Method and system for intelligently storing electroplating hangers

Abstract

The embodiment of the invention provides an intelligent storage method and system for electroplating hangers, and the method comprises the steps: building a three-dimensional space rectangular coordinate positioning system, and carrying out the positioning calibration of the electroplating hangers in the positioning system; the method for controlling the manipulator to pick and place the electroplating hanger comprises the following steps: adopting a linear control system with state feedback, and controlling the manipulator to adopt a first control gain to carry out rapid movement control when the distance between the manipulator and a target position is greater than a preset distance threshold value, when the distance between the manipulator and the target position is smaller than or equal to the preset distance threshold value, the manipulator is controlled to carry out accurate positioning control by adopting a second control gain, and inertial errors and mechanical friction in the movement process of the manipulator are compensated in real time; and storing the position information, the motion state and the operation record of the electroplating hanger by adopting a distributed data storage architecture so as to realize multi-dimensional query and statistical analysis of the storage information of the electroplating hanger.

Description

Technical Field

[0001] The present application relates to the field of intelligent manufacturing, in particular to the field of electroplating production line automation, Internet of Things and intelligent control technology, and specifically to a method and system for intelligent storage of electroplating hangers. Background Art

[0002] The storage management of the racks in the electroplating production line is an important part of the electroplating manufacturing process. In the process of implementing the invention, the embodiments of the present application found that the storage management of the electroplating racks in the prior art has the following problems:

[0003] Traditional electroplating rack storage management mainly relies on manual operation, which is not only inefficient but also prone to errors, affecting production efficiency. Although some automated storage systems can achieve basic storage and retrieval functions, they lack key functions such as real-time positioning and intelligent scheduling, and cannot accurately manage electroplating racks, affecting the efficiency of the storage system. The storage systems in the existing technology generally have the problems of low system integration and difficulty in real-time data traceability, making it difficult to achieve intelligent management of the entire process of electroplating racks.

[0004] The existing technology has technical problems such as low automation and intelligence level of electroplating rack storage system, insufficient positioning and scheduling capabilities, and low system integration. Summary of the invention

[0005] In view of this, the embodiments of the present application provide a method and system for intelligent storage of electroplating hangers, which solve the technical problems in the prior art of low automation and intelligence levels of electroplating hanger storage systems, insufficient positioning and scheduling capabilities, and low system integration.

[0006] The present application embodiment provides a method for intelligent storage of electroplating racks, comprising:

[0007] Establishing a three-dimensional rectangular coordinate positioning system, and positioning and calibrating the electroplating rack in the positioning system;

[0008] Controlling a manipulator to pick up and place an electroplating rack, comprising: using a linear control system with state feedback, when the distance between the manipulator and a target position is greater than a preset distance threshold, controlling the manipulator to use a first control gain for rapid movement control, and when the distance between the manipulator and the target position is less than or equal to the preset distance threshold, controlling the manipulator to use a second control gain for precise positioning control, and real-time compensation for inertia error and mechanical friction during the movement of the manipulator; wherein, during the operation of picking up and placing the electroplating rack, a state transition model based on a Markov chain is used to optimize the path planning of picking up and placing the electroplating rack;

[0009] A distributed data storage architecture is used to store the location information, movement status and operation records of the electroplating racks, so as to realize multi-dimensional query and statistical analysis of the electroplating rack storage information.

[0010] Optionally, a state transition model based on a Markov chain is used to optimize the path planning of picking up and placing the electroplating rack, including:

[0011] Collect historical operation data of the electroplating rack to train a state transition probability matrix;

[0012] predicting future pick and place requirements based on the state transition probability matrix;

[0013] A dynamic programming algorithm is used to calculate the scheduling path that meets the shortest path and minimum waiting time constraints.

[0014] Optionally, before using the state transition model based on the Markov chain to optimize the path planning of the electroplating rack pick-up and placement, the method further includes:

[0015] Establishing a state space describing the position and state changes of the electroplating rack;

[0016] Calculating the transition probability of the state space based on the historical operation data;

[0017] Introducing robustness constraints into the dynamic programming algorithm to ensure scheduling reliability under uncertain conditions;

[0018] Distributing the path planning task to multiple computing nodes for parallel processing;

[0019] The state transition probability matrix is ​​updated in real time.

[0020] Optionally, the method further comprises:

[0021] Establish a digital requirements management system, including:

[0022] Establish a structural view, a behavior view, a demand view and a parameter view describing the electroplating rack intelligent storage system;

[0023] The functional requirements and performance requirements of the electroplating rack intelligent storage system are decomposed into multiple technical indicators through the demand decomposition matrix.

[0024] Optionally, the digital demand management system is constructed based on the SysML metamodel derived from INCOSE, and the functional requirements and performance requirements of the electroplating rack intelligent storage system are decomposed into multiple technical indicators through the demand decomposition matrix, including:

[0025] Use SysML requirement diagram to describe the functional and performance requirements of the electroplating rack intelligent storage system;

[0026] A module definition diagram is used to establish a structural model of the electroplating rack intelligent storage system and determine structural technical indicators;

[0027] Define the connection relationship within the electroplating rack intelligent storage system through an internal block diagram and determine the connection technical indicators;

[0028] Use activity diagrams and state machine diagrams to describe the dynamic behavior of the electroplating rack intelligent storage system and determine it as a dynamic behavior technical indicator;

[0029] Establish a two-way traceability relationship between requirements and design elements.

[0030] Optionally, a three-dimensional rectangular coordinate positioning system is established, and the electroplating rack is positioned and calibrated in the positioning system, including:

[0031] Laser distance measuring sensors with an accuracy of ±1mm are installed at key node positions of the storage racks, and the laser distance measuring sensors form a sensor network matrix;

[0032] Each of the electroplating racks is equipped with an active RFID tag, and the RFID tag stores identification information including a unique identification code of the electroplating rack, a type of the electroplating rack, and a load parameter;

[0033] Using multiple groups of parallel laser beam array transmitters to collect the reflection signal of the electroplating rack in real time;

[0034] The distance between the electroplating rack and a fixed reference point is calculated based on the time-of-flight ranging principle, and the distance is calibrated with a preset fixed calibration point.

