Rebar processing control system and method based on digital twin

Abstract

The present invention discloses a steel bar processing control system and method based on digital twin, and relates to the field of digital twin technology. The present invention includes a data acquisition module, a monitoring module, a construction module, an operation module and a control module. The present invention constructs a steel bar processing system, establishes a preset construction library, a basic database and a sample database; uses OSG to establish a digital twin model of the steel bar processing system; realizes virtual-real linkage with the steel bar processing system through the digital twin model; uses the digital twin model for simulation, and drives the movement of the digital twin model by reading the motion data of each node; reads the node displacement data, and saves it in the twin database to realize real-time monitoring; establishes a mapping model of the key process parameter factors of steel bar processing and the quality index coefficient of the processed steel bar through the regression model, obtains the quality index coefficient of steel bar processing under different process parameter factor combinations, and finds the best process parameter factor combination.

Description

Technical Field

[0001] The present invention belongs to the technical field of digital twins, and in particular relates to a steel bar processing control system and method based on digital twins. Background Art

[0002] The existing technical solutions for steel bar processing are manual or semi-automatic steel bar processing and intelligent automatic steel bar processing. Manual or semi-automatic steel bar processing has great safety risks, low production efficiency, poor processing accuracy, high scrap rate, high labor intensity, great waste of manpower and materials, and great management difficulty.

[0003] Intelligent and automated steel bar processing can, to a large extent, reduce the number of people and material waste, improve production efficiency, and improve product quality to a certain extent. However, there are still many shortcomings in management, such as the difficulty in avoiding abnormal losses due to repeated processing, difficulty in inventory, lack of traceability, and difficulty in forming effective linkage among various management departments.

[0004] Existing technical solutions cannot fully supervise steel bar processing, are subject to human manipulation, have many management loopholes, and cannot truly reflect the progress, quality, and cost control of the steel bar processing construction process. They lack macroscopicity and traceability. At the same time, there are delays in the linkage of various departments, and the production situation, the number of raw materials in stock, the arrival of materials, and the inspection and acceptance of materials are not closely coordinated. The production chain often freezes, affecting normal production. In addition, it is impossible to effectively trace the source of steel bar processing errors and quality problems, and thus it is impossible to quickly make breakthroughs and improvements. Summary of the invention

[0005] In order to overcome the shortcomings and deficiencies of the above-mentioned prior art, the first purpose of the present invention is to provide a steel bar processing control system based on digital twin; the second purpose of the present invention is to provide a steel bar processing control method based on digital twin.

[0006] The first object of the present invention adopts the following technical solution:

[0007] The rebar processing control system based on digital twin includes data acquisition module, monitoring module, construction module, operation module and control module;

[0008] The data acquisition module is used to collect the data required by the steel bar processing system and its digital twin model;

[0009] The monitoring module is used to monitor the rebar processing system and its digital twin model in real time;

[0010] The construction module is used to construct a digital twin model of the steel bar processing system and set the digital twin model in the operation module;

[0011] The operation module is used to input the operation data in the operation request into the digital twin model, and the digital twin model outputs and displays the results;

[0012] The control module is used to control the execution devices in the steel bar processing system, which include a steel bar shearing machine, a steel bar bending machine and a temperature-controlled cooling box.

[0013] The second object of the present invention adopts the following technical solution:

[0014] The steel bar processing control method based on digital twin includes the following steps:

[0015] Step 1: Establish a steel bar processing system, including a controller, a sensor, an actuator and an interactive interface;

[0016] The sensor is used to monitor the physical parameters of the steel bar in real time, including position, angle, and length, and convert them into parameter signals and transmit them to the controller;

[0017] The controller is used to receive parameter signals sent by the sensor and process instructions from the user, and control the movement of the actuator during the steel bar processing according to the instructions;

[0018] The execution device is used to process the steel bars according to the instructions of the controller, and the execution device includes a steel bar shearing machine, a steel bar bending machine and a temperature-controlled cooling box;

[0019] The interactive interface is the interface for users to interact with the steel bar processing system. Users can input instructions and view the status of the steel bar processing process through the interactive interface;

[0020] Step 2: According to the steel bar processing system, establish a preset construction library, a basic database and a sample database;

