An Internet of Things training platform, working method and program quality assessment method

Abstract

The present invention provides an Internet of Things training platform, a working method and a program quality assessment method. In the training platform, the length of the base does not exceed 1m and the width does not exceed 0.5m. The electric control system includes a shell, a control component, a touch screen and a storage table. The control component is configured with an expansion port for receiving programs, and the storage table is provided with multiple storage positions. The touch screen is used to send control commands to the control component; the laser engraving system includes a marking position, a first frame, a second frame, a laser head and a first drive component. The first drive component is used to drive the second frame and the laser head to move so that the laser head moves in the horizontal and vertical directions above the marking position; the transport system includes a support seat, a mechanical arm and a second drive component. The second drive component is used to drive the support seat and the mechanical arm to move and drive the support seat to rotate so as to transport the workpiece placed in the storage position to the marking position; the control component is used to control the actions of the first drive component and the second drive component.

Description

Technical Field

[0001] One or more embodiments of the present specification relate to the field of training platform application technology, and in particular, to an Internet of Things training platform, a working method, and a program quality assessment method. Background Art

[0002] With the development of intelligent manufacturing, the demand for intelligent manufacturing training equipment is also increasing day by day.

[0003] In the related technologies, some IoT training platforms occupy a large area, are inconvenient to move, and lack interaction with trainees.

[0004] Therefore, there is an urgent need for an Internet of Things training platform, working method and program quality assessment method to solve the above technical problems. Summary of the invention

[0005] One or more embodiments of this specification describe an Internet of Things training platform, a working method, and a program quality assessment method, which occupy a small area, are easy to move, and can effectively interact with trainees.

[0006] According to a first aspect, an Internet of Things training platform is provided, comprising:

[0007] A base having a length not exceeding 1 m and a width not exceeding 0.5 m, wherein a linear guide rail is arranged on the base;

[0008] The electric control system comprises a housing fixed on the base, a control component arranged inside the housing, a touch screen arranged on the housing, and a storage table, wherein the control component is provided with an expansion port for receiving programs, the storage table is provided with a plurality of storage positions for placing workpieces, the touch screen is electrically connected to the control component, and the touch screen is used to send control commands to the control component;

[0009] The laser engraving system comprises a marking position fixed on the base, a first frame fixed on the base, a second frame movably arranged on the first frame, a laser head movably arranged on the second frame, and a first driving component, wherein the first driving component is electrically connected to the control component, and the first driving component is used to drive the second frame and the laser head to move, so that the laser head moves in the horizontal and vertical directions above the marking position;

[0010] A handling system, comprising a support base movably arranged on the linear guide rail, a mechanical arm arranged on the support base, and a second driving component, wherein the support base can rotate relative to the linear guide rail, the second driving component is electrically connected to the control component, and the second driving component is used to drive the support base and the mechanical arm to move and drive the support base to rotate, so as to transport the workpiece placed at the storage position to the marking position;

[0011] The control component is used to control the actions of the first driving component and the second driving component based on a program received from an external input.

[0012] According to a second aspect, a working method of the Internet of Things training platform based on the above embodiment is provided, including:

[0013] Step S1, initializing the electric control system, the laser engraving system and the transport system to determine the workpiece storage information of the storage position, return the second frame to zero position and return the robot arm to zero position;

[0014] Step S2, using the control component to control the second drive component to move, so that the second drive component drives the mechanical arm to move;

[0015] Step S3, using the control component to control the air pump to make the air nozzle suck the workpiece;

[0016] Step S4, transferring the workpiece sucked by the air nozzle to the RFID card reader by the robot arm, so as to use the RFID card reader to read the marking information of the workpiece;

[0017] Step S5, the robot arm continues to transfer the workpiece sucked by the air nozzle to the marking position, and the air nozzle is deflated;

[0018] Step S6, using the control component to control the laser engraving system to operate based on the received marking information sent by the RFID card reader, so that the laser head marks the workpiece located at the marking position;

[0019] Step S7, when the laser head completes marking the workpiece located at the marking position, using the control component to control the second frame to return to the zero position;

[0020] Step S8, transferring the marked workpiece to the original storage position by the robot arm and the air nozzle, and continuing to perform steps S1-S8 on the workpiece in the next storage position;

[0021] Step S9: After all workpieces are marked, the control component is used to control the robot arm to return to zero position.