[0035] Optionally, the linear control system is a predictor-feedback control system with a transient switch, and the method further comprises:

[0036] A transient switch is provided in the state feedback link for dynamically switching the first control gain and the second control gain;

[0037] Using a predictor model to calculate the motion trajectory of the manipulator in advance;

[0038] Establishing a feedback correction mechanism according to the actual position of the manipulator;

[0039] Based on the weighting of the predictor calculation result and the feedback signal, a control instruction of the manipulator is generated.

[0040] Optionally, the method further comprises:

[0041] Perform smooth transition operation during the transient switch switching process to avoid control quantity jump;

[0042] Adaptively adjusting the predictor model parameters according to the manipulator load;

[0043] Realize adaptive weight allocation of predictive control and feedback control, and set control quantity limit and change rate constraints.

[0044] Optionally, the use of a distributed data storage architecture to store the location information, movement status and operation records of the electroplating rack includes:

[0045] Establish a master data node and multiple slave data nodes of a distributed data storage architecture, wherein the master data node is responsible for data writing and distribution, and the slave data nodes are responsible for data backup and query;

[0046] Using a time series database in the master data node and the slave data node to store the position information and movement status of the hanger;

[0047] Using a relational database in the master data node and the slave data node to store the operation records of the hanger;

[0048] Synchronize data between the master data node and the slave data node based on a data consistency protocol;

[0049] A data sharding strategy is set to partition and store the hanger information according to the time dimension and the space dimension.

[0050] The present application also provides a system for intelligent storage of electroplating racks, which is used to implement the above method, including:

[0051] Multi-layer three-dimensional storage racks include several layers of shelf units, each layer of shelf units is connected through a standard interface;

[0052] An automated transmission system, comprising a six-degree-of-freedom manipulator and a conveyor belt with an encoder, wherein the manipulator is connected to a control system via a control bus, and the conveyor belt is connected to the control system via industrial Ethernet;

[0053] Identification system, including a laser scanner with an accuracy of ±1mm and an RFID reader supporting the EPC Gen2 protocol;

[0054] Distributed control system adopts master-slave architecture to realize multi-task collaborative control.

[0055] The embodiments of the present application have the following technical effects:

[0056] By establishing a three-dimensional rectangular coordinate positioning system to accurately position the electroplating hanger, and using a predictor-feedback control system with a transient switch to achieve high-precision control of the manipulator, the automation level and positioning accuracy of the warehousing system have been significantly improved.

[0057] By performing path planning based on the state transition model of Markov chain, intelligent scheduling of the warehousing system is achieved, which greatly improves the efficiency of picking and placing electroplating hangers.

[0058] The distributed data storage architecture enables the collection and management of data over the entire life cycle of electroplating hangers, improving the system's integration and reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] 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 in the following description are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0060] Figure 1 It is a schematic diagram of the process of the intelligent storage method of electroplating racks provided in the embodiment of the present application;

[0061] Figure 2 It is a structural schematic diagram of a three-dimensional rectangular coordinate positioning system provided in an embodiment of the present application;

[0062] Figure 3 is a principle block diagram of a predictor-feedback control system provided in an embodiment of the present application;

[0063] Figure 4 It is a schematic diagram of a state transition model based on a Markov chain provided in an embodiment of the present application;

[0064] Figure 5 This is a schematic diagram of a distributed data storage architecture provided by an embodiment of the present application;

[0065] Figure 6 It is a structural schematic diagram of the intelligent storage system for electroplating racks provided in an embodiment of the present application;

[0066] Figure 7 It is a schematic diagram of the actual structure of a factory including an intelligent warehousing system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0067] For ease of explanation, the terms in the embodiments of the present application may be explained here. "Plating hanger" refers to a fixture used to carry and fix the workpiece to be electroplated in the electroplating production line. "Predictor-feedback control" refers to a control system that has both feedforward prediction and feedback correction functions. "Transient switch" refers to a mechanism that can achieve rapid switching of control parameters. "Markov chain" refers to a probabilistic model that predicts future states based on the current state. "SysML" refers to System Modeling Language, a modeling language used in system engineering.

[0068] See also Figure 1 The intelligent storage method for electroplating hangers provided in the embodiment of the present application includes three main steps: establishing a three-dimensional rectangular coordinate positioning system and performing positioning calibration, controlling a manipulator to pick up and place electroplating hangers, and using a distributed data storage architecture for data management.

[0069] Specifically, the method includes:

[0070] S1. Establishing a three-dimensional rectangular coordinate positioning system, in which the electroplating rack is positioned and calibrated;

[0071] S2. Controlling a manipulator to pick up and place an electroplating rack, comprising: using a linear control system with state feedback, when the distance between the manipulator and the target position is greater than a preset distance threshold, controlling the manipulator to use a first control gain for rapid movement control, and when the distance between the manipulator and the target position is less than or equal to the preset distance threshold, controlling the manipulator to use a second control gain for precise positioning control, and real-time compensation for inertia error and mechanical friction during the movement of the manipulator; wherein, during the operation of picking up and placing the electroplating rack, a state transition model based on a Markov chain is used to optimize the path planning of picking up and placing the electroplating rack;

[0072] S3. Use a distributed data storage architecture to store the location information, movement status and operation records of the electroplating rack to achieve multi-dimensional query and statistical analysis of the electroplating rack storage information.

[0073] The specific implementation process and interrelationships of these three main steps are described in detail below.