[0021] The preset construction library is used to store system preset usage data, including system optimization data, system fault detection data, system operation simulation data, and system accident warning data; each type of data included in the preset usage data corresponds to its digital twin model parameters, processing flow, and configuration data;

[0022] The basic database is used to store basic data of the steel bar processing system, basic data of the digital twin model and basic data of the production environment; the basic data of the steel bar processing system includes the geometric dimensions, material properties and processing requirements of the steel bars; the basic data of the digital twin model includes the geometric model, physical properties and kinematic parameters of the actuator; the basic data of the production environment includes the workshop layout, location and processing capacity data of the actuator;

[0023] The sample database is used to store sample data, including historical processing data, quality inspection results and fault record data in the actual steel bar processing process;

[0024] Step 3: Use OSG to build a digital twin model of the steel bar processing system;

[0025] Step 4: Realize virtual-real linkage with the steel bar processing system through the digital twin model;

[0026] Step 5: Use the digital twin model for simulation, drive the movement of the digital twin model by reading the motion data of each node; read the node displacement data and save it in the twin database;

[0027] Step 6: Establish a mapping model between the key process parameter factors of steel bar processing and the quality index coefficients of steel bars after processing through a regression model, obtain the quality index coefficients of steel bar processing under different process parameter factor combinations, and find the best process parameter factor combination; the process parameter factors include shear speed factor, bending radius factor and cooling time factor;

[0028] Step 7: Real-time monitoring and visual display.

[0029] As a preference, step three is specifically as follows:

[0030] Create a 3D model of the rebar processing system;

[0031] Export the model to a format supported by OSG, use the API to import the 3D model into the virtual scene, arrange the model in the virtual scene, and create a rebar processing system environment;

[0032] Add physical properties to the model, including mass, stiffness, and damping, to perform physical simulation and enable user interaction with the virtual scene.

[0033] As a preference, step four is specifically as follows:

[0034] Each node of the digital twin virtual model consists of four parts: X-axis, Y-axis, Z-axis and base;

[0035] The movement of each component is realized by calculating between the node and the pose change matrix. The calculation formula is as follows:

[0036] X' = ​​TX;

[0037] Where X represents the coordinates before the node changes, X'=[x,y,z,1] T ; T represents the coordinate transformation homogeneous matrix;

[0038] When the node coordinates are translated, the transformation homogeneous matrix formula is as follows:

[0039]

[0040] Among them, tx is the translation vector of the node coordinate in the x direction, ty is the translation vector of the node coordinate in the y direction, and tz is the translation vector of the node coordinate in the z direction;

[0041] When the node coordinates are scaled relative to another point ax,ay,az, the transformation matrix formula is as follows:

[0042]

[0043] Among them, sx is the scaling factor of the node coordinates in the x direction, sy is the scaling factor of the node coordinates in the y direction, and sz is the scaling factor of the node coordinates in the z direction;

[0044] When the node coordinates are rotated α degrees around the X-axis, the rotation matrix formula is as follows:

[0045]

[0046] Among them, counterclockwise rotation is "+" and clockwise rotation is "-";

[0047] When the node coordinates are rotated β degrees around the Y axis relative to another point (ax,ay,(az), the rotation matrix formula is as follows:

[0048]

[0049] Among them, counterclockwise rotation is "+" and clockwise rotation is "-";

[0050] When the node coordinates are rotated γ degrees around the Z axis relative to another point (ax, ay, az), the rotation matrix formula is as follows:

[0051]

[0052] Among them, counterclockwise rotation is "+" and clockwise rotation is "-".

[0053] As a preference, step five is specifically as follows:

[0054] Obtain real-time data of the rebar processing system, including the progress and status of rebar processing, and update it to the digital twin model;

[0055] The digital twin model is used for simulation to predict the behavior of the rebar processing system within a set time. Based on the simulation results, control instructions are generated and sent to the execution device through the controller to achieve virtual-reality linkage between the rebar processing system and the digital twin model.