[0022] According to a third aspect, a method for evaluating program quality of an Internet of Things training platform is provided, comprising:

[0023] receiving programs for evaluation;

[0024] Execute the procedures to be evaluated;

[0025] Based on the work completion of the Internet of Things training platform, the quality of the program to be evaluated is evaluated; wherein the work completion of the Internet of Things training platform is determined by the working method of the Internet of Things training platform as described in the above embodiment.

[0026] According to the Internet of Things training platform, working method and program quality assessment method provided in the embodiments of this specification, since the length of the base does not exceed 1m, the width does not exceed 0.5m, and the electronic control system, laser engraving system and handling system are all located on the base, the overall footprint of the Internet of Things training platform does not exceed 0.5 square meters, and it is also convenient to move; the control component is configured with an expansion port for receiving programs, so that program codes provided by different trainees can be uploaded, so that the Internet of Things training platform can execute different program codes to better observe the quality of different program codes, so this can effectively interact with trainees. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0028] Figure 1 A schematic diagram of the structure of an Internet of Things training platform according to an embodiment is shown;

[0029] Figure 2 Shows Figure 1 The floor plan of the storage table of the Internet of Things training platform shown;

[0030] Figure 3 A flow chart of a method for evaluating program quality of an Internet of Things training platform according to an embodiment is shown.

[0031] Reference numerals:

[0032] 1- Base;

[0033] 11-Linear guide;

[0034] 2-Electronic control system;

[0035] 21-housing;

[0036] 22- Touch screen;

[0037] 23-Storage table;

[0038] 231-storage position;

[0039] 232-RFID card reader;

[0040] 233-photoresistor sensor;

[0041] 234-sensitivity adjustment potentiometer;

[0042] 235- switch indicator light;

[0043] 236-power indicator light; 3-laser engraving system;

[0044] 31-Marking position;

[0045] 32- first frame;

[0046] 33- second frame;

[0047] 34-Laser head;

[0048] 35-first drive assembly; 4-handling system;

[0049] 41-support seat;

[0050] 42-Robotic arm;

[0051] 43- second drive assembly;

[0052] 44-air nozzle;

[0053] 45- Rotating motor. DETAILED DESCRIPTION

[0054] The solution provided in this specification is described below in conjunction with the accompanying drawings.

[0055] Figure 1 FIG. 1 shows a schematic diagram of the structure of an Internet of Things training platform according to an embodiment. Figure 1 As shown, the Internet of Things training platform includes a base 1, an electric control system 2, a laser engraving system 3 and a handling system 4, wherein:

[0056] A base 1 having a length not exceeding 1 m and a width not exceeding 0.5 m, and a linear guide rail 11 is provided on the base 1;

[0057] The electric control system 2 includes a housing 21 fixed on the base 1, a control component (not shown) disposed inside the housing 21, a touch screen 22 disposed on the housing 21, and a storage table 23, wherein the control component is provided with an expansion port for receiving a program, the storage table 23 is provided with a plurality of storage positions 231 for placing workpieces, the touch screen 22 is electrically connected to the control component, and the touch screen 22 is used to send control commands to the control component;

[0058] The laser engraving system 3 includes a marking position 31 fixed on the base 1, a first frame 32 fixed on the base 1, a second frame 33 movably arranged on the first frame 32, a laser head 34 movably arranged on the second frame 33, and a first driving component 35, wherein the first driving component 35 is electrically connected to the control component, and the first driving component 35 is used to drive the second frame 33 and the laser head 34 to move, so that the laser head 34 moves in the horizontal and vertical directions above the marking position 31;

[0059] The transport system 4 includes a support base 41 movably disposed on the linear guide rail 11, a mechanical arm 42 disposed on the support base 41, and a second drive assembly 43, the support base 41 can rotate relative to the linear guide rail 11, the second drive assembly 43 is electrically connected to the control assembly, and the second drive assembly 43 is used to drive the support base 41 and the mechanical arm 42 to move and drive the support base 41 to rotate, so as to transport the workpiece placed in the storage position 231 to the marking position 31;

[0060] The control component is used to control the first driving component 35 and the second driving component 43 based on a program received from an external input.

[0061] In this embodiment, since the length of the base 1 does not exceed 1m, the width does not exceed 0.5m, and the electronic control system 2, the laser engraving system 3 and the handling system 4 are all located on the base 1, the overall footprint of the Internet of Things training platform does not exceed 0.5 square meters, and it is also convenient to move; the control component is configured with an expansion port for receiving programs, so that program codes provided by different trainees can be uploaded, so that the Internet of Things training platform can execute different program codes to better observe the quality of different program codes, so this can effectively interact with trainees.