[0074] In the first major step, the embodiment of the present application first establishes a three-dimensional rectangular coordinate positioning system, which is used to achieve precise positioning of the electroplating rack in the storage space. Specifically, the system installs laser ranging sensors with an accuracy of ±1mm at key node positions such as the four corners and center of the storage rack, and these sensors are connected through an industrial bus to form a sensor network matrix. At the same time, the system is equipped with an active RFID tag on each electroplating rack to store identification information including the unique identification code of the electroplating rack, the type of electroplating rack, and the load parameters. The RFID tag is used in conjunction with the laser ranging sensor to achieve unique identification and spatial positioning of the electroplating rack.

[0075] In the second main step, the embodiment of the present application controls the manipulator to perform the pick-up and placement operation of the electroplating rack. The system adopts a linear control system with state feedback, and dynamically adjusts the control strategy according to the real-time position during the movement of the manipulator. When the manipulator is greater than the preset distance threshold from the target position, the first control gain is used to achieve rapid approach; when the distance is less than or equal to the preset threshold, it switches to the second control gain to achieve precise positioning. In this process, the system uses a state transition model based on a Markov chain to optimize the pick-up and placement path of the electroplating rack, and by collecting and analyzing historical operation data, predicts future pick-up and placement needs and calculates the optimal scheduling path.

[0076] In the third major step, the embodiment of the present application uses a distributed data storage architecture to manage various information of the electroplating rack. The architecture includes a master data node and multiple slave data nodes, which are responsible for data writing distribution and backup query respectively. Among them, the system uses a time series database to store the location information and movement status of the electroplating rack, and a relational database to store operation records. Through this distributed architecture, the system realizes reliable storage and efficient query of electroplating rack data throughout its life cycle.

[0077] It should be noted that there is a close logical relationship between the three main steps mentioned above. First, the three-dimensional rectangular coordinate positioning system provides position feedback information for the precise control of the manipulator. Secondly, the motion control process of the manipulator generates a large amount of real-time data, which is managed through a distributed storage architecture and provides data support for subsequent path optimization. In addition, the historical data accumulated in the distributed data storage architecture can be used to optimize the calibration parameters of the positioning system, thus forming a closed-loop optimization process.

[0078] See also Figure 2 , the embodiment of the present application describes in detail the specific steps of establishing a three-dimensional rectangular coordinate positioning system. The system installs laser ranging sensors with an accuracy of ±1mm at the key node positions of the storage rack, and these sensors form a sensor network matrix. In addition, the system is equipped with an active RFID tag on each electroplating rack to store identification information such as the unique identification code of the electroplating rack, the type of the electroplating rack, and the load parameters.

[0079] During the positioning and calibration process, the system uses multiple sets of parallel laser beam array transmitters to collect the reflected signals of the electroplating rack in real time. It should be noted that the system calculates the distance between the electroplating rack and the fixed reference point based on the principle of time-of-flight ranging. In addition, the system will calibrate the calculated distance with the preset fixed calibration point to achieve accurate positioning of the electroplating rack.

[0080] The embodiment of the present application describes the process of controlling a manipulator to pick up and place electroplating hangers. The system adopts a linear control system with state feedback. The core feature of the control system is to dynamically adjust the control strategy according to the distance between the manipulator and the target position. Specifically, when the distance between the manipulator and the target position is greater than a preset distance threshold, the system controls the manipulator to use a first control gain for rapid movement control; when the distance is less than or equal to the preset distance threshold, it switches to a second control gain for precise positioning control. In this process, the system compensates for the inertia error and mechanical friction during the movement of the manipulator in real time.

[0081] See also Figure 3 The present application embodiment describes in detail the process of controlling the manipulator to pick up and place the electroplating hanger. The control process of the manipulator mainly involves three aspects: the basic architecture of the control system, the dynamic adjustment of the control gain, and the real-time compensation mechanism.

[0082] First, the system adopts a linear control system with state feedback as the basic control architecture. Figure 3 As shown in the figure, the control system includes state feedback link, feedforward control link and compensation control link. The state feedback link collects the position, velocity and acceleration information of the manipulator in real time through a high-precision encoder to form a complete state vector. The feedforward control link calculates the theoretical control quantity based on the kinematic model, and the compensation control link makes corrections based on the actual motion state. The outputs of the three control links are weighted and synthesized to generate the final control command.

[0083] Secondly, the core feature of the control system is to dynamically adjust the control strategy according to the distance between the manipulator and the target position. The system first obtains the Euclidean distance between the current position of the manipulator and the target position, and compares it with the preset distance threshold (usually set to 100mm). Depending on the comparison result, the system adopts different control strategies:

[0084] When the distance between the manipulator and the target position is greater than the preset distance threshold, the system controls the manipulator to use the first control gain for rapid movement control. The first control gain is usually set between 0.8-0.9. At this stage, the control system focuses on the rapid response performance of the manipulator, so that it can approach the target position at a faster speed. At the same time, the system will set the corresponding acceleration limit according to the movement speed, with a typical value of 2m / s 2 , to prevent excessive inertial impact.

[0085] When the distance is less than or equal to the preset distance threshold, the system switches to the second control gain for precise positioning control. The second control gain is usually set between 0.1-0.2. At this stage, the control system focuses on the positioning accuracy of the manipulator, and reduces the response speed of the system and the overshoot through a smaller control gain. At this time, the system will also reduce the speed and acceleration limits accordingly, with typical values ​​of 0.1m / s and 0.2m / s respectively. 2 .

[0086] In addition, during the control process, the system compensates for the inertia error and mechanical friction of the manipulator in real time. Inertia error compensation is mainly aimed at the dynamic error generated by the manipulator during high-speed movement and rapid speed change. The system establishes a dynamic model of the manipulator, calculates the inertia moment of each joint, and adds corresponding compensation items to the control quantity. Typical compensation strategies include:

[0087] 1) Acceleration feedforward compensation: Calculate the inertia compensation based on the acceleration of the desired trajectory

[0088] F_inertia=M*a_desired

[0089] Where M is the equivalent mass matrix of the manipulator and a_desired is the desired acceleration.

[0090] 2) Coriolis force and centrifugal force compensation: for the additional dynamic forces generated by multi-joint coupling motion

[0091]

[0092] where C is the Coriolis force coefficient matrix, and q and q· are the joint position and velocity vectors, respectively.