[0056] As a preference, step six is ​​specifically as follows:

[0057] The cost function is used to calculate the error between the predicted value and the actual value of the regression model. The cost function formula is as follows:

[0058]

[0059] Among them, h ω xi is the predicted value, yi is the actual value;

[0060] When the Jθ value reaches the minimum value, the corresponding mapping model is obtained, as well as the corresponding shear velocity factor, bending radius factor and cooling time factor values ​​under the mapping model;

[0061] Select shear speed factor, bending radius factor and cooling time factor as experimental factors, design orthogonal experiment of process parameter factors, substitute shear speed factor, bending radius factor and cooling time factor into the correlation formula, and obtain steel bar processing quality index coefficient M. The correlation formula is as follows:

[0062] M=ρ1*a1+ρ2*a2+ρ3*a3+δ;

[0063] Among them, ρ1 is the shear velocity factor, ρ2 is the bending radius factor, ρ3 is the cooling time factor; a1, a2 and a3 are their corresponding weights respectively; δ is the correction coefficient;

[0064] When the steel bar processing quality index coefficient M value reaches the maximum value, the shear speed factor, bending radius factor and cooling time factor corresponding to this value are marked as the optimal process parameter factors.

[0065] As a preference, step seven is specifically as follows:

[0066] Create a timer and configure the timer interval to t1-t2, which is used to periodically trigger interactive simulation and monitor operation instructions; t1 is the time to trigger the timer, and t2 is the time to stop the timer;

[0067] In the callback function of the timer, the status of the rebar processing system and the digital twin model are synchronized, and the status levels include excellent, good, and poor;

[0068] Use the timer to complete the real-time interactive simulation between the steel bar processing system and the digital twin model. During the synchronization and interaction process, monitor the status of the steel bar processing system in real time to achieve real-time monitoring of the steel bar processing system.

[0069] The simulation results of the digital twin model and the real-time status visualization interface of the steel bar processing system are displayed.

[0070] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0071] 1. The present invention uses OSG to establish a digital twin model of the steel bar processing system to achieve virtual-real linkage with the steel bar processing system, realize intelligent management of the entire process, reduce manpower, and improve work efficiency and supervision;

[0072] 2. The present invention establishes a mapping model between key process parameter factors of steel bar processing and quality index coefficients of processed steel bars through a regression model, obtains the quality index coefficients of steel bar processing under different process parameter factor combinations, finds the best process parameter factor combination, improves product quality, and optimizes the process;

[0073] 3. The present invention uses a timer to complete the real-time interactive simulation between the steel bar processing system and the digital twin model. During the synchronization and interaction process, the status of the steel bar processing system is monitored in real time, thereby realizing real-time monitoring of the steel bar processing system, which helps operators to intuitively understand the system status, promptly discover abnormal losses, and avoid repeated processing waste caused by human errors. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0075] Figure 1 The module diagram of the steel bar processing control system based on digital twin of the present invention is shown;

[0076] Figure 2 A flow chart of the steel bar processing control method based on digital twin of the present invention is shown. DETAILED DESCRIPTION

[0077] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0078] Example 1

[0079] See also Figure 1 As shown, the steel bar processing control system based on digital twin of this embodiment includes a data acquisition module, a monitoring module, a construction module, an operation module and a control module.

[0080] Data acquisition module: The data acquisition module is used to collect the data required for the steel bar processing system and its digital twin model.

[0081] Monitoring module: used for real-time monitoring of the steel bar processing system and its digital twin model.

[0082] Construction module: used to build a digital twin model of the steel bar processing system and set the digital twin model in the running module.

[0083] Run module: used to input the run data in the run request into the digital twin model, and the digital twin model outputs and displays the results.

[0084] Control module: used to control the actuators in the steel bar processing system, including steel bar shears, steel bar benders and temperature-controlled cooling boxes.

[0085] The beneficial effects of this embodiment are: by constructing a data acquisition module, a monitoring module, a construction module, an operation module and a control module, a rebar processing control system based on a digital twin is established, and the digital twin technology is combined with the rebar processing control process, so that the operation of the equipment can be monitored more intuitively.

[0086] Example 2

[0087] See also Figure 2 As shown, the steel bar processing control method based on digital twin in this embodiment has the following process:

[0088] Step 1: Establish a steel bar processing system, including controllers, sensors, actuators and interactive interfaces.

[0089] The sensor is used to monitor the physical parameters of the steel bars, such as position, angle, and length, in real time, and convert them into parameter signals for transmission to the controller.

[0090] The controller is used to receive parameter signals sent by the sensor and process instructions from the user, and control the movement of the actuator during the steel bar processing according to the instructions.