[0062] For example, the control component in the embodiment of this specification includes three Arduino mega2560s. The first Arduino mega2560 is used as the main control board to receive commands from the touch screen 22. The second Arduino mega2560 uses marlin firmware 1.0. The main control board Arduino mega2560 controls the specified position of the robot arm movement by sending G code through the serial port. The third Arduino mega2560 is responsible for controlling the laser engraving system.

[0063] In some embodiments, the first drive assembly 35 and the second drive assembly 43 are both implemented by motors, and the motor drive module uses, for example, an A4988 stepper motor driver. Specifically, the first drive assembly 35 may include an X-direction motor and a Y-direction motor, and the second drive assembly 43 includes an X-direction motor, a Y-direction motor, and a Z-direction motor.

[0064] The upper arm and the lower arm of the robot arm move in a vertical plane under the joint drive of the stepper motor (X) and the stepper motor (Y). The upper arm and the lower arm of the robot arm use the lever, the connecting rod principle and the quadrilateral structure to ensure that the end bracket of the robot arm is always in a horizontal posture.

[0065] In some embodiments, the support base 41 is driven by a stepper motor of model 57BYGH56 to drive the mechanical arm 42 fixed on the synchronous belt to perform linear motion.

[0066] The stepper motor (laser X-axis) and stepper motor (laser Y-axis) of the laser engraving system move together to move the laser head to the engraving start position of the engraving operation table, turn on the laser head 34 by controlling the enable signal, control the output PWM signal to control the laser output power, and drive the laser head 34 to continue moving along the engraving track through the stepper motor until the engraving work is completed. Table 1 shows the main parameters of the laser head, Table 2 shows the main structural parameters of the laser engraving system, and Table 3 shows the main parameters of the touch screen.

[0067] Table 1 Main parameters of laser head

[0068]

[0069]

[0070] Table 2 Main structural parameters of laser engraving system

[0071] Overall dimensions 410mm×470mm Working area 165mm×320mm Motor specifications (X, Y axis) 42 Control accuracy (X, Y axis) 0.5mm

[0072] Table 3 Main technical parameters of touch screen

[0073]

[0074]

[0075] In summary, the control components include three Arduino Mega 2560 processors (one as the host computer and two as the slave computers), and the stepper motors are driven by power chips A4988 and M542C. The entire training platform is powered by a 12V switching power supply. The 5V and 3.3V power supplies required inside the training platform are converted by the voltage regulator chip LM2596S from the 12VDC input, and the motor drive is powered by 12V.

[0076] Figure 2 Shows Figure 1 The floor plan of the storage platform of the Internet of Things training platform is shown in FIG. Figure 2 As shown, in one embodiment of the present specification, the storage station 23 is further provided with an RFID card reader 232 for communicating with the workpiece, and the RFID card reader 232 is electrically connected to the control component;

[0077] The control component controls the laser head 34 to mark the workpiece located at the marking position 31 by receiving the marking information sent by the RFID card reader 232 ; wherein the marking information is obtained by reading the workpiece by the RFID card reader 232 .

[0078] In this embodiment, an RFID card reader 232 is provided on the storage table 23 , so that the control component can control the laser head 34 to mark the workpiece located at the marking position 31 by receiving the marking information sent by the RFID card reader 232 .

[0079] Of course, the RFID card reader 232 may not be provided. For example, the information to be marked may be stored in advance in the electric control system 2 , which is not limited here.

[0080] Please continue reading Figure 2 In one embodiment of the present specification, each storage position 231 is provided with a photoresistor sensor 233, a sensitivity adjustment potentiometer 234, a switch indicator light 235 and a power indicator light 236. The photoresistor sensor 233 is used to sense whether there is a workpiece at the current position, the sensitivity adjustment potentiometer 234 is used to adjust the sensing light intensity threshold of the photoresistor sensor 233, the switch indicator light 235 is used to indicate whether there is a workpiece at the current position, and the power indicator light 236 is used to indicate whether the power is turned on;

[0081] The touch screen 22 is also used to display the workpiece storage information of the storage location 231 .