[0093] In addition, mechanical friction compensation mainly includes Coulomb friction compensation and viscous friction compensation:

[0094] 1) Coulomb friction compensation uses a piecewise continuous function:

[0095] F_coulomb=Fc*sign(v),|v|>v_threshold

[0096] F_coulomb=Fc*(v / v_threshold),|v|≤v_threshold

[0097] Where Fc is the Coulomb friction coefficient, v is the movement speed, and v_threshold is the speed threshold.

[0098] 2) Viscous friction compensation is proportional to speed:

[0099] F_viscous=Fv*v

[0100] Where Fv is the viscous friction coefficient.

[0101] It should be noted that in order to prevent the control amount from jumping when the control gain is switched, the system has designed a smooth transition mechanism. Specifically, the sigmoid function is used to achieve the gradual change of the control gain:

[0102] K=K1+(K2-K1)*(1 / (1+e^(-α(d-d_threshold))))

[0103] Where K1 is the first control gain, K2 is the second control gain, d is the current distance, d_threshold is the distance threshold, and α is the adjustment coefficient.

[0104] Through the above control strategy, the embodiment of the present application achieves the unity of rapid response and precise positioning during the movement of the manipulator, while ensuring the stability of the movement process. According to the test, the maximum speed of the control system in the rapid movement stage can reach 1m / s, and the positioning accuracy in the precise positioning stage is better than ±0.1mm.

[0105] It should be noted that the linear control system adopts a predictor-feedback control structure. Figure 3 A transient switch is provided in the state feedback link to dynamically switch the first control gain and the second control gain. The system uses a predictor model to calculate the motion trajectory of the manipulator in advance, and establishes a feedback correction mechanism based on the actual position of the manipulator. Finally, the system generates a control instruction for the manipulator based on the weighted predictor calculation result and the feedback signal.

[0106] Specifically, the predictor model adopts a feedforward predictive control method based on the dynamic equation of the manipulator, and accurately predicts the motion trajectory of the manipulator by establishing the kinematic and dynamic models of the manipulator. The predictor model includes the following key components:

[0107] First, the system established a kinematic model of the manipulator based on Denavit-Hartenberg parameters. Through this model, the Cartesian spatial position of the manipulator end effector can be correspondingly related to the angles of each joint:

[0108] X=f(θ)

[0109] where X is the position and attitude vector of the end effector in Cartesian space, θ is the joint angle vector, and f(·) is the forward kinematics equation.

[0110] Secondly, the system established a dynamic model that takes into account the effects of joint inertia, Coriolis force and gravity:

[0111]

[0112] Where M(θ) is the inertia matrix, C(θ, θ) is the Coriolis force and centrifugal force terms, G(θ) is the gravity term, and τ is the joint driving torque.

[0113] Based on the above model, the predictor uses a recursive algorithm to predict the state of the manipulator in the next N sampling periods:

[0114] 1. First, based on the current position p(k) and the target position p_target, a quintic polynomial interpolation is used to generate the desired trajectory:

[0115] p_d(t)=a0+a1t+a2t 2 +a3t 3 +a4t 4 +a5t 5

[0116] The coefficients a0 to a5 are solved through the boundary conditions of position, velocity and acceleration.

[0117] 2. Then, for each prediction time k+i (i=1, 2, ..., N), the prediction state is calculated using the following steps:

[0118] Solve the desired joint angle θ_d(k+i) through inverse kinematics;

[0119] Substitute into the dynamic equation to calculate the required driving torque τ(k+i);

[0120] Considering the dynamic characteristics of the actuator, the actual joint motion response θ(k+i) is predicted;

[0121] The predicted position p(k+i) is calculated by forward kinematics;

[0122] 3. In addition, the predictor also takes into account the following factors to improve the prediction accuracy:

[0123] Introduce state estimation based on Kalman filtering to identify and update model parameters online;

[0124] Considering the nonlinear characteristics of mechanical friction, the LuGre friction model is used for compensation;

[0125] Add a load mass adaptive estimation mechanism to adjust the dynamic model parameters in real time;

[0126] 4. In order to improve computing efficiency, the system adopts a rolling time domain optimization strategy:

[0127] In each control cycle, only the predicted values ​​of the next N sampling points are calculated;

[0128] Adopt parallel computing architecture to calculate the status of multiple prediction moments simultaneously;

[0129] Set up an adaptive adjustment mechanism for the prediction length N and the control period;

[0130] Through the above predictor model, the system can predict the motion trajectory of the manipulator 0.1 seconds in advance, with a prediction error within ±0.5mm, providing effective support for high-precision motion control. The predictor model works in conjunction with the state feedback controller to significantly improve the system's dynamic tracking performance and positioning accuracy.

[0131] In the specific implementation of the transient switch, the embodiment of the present application performs a smooth transition operation during the switching process to avoid jumps in the control quantity. At the same time, the system will adaptively adjust the predictor model parameters according to the manipulator load. In addition, the system implements adaptive weight distribution of predictive control and feedback control, and sets control quantity limit and change rate constraints to ensure the stability of the control system.

[0132] In the collaborative control framework of predictive control and feedback control, the system uses an adaptive weight allocation mechanism to balance the effects of the two control strategies. The specific implementation plan includes the following key aspects:

[0133] First, the system establishes a hybrid control law of predictive control and feedback control:

[0134] u(k)=λ(k)u_pred(k)+(1-λ(k))u_fb(k)

[0135] Where u(k) is the final control quantity, u_pred(k) is the predicted control quantity, u_fb(k) is the feedback control quantity, and λ(k) is the adaptive weight coefficient.

[0136] Secondly, the system designs an adaptive weight adjustment mechanism based on multi-objective evaluation:

[0137] 1. The weight adjustment adopts the following dynamic equation:

[0138] λ(k)=λ(k-1)+η[J_pred(k)-J_fb(k)]

[0139] Where η is the learning rate (typical value is 0.01), J_pred and J_fb are the performance indicators of predictive control and feedback control, respectively.