[0091] The execution device is used to perform processing operations on the steel bars according to the instructions of the controller. The execution device includes a steel bar shearing machine, a steel bar bending machine and a temperature-controlled cooling box.

[0092] The interactive interface is the interface for users to interact with the steel bar processing system. Users input instructions and view the status of the steel bar processing process through the interactive interface.

[0093] Step 2: According to the steel bar processing system, establish a preset construction library, basic database and sample database.

[0094] The preset construction library is used to store system preset usage data, including system optimization data, system fault detection data, system operation simulation data, and system accident warning data. Each type of data included in the preset usage data corresponds to its digital twin model parameters, processing flow, and configuration data.

[0095] The basic database is used to store multiple basic project data, including basic data of the steel bar processing system, such as the geometric dimensions, material properties and processing requirements of the steel bars; basic data of the digital twin model, such as the geometric model, physical properties and kinematic parameters of the actuator; basic data of the production environment, such as the workshop layout, location and processing capacity data of the actuator.

[0096] The sample database is used to store multiple sample data, including historical processing data, quality inspection results and fault record data in the actual steel bar processing process.

[0097] Step 3: Use OSG to build a digital twin model of the steel bar processing system.

[0098] Create a 3D model of the rebar processing system;

[0099] Export the model to a format supported by OSG, use the API to import the 3D model into the virtual scene, arrange the model in the virtual scene, and create a rebar processing system environment;

[0100] Add physical properties to the model, including mass, stiffness, damping, etc., to facilitate physical simulation and enable user interaction with the virtual scene.

[0101] Step 4: Realize virtual-reality linkage with the steel bar processing system through the digital twin model.

[0102] Each node of the digital twin virtual model consists of four parts: X-axis, Y-axis, Z-axis and base.

[0103] The movement of each component is realized by calculating between the node and the pose change matrix. The calculation formula is as follows:

[0104]

[0105] Where X represents the coordinates before the node changes, X'=[x,y,z,1] T ; T represents the coordinate transformation homogeneous matrix.

[0106] When the node coordinates are translated, the transformation homogeneous matrix formula is as follows:

[0107]

[0108] Among them, tx is the translation vector of the node coordinates in the x direction, ty is the translation vector of the node coordinates in the y direction, and tz is the translation vector of the node coordinates in the z direction.

[0109] When the node coordinates are scaled relative to another point (ax,ay,az), the transformation matrix formula is as follows:

[0110]

[0111] Among them, sx is the scaling factor of the node coordinates in the x direction, sy is the scaling factor of the node coordinates in the y direction, and sz is the scaling factor of the node coordinates in the z direction.

[0112] When the node coordinates are rotated α degrees around the X-axis, the rotation matrix formula is as follows:

[0113]

[0114] Among them, counterclockwise rotation is "+" and clockwise rotation is "-".

[0115] When the node coordinates are rotated β degrees around the Y axis relative to another point (ax,ay,az), the rotation matrix formula is as follows:

[0116]

[0117] Among them, counterclockwise rotation is "+" and clockwise rotation is "-".

[0118] When the node coordinates are rotated γ degrees around the Z axis relative to another point (ax, ay, az), the rotation matrix formula is as follows:

[0119]

[0120] Among them, counterclockwise rotation is "+" and clockwise rotation is "-".

[0121] Step 5: Use the digital twin model for simulation, drive the movement of the digital twin model by reading the motion data of each node; read the node displacement data and save it in the twin database.

[0122] Obtain real-time data of the rebar processing system, such as rebar processing progress and status, and update it to the digital twin model;

[0123] The digital twin model is used for simulation to predict the behavior of the rebar processing system within a set time. Based on the simulation results, control instructions are generated and sent to the execution device through the controller to achieve virtual-reality linkage between the rebar processing system and the digital twin model.

[0124] Step 6. A mapping model between the key process parameter factors of steel bar processing and the quality index coefficients of the processed steel bars is established through the regression model to obtain the quality index coefficients of steel bar processing under different combinations of process parameter factors and find the best combination of process parameter factors; the process parameter factors include shear speed factor, bending radius factor and cooling time factor.

[0125] The cost function is used to calculate the error between the predicted value and the actual value of the regression model. The cost function formula is as follows:

[0126]

[0127] Among them, h ω (xi) is the predicted value and yi is the actual value.

[0128] When the J(θ) value reaches the minimum value, the corresponding mapping model is obtained, as well as the corresponding shear velocity factor, bending radius factor and cooling time factor values ​​under the mapping model.

[0129] Select shear speed factor, bending radius factor and cooling time factor as experimental factors, design orthogonal experiment of process parameter factors, substitute shear speed factor, bending radius factor and cooling time factor into the correlation formula, and obtain steel bar processing quality index coefficient M. The correlation formula is as follows:

[0130] M=ρ1*a1+ρ2*a2+ρ3*a3+δ;

[0131] Among them, ρ1 is the shear velocity factor, ρ2 is the bending radius factor, ρ3 is the cooling time factor; a1, a2 and a3 are their corresponding weights respectively; δ is the correction coefficient.

[0132] When the steel bar processing quality index coefficient M value reaches the maximum value, the shear speed factor, bending radius factor and cooling time factor corresponding to this value are marked as the optimal process parameter factors.

[0133] Step 7: Real-time monitoring and visual display.

[0134] Create a timer and configure the timer interval to t1-t2, which is used to periodically trigger interactive simulation and monitor operation instructions; t1 is the time to trigger the timer, and t2 is the time to stop the timer;

[0135] In the callback function of the timer, the status of the rebar processing system and the digital twin model are synchronized, and the status levels include excellent, good, and poor;

[0136] The timer is used to complete the real-time interactive simulation between the steel bar processing system and the digital twin model. During the synchronization and interaction process, the status of the steel bar processing system is monitored in real time to achieve real-time monitoring of the steel bar processing system.

[0137] The simulation results of the digital twin model and the real-time status visualization interface of the steel bar processing system are displayed.

[0138] The beneficial effects of this embodiment are as follows: by establishing a digital twin model of the steel bar processing system, virtual-reality linkage with the steel bar processing system is achieved; a mapping model between key process parameter factors of steel bar processing and quality index coefficients of processed steel bars is established through a regression model, the quality index coefficients of steel bar processing under different combinations of process parameter factors are obtained, and the optimal combination of process parameter factors is found; a timer is used to complete real-time interactive simulation between the steel bar processing system and the digital twin model, thereby achieving real-time monitoring of the steel bar processing system.

[0139] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

[0140] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A steel bar processing control method based on digital twins, characterized in that: The steps include: Step 1: Establish a steel bar processing system, including a controller, a sensor, an actuator and an interactive interface; The sensor is used to monitor the physical parameters of the steel bar in real time, including position, angle, and length, and convert them into parameter signals and transmit them to the controller; The controller is used to receive parameter signals sent by the sensor and process instructions from the user, and control the movement of the actuator during the steel bar processing according to the instructions; The execution device is used to process the steel bars according to the instructions of the controller, and the execution device includes a steel bar shearing machine, a steel bar bending machine and a temperature-controlled cooling box; The interactive interface is the interface for users to interact with the steel bar processing system. Users can input instructions and view the status of the steel bar processing process through the interactive interface; Step 2: According to the steel bar processing system, establish a preset construction library, a basic database and a sample database; The preset construction library is used to store system preset usage data, including system optimization data, system fault detection data, system operation simulation data, and system accident warning data; each type of data included in the preset usage data corresponds to its digital twin model parameters, processing flow, and configuration data; The basic database is used to store basic data of the steel bar processing system, basic data of the digital twin model and basic data of the production environment; the basic data of the steel bar processing system includes the geometric dimensions, material properties and processing requirements of the steel bars; the basic data of the digital twin model includes the geometric model, physical properties and kinematic parameters of the actuator; the basic data of the production environment includes the workshop layout, location and processing capacity data of the actuator; The sample database is used to store sample data, including historical processing data, quality inspection results and fault record data in the actual steel bar processing process; Step 3: Use OSG to build a digital twin model of the steel bar processing system; Step 4: Realize virtual-real linkage with the steel bar processing system through the digital twin model; Step 5: Use the digital twin model for simulation, drive the movement of the digital twin model by reading the motion data of each node; read the node displacement data and save it in the twin database; Step 6: Establish a mapping model between the key process parameter factors of steel bar processing and the quality index coefficients of steel bars after processing through a regression model, obtain the quality index coefficients of steel bar processing under different process parameter factor combinations, and find the best process parameter factor combination; the process parameter factors include shear speed factor, bending radius factor and cooling time factor; Step 7: Real-time monitoring and visual display; The step six is ​​as follows: The cost function is used to calculate the error between the predicted value and the actual value of the regression model. The cost function formula is as follows: Among them, h ω (xi) is the predicted value, yi is the actual value; When the J(θ) value reaches the minimum value, the corresponding mapping model is obtained, as well as the corresponding shear velocity factor, bending radius factor and cooling time factor values ​​under the mapping model; Select shear speed factor, bending radius factor and cooling time factor as experimental factors, design orthogonal experiment of process parameter factors, substitute shear speed factor, bending radius factor and cooling time factor into the correlation formula, and obtain steel bar processing quality index coefficient M. The correlation formula is as follows: M=ρ1*a1+ρ2*a2+ρ3*a3+δ; Among them, ρ1 is the shear velocity factor, ρ2 is the bending radius factor, ρ3 is the cooling time factor; a1, a2 and a3 are their corresponding weights respectively; δ is the correction coefficient; When the steel bar processing quality index coefficient M value reaches the maximum value, the shear speed factor, bending radius factor and cooling time factor corresponding to this value are marked as the optimal process parameter factors.

2. The steel bar processing control method based on digital twin according to claim 1 is characterized in that: The step three is as follows: Create a 3D model of the rebar processing system; Export the model to a format supported by OSG, use the API to import the 3D model into the virtual scene, arrange the model in the virtual scene, and create a rebar processing system environment; Add physical properties to the model, including mass, stiffness, and damping, to perform physical simulation and enable user interaction with the virtual scene.

3. The steel bar processing control method based on digital twin according to claim 1 is characterized in that: The step 4 is specifically as follows: Each node of the digital twin virtual model consists of four parts: X-axis, Y-axis, Z-axis and base; The movement of each component is realized by calculating between the node and the pose change matrix. The calculation formula is as follows: X′=TX; Where X represents the coordinates before the node changes, X'=[x,y,z,1] T ; T represents the coordinate transformation homogeneous matrix; When the node coordinates are translated, the transformation homogeneous matrix formula is as follows: Among them, tx is the translation vector of the node coordinate in the x direction, ty is the translation vector of the node coordinate in the y direction, and tz is the translation vector of the node coordinate in the z direction; When the node coordinates are scaled relative to another point (ax,ay,az), the transformation matrix formula is as follows: Among them, sx is the scaling factor of the node coordinates in the x direction, sy is the scaling factor of the node coordinates in the y direction, and sz is the scaling factor of the node coordinates in the z direction; When the node coordinates are rotated α degrees around the X-axis, the rotation matrix formula is as follows: Among them, counterclockwise rotation is "+" and clockwise rotation is "-"; When the node coordinates are rotated β degrees around the Y axis relative to another point (ax,ay,az), the rotation matrix formula is as follows: Among them, counterclockwise rotation is "+" and clockwise rotation is "-"; When the node coordinates are rotated γ degrees around the Z axis relative to another point (ax, ay, az), the rotation matrix formula is as follows: Among them, counterclockwise rotation is "+" and clockwise rotation is "-".

4. The steel bar processing control method based on digital twin according to claim 1 is characterized in that: The step five is as follows: Obtain real-time data of the rebar processing system, including the progress and status of rebar processing, and update it to the digital twin model; The digital twin model is used for simulation to predict the behavior of the rebar processing system within a set time. Based on the simulation results, control instructions are generated and sent to the execution device through the controller to achieve virtual-reality linkage between the rebar processing system and the digital twin model.

5. The steel bar processing control method based on digital twin according to claim 1 is characterized in that: The step seven is as follows: Create a timer and configure the timer interval to t1-t2, which is used to periodically trigger interactive simulation and monitor operation instructions; t1 is the time to trigger the timer, and t2 is the time to stop the timer; In the callback function of the timer, the status of the rebar processing system and the digital twin model are synchronized, and the status levels include excellent, good, and poor; Use the timer to complete the real-time interactive simulation between the steel bar processing system and the digital twin model. During the synchronization and interaction process, monitor the status of the steel bar processing system in real time to achieve real-time monitoring of the steel bar processing system. The simulation results of the digital twin model and the real-time status visualization interface of the steel bar processing system are displayed.

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