[0082] In this embodiment, the control component can determine the workpiece storage information (i.e., the workpiece in-place status) in the storage position 231 based on the photoresistor signal installed in the storage position 231, and refresh the information to the touch screen 22. When the user clicks on the touch screen 22 to enter the workpiece number to be processed and clicks the start button, the control component will query whether there is a workpiece in the storage position 231. If there is no workpiece in place, no marking operation will be performed. This method can provide real-time feedback on the workpiece storage information to the handling system 4 to facilitate system logic decision-making.

[0083] In one embodiment of the present specification, an air nozzle 44 is provided at the end of the robot arm 42, and the air nozzle 44 is connected to an air pump (not shown in the figure) and a rotating motor 45. The air pump is electrically connected to the control component, and the air pump is used to provide suction to the air nozzle 44 to suck the workpiece, and the rotating motor 45 is used to adjust the posture of the workpiece sucked by the air nozzle 44.

[0084] In this embodiment, the workpiece can be grasped by providing the air nozzle 44 , and the posture of the workpiece sucked by the air nozzle 44 can be adjusted by providing the rotating motor 45 , so that the sucked workpiece can be accurately placed in the marking position 31 .

[0085] Of course, magnetic attraction may also be used to replace the air nozzle 44 and the air pump, but this method requires the workpiece to be a metal part, and the specific method of grabbing the workpiece is not limited here.

[0086] In one embodiment of the present specification, both the linear guide rail 11 and the first frame 32 are provided with a limit switch (not shown in the figure), and the limit switch is electrically connected to the control assembly;

[0087] A control component is used to perform the following operations: in response to receiving a control command for returning to zero position, controlling the first drive component 35 or the second drive component 43 to operate so that the second frame 33 or the robotic arm 42 moves toward their respective zero positions at a first preset speed; in response to receiving the first trigger feedback of the limit switch, controlling the first drive component 35 or the second drive component 43 to operate again so that the second frame 33 or the robotic arm 42 retreats a preset distance and moves toward their respective zero positions at a second preset speed; wherein the first preset speed is greater than the second preset speed.

[0088] In this embodiment, by setting the operation of returning the second frame 33 or the mechanical arm 42 to zero, the reset error caused by the mistaken touch of the limit switch and the reset accuracy problem caused by the excessive reset speed can be solved.

[0089] Generally speaking, the limit switch is triggered only once, that is, a triggering action is generated when the execution terminal (i.e., the second frame 33 or the mechanical arm 42) returns to the zero position. However, if the staff accidentally blocks the limit switch (i.e., accidentally touches it), causing the limit switch to generate a trigger feedback, the control component will control the execution terminal to stop moving. There is also a situation where the reset speed of the execution terminal is too fast during the zeroing process, which will cause low reset accuracy. After repeated work, the reset accuracy becomes lower and lower, which will cause errors in subsequent handling and marking. Therefore, in response to the first trigger feedback received from the limit switch, the control component controls the first drive component 35 or the second drive component 43 to move again, so that the second frame 33 or the mechanical arm 42 retreats a preset distance and moves toward their respective zero positions at a second preset speed; wherein the first preset speed is greater than the second preset speed. In this way, the reset error caused by the mistaken touch of the limit switch and the reset accuracy problem caused by the excessive reset speed can be solved.

[0090] It should also be noted that the above-mentioned IoT training platform also has the following advantages:

[0091] 1) Advantages of robot arm motor motion strategy

[0092] When the system completes the entire reset process, the user selects the workpiece for processing, and the synchronous belt linear guide cooperates with the robotic arm to move in a differential compensation manner. Multiple motors move in coordination to align the air nozzle at the end of the robotic arm with the corresponding workpiece position. After the motor moves into place, the system controls the air pump to start working, and after the workpiece is adsorbed on the air nozzle at the end of the robotic arm, multiple motors of the robotic arm move in coordination, causing the workpiece to undergo a certain displacement in the direction of the normal vector of the operating plane. This method ensures that the workpiece contacts the edge of the storage position groove of the storage system as little as possible during the rising process, to prevent the air nozzle from failing to absorb or the absorption position from shifting due to scratching the edge. The robotic arm motor moves in a differential compensation manner, making the entire movement process smoother and smoother from a visual perspective than single-axis sequential movement.

