Vacuum low-temperature rotary test tool intelligent control method based on digital twinning

Abstract

The application is a kind of vacuum low-temperature rotary test tool intelligent control method based on digital twinning. The application relates to the technical field of digital twinning modeling and intelligent control, and realizes real-time mapping, dynamic synchronization and closed-loop feedback between physical tooling and virtual model by constructing a digital twin model of vacuum low-temperature rotary test tooling. The application unifies multi-source operation information such as temperature state, motion state, heating state, angle feedback state, locking state and limiting state into the digital twin system for comprehensive analysis. Compared with the conventional rotary test tooling which only relies on temperature feedback and angle feedback for control, the application can predict the key component temperature field evolution law, structural thermal deformation trend and angle positioning error based on real-time data acquisition and digital twin model, and generate heating control quantity and angle compensation quantity accordingly, realizing the collaborative optimization of temperature control and attitude positioning.

Description

Technical Field

[0001] This invention relates to the field of digital twin modeling and intelligent control technology, and is a method for intelligent control of vacuum cryogenic rotary testing fixtures based on digital twins. Background Technology

[0002] During the development and testing of optical payloads such as space cameras, imaging performance testing, field-of-view adjustment, and attitude calibration are typically conducted in a sealed, vacuum, and cryogenic environment. To meet these testing requirements, a rotating test fixture is usually used to adjust the attitude of the camera under test, enabling it to complete imaging tests at different field-of-view angles or operating postures. Existing rotating test fixtures generally consist of a mechanical turntable, a load platform, a drive mechanism, an angle feedback device, a temperature control system, and a host computer control system. The drive mechanism rotates the load platform, and the angle feedback device detects the position. Simultaneously, temperature sensors and heating devices control the temperature of key components to ensure the equipment can operate in a vacuum and cryogenic environment.

[0003] However, existing vacuum cryogenic rotary testing fixtures still have certain shortcomings in practical use. Because the equipment is in a closed, low-temperature vacuum environment for extended periods, the base, rotating shaft system, load platform, circular grating mounting location, motor, bearings, and other critical structures are susceptible to thermal deformation due to temperature changes, leading to rotation axis misalignment, angle feedback errors, and attitude positioning deviations. Furthermore, temperature control, angle control, and status monitoring in existing systems are typically relatively independent, lacking a unified mapping relationship between temperature field changes and attitude errors, making it difficult to predict structural deformation and positioning errors in a timely manner based on temperature changes. In addition, traditional host computer systems primarily focus on data acquisition, status display, and command issuance, usually only reflecting the current operating status and lacking the ability to predict future temperature change trends, angle drift trends, and abnormal states of critical components. During long-term operation, it is difficult to detect potential abnormalities in components such as heating elements, temperature sensors, circular gratings, bearings, motors, or locking mechanisms in a timely manner.

[0004] Therefore, there is an urgent need to propose a technical solution that can unify the modeling and coordinated control of the physical rotation test fixture, temperature control system, angle feedback system and operation status monitoring system, so as to improve the temperature control stability, attitude positioning accuracy and long-term operation reliability of the rotation test fixture in a vacuum cryogenic environment. Summary of the Invention

[0005] To address the problems of existing technologies, this invention discloses an intelligent control method for vacuum cryogenic rotary testing fixtures based on digital twins.

[0006] This invention provides the following technical solutions: A method for intelligent control of a vacuum cryogenic rotary testing fixture based on digital twins, the method comprising the following steps: Step 1: Establish a spatial coordinate system and perform virtual-physical registration to achieve seamless overlay of virtual tooling models and physical tooling; Step 2: After the vacuum tank is sealed and vacuuming and cooling begins, the component temperature data is collected in real time through a distributed sensor network to perform active and passive composite thermal control and precision drive. Step 3: Visualize the sensor data through a data geometry mapping function using the AR-assisted system. Temperature is displayed with gradient colors and floating labels, angle deviation is indicated by dynamic arrows, and operation guidance is rendered with a smooth trajectory. When the sensor data exceeds the safety threshold, the AR interface triggers a highlight flashing warning. Follow the guidance to handle the situation or initiate remote expert collaboration until the test is completed.

[0007] Preferably, a transformation relationship is established between four coordinate systems: the world coordinate system {W} takes a fixed point inside the vacuum tank or a reference point outside the tank as its origin; the tooling body coordinate system {T} is fixed to the rotation center of the load platform; the AR head-mounted display coordinate system {H} is fixed to the operator's head; and the image projection coordinate system {I} is the virtual image projection surface of the head-mounted display. The three-dimensional rigid body transformation matrix is ​​solved by virtual-real registration. It can be expressed by the following formula. :

[0008] in, Let {H} be the homogeneous transformation matrix from the world coordinate system to the AR head-mounted display coordinate system. Real-time calculation using spatial positioning units results in a 4x4 homogeneous transformation matrix:

[0009] in, Let be a rotation matrix. Let R be the translation variable, and R be a 3×3 rotation matrix. T It is the transpose of the three-dimensional zero vector; Any point P on the virtual tooling model T The coordinates in the {T} system are projected onto the virtual image plane of the head-mounted display, and the projection process uses a pinhole camera model:

[0010] Where K is the intrinsic parameter matrix of the head-mounted virtual camera, including the focal length f x , f y , and the principal point c x c y , (u,v () represents virtual pixel coordinates. is the projection scale factor.

