Multi-actuator collaborative system and production line based on real-time operation

Abstract

This invention relates to the field of collaborative control system technology and discloses a multi-actuator collaborative system and production line based on real-time operation. The system includes: collecting motion parameters of the actuators and the deviation rate of the previous-level actuators based on a multi-actuator collaborative control environment; transmitting the motion parameters to a local model for analyzing the motion state of the actuator; analyzing the collected parameters of other actuators based on the local model and calculating the deviation rate of the actuators deployed with the local model in the current operating state; and determining whether to optimize the actuator's action based on a threshold of the deviation rate in the operating state. This invention accurately quantifies the position deviation of the actuators using the Euclidean distance formula in three-dimensional space, providing basic data for deviation rate calculation, ensuring the accuracy of position control, and avoiding collaborative control failure due to accumulated errors when multiple operating systems work together.

Description

Technical Field

[0001] This invention relates to the field of collaborative control system technology, and in particular to a multi-actuator collaborative system based on real-time operation and a production line. Background Technology

[0002] In the kitchenware manufacturing industry, multi-actuator collaborative control technology is widely used in processes such as cutting, stamping, forming, welding, and assembly. Traditional kitchenware production lines typically employ independently controlled actuators, lacking real-time data interaction and dynamic adjustment mechanisms between processes. For example, in the production of stainless steel cookware, a robotic arm is responsible for the precise cutting of metal sheets, a conveyor belt needs to simultaneously transport semi-finished products to the stamping station, and a welding robot needs to adjust the welding path in real time according to the component position. However, existing systems largely rely on non-real-time communication networks, resulting in low efficiency in inter-process collaboration and a tendency to accumulate errors. For instance, positional deviations in the cutting robotic arm may lead to inaccurate positioning in subsequent stamping, while torque output deviations in the welding robot can cause weak welds or deformation. Furthermore, the threshold settings for break-in errors in existing technologies are mostly based on static rules, which cannot adapt to the dynamic needs of producing different models of kitchenware, often leading to unintended shutdowns or quality defects.

[0003] An existing patent discloses a collaborative control system and command communication method for multiple mobile robotic arms (publication number CN111761587A). The collaborative control system includes a terminal, a server, several lower-level machines, and several mobile robotic arms. This patent's collaborative control system not only suffers from the aforementioned problems but also has issues with its alarm threshold for break-in errors. This threshold is typically based on manual experience or static rules, lacking adaptability to historical data and real-time operating conditions. Such issues can lead to misjudgments or missed judgments, affecting production efficiency and equipment safety. Summary of the Invention

[0004] This invention provides a multi-actuator collaborative system and production line based on real-time operation to solve the existing technical problems, thereby resolving the issues mentioned in the background art.

[0005] To solve the above-mentioned technical problems, according to one aspect of the present invention, more specifically, a multi-actuator collaborative system and production line based on real-time operation, comprising:

[0006] The data acquisition module is configured in a multi-actuator collaborative control environment to collect the motion parameters of the current actuator and the deviation rate of the previous stage actuator;

[0007] The local model module is communicatively connected to the data acquisition module and is used to receive the motion parameters to analyze the motion state of the current actuator; and to calculate the deviation rate of the current actuator in the current operating state based on the collected deviation rate of the previous stage actuator and other mechanism parameters.

[0008] The action optimization decision module is connected to the local model module. Based on the local model prediction technology, it determines whether to optimize the action of the current actuator according to the comparison result of the deviation rate and the preset threshold.

[0009] The motion feedback module, connected to the motion optimization decision module, is used to feed back optimization instructions to the current actuator to adjust its motion state;

[0010] The real-time communication transmission module is configured as follows:

[0011] 1) Transmit the optimized motion state parameters of the current actuator to the control system of the next-level actuator;

[0012] 2) Receive the deviation rate calculated by the local model module of the previous level actuator and transmit it to the local model module of the current actuator;

[0013] 3) Transfer the deviation rate of the current actuator to the local model module of the next level actuator.

[0014] Furthermore, the motion parameters are bound to the collected information of a single actuator.