[0093] 2) System module radio frequency identification (RFID) and information mapping advantages

[0094] After the robot arm takes the workpiece out of the storage position, the motor of the robot arm will move in a differential compensation manner to move the workpiece to the top of the RFID module. At this time, the system will identify the workpiece information. If the workpiece is the number information registered in the system storage system and matches the corresponding number bin where the workpiece is located, the system buzzer will make a "di di" sound, indicating that the workpiece identity information is correct and the workpiece is transported to the laser marking area for subsequent processing. If the workpiece is not the number information registered in the system storage system or does not match the corresponding number bin where the workpiece is located, it indicates that the workpiece identity information is wrong. If the storage position is wrong, the robot arm will place the workpiece in its corresponding correct storage position. At this time, manual intervention is required to adjust the storage position workpiece; if the workpiece is not the number information registered in the system storage system, the robot arm will place the workpiece outside the storage position on the operating platform. This decision-making method enables the system to have the function of identifying the storage position workpiece information to prevent processing errors.

[0095] 2.5. Advantages of laser marking personalized decision-making

[0096] When the system successfully identifies and transports the workpiece to the laser marking area, the system will send the identified workpiece information to the laser engraving system based on the results of the radio frequency identification. The laser engraving system will perform personalized laser marking based on the information received. After marking, the robot arm and the synchronous belt linear guide will transport it to the corresponding position in the warehouse. When the robot arm arrives at the designated position, the system will verify the photoresistor status information at the corresponding position. After the double authentication, the storage system display will be refreshed, and the relay will control the opening of the air release valve to place the workpiece in the corresponding storage position.

[0097] In addition, the embodiment of this specification also provides a working method of the Internet of Things training platform mentioned in the above embodiment, which specifically includes:

[0098] Step S1, initializing the electric control system 2, the laser engraving system 3 and the transport system 4 to determine the workpiece storage information of the storage position 231, returning the second frame 33 to the zero position and returning the robot arm 42 to the zero position;

[0099] Step S2, using the control component to control the second driving component 43 to move, so that the second driving component 43 drives the mechanical arm 42 to move;

[0100] Step S3, using the control component to control the air pump to make the air nozzle 44 suck the workpiece;

[0101] Step S4, transferring the workpiece sucked by the air nozzle 44 to the RFID card reader 232 by the robot arm 42, so as to use the RFID card reader 232 to read the marking information of the workpiece;

[0102] Step S5, the robot arm 42 continues to transfer the workpiece sucked by the air nozzle 44 to the marking position 31, and the air nozzle 44 is deflated;

[0103] Step S6: Using the control component to control the laser engraving system 3 based on the marking information received from the RFID card reader 232, so that the laser head 34 marks the workpiece located at the marking position 31;

[0104] Step S7, when the laser head 34 completes marking the workpiece located at the marking position 31, the control component is used to control the second frame 33 to return to the zero position;

[0105] Step S8, transfer the marked workpiece to the original storage position 231 through the robot arm 42 and the air nozzle 44, and continue to perform steps S1-S8 on the workpiece in the next storage position 231;

[0106] Step S9: After all workpieces are marked, the control component is used to control the robot arm 42 to return to the zero position.

[0107] Since the working method provided in the embodiments of this specification is based on the Internet of Things training platform provided in the above embodiments (that is, the two belong to the same inventive concept), the two have the same beneficial effects, and the beneficial effects of the working method of the Internet of Things training platform will not be repeated here.

[0108] Figure 3 FIG. 1 is a flowchart of a method for evaluating program quality of an Internet of Things training platform according to an embodiment. Figure 3 As shown, in this embodiment, the program quality assessment method of the Internet of Things training platform specifically includes:

[0109] Step 300: receiving a program to be evaluated;

[0110] Step 302: Execute the program to be evaluated;

[0111] Step 304: Evaluate the quality of the program to be evaluated based on the work completion of the Internet of Things training platform; wherein the work completion of the Internet of Things training platform is determined by the working method of the Internet of Things training platform mentioned in the above embodiment.

[0112] In this embodiment, since the control component is equipped with an expansion port for receiving programs, program codes provided by different trainees can be uploaded, so that the Internet of Things training platform can execute different program codes to better observe the quality of different program codes, so this can effectively interact with trainees. Moreover, based on the work completion of the Internet of Things training platform, the quality of the program to be evaluated is evaluated; wherein, the work completion of the Internet of Things training platform is determined by the working method of the Internet of Things training platform mentioned in the above embodiment, which is conducive to the effective evaluation of the program quality of the Internet of Things training platform.