[0011] Preferably, the tooling includes a mechanical body system, an active-passive composite thermal control system, a precision drive system and an angle measurement system, and a distributed sensor network system; The active-passive combined thermal control system uses the Smith prediction method to overcome the large time delay characteristic of temperature control. The transfer function of the controlled object is:

[0012]

[0013]

[0014] Where K is the gain, T is the time constant, and τ is the pure time delay. The controlled object is a transfer function, where s is the Laplace operator. Smith predicts the transfer function of the compensation stage. The predicted model output value at the k-th sampling time. Sampling period of discrete control system For the kth The predicted model output value at one sampling time, where m is the number of sampling steps corresponding to the pure time delay; The precision drive system uses a worm gear pair and a vacuum stepper motor; A circular grating enables absolute angle feedback, with a closed-loop step size better than 10″. The distributed sensor network deploys more than 20 PT100 sensors, distributed in key components such as bearings, motors, and grating readout heads, to collect temperature data via CAN bus, with a sampling frequency of 1Hz and a resolution of 0.1℃.

[0015] Preferably, the data-geometry mapping function is set as follows: It can be expressed by the following formula. :

[0016] in, x i (t) Let be the measurement value of the i-th sensor at time t. G i For the geometric region of the virtual component associated with the sensor, C i To visualize coding rules, Let i be the data-geometry mapping function for the i-th sensor; Temperature mapping is achieved using a color lookup table:

[0017] in, The maximum time constant, It is the minimum time constant. It is the largest hue component in the color space. It is the smallest hue component in the color space; Angular deviation is represented by a dynamic arrow, and the rotation matrix R of the arrow is... arrow There is a difference between the current angle and the target angle. Decide:

[0018] Where k is the vertically upward rotation axis, and [·]x is the antisymmetric operator. From the perspective of the target, From the current perspective; Dynamic operation guidance uses a fifth-order polynomial to generate a smooth trajectory:

[0019] in, The command angle value corresponding to the normalized stroke s, The termination angle of the trajectory, The starting angle of the trajectory, The total duration of the entire guided trajectory movement.

[0020] Preferably, set Corresponding to red H=0°, Corresponding to green H=120°, Corresponding to red-purple H=300°, the component color changes in real time.

[0021] Preferably, when the remote expert mode is activated, the system will display the video stream and pose of the operator's AR field of view. With the synchronous transmission of intrinsic parameter K, the two-dimensional annotation points drawn by remote experts are mapped to the three-dimensional tooling coordinate system through point-ray inverse projection:

[0022] in, A three-dimensional ray originating from the camera's optical center and passing through the marked pixels. for The rotation submatrix in The inverse of the camera intrinsic parameter matrix, Translation vector from the tooling coordinate system to the AR head-mounted display coordinate system.

[0023] Preferably, by optimizing λ so that r(λ) falls on the known geometric surface of the tooling, precise collaboration across devices is achieved. Finally, the AR headset displays all virtual elements in a mixed manner according to depth and semi-transparency attributes, thereby enabling operators to obtain transparent and intelligent perception and guidance of the tooling status in a vacuum cryogenic environment.

[0024] Preferably, when a sensor's data exceeds a safety threshold during several days of continuous testing, a bright, flashing warning box immediately appears on the corresponding virtual component in the AR headset, along with a fault message.

[0025] A computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement an intelligent control method for a vacuum cryogenic rotary testing fixture based on digital twins.

[0026] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement an intelligent control method for a vacuum cryogenic rotary testing fixture based on digital twins.

[0027] The present invention has the following beneficial effects: This invention constructs a digital twin model of a vacuum cryogenic rotary testing fixture, achieving real-time mapping, dynamic synchronization, and closed-loop feedback between the physical fixture and the virtual model. It integrates multi-source operational information, including temperature, motion, heating, angle feedback, locking, and limit states, into the digital twin system for comprehensive analysis. Compared to traditional rotary testing fixtures that rely solely on temperature and angle feedback for control, this invention can predict the evolution of the temperature field, structural thermal deformation trends, and angle positioning errors of key components based on real-time data acquisition and the digital twin model. It then generates heating control and angle compensation quantities accordingly, achieving coordinated optimization of temperature control and attitude positioning. Simultaneously, by comparing the deviation between the measured and predicted states, it can assess the operational status and provide fault warnings for key components such as the heater, temperature sensor, circular grating, rotating shaft system, motor, and locking mechanism. This improves the temperature control stability, attitude positioning accuracy, status observability, anomaly warning capability, and long-term operational reliability of the rotary testing fixture under vacuum cryogenic conditions. Attached Figure Description

[0028] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0029] Figure 1The diagram shows a flowchart of the digital twin control method of the present invention. Figure 2 The diagram shows the overall structure of the vacuum cryogenic rotary testing fixture. Figure 3 The diagram shows the components of a digital twin model. Detailed Implementation

[0030] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0031] The present invention will be described in detail below with reference to specific embodiments. Specific Implementation Example 1: according to Figures 1 to 3 As shown, the specific optimized technical solution adopted by the present invention to solve the above-mentioned technical problems is: The present invention relates to an intelligent control method for vacuum low-temperature rotary testing fixture based on digital twin.

[0033] This invention provides an intelligent control system and method for a vacuum cryogenic rotating testing fixture based on digital twins. The method establishes a digital twin model of the rotating testing fixture, collects real-time operational data such as temperature, angle, speed, heating state, locking state, and limit state of the physical fixture, and synchronizes the collected data to the digital twin model. The digital twin model is used to predict temperature field changes, structural thermal deformation, and angle positioning errors, thereby generating heating control quantities and angle compensation quantities. This achieves virtual-physical synchronization of the rotating testing fixture, temperature-attitude coordinated control, and operational status early warning. The overall flowchart is shown below. Figure 1 As shown.

[0034] Step 1: Establish a digital twