2j, Y2j), where m is a preset value, j∈[1,...,m]; select m pairs of coordinate points on the set segment corresponding to the abnormal segment at an angle and mark them as (Z1j, W1j) and (Z2j, W2j); By formula Calculate the similarity S and filter the trajectory segments whose similarity is lower than the preset threshold.

8. The industrial monitoring system based on the Internet of Things and 5G lightweight terminals according to claim 1 is characterized in that: The central processor also includes a data storage unit for storing processed movement data and motion trajectory, and a ratio value sequence.

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