[0140] 2. The calculation of performance indicators takes into account the following factors:

[0141] Tracking error: e(k) = ||p_d(k)p(k)||

[0142] Control energy consumption: E(k) = ||u(k)||2

[0143] Overshoot: σ(k) = max(0, p(k)p_d(k))

[0144] Setting time: t_s(k)

[0145] 3. The calculation formula of comprehensive performance index is:

[0146] J(k)=w1·e(k)+w2·E(k)+w3·σ(k)+w4·t_s(k)

[0147] Among them, w1~w4 are weight coefficients, which are determined through experimental optimization.

[0148] In order to ensure the stability of the control system, the system sets strict control amount limit and change rate constraints:

[0149] 1. Control quantity limit constraint:

[0150] |u(k)|≤u_max

[0151] Among them, u_max is determined according to the rated torque of each joint of the manipulator, and the typical value is:

[0152] Large joint: ±100N·m

[0153] Middle joint: ±50N·m

[0154] Small joints: ±20N·m

[0155] 2. Control variable change rate constraint:

[0156] |u(k)-u(k-1)|≤Δu_max

[0157] Where Δu_max is the maximum allowable rate of change, which is determined by the following adaptive mechanism:

[0158] Δu_max(k)=min(α·|e(k)|, Δu_limit)

[0159] Where α is the proportional coefficient and Δu_limit is the hard limit value.

[0160] 3. The system also introduces a constraint adjustment mechanism based on Lyapunov stability theory:

[0161] Define the Lyapunov function:

[0162] V(k)=e(k) T Pe(k)+λ(k) T Qλ(k)

[0163] Where P and Q are positive definite weight matrices.

[0164] The system is required to meet the stability conditions:

[0165] ΔV(k)=V(k+1)-V(k)<0

[0166] 4. In order to further improve the robustness of the system, adaptive dead zone control is introduced:

[0167] u_dead(k)={

[0168] u(k),|e(k)|>ε

[0169] 0,|e(k)|≤ε

[0170] }

[0171] Where ε is the dead zone threshold, which is determined by online identification.

[0172] In addition, the system also implements the following optimization mechanisms:

[0173] 1. Use soft constraint processing method to convert control constraints into penalty terms:

[0174] J_constraint(k)=μ1·max(0, |u(k)|-u_max) 2 +μ2·max(0,|u(k)-u(k-1)|-Δu_max) 2

[0175] Where μ1 and μ2 are penalty factors.

[0176] 2. Designed an online parameter optimization algorithm based on gradient descent:

[0177]

[0178] Where θ is the controller parameter vector and γ is the learning step size.

[0179] 3. Implemented the anti-saturation mechanism of control parameters:

[0180] Adopting an integral separation strategy;

[0181] Set the upper and lower limits of the integral term;

[0182] Introduce feedback anti-integral saturation link;

[0183] Through the above design, the system achieves the coordination of predictive control and feedback control, ensuring the stability of the system while ensuring the control performance. The experimental results show that the control strategy can enable the manipulator to achieve a good balance between fast response and precise positioning, with a positioning accuracy better than ±0.1mm, an overshoot less than 1%, and a setting time of no more than 0.5s.

[0184] See also Figure 4In the process of picking and placing the electroplating rack, the embodiment of the present application uses a state transition model based on a Markov chain to optimize path planning. First, the system collects historical operation data of the electroplating rack to train the state transition probability matrix. Then, the system predicts future pick-and-place requirements based on the probability matrix. Finally, the system uses a dynamic programming algorithm to calculate the scheduling path that meets the shortest path and minimum waiting time constraints.

[0185] First, the system established a state transition model based on an unbounded Markov chain to describe the dynamic scheduling process of hangers in the storage system. Secondly, by collecting historical operation data, the system uses a machine learning algorithm to train the state transition probability matrix to achieve intelligent prediction of future pick-and-place requirements. Thirdly, based on the prediction results, the system uses a dynamic programming algorithm to calculate the optimal scheduling path, while considering multiple optimization objectives, including the shortest path, minimum waiting time, and optimal energy consumption. Among them, in order to deal with random interference factors, the algorithm introduces robustness constraints to ensure that good scheduling performance can be maintained under uncertain conditions. In addition, the system designs a priority-based task scheduling mechanism that can dynamically adjust the order of operations according to production needs. It should be noted that in order to improve the parallel processing capability of the system, the scheduling algorithm adopts a distributed computing architecture, and multiple scheduling units work together. Finally, the system continuously optimizes the scheduling strategy through real-time monitoring and feedback mechanisms to adapt to the dynamically changing production environment.

[0186] Before using the Markov chain state transition model, the system needs to first establish a state space that describes the position and state changes of the electroplating hanger. Then, the system calculates the transition probability of the state space based on historical operation data. It is worth noting that the system introduces robustness constraints in the dynamic programming algorithm to ensure scheduling reliability under uncertain conditions. In addition, in order to improve computing efficiency, the system distributes path planning tasks to multiple computing nodes for parallel processing and updates the state transition probability matrix in real time.

[0187] The following describes in detail the construction of the state space, the training of the state transition probability matrix, and the specific process of path optimization.

[0188] First, the embodiment of the present application establishes a state space that describes the position and state changes of the electroplating rack. The state space is a multidimensional vector space, and each state vector contains the following dimensional information:

[0189] 1) Spatial position information: including the position coordinates (x, y, z) of the electroplating rack in the three-dimensional coordinate system;

[0190] 2) Motion state information: including the motion speed v and acceleration a of the electroplating rack;

[0191] 3) Operation status information: including the occupancy status of the electroplating rack (idle / occupied), task type (pick / place) and task priority (high / medium / low);

[0192] 4) Time status information: including the job start time t_start and expected completion time t_end.