[0113] In one embodiment of this specification, the work completion degree of the Internet of Things training platform is specifically determined in the following manner:

[0114] Obtain the working node when the IoT training platform stops working; the working node includes the number of workpieces that have completed marking and the step node of the current cycle process;

[0115] Based on the working nodes and the working methods of the Internet of Things training platform, determine the work completion degree of the Internet of Things training platform.

[0116] For example, the working method of the Internet of Things training platform includes processing 8 workpieces, and each workpiece needs to perform 8 steps. Therefore, when the Internet of Things training platform executes different program codes, the number of workpieces marked by each program code (for example, 4) and the error in the 5th step of the cycle process when marking the 5th workpiece can be counted. Therefore, based on the above data information, the work completion degree of the Internet of Things training platform can be determined.

[0117] In one embodiment of the present specification, based on the working node and the working method of the Internet of Things training platform, determining the work completion degree of the Internet of Things training platform includes:

[0118] Determine the number of all workpieces corresponding to the working method of the IoT training platform and the number of steps included in each cycle process;

[0119] The following formula is used to determine the work completion of the Internet of Things training platform:

[0120]

[0121] In the formula, For work completion, α is the first preset coefficient, 0.5≤α≤0.8, a is the number of workpieces that have completed marking, A is the number of all workpieces, a≤A, β is the first preset coefficient, 0.2≤β≤0.5, α+β=1, b is the step node of the current cycle process, B is the number of steps included in each cycle process, b≤B.

[0122] For example, in the above embodiment, α is equal to 0.8, a is equal to 4, A is equal to 8, β is equal to 0.2, b is equal to 5, and B is equal to 8. Equal to 0.525.

[0123] In one embodiment of the present specification, the work completion degree of the Internet of Things training platform also includes at least one of the following: work completion time, and work completion energy consumption.

[0124] In this embodiment, since the logic of different program codes is not exactly the same, the ways of completing the same event may be different (for example, one is that the robot arm remains motionless after moving the workpiece to the marking position, and the other is that the robot arm returns to the zero position after moving the workpiece to the marking position. Obviously, the former consumes less energy). Therefore, it is necessary to consider the work completion time and the work completion energy consumption as evaluation indicators of work completion.

[0125] In some implementations, the energy consumption may be calculated by an electric meter, which will not be described in detail here.

[0126] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0127] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0128] Those skilled in the art should be aware that in one or more of the above examples, the functions described in the present invention can be implemented by hardware, software, firmware or any combination thereof. When implemented by software, these functions can be stored in a computer-readable medium or transmitted as one or more instructions or codes on a computer-readable medium.

[0129] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made on the basis of the technical solution of the present invention should be included in the scope of protection of the present invention.

