A production task control method based on the Internet of Things

Abstract

The present invention relates to the technical field of intelligent control, and specifically discloses a production task control method based on the Internet of Things, including the following steps: S1: Collect images of the robotic arm, establish a coordinate system with the central pixel point of the image as the origin, and set the picking range; S2: Obtain the picking values; sort the picking values in the order of the time axis, and determine the picking period according to the sorting; S3: Sort the picking periods in the order of the time axis, determine the judgment period according to the sorting, determine the normal period according to the speed of the reference point moving within the judgment period, and determine the total number of normal periods; S4: Calculate the evaluation score, and judge whether there is an abnormality in the robotic arm according to the evaluation score. The present invention can judge whether there is an abnormality in the robotic arm according to the movement of the reference point on the robotic arm, and prevent the situation where the production task cannot be completed due to the abnormality of the robotic arm.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent control, and particularly relates to a production task control method based on the Internet of Things. Background Art

[0002] The Internet of Things (IoT) is a technology that connects various physical devices and objects through the Internet to enable information exchange and communication between these devices. The core of the Internet of Things lies in integrating the physical world and the digital world through sensors, software, and other technologies to create an intelligent environment.

[0003] A production task refers to a series of specific tasks and goals that an enterprise or factory needs to complete during the production process to ensure the smooth progress of the production process and meet the expected production volume and quality standards. For example, according to market demand and order situation, predict the quantity of products to be produced, determine the required raw materials, equipment, and human resources, and make reasonable allocations.

[0004] With the progress of the times, automation and intelligent technologies have been widely applied in the manufacturing industry. Especially on the assembly line, robotic arms have gradually replaced manual operations. Robotic arms can perform highly repetitive and precise operations, reducing errors and fatigue problems in manual operations. At the same time, they can work around the clock without rest, significantly improving the continuity and stability of production. However, during the use of robotic arms, the work efficiency may decrease due to reasons such as equipment aging or malfunctions. If abnormal robotic arms cannot be detected in a timely manner, it will lead to a decrease in overall production volume and affect the completion of production tasks. Summary of the Invention

[0005] The purpose of the present invention is to provide a production task control method based on the Internet of Things to solve the above technical problems.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] A production task control method based on the Internet of Things includes the following steps:

[0008] S1: Periodically collect n images of the robotic arm at a preset position, where n is a preset value, the time interval between two adjacent image collections is Δt, establish a coordinate system with the central pixel point of the image as the origin, draw a straight line passing through a preset position on the y-axis and parallel to the x-axis, and its function expression is f(x), and set a picking range, and the picking range satisfies the following constraints:

[0009]

[0010] Wherein, r' represents a preset picking distance, and Δr represents a correction value of the preset picking distance;

[0011] S2: Obtain a picking value. When the reference point belongs to the picking range, the picking value is T, and the reference point is a preset position on the robotic arm; when the reference point does not belong to the picking range, the picking value is F;

[0012] Sort the picking values in chronological order. Starting from the first picking value in the sorting, determine the first picking value a that is T in the sorting, and starting from the picking value a, determine the first picking value b that is F in the sorting. Take the time interval between the picking value a and the picking value b as a picking cycle;

[0013] Remove the picking values before the picking value b from the sorting to generate a new sorting, and repeat the above steps to determine all picking cycles;

[0014] S3: Sort the picking cycles in chronological order, determine the time node Cend when the C-th picking cycle ends, and determine the time node C+1sta when the next adjacent picking cycle C+1 starts. Take [Cend, C+1sta] as a judgment cycle, and determine the total number mfre of normal cycles. The normal cycle is a judgment cycle in which the speed of the reference point movement does not appear abnormal;

[0015] S4: Calculate an evaluation score K, and determine whether there is an abnormality in the robotic arm according to the evaluation score.

[0016] As a further solution of the present invention: In the step S2, obtain the length of the picking cycle. When it is greater than a preset duration threshold, mark the picking cycle as an abnormal cycle, and count the proportion A of the abnormal cycles in the picking cycles. When the proportion A is greater than a preset proportion threshold A', do not perform the subsequent steps and determine that there is an abnormality in the robotic arm.

[0017] As a further solution of the present invention: In the step S3, the process of obtaining the speed of the reference point movement specifically includes:

[0018] Obtain the coordinates (Xd, Yd) of the reference point in the d-th image, and obtain the next adjacent image d+1 to the d-th image, and determine the coordinates (Xd +1 , Yd +1 ) of the reference point in the (d+1)-th image;

[0019] Calculate the moving distance And calculate the speed Vd = Dd / Δt of the reference point between the d-th image and the (d+1)-th image;

[0020] Repeat the above steps to determine the speed at which the reference point moves between two adjacent images.

[0021] As a further solution of the present invention: in the step S3, the process of determining the normal period specifically includes:

[0022] Regarding the robotic arms without anomalies within the preset acquisition period as normal robotic arms, obtaining the speed at which the reference point on the normal robotic arms moves between two adjacent images, and calculating the mean value as the standard speed Vmea;

[0023] During the judgment period, obtaining the speed at which the reference point moves between two adjacent images, and calculating the mean value as the judgment speed Vjud;

[0024] Calculating the speed difference ΔV = Vmea - Vjud, and when the speed difference is less than the preset difference threshold, regarding the judgment period as the normal period.

[0025] As a further solution of the present invention: during the process of calculating the standard speed, when the difference between a certain speed Vcer and the judgment speed is greater than or equal to the preset speed threshold, removing the speed Vcer and recalculating the mean value as the standard speed;

[0026] During the process of calculating the judgment speed, when the difference between a certain speed Vcer' and the judgment speed is greater than or equal to the speed threshold, removing the speed Vcer' and recalculating the mean value as the judgment speed.

[0027] As a further solution of the present invention: in the step S4, the process of calculating the evaluation score specifically includes:

[0028] Determining the number of pickup periods of the normal robotic arm within the acquisition period, and calculating the mean value m', obtaining the number of pickup periods m of the current robotic arm, and when m < m' - Δm' (where Δm' is the preset period number correction value), calculating the evaluation score K through the following formula:

[0029]

[0030] where ti' represents the length of the i-th pickup period;

[0031] When m ∈ [m' - Δm', m' + Δm'], calculating the evaluation score K through the following formula:

[0032]

[0033] where γ is the preset correction coefficient.

[0034] As a further solution of the present invention: when m > m' + Δm', the subsequent steps are not executed, and it is determined that the robotic arm is not abnormal;

[0035] When m ≤ m' - λ * Δm', it is determined that the robotic arm is abnormal, where λ is a preset quantity correction coefficient and λ > 1.

[0036] As a further solution of the present invention: in the step S4, the process of judging whether the robotic arm of the employee is abnormal according to the evaluation score specifically includes:

[0037] When 0 < K ≤ K1, it is judged that the working state of the robotic arm is good and there is no abnormality, and no treatment is performed;

[0038] When K1 < K ≤ K2, it is judged that the working state of the robotic arm is normal, and the current robotic arm is marked to remind the management staff to pay attention;

[0039] When K2 < K, it is judged that the working state of the robotic arm is poor and there is an abnormality, and the management staff is reminded;

[0040] Among them, K1 and K2 are preset judgment thresholds, and K1 < K2.

[0041] Advantages of the present invention: In the present invention, first, images of the robotic arm are periodically collected, and the picking range is determined. Periodically collecting images of the robotic arm can determine the positions of reference points on the robotic arm at different times, providing a basis for subsequent further analysis and processing. The picking range is the conveyor belt on the assembly line. After the robotic arm finishes processing an item, it needs to obtain a new unprocessed product from the assembly line for processing. Then, according to the relationship between the reference point and the picking range, the picking value is determined. When the reference point belongs to the picking range, it means that the robotic arm has obtained an unprocessed product from the assembly line and proceeds with subsequent processing. The picking cycle is the time between two adjacent times when an employee picks up an item from the assembly line. It should be noted that for a normal robotic arm, the number and time of its corresponding picking cycles should be relatively stable. For an abnormal robotic arm, due to reasons such as faults or aging, the speed of processing products becomes slower, resulting in a longer picking cycle, and thus the total number of picking cycles is less than that of a normal robotic arm. Then, the judgment cycle is determined, that is, the time interval between the last time the reference point leaves the picking range and the next adjacent time it enters the picking range, and the speed at which the reference point moves during this process is obtained and compared with the speed of a normal robotic arm during this process to screen out normal cycles. It is worth noting that for a normal robotic arm, the speed at which the reference point moves should be within a certain range, which is set by the control program for operating the robotic arm. If the robotic arm has faults or aging, etc., it will cause the speed of the robotic arm to become slower, which will be less than the speed of a normal robotic arm. In order to improve the accuracy of judgment, a difference threshold is set for judgment. When the speed difference is greater than or equal to the difference threshold, the corresponding cycle will be regarded as an abnormal cycle, otherwise it is a normal cycle. Finally, an evaluation score is calculated, and based on the evaluation score, it is judged whether the robotic arm is abnormal. It can be understood that the evaluation score is calculated based on the movement of the reference point and can reflect the movement of the reference point. The movement of the reference point of a normal robotic arm and an abnormal robotic arm has a very large difference. Therefore, it is judged whether the robotic arm is abnormal based on the movement of the reference point. And when the working state of the robotic arm is good, no treatment is done. When the working state of the robotic arm is average, the management personnel need to be reminded to pay more attention. When the working state of the robotic arm is poor, the management personnel are reminded that the robotic arm is abnormal. The present invention can judge whether the robotic arm is abnormal based on the movement of the reference point on the robotic arm, preventing the situation where the production task cannot be completed due to the abnormality of the robotic arm. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The present invention will be further described below with reference to the accompanying drawings.

[0043] Figure 1 is a flowchart of a production task control method based on the Internet of Things according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0044] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a 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 those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0045] Please refer to Figure 1 As shown, the present invention is a production task control method based on the Internet of Things, including the following steps:

[0046] S1: Periodically collect n images of the robotic arm at a preset position, where n is a preset value, the time interval between two adjacent image collections is Δt, and establish a coordinate system with the central pixel point of the image as the origin. Draw a straight line passing through a preset position on the y-axis and parallel to the x-axis, and its function expression is f(x). Set the picking range, and the picking range satisfies the following constraints:

[0047]

[0048] Among them, r' represents the preset picking distance, and Δr represents the preset picking distance correction value;

[0049] S2: Obtain the picking value. When the reference point belongs to the picking range, the picking value is T, and the reference point is a preset position on the robotic arm; when the reference point does not belong to the picking range, the picking value is F;

[0050] Sort the picking values in the order of the time axis. Starting from the first picking value in the sorting, determine the first picking value a that is T in the sorting, and starting from the picking value a, determine the first picking value b that is F in the sorting. Take the time interval between the picking value a and the picking value b as a picking cycle;

[0051] Remove the picking values before the picking value b from the sorting to generate a new sorting, and repeat the above steps to determine all the picking cycles;

[0052] S3: Sort the picking cycles in the order of the time axis. Determine the time node Cend when the Cth picking cycle ends, and determine the time node C+1sta when the next adjacent picking cycle C+1 starts. Take [Cend, C+1sta] as the judgment cycle, and determine the total number mfre of normal cycles. The normal cycle is the judgment cycle when the moving speed of the reference point does not show an abnormality;

[0053] S4: Calculate the evaluation score K, and judge whether there is an abnormality in the robotic arm according to the evaluation score.

[0054] It should be noted that, first, images of the robotic arm are collected periodically, and the picking range is determined. Periodically collecting images of the robotic arm can determine the positions of reference points on the robotic arm at different times, providing a basis for subsequent further analysis and processing. The picking range is the conveyor belt on the assembly line. After the robotic arm finishes processing an item, it needs to obtain a new unprocessed product from the assembly line for processing. Then, according to the relationship between the reference point and the picking range, the picking value is determined. When the reference point belongs to the picking range, it means that the robotic arm has obtained an unprocessed product from the assembly line and proceeds with subsequent processing. The picking cycle is the time interval between two adjacent times when an employee picks up an item from the assembly line. It should be noted that for a normal robotic arm, the number and time of its corresponding picking cycles should be relatively stable. For an abnormal robotic arm, due to reasons such as faults or aging, the speed of processing products becomes slower, resulting in a longer picking cycle, and thus the total number of picking cycles is less than that of a normal robotic arm. Then, the judgment cycle is determined, that is, the time interval between the last time the reference point leaves the picking range and the next time it enters the picking range, and the speed at which the reference point moves during this process is obtained and compared with the speed of a normal robotic arm during this process, so as to screen out normal cycles. It is worth noting that for a normal robotic arm, the speed at which the reference point moves should be within a certain range, which is set by the control program for operating the robotic arm. If the robotic arm has faults or aging, etc., it will cause the speed of the robotic arm to slow down and be less than the speed of a normal robotic arm. In order to improve the accuracy of judgment, a difference threshold is set for judgment. When the speed difference is greater than or equal to the difference threshold, the corresponding cycle will be regarded as an abnormal cycle, otherwise it is a normal cycle. Finally, an evaluation score is calculated, and based on the evaluation score, it is judged whether the robotic arm is abnormal. It can be understood that the evaluation score is calculated based on the movement of the reference point and can reflect the movement of the reference point. The movement of the reference point of a normal robotic arm and an abnormal robotic arm differs significantly. Therefore, it is judged whether the robotic arm is abnormal based on the movement of the reference point. And when the working state of the robotic arm is good, no processing is performed. When the working state of the robotic arm is average, the management personnel need to be reminded to pay more attention. When the working state of the robotic arm is poor, the management personnel are reminded that the robotic arm is abnormal.

[0055] In another preferred embodiment of the present invention, in step S2, the length of the picking cycle is obtained. When it is greater than a preset duration threshold, the picking cycle is marked as an abnormal cycle, and the proportion A of the abnormal cycles in the picking cycles is statistically calculated. When the proportion A is greater than a preset proportion threshold A', the subsequent steps are not executed, and it is determined that the robotic arm is abnormal.

[0056] It is worth noting that by obtaining the length of the picking cycle, it is possible to identify whether there are abnormal behaviors during the operation of the robotic arm. If the picking cycle of the robotic arm is significantly longer, it indicates that the robotic arm has not achieved the expected working efficiency. Therefore, it is determined that the robotic arm is abnormal and the subsequent steps are not carried out, thereby saving computing resources and improving processing efficiency.

[0057] In another preferred embodiment of the present invention, in the step S3, the process of obtaining the speed of the reference point moving specifically includes:

[0058] Obtain the coordinates (Xd, Yd) of the reference point in the d-th image, and obtain the next adjacent image d + 1 to the d-th image, and determine the coordinates (Xd +1 , Yd +1 ) of the reference point in the (d + 1)-th image;

[0059] Calculate the moving distance and calculate the speed Vd of the reference point between the d-th image and the (d + 1)-th image as Vd = Dd / Δt;

[0060] Repeat the above steps to determine the speed of the reference point moving between two adjacent images.

[0061] In another preferred embodiment of the present invention, in the step S3, the process of determining the normal cycle specifically includes:

[0062] Take the robotic arm without abnormalities within the preset acquisition cycle as the normal robotic arm, obtain the speed of the reference point on the normal robotic arm moving between two adjacent images, and calculate the average value as the standard speed Vmea;

[0063] In the judgment cycle, obtain the speed of the reference point moving between two adjacent images, and calculate the average value as the judgment speed Vjud;

[0064] Calculate the speed difference ΔV = Vmea - Vjud. When the speed difference is less than the preset difference threshold, take the judgment cycle as the normal cycle.

[0065] It should be noted that the moving speed of the robotic arm can reflect the actual operation situation of the robotic arm during the working process. By calculating the moving speed of the robotic arm, the moving situation of the robotic arm during the working process is quantified; and if the speed difference is greater than or equal to the difference threshold, it indicates that the moving speed of the robotic arm in the judgment cycle is too slow, which may be caused by the slow speed of the robotic arm in processing the product or the slow moving speed. Therefore, when the speed difference is greater than or equal to the difference threshold, the judgment cycle is marked as an abnormal cycle, and vice versa as a normal cycle. The more normal cycles there are, the smaller the possibility that the robotic arm has abnormalities.