[0193] Secondly, the system trains the state transition probability matrix based on historical operation data. The specific training process includes:

[0194] 1) Data preprocessing: Collect the electroplating rack operation data of the last 30 days, and clean and standardize the data. Mainly includes:

[0195] Remove outliers and noisy data;

[0196] Unified timestamp format;

[0197] Standardize spatial coordinate values;

[0198] 2) State sequence generation: Generate state transition sequences from preprocessed data in chronological order. Each sequence contains:

[0199] Initial state s_t;

[0200] Subsequent state s_(t+1);

[0201] Transfer time interval Δt;

[0202] Environmental conditions c_t;

[0203] 3) Probability matrix calculation: Calculate the transition probability based on the state transition sequence:

[0204] P(s_(t+1)|s_t)=N(s_t→s_(t+1)) / N(s_t)

[0205] Where N(s_t→s_(t+1)) represents the number of samples that transfer from state s_t to s_(t+1), and N(s_t) represents the total number of samples in which state s_t appears.

[0206] Again, the system predicts future pick-and-place requirements based on the state transition probability matrix P obtained through training. The prediction process is iterative:

[0207] 1) Given the current state s_t, calculate the most likely state at the next moment:

[0208] s_(t+1)=argmax_s P(s|s_t)

[0209] 2) Prediction confidence assessment:

[0210] conf(s_(t+1))=P(s_(t+1)|s_t) / max(P(s|s_t))

[0211] When the confidence is below a threshold (usually set to 0.6), the system considers multiple possible subsequent states simultaneously.

[0212] In addition, the system uses a dynamic programming algorithm to calculate the optimal scheduling path that meets the constraints. The specific implementation process includes:

[0213] 1) Define the optimization objective function:

[0214] J=w1*L+w2*T+w3*E

[0215] Where L is the path length, T is the waiting time, E is the energy consumption, and w1, w2, and w3 are weight coefficients.

[0216] 2) Establish the state transfer equation:

[0217] s_(t+1)=f(s_t, a_t)

[0218] Where a_t is the action decision at time t.

[0219] 3) Introducing robustness constraints:

[0220] Path feasibility constraints: ensure that the planned path satisfies the manipulator kinematic constraints;

[0221] Collision avoidance constraint: ensure that the safe distance from other electroplating hangers and fixed obstacles is not less than 200mm;

[0222] Time window constraint: ensure that the task completion time meets the production cycle requirements;

[0223] Load constraint: Ensure that the maximum load capacity of the robot is not exceeded.

[0224] Finally, in order to improve computing efficiency, the system uses a parallel computing architecture to process path planning tasks:

[0225] 1) Task decomposition: Decompose the global path planning into multiple local planning subtasks, each of which is responsible for path optimization in a specific area or time period.

[0226] 2) Parallel Processing:

[0227] Use Map-Reduce mode for parallel computing;

[0228] Map phase: multiple computing nodes process different subtasks simultaneously;

[0229] Reduce phase: merge the results of each subtask to obtain the global optimal solution;

[0230] 3) Real-time update mechanism:

[0231] Update the state transition probability matrix every 5 minutes;

[0232] When an abnormal state transition is detected (probability change exceeds 20%), an update is triggered immediately;

[0233] Adopting incremental update method, only the probability values ​​that have changed are updated;

[0234] It should be noted that the system is also designed with an adaptive adjustment mechanism to dynamically adjust the optimization parameters according to the actual operation results:

[0235] When the actual running time exceeds the expected time by 20%, the time weight w2 is automatically adjusted;

[0236] When energy consumption exceeds expectations by 15%, the energy consumption weight w3 is automatically adjusted;

[0237] When the prediction accuracy is lower than 85%, increase the historical data sampling window;

[0238] Through the above design, the embodiment of the present application realizes the intelligent planning of the pick-up and placement path of the electroplating hanger. After testing, the solution can shorten the average pick-up and placement time by 30%, and the accuracy of the planned path reaches 92%.

[0239] See also Figure 5 , the embodiment of the present application adopts a distributed data storage architecture to store and manage various types of information of the electroplating hanger. Specifically, the system establishes a master data node and multiple slave data nodes, where the master data node is responsible for data writing and distribution, and the slave data node is responsible for data backup and query. In actual applications, the system uses a time series database in the master data node and the slave data node to store the location information and movement status of the electroplating hanger, and uses a relational database to store job records.

[0240] During the data storage process, the system synchronizes data between the master data node and the slave data node based on the data consistency protocol. In addition, the system sets a data sharding strategy to partition and store the electroplating hanger information according to the time dimension and space dimension. This distributed architecture not only improves the reliability of data storage, but also realizes multi-dimensional query and statistical analysis of electroplating hanger storage information.

[0241] In addition, the system has established a multi-level data collection network, including the equipment layer, control layer and management layer, to achieve comprehensive data collection and real-time transmission. Secondly, each hanger is equipped with an intelligent sensor with a unique identity, which can record the full life cycle data including the number of uses, maintenance records, working status, etc. Thirdly, the system adopts a distributed data storage architecture, combined with blockchain technology to ensure the integrity and immutability of the data. Among them, by setting up multiple data verification mechanisms, the accuracy and reliability of data collection are guaranteed. In addition, the system has developed an intelligent data analysis module that can monitor the operating status of the equipment in real time and predict potential failures. It should be noted that the data traceability system supports multi-dimensional query and statistical analysis, which can quickly locate the root cause of any abnormal situation. However, considering the data security requirements, the system implements strict access control and encryption measures. Finally, by establishing a complete data backup and recovery mechanism, the security and availability of key data are ensured.

[0242] The embodiment of the present application also establishes a digital demand management system. The system first establishes a structural view, a behavior view, a demand view, and a parameter view describing the electroplating rack intelligent storage system. Then, through the demand decomposition matrix, the functional requirements and performance requirements of the system are decomposed into multiple technical indicators.