[0015] Furthermore, the motion parameters include spatial dimension deviation information and torque output deviation information.

[0016] Furthermore, the local model obtains the total correction product based on the deviation rate of the previous stage actuator cooperating with the current actuator and the Euclidean distance deviation between the actual position and the target position of the current actuator, and compensates the total correction product based on the percentage deviation between the actual output torque of the current actuator and the expected value, so as to obtain the deviation rate of the current actuator in this operating state.

[0017] The local model obtains the deviation rate calculation formula for the current actuator in this operating state as follows:

[0018] ;

[0019] In the formula, It represents the deviation rate of the current actuator in this operating state; it is used to comprehensively quantify the collaborative control error of the current actuator and to determine whether action optimization is needed.

[0020] It represents the deviation rate of the upstream executing agency in coordinating with the current agency; it is used to reflect the impact of the accumulated coordination error of the upstream agency on the current agency.

[0021] This indicates the Euclidean distance deviation between the current actuator's actual position and the target position; This indicates the percentage deviation between the actual output torque of the current actuator and the expected value. , , They represent the functions used for adjustment. , , The model sensitivity.

[0022] in, This represents the total correction product in the local model; and the total correction product integrates the upstream deviation rate and the position deviation, thereby reflecting the coupled effect of cumulative error and spatial positioning accuracy.

[0023] This represents the compensation value used to compensate for the total correction product. This compensation value is based on dynamic compensation of the total correction product according to the torque deviation. The larger the torque deviation, the smaller the compensation value, and the larger the deviation rate g.

[0024] Furthermore, when the deviation rate exceeds the set threshold, it indicates that the running-in error between the current actuator and the previous stage actuator is sufficient to affect the production and use of the equipment.

[0025] When the deviation rate is less than the set threshold, it means that the running-in error between the current actuator and the previous stage actuator will not affect the production and use of the equipment.

[0026] Furthermore, the local model calculates the Euclidean distance deviation between the current actuator's actual position and the target position. ,have:

[0027] ;

[0028] In the formula, , , These represent the actual position coordinates of the current actuator, in millimeters (mm). , , These represent the target position coordinates of the current executing mechanism, in millimeters (mm).

[0029] Furthermore, the local model calculates the percentage deviation between the actual output torque of the current actuator and the expected value. ,have:

[0030] ;

[0031] In the formula, This indicates the actual output torque of the current actuator, in units of... ; This indicates the target output torque of the current actuator, in units of... .

[0032] Furthermore, the local model prediction technology is used to provide a threshold based on excessive sample data and feedback, which can reflect that the break-in error between the current actuator and the previous stage actuator is sufficient to affect the production and use of the equipment.

[0033] Furthermore, a multi-actuator collaborative production line based on real-time operation includes: the local assembly line, the local execution table, the local actuator, the next-level assembly line, and kitchen utensils.

[0034] The multi-actuator collaborative system and production line based on real-time operation provided by this invention have the following advantages compared with the prior art:

[0035] 1. This invention uses the Euclidean distance formula in three-dimensional space to accurately quantify the position deviation of the actuator, providing basic data for deviation rate calculation, ensuring the accuracy of position control, and avoiding the failure of collaborative control due to accumulated errors when multiple implementation operating systems work together.

[0036] 2. This invention uses a percentage deviation formula to evaluate the difference between the actual torque and the target torque, ensuring the stability of the torque output and thus preventing uneven load on the robotic arm or equipment damage due to excessive torque deviation.

[0037] 3. This invention dynamically calculates the current deviation rate by combining position deviation, torque deviation, and the deviation rate of the previous stage actuator, comprehensively evaluates the status of the actuator, and judges whether the action needs to be optimized by comprehensive parameters, thereby improving the system's adaptive capability.