Claims

1. A program quality assessment method for an Internet of Things training platform, characterized in that: include: receiving programs for evaluation; Execute the procedure to be evaluated; Based on the work completion of the Internet of Things training platform, the quality of the program to be evaluated is evaluated; The Internet of Things training platform includes: A base (1) having a length not exceeding 1 m and a width not exceeding 0.5 m, wherein a linear guide rail (11) is arranged on the base (1); The electric control system (2) comprises a housing (21) fixed on the base (1), a control component arranged inside the housing (21), a touch screen (22) arranged on the housing (21), and a storage table (23), wherein the control component is provided with an expansion port for receiving a program, the storage table (23) is provided with a plurality of storage positions (231) for placing workpieces, the touch screen (22) is electrically connected to the control component, and the touch screen (22) is used to send control commands to the control component; The laser engraving system (3) comprises a marking position (31) fixed on the base (1), a first frame (32) fixed on the base (1), a second frame (33) movably arranged on the first frame (32), a laser head (34) movably arranged on the second frame (33), and a first driving component (35), wherein the first driving component (35) is electrically connected to the control component, and the first driving component (35) is used to drive the second frame (33) and the laser head (34) to move, so that the laser head (34) moves in the horizontal and vertical directions above the marking position (31); The transport system (4) comprises a support base (41) movably arranged on the linear guide rail (11), a mechanical arm (42) arranged on the support base (41) and a second drive component (43), wherein the support base (41) can rotate relative to the linear guide rail (11), the second drive component (43) is electrically connected to the control component, and the second drive component (43) is used to drive the support base (41) and the mechanical arm (42) to move and drive the support base (41) to rotate, so as to transport the workpiece placed in the storage position (231) to the marking position (31); The control component is used to control the actions of the first drive component (35) and the second drive component (43) based on a program received from an external input; The storage platform (23) is also provided with an RFID card reader (232) for communicating with the workpiece, and the RFID card reader (232) is electrically connected to the control component; The control component controls the laser head (34) to mark the workpiece located at the marking position (31) by receiving the marking information sent by the RFID card reader (232); wherein the marking information is obtained by reading the workpiece by the RFID card reader (232); Each storage position (231) is provided with a photoresistor sensor (233), a sensitivity adjustment potentiometer (234), a switch indicator light (235) and a power indicator light (236); the photoresistor sensor (233) is used to sense whether a workpiece exists at the current position; the sensitivity adjustment potentiometer (234) is used to adjust the sensing light intensity threshold of the photoresistor sensor (233); the switch indicator light (235) is used to indicate whether a workpiece exists at the current position; and the power indicator light (236) is used to indicate whether the power is turned on; The touch screen (22) is also used to display the workpiece storage information of the storage position (231); An air nozzle (44) is provided at the end of the mechanical arm (42), and the air nozzle (44) is connected to an air pump and a rotating motor (45). The air pump is electrically connected to the control component, and the air pump is used to provide suction force for the air nozzle (44) to suck the workpiece, and the rotating motor (45) is used to adjust the posture of the workpiece sucked by the air nozzle (44); The linear guide rail (11) and the first frame (32) are both provided with limit switches, and the limit switches are electrically connected to the control component; The control component is used to perform: in response to receiving a control command for returning to zero position, controlling the first drive component (35) or the second drive component (43) to move, so that the second frame (33) or the mechanical arm (42) moves toward their respective zero positions at a first preset speed; in response to receiving a first trigger feedback of the limit switch, controlling the first drive component (35) or the second drive component (43) to move again, so that the second frame (33) or the mechanical arm (42) retreats a preset distance and moves toward their respective zero positions at a second preset speed; wherein the first preset speed is greater than the second preset speed; The work completion of the Internet of Things training platform is determined by the working method of the Internet of Things training platform as described below: Step S1, initializing the electric control system (2), the laser engraving system (3) and the handling system (4) to determine the workpiece storage information of the storage position (231), return the second frame (33) to the zero position and return the robot arm (42) to the zero position; Step S2, using the control component to control the second drive component (43) to move, so that the second drive component (43) drives the mechanical arm (42) to move; Step S3, using the control component to control the air pump to operate so that the air nozzle (44) sucks the workpiece; Step S4, transferring the workpiece sucked by the air nozzle (44) to the RFID card reader (232) by the mechanical arm (42), so as to use the RFID card reader (232) to read the marking information of the workpiece; Step S5, continuing to transfer the workpiece sucked by the air nozzle (44) to the marking position (31) through the robot arm (42), and deflating the air nozzle (44); Step S6, using the control component to control the laser engraving system (3) to operate based on the marking information received from the RFID card reader (232), so that the laser head (34) marks the workpiece located at the marking position (31); Step S7, when the laser head (34) completes marking the workpiece located at the marking position (31), the control component is used to control the second frame (33) to return to the zero position; Step S8, transferring the marked workpiece to the original storage position (231) through the robot arm (42) and the air nozzle (44), and continuing to perform steps S1-S8 on the workpiece in the next storage position (231); Step S9, after completing marking of all workpieces, using the control component to control the robot arm (42) to return to zero position; The work completion degree of the Internet of Things training platform is specifically determined by the following methods: Obtaining the working node when the Internet of Things training platform stops working; wherein the working node includes the number of workpieces that have completed marking and the step node of the current cycle process; Based on the working node and the working method of the Internet of Things training platform, determining the work completion degree of the Internet of Things training platform; The determining the work completion degree of the Internet of Things training platform based on the working node and the working method of the Internet of Things training platform includes: Determine the number of all workpieces corresponding to the working method of the Internet of Things training platform and the number of steps included in each cycle process; The following formula is used to determine the work completion of the Internet of Things training platform: In the formula, For work completion, α is the first preset coefficient, 0.5≤α≤0.8, a is the number of workpieces that have completed marking, A is the number of all workpieces, α≤A, β is the first preset coefficient, 0.2≤β≤0.5, α+β=1, b is the step node of the current cycle process, B is the number of steps included in each cycle process, b≤B; The work completion degree of the Internet of Things training platform also includes at least one of the following: work completion time and work completion energy consumption.

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