[0243] It should be noted that the digital demand management system is built based on the SysML metamodel derived from INCOSE. Specifically, the system uses SysML requirement diagrams to describe the functional and performance requirements of the electroplating hanger intelligent storage system, and uses module definition diagrams to establish the system structure model and determine the structural technical indicators. At the same time, the system defines the internal connection relationship of the system through internal block diagrams and determines the connection technical indicators, and uses activity diagrams and state machine diagrams to describe the dynamic behavior of the system and determine the dynamic behavior technical indicators. Finally, the system establishes a two-way traceability relationship between requirements and design elements.

[0244] Specifically, the system uses the SysML modeling language of INCOSE (International Council on Systems Engineering) standard to build a complete metamodel of the electroplating hanger storage system. Secondly, the metamodel contains four key views: structural view, behavioral view, requirement view and parameter view, which are used to fully describe the functional and performance requirements of the system. Thirdly, through the requirement decomposition matrix, the system decomposes high-level business requirements into specific technical indicators and implementation plans step by step. Among them, each requirement contains clear verification criteria and traceability links to ensure the integrity and consistency of the requirements. In addition, the system integrates a requirement change management mechanism, which can track and evaluate the impact of requirement changes on the system in real time. It should be noted that in order to improve the reusability of the requirement model, the system has established a standardized requirement template library. However, considering the personalized needs of different enterprises, the model design retains sufficient flexibility and scalability. Finally, through the continuous requirement verification and confirmation process, ensure that the system functions are consistent with user expectations.

[0245] See also Figure 6 The embodiment of the present application also provides an intelligent storage system for electroplating racks, which is used in conjunction with a production workshop to implement the above method. The system includes a multi-layer three-dimensional storage rack, which includes several layers of shelf units, and each layer of shelf units is connected through a standard interface. The automated transmission system of the system includes a six-degree-of-freedom manipulator and a conveyor belt with an encoder, wherein the manipulator is connected to the control system through a control bus, and the conveyor belt is connected to the control system through industrial Ethernet.

[0246] The identification system of the system includes a laser scanner with an accuracy of ±1mm and an RFID reader that supports the EPC Gen2 protocol. In addition, the system adopts a distributed control system to achieve multi-task collaborative control through a master-slave architecture. The hardware system architecture provides reliable infrastructure support for the implementation of the above method.

[0247] The intelligent storage method for electroplating hangers provided in the embodiments of the present application realizes the precise positioning, intelligent scheduling and full-process management of electroplating hangers by establishing a three-dimensional rectangular coordinate positioning system, adopting a predictor-feedback control system, introducing a Markov chain state transition model and using a distributed data storage architecture, thereby significantly improving the degree of automation and operating efficiency of the storage system.

[0248] It should be noted that the technical features not described in detail in the embodiments of the present application can be reasonably inferred by those skilled in the art based on the above content. For example, the specific parameter settings of each module in the system can be adjusted according to the actual application scenario. In addition, the specific implementation of the embodiments of the present application is not limited to the above description, and those skilled in the art can make equivalent replacements or improvements according to actual needs.

[0249] Figure 7is a schematic diagram of a factory including intelligent warehousing in another embodiment of the present application, such as Figure 7 The factory includes intelligent warehousing 200 and main production line 100. Among them, intelligent warehousing 200 cooperates with the main production line 100 to achieve precise positioning, intelligent scheduling and full-process management of electroplating hangers by establishing a three-dimensional rectangular coordinate positioning system, adopting a predictor-feedback control system, introducing a Markov chain state transfer model, and using a distributed data storage architecture. This significantly improves the automation level and operation efficiency of the warehousing system.

[0250] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.

[0251] The present invention also provides a computer system suitable for implementing a dual-starter switching and control method for an intelligent device based on artificial intelligence, wherein the computer system includes a central processing unit (CPU), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) or a program loaded from a storage part to a random access memory (RAM), such as executing the method described in the above embodiment. Various programs and data required for system operation are also stored in the RAM. The CPU, ROM, and RAM are connected to each other via a bus. The input / output (I / O) interface is also connected to the bus.

[0252] The following components are connected to the I / O interface: an input part including a keyboard, a mouse, etc.; an output part including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker; a storage part including a hard disk, etc.; and a communication part including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication part performs communication processing via a network such as the Internet. The drive 310 is also connected to the I / O interface as needed. Removable media, such as magnetic disks, optical disks, magneto-optical disks, semiconductor memories, etc., are installed on the drive as needed so that the computer program read therefrom is installed into the storage part as needed.

[0253] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication part, and / or installed from a removable medium. When the computer program is executed by a central processing unit (CPU), various functions defined in the system of the present application are executed.

[0254] It should be noted that the computer-readable medium shown in the embodiment of the present application can be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium can be, for example, but not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples of computer-readable storage media can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (Compact Disc Read-Only Memory, CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0255] In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, apparatus, or device. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable computer program. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in combination with an instruction execution system, apparatus, or device. The computer program contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0256] The flowchart and block diagram in the accompanying drawings illustrate the possible architecture, functions and operations of the system, method and computer program product according to various embodiments of the present application. Wherein, each box in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0257] The units involved in the embodiments of the present invention may be implemented by software or hardware, and the units described may also be arranged in a processor. The names of these units do not, in some cases, limit the units themselves.

[0258] According to one aspect of the present application, a computer program product or a computer program is provided, the computer program product or the computer program comprising computer instructions, the computer instructions being stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the methods provided in the above-mentioned various optional implementations.

[0259] As another aspect, the present application also provides a computer-readable medium, which may be included in the electronic device described in the above embodiment; or may exist independently without being assembled into the electronic device. The above computer-readable medium carries one or more programs, and when the above one or more programs are executed by an electronic device, the electronic device implements the method described in the above embodiment.

[0260] It should be noted that, although several modules or units of the equipment for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present application, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into being embodied by multiple modules or units.

[0261] Through the description of the above implementation methods, it is easy for those skilled in the art to understand that the example implementation methods described here can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to the implementation methods of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the implementation methods of the present application.