[0038] 4. This invention determines thresholds through a large amount of sample data to achieve intelligent classification of equipment status, including red error, yellow warning, and green pass. This can provide early warning of potential faults, reduce downtime, and ensure production efficiency. Attached Figure Description

[0039] Figure 1 This is a schematic diagram of the structure of the collaborative control system in this invention;

[0040] Figure 2 This is a flowchart of a single actuator in the present invention;

[0041] Figure 3 In this invention, the deviation rate g and the deviation rate Relationship diagram;

[0042] Figure 4 This is a graph showing the relationship between the deviation rate g and the Euclidean distance deviation p in this invention.

[0043] Figure 5 This is a graph showing the relationship between the deviation rate g and the percentage deviation d in this invention.

[0044] Figure 6 In this invention, the deviation rate g and the deviation rate The relationship between the Euclidean distance deviation p;

[0045] Figure 7 In this invention, the deviation rate g and the deviation rate A graph showing the relationship between percentage deviation d;

[0046] Figure 8 This is a graph showing the relationship between the deviation rate g, the Euclidean distance deviation p, and the percentage deviation d in this invention.

[0047] Figure 9 This is a schematic diagram of the production line in this invention.

[0048] In the diagram: 1. This level of production line; 2. The next level of production line; 3. This level of execution station; 4. This level of execution mechanism; 5. Kitchenware. Detailed Implementation

[0049] To make the technical solution of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0050] like Figure 1 As shown in Figure 2, according to one aspect of the present invention, a multi-actuator collaborative system based on real-time operation is provided, comprising:

[0051] Based on a multi-actuator collaborative control environment, the motion parameters of the current actuator and the deviation rate of the previous stage actuator are collected; the motion parameters are transmitted to a local model used to analyze the motion state of the current actuator.

[0052] The parameters of other mechanisms collected are analyzed based on the local model, and the deviation rate of the current actuator in which the local model is deployed is calculated under this operating state.

[0053] Based on local model prediction technology, a threshold of the deviation rate of the current actuator in its operating state is used to determine whether to optimize the action of the current actuator; the action optimization can be fed back to the current actuator that has been collected.

[0054] After optimization, the motion state parameters of the current actuator are transmitted to the control system of the next-level actuator through a real-time communication network;

[0055] The specific steps for calculating the deviation rate of the actuator are as follows:

[0056] The deviation rate of the higher-level actuator is calculated from the local model of the higher-level actuator, and the deviation rate of the higher-level actuator is transmitted to the local model of the current actuator through the communication network.

[0057] The local model of the current actuator collects the motion parameters of the current actuator and the deviation rate of the previous stage actuator to calculate the deviation rate of the current actuator; at the same time, the deviation rate of the current actuator is transmitted to the next stage actuator through the communication network.

[0058] The above-described technical solutions address the limitations of traditional control systems that rely on non-real-time communication networks or centralized data processing architectures, making it difficult to meet the millisecond-level response requirements of collaborative multi-actuator operations. Communication delays or data processing bottlenecks can lead to asynchronous actions of actuators, resulting in accumulated errors or even equipment failure.

[0059] Example 1

[0060] like Figure 1 As shown, the local model calculates the Euclidean distance deviation between the actual position and the target position of the current actuator. ,have:

[0061] ;

[0062] In the formula, , , These represent the actual position coordinates of the current actuator, in millimeters (mm). , , These represent the target position coordinates of the current executing mechanism, in millimeters (mm).

[0063] In the collaborative control system for multi-joint robotic arm collaborative welding, the Euclidean distance deviation between the actual position and the target position of the multi-joint robotic arm in one process is calculated. The actual position coordinates of the end effector of the multi-joint robotic arm are taken as (521, 233, 45). The target position coordinates of the end effector are (517, 225, 50), therefore:

[0064]

[0065] The above calculations show that the Euclidean distance deviation between the actual position and the target position of the multi-joint robotic arm is [value missing]. .

[0066] Example 2

[0067] like Figure 1 As shown, the local model calculates the percentage deviation between the actual output torque of the current actuator and the expected value. ,have:

[0068] ;

[0069] In the formula, This indicates the actual output torque of the current actuator, in units of... ; This indicates the target output torque of the current actuator, in units of... .