[0066] In another preferred embodiment of the present invention, during the process of calculating the standard speed, when the difference between a certain speed Vcer and the judgment speed is greater than or equal to a preset speed threshold, the speed Vcer is removed, and the mean value is recalculated as the standard speed;

[0067] During the process of calculating the judgment speed, when the difference between a certain speed Vcer' and the judgment speed is greater than or equal to the speed threshold, the speed Vcer' is removed, and the mean value is recalculated as the judgment speed.

[0068] It should be noted that in actual situations, the actual measurement process often contains random errors, which may cause a certain speed to deviate from the true value. By excluding these outliers, the influence of errors on the overall data can be reduced, making the remaining data closer to the true situation and improving the accuracy of the data.

[0069] In another preferred embodiment of the present invention, in step S4, the calculation process of the evaluation score specifically includes:

[0070] Determine the number of pickup cycles of the normal robotic arm within the acquisition period, calculate the mean value m', obtain the number of pickup cycles m of the current robotic arm, and when m < m' - Δm', calculate the evaluation score K through the following formula, where Δm' is a preset cycle number correction value:

[0071]

[0072] where ti' represents the length of the i-th pickup cycle;

[0073] When m ∈ [m' - Δm', m' + Δm'], calculate the evaluation score K through the following formula:

[0074]

[0075] where γ is a preset correction coefficient.

[0076] In another preferred embodiment of the present invention, when m > m' + Δm', the subsequent steps are not executed, and it is determined that the robotic arm is normal;

[0077] When m ≤ m' - λ * Δm', it is determined that the robotic arm is abnormal, where λ is a preset quantity correction coefficient and λ > 1.

[0078] It can be understood that when m > m' + Δm', it indicates that the number of pickup cycles corresponding to the current robotic arm is greater than that of the normal robotic arm, which can reflect that the working state of the current robotic arm is good. Therefore, it is determined that the robotic arm is normal;

[0079] When m ≤ m' - λ * Δm', it indicates that the number of pick-up cycles of the current robotic arm is significantly lower than normal, suggesting that the working efficiency of the robotic arm is low. Therefore, it is determined that the robotic arm is abnormal.

[0080] In these two cases, it is already possible to determine whether the robotic arm is abnormal. Therefore, no further operations are performed, thereby reducing the amount of data to be processed and improving the processing efficiency.

[0081] In another preferred embodiment of the present invention, in step S4, the process of determining whether the robotic arm is abnormal based on the evaluation score specifically includes:

[0082] When 0 < K ≤ K1, it is determined that the working state of the robotic arm is good and there is no abnormality, and no treatment is required;

[0083] When K1 < K ≤ K2, it is determined that the working state of the robotic arm is average, and the current robotic arm is marked to remind the management to pay attention;

[0084] When K2 < K, it is determined that the working state of the robotic arm is poor and there is an abnormality, and the management is reminded;

[0085] Among them, K1 and K2 are preset judgment thresholds, and K1 < K2.

[0086] The above has described a detailed embodiment of the present invention, but the content described is only a preferred embodiment of the present invention and cannot be considered as limiting the scope of implementation of the present invention. Any equivalent changes and improvements made within the scope of the application of the present invention should still fall within the scope covered by the patent of the present invention.

Claims

1. An Internet of Things-based production task control method, characterized in that It includes the following steps: S1: Periodically collect n images of the robotic arm at a preset position, where n is a preset value, the time interval between two adjacent image collections is Δt, establish a coordinate system with the central pixel point of the image as the origin, draw a straight line passing through a preset position on the y-axis and parallel to the x-axis, and its function expression is f(x). Set the picking range, and the picking range satisfies the following constraints: ； where r' represents the preset picking distance, and Δr represents the preset picking distance correction value; S2: Obtain the picking value. When the reference point belongs to the picking range, the picking value is T, and the reference point is a preset position on the robotic arm; when the reference point does not belong to the picking range, the picking value is F; Sort the picking values in the order of the time axis. Starting from the first picking value in the sorting, determine the first picking value a that is T in the sorting, and starting from the picking value a, determine the first picking value b that is F in the sorting. Take the time interval between the picking value a and the picking value b as a picking cycle; Remove the picking values before the picking value b from the sorting to generate a new sorting, and repeat the above steps to determine all the picking cycles; S3: Sort the pickup cycles in the order of the time axis, and determine the time node C when the C-th pickup cycle ends end , and determine the time node C+1 when the next adjacent pickup cycle C+1 starts sta , and use [C end , C+1 sta as the judgment cycle, and determine the total number m of normal cycles fre , where the normal cycle is the judgment cycle in which the speed of the reference point movement does not show abnormalities; S4: Calculate the evaluation score K, and determine whether the robotic arm is abnormal according to the evaluation score; The process of obtaining the speed at which the reference point moves specifically includes: Obtain the coordinates (X d , Y d ) of the reference point in the d-th image, obtain the next image d + 1 adjacent to the d-th image, and determine the coordinates (X d+1 , Y d+1 ) of the reference point in the (d + 1)-th image; Calculate the moving distance , and calculate the speed V of the reference point between the d-th image and the (d + 1)-th image d =D d / Δt; Repeat the above steps to determine the speed at which the reference point moves between two adjacent images; The calculation process of the evaluation score specifically includes: Determine the number of picking cycles of a normal robotic arm within the collection cycle, and calculate the mean value m'. Obtain the number of picking cycles m of the current robotic arm, and when m < m' - Δm', calculate the evaluation score K through the following formula, where Δm' is a preset cycle number correction value: where t i ' represents the length of the i-th pick-up cycle; When \(m\in[m'-\Delta m',m'+\Delta m']\), the evaluation score \(K\) is calculated by the following formula: ; where \(\gamma\) is a preset correction coefficient.

2. The production task control method based on the Internet of Things according to claim 1, wherein In step S2, obtain the length of the picking cycle. When it is greater than the preset time threshold, mark the picking cycle as an abnormal cycle, and count the proportion A of the abnormal cycles in the picking cycles. When the proportion A is greater than the preset proportion threshold A', do not perform the subsequent steps, and determine that the robotic arm is abnormal.

3. The production task control method based on the Internet of Things according to claim 1, characterized in that In step S3, the process of determining the normal cycle specifically includes: The robotic arm without anomalies within a preset acquisition period is regarded as a normal robotic arm, and the speed at which the reference point on the normal robotic arm moves between two adjacent images is obtained, and the average value is calculated as the standard speed V mea ; In the judgment period, obtain the speed at which the reference point moves between two adjacent images, and calculate the average value as the judgment speed V jud ; Calculate the speed difference ΔV = V mea - V jud , when the speed difference is less than a preset difference threshold, the determination period is taken as a normal period.

4. The production task control method based on the Internet of Things according to claim 3, wherein In the process of calculating the standard speed, when there is a certain speed V cer whose difference from the judgment speed is greater than or equal to a preset speed threshold, remove the speed V cer , and recalculate the mean value as the standard speed; in the process of calculating the judgment speed, when there is a certain speed V cer ' whose difference from the judgment speed is greater than or equal to the speed threshold, remove the speed V cer ', and recalculate the mean value as the judgment speed.

5. The production task control method based on the Internet of Things according to claim 1, characterized in that When m > m' + Δm', do not perform the subsequent steps, and determine that the robotic arm is not abnormal; When m ≤ m' - λ * Δm', and determine that the robotic arm is abnormal, where λ is a preset quantity correction coefficient, and λ > 1.

6. The production task control method based on the Internet of Things according to claim 1, wherein In step S4, the process of determining whether the robotic arm is abnormal according to the evaluation score specifically includes: When 0 < K ≤ K1, determine that the working state of the robotic arm is good and there is no abnormality, and no processing is required; When K1 < K ≤ K2, determine that the working state of the robotic arm is average, mark the current robotic arm, and remind the management personnel to pay attention; When K2 < K, determine that the working state of the robotic arm is poor and there is an abnormality, and remind the management personnel; where K1 and K2 are preset judgment thresholds, and K1 < K2.

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