[0262] Although the embodiments of this specification are disclosed as above, the present invention is not limited thereto. Any person skilled in the art can make various changes and modifications without departing from the spirit and scope of the present invention, so the protection scope of the present invention shall be subject to the scope defined by the claims.

Claims

1. A method for intelligent storage of electroplating racks, characterized in that ,include: Establishing a three-dimensional rectangular coordinate positioning system, and positioning and calibrating the electroplating rack in the positioning system; Controlling a manipulator to pick up and place an electroplating rack, comprising: using a linear control system with state feedback, when the distance between the manipulator and a target position is greater than a preset distance threshold, controlling the manipulator to use a first control gain for rapid movement control, and when the distance between the manipulator and the target position is less than or equal to the preset distance threshold, controlling the manipulator to use a second control gain for precise positioning control, and real-time compensation for inertia error and mechanical friction during the movement of the manipulator; wherein, during the operation of picking up and placing the electroplating rack, a state transition model based on a Markov chain is used to optimize the path planning of picking up and placing the electroplating rack; A distributed data storage architecture is used to store the location information, movement status and operation records of the electroplating rack, so as to realize multi-dimensional query and statistical analysis of the storage information of the electroplating rack.

2. The method according to claim 1, characterized in that , using a state transition model based on a Markov chain to optimize the path planning of the electroplating rack pick-up and placement, including: Collect historical operation data of the electroplating rack to train a state transition probability matrix; predicting future pick and place requirements based on the state transition probability matrix; A dynamic programming algorithm is used to calculate the scheduling path that meets the shortest path and minimum waiting time constraints.

3. The method according to claim 2, characterized in that Before optimizing the path planning of the electroplating rack pick-up and placement using the state transition model based on the Markov chain, the method further includes: Establishing a state space describing the position and state changes of the electroplating rack; Calculating the transition probability of the state space based on the historical operation data; Introducing robustness constraints into the dynamic programming algorithm to ensure scheduling reliability under uncertain conditions; Distributing the path planning task to multiple computing nodes for parallel processing; The state transition probability matrix is ​​updated in real time.

4. The method according to claim 1, characterized in that , the method further comprises: Establish a digital requirements management system, including: Establish a structural view, a behavior view, a demand view and a parameter view describing the electroplating rack intelligent storage system; The functional requirements and performance requirements of the electroplating rack intelligent storage system are decomposed into multiple technical indicators through the demand decomposition matrix.

5. The method according to claim 4, characterized in that The digital demand management system is built based on the SysML metamodel derived from INCOSE. The functional requirements and performance requirements of the electroplating hanger intelligent storage system are decomposed into multiple technical indicators through the demand decomposition matrix, including: Use SysML requirement diagram to describe the functional and performance requirements of the electroplating rack intelligent storage system; A module definition diagram is used to establish a structural model of the electroplating rack intelligent storage system and determine structural technical indicators; Define the connection relationship within the electroplating rack intelligent storage system through an internal block diagram and determine the connection technical indicators; Use activity diagrams and state machine diagrams to describe the dynamic behavior of the electroplating rack intelligent storage system and determine it as a dynamic behavior technical indicator; Establish a two-way traceability relationship between requirements and design elements.

6. The method according to claim 1, characterized in that , establish a three-dimensional rectangular coordinate positioning system, and perform positioning and calibration on the electroplating hanger in the positioning system, including: Laser distance measuring sensors with an accuracy of ±1mm are installed at key node positions of the storage racks, and the laser distance measuring sensors form a sensor network matrix; Each of the electroplating racks is equipped with an active RFID tag, and the RFID tag stores identification information including a unique identification code of the electroplating rack, a type of the electroplating rack, and a load parameter; Using multiple groups of parallel laser beam array transmitters to collect the reflection signal of the electroplating rack in real time; The distance between the electroplating rack and a fixed reference point is calculated based on the time-of-flight ranging principle, and the distance is calibrated with a preset fixed calibration point.

7. The method according to claim 1, characterized in that , the linear control system is a predictor-feedback control system with a transient switch, the method further comprising: A transient switch is provided in the state feedback link, for dynamically switching the first control gain and the second control gain; Using a predictor model to calculate the motion trajectory of the manipulator in advance; Establishing a feedback correction mechanism according to the actual position of the manipulator; Based on the weighting of the predictor calculation result and the feedback signal, a control instruction of the manipulator is generated.

8. The method according to claim 7, characterized in that , the method further comprises: Perform smooth transition operation during the transient switch switching process to avoid control quantity jump; Adaptively adjusting the predictor model parameters according to the manipulator load; Realize adaptive weight allocation of predictive control and feedback control, and set control quantity limit and change rate constraints.

9. The method according to claim 1, characterized in that: The distributed data storage architecture is used to store the location information, movement status and operation records of the electroplating rack, including: Establish a master data node and multiple slave data nodes of a distributed data storage architecture, wherein the master data node is responsible for data writing and distribution, and the slave data nodes are responsible for data backup and query; Using a time series database in the master data node and the slave data node to store the position information and movement status of the hanger; Using a relational database in the master data node and the slave data node to store the operation records of the hanger; Synchronize data between the master data node and the slave data node based on a data consistency protocol; A data sharding strategy is set to partition and store the hanger information according to the time dimension and the space dimension.

10. A system for intelligent storage of electroplating racks, characterized in that , used to implement the method described in any one of claims 1 to 9, comprising: Multi-layer three-dimensional storage racks include several layers of shelf units, each layer of shelf units is connected through a standard interface; An automated transmission system, comprising a six-degree-of-freedom manipulator and a conveyor belt with an encoder, wherein the manipulator is connected to a control system via a control bus, and the conveyor belt is connected to the control system via industrial Ethernet; Identification system, including a laser scanner with an accuracy of ±1mm and an RFID reader supporting the EPC Gen2 protocol; Distributed control system adopts master-slave architecture to realize multi-task collaborative control.

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