[0070] In the collaborative control system for multi-joint robotic arm collaborative welding, the percentage deviation between the actual output torque of the multi-joint robotic arm and the expected value in one process is calculated. The actual output torque of the multi-joint robotic arm is taken as... ( The target output torque of the multi-joint robotic arm is taken as... ( ), then we have:

[0071]

[0072] The above calculations show that the percentage deviation between the actual output torque of the multi-joint robotic arm and the expected value is [missing value]. Among them, when the actual output torque of a multi-joint robotic arm is exactly the same as the target output torque, then take... .

[0073] Example 3

[0074] like Figure 3 As shown, the local model obtains the total correction product based on the deviation rate of the previous stage actuator in cooperating with the current actuator, and the Euclidean distance deviation between the actual position and the target position of the current actuator. The total correction product is then compensated based on the percentage deviation between the actual output torque of the current actuator and the expected value, in order to obtain the deviation rate of the current actuator in this operating state.

[0075] The local model uses the following formula to calculate the deviation rate of the current actuator in this operating state:

[0076] ;

[0077] In the formula, It represents the deviation rate of the current actuator in this operating state; it is used to comprehensively quantify the collaborative control error of the current actuator and to determine whether action optimization is needed.

[0078] It represents the deviation rate of the upstream executing agency in coordinating with the current agency; it is used to reflect the impact of the accumulated coordination error of the upstream agency on the current agency.

[0079] This indicates the Euclidean distance deviation between the current actuator's actual position and the target position; This indicates the percentage deviation between the actual output torque of the current actuator and the expected value. , , They represent the functions used for adjustment. , , The model sensitivity.

[0080] in, This represents the total correction product in the local model; and this total correction product integrates the upstream deviation rate and the position deviation, thereby reflecting the coupled effect of cumulative error and spatial positioning accuracy. Furthermore, 1 and 12 in this section both represent constants used to increase the amplitude of the formula response.

[0081] This represents the compensation value used to compensate for the total correction product. This compensation value is based on dynamic compensation of the total correction product according to the torque deviation; the larger the torque deviation, the smaller the compensation value, and the larger the deviation rate g. Similarly, 0.1 represents a constant used to adjust the strength of this formula.

[0082] The above formula is an analysis conducted to solve practical problems for enterprises, which is called empirical analysis. The data obtained in sequence and the characteristic relationships between the data are the external manifestations of the empirical formula. The reasoning process is as follows:

[0083] 1) Fit the formula for the deviation rate g of the current actuator under this operating state.

[0084] Among them, the deviation rate g of the current actuator in this operating state can be represented by the number of abnormal state mechanisms in the sample mechanism.

[0085] For example, if data is collected from 100 actuator samples, and the degree of abnormality of a certain actuator exceeds that of the data in the other 50 samples after manual evaluation, then it means that the deviation rate g=50% of the current actuator in this operating state.

[0086] All other data were collected by the device itself for subsequent calculations and fitting statistics.

[0087] 2) Regarding the deviation rate g and the deviation rate Establish mathematical models for the relationships between them (such as...) Figure 3 As shown in the figure (where the red dots represent the distribution of the 100 collected samples), then:

[0088] (Formula 1);

[0089] In Formula 1 above, k represents an empirical constant for adjusting the sensitivity of the model. And from... Figure 3 The data in the middle can be determined When, in Formula 1 It approaches the approximation of the deviation rate g.

[0090] 3) Establish a mathematical model for the relationship between the deviation rate g and the Euclidean distance deviation p (e.g.) Figure 4 As shown in the figure (where the red dots represent the distribution of the 100 collected samples), then:

[0091] (Formula 2);

[0092] In Formula 2 above, k represents an empirical constant for adjusting the sensitivity of the model. And from... Figure 4 The data in the middle can be determined When k is used, the value of k in Formula 2 is approximately the same as the value of k in Formula 1.

[0093] 4) Establish a mathematical model for the relationship between the deviation rate g and the percentage deviation d (e.g., Figure 5 As shown in the figure (where the red dots represent the distribution of the 100 collected samples), then:

[0094] (Formula 3);

[0095] In Formula 3 above, k represents an empirical constant for adjusting the sensitivity of the model. And from... Figure 5 The data in the middle can be determined At that time, the value of k in Formula 3 is approximately the same as the value of k in Formula 1 and Formula 2.

[0096] 5) Regarding the deviation rate g and the deviation rate A mathematical model is established to establish the relationship between the Euclidean distance deviation p (e.g.) Figure 6 As shown, the deviation rate can be known through this model. The Euclidean distance deviation p is positively correlated with the above. Combining this with the characteristic relationship from Formula 1 and Formula 2 above, we have:

[0097] g=(Formula 1)×(Formula 2);

[0098] 5) Regarding the deviation rate g and the deviation rate Establish a mathematical model for the relationship between percentage deviation d (e.g.) Figure 7 As shown, the deviation rate can be known through this model. The percentage deviation d is positively correlated with the given information. Combining this with the characteristic relationship between Formula 1 and Formula 2 above, we have:

[0099] g=(Formula 1)×(Formula 3);

[0100] 5) Establish a mathematical model for the relationship between the deviation rate g and the Euclidean distance deviation p and percentage deviation d (e.g.) Figure 8 As shown, this model reveals a positive correlation between Euclidean distance deviation p and percentage deviation d. Combining this with the characteristic relationship derived from Formulas 1 and 2 above, we have:

[0101] g=(Formula 2)×(Formula 3);

[0102] Therefore, based on the above derivation, the condition coefficient g for the energy storage cabinet to call the backup power module is:

[0103] .

[0104] Therefore, in the collaborative control system of multi-joint robotic arms for collaborative welding, the deviation rate of a multi-joint robotic arm in one of the processes is calculated in this operating state. The deviation rate of the multi-joint robotic arm at the previous stage of the conveyor belt coordinating with the current multi-joint robotic arm is taken as... The Euclidean distance deviation between the actual position and the target position of the multi-joint robotic arm is taken as... The percentage deviation between the actual output torque of the multi-joint robotic arm and the expected value is taken as... Therefore, we have:

[0105]

[0106] The above calculations show that the deviation rate of the multi-joint robotic arm during this operation is [missing information]. This means that the break-in error between the multi-joint robotic arm and the previous multi-joint robotic arm is sufficient to affect the production and use of the equipment.

[0107] Example 4

[0108] Local model prediction technology is used to determine a threshold, based on excessive sample data and feedback, that reflects a break-in error between the actuator and its upstream actuator that could significantly impact equipment production and use. By comparing data from multiple sets of implementation examples, the following can be observed:

[0109] Table 1. Data from some embodiments and the status of device operation.

[0110]

[0111] The data in Table 1 above shows that when the sample data approaches infinity, the relationship between equipment error rates and deviation rates g can be analyzed using local model prediction techniques. This is achieved through the actuator deviation rate. The numerical values ​​are used to determine whether a specific device is reporting an error, issuing a warning, or allowing safe passage. That is, in If the running-in error between the actuator and the preceding actuator is sufficient to affect the production and use of the equipment, then it indicates that the running-in error between the actuator and the preceding actuator is sufficient to affect the production and use of the equipment. If the timing is right, it means that the running-in error between the actuator and the actuator at the next higher level will not affect the production and use of the equipment.

[0112] Example 5

[0113] like Figure 9 As shown, a multi-actuator collaborative production line based on real-time operation includes: a local assembly line 1, a local execution table 3, a local actuator 4, a previous-level assembly line 2, and kitchen equipment 5. The local execution table 3 is installed at the head of the local assembly line 1. The local actuator 4 is installed on the local execution table 3. The local actuator 4 is the actuator in the multi-actuator collaborative system based on real-time operation.

[0114] The working process is as follows: After the kitchen utensils 5 on the previous production line 2 have been processed by grinding, polishing and other steps, they will enter the working range of the current execution mechanism 4 through the previous production line 2. After being processed by the current execution mechanism 4 through the corresponding packaging, installation and other steps, they can be transported to the next destination through the current production line 1.

[0115] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A multi-actuator collaborative system based on real-time operation, characterized in that, include: The data acquisition module is configured in a multi-actuator collaborative control environment to collect the motion parameters of the current actuator and the deviation rate of the previous stage actuator; The local model module is communicatively connected to the data acquisition module and is used to receive the motion parameters to analyze the motion state of the current actuator; and to calculate the deviation rate of the current actuator in the current operating state based on the collected deviation rate of the previous stage actuator and other mechanism parameters. The action optimization decision module is connected to the local model module. Based on the local model prediction technology, it determines whether to optimize the action of the current actuator according to the comparison result of the deviation rate and the preset threshold. The motion feedback module, connected to the motion optimization decision module, is used to feed back optimization instructions to the current actuator to adjust its motion state; The real-time communication transmission module is configured as follows: 1) Transmit the optimized motion state parameters of the current actuator to the control system of the next-level actuator; 2) Receive the deviation rate calculated by the local model module of the previous level actuator and transmit it to the local model module of the current actuator;    3) Transfer the deviation rate of the current actuator to the local model module of the next level actuator; The local model uses the following formula to calculate the deviation rate of the current actuator in this operating state: ； In the formula, This indicates the deviation rate of the current actuator in this operating state; This indicates the deviation rate in the coordination between the higher-level implementing agency and the operation of this agency; This indicates the Euclidean distance deviation between the current actuator's actual position and the target position; This indicates the percentage deviation between the actual output torque of the current actuator and the expected value; where, , , They represent the functions used for adjustment. , , The model sensitivity.

2. The multi-actuator collaborative system based on real-time operation according to claim 1, characterized in that: The motion parameters are bound to the collected information of a single actuator.

3. The multi-actuator collaborative system based on real-time operation according to claim 1, characterized in that: The motion parameters include spatial dimension deviation information and torque output deviation information.

4. The multi-actuator collaborative system based on real-time operation according to claim 1, characterized in that: The local model obtains the total correction product based on the deviation rate of the upper-level actuator cooperating with the current actuator and the Euclidean distance deviation between the actual position and the target position of the current actuator. The total correction product is compensated based on the percentage deviation between the actual output torque of the current actuator and the expected value, so as to obtain the deviation rate of the current actuator in this operating state; in, This represents the total corrected product in the local model; This represents the compensation value used to compensate for the total correction product.

5. The multi-actuator collaborative system based on real-time operation according to claim 4, characterized in that: When the deviation rate exceeds the set threshold, it indicates that the running-in error between the current actuator and the previous stage actuator is sufficient to affect the production and use of the equipment. When the deviation rate is less than the set threshold, it means that the running-in error between the current actuator and the previous stage actuator will not affect the production and use of the equipment.

6. The multi-actuator collaborative system based on real-time operation according to claim 4, characterized in that: The local model calculates the Euclidean distance deviation between the current actuator's actual position and the target position. ,have: ； In the formula, , , These represent the actual position coordinates of the current actuator, in millimeters (mm). , , These represent the target position coordinates of the current executing mechanism, in millimeters (mm).

7. The multi-actuator collaborative system based on real-time operation according to claim 4, characterized in that: The local model calculates the percentage deviation between the actual output torque of the current actuator and the expected value. ,have: ； In the formula, This indicates the actual output torque of the current actuator, in units of... ; This indicates the target output torque of the current actuator, in units of... .

8. The multi-actuator collaborative system based on real-time operation according to claim 1 or 5, characterized in that: The local model prediction technology is used to provide a threshold based on excessive sample data and feedback, which can reflect that the break-in error between the current actuator and the previous stage actuator is sufficient to affect the production and use of the equipment.

9. A multi-actuator collaborative production line based on real-time operation, characterized in that, The multi-actuator collaborative system based on real-time operation as described in any one of claims 1-8, the multi-actuator collaborative production line based on real-time operation, includes: a local production line (1), a local execution table (3), a local execution mechanism (4), a previous production line (2), and kitchen utensils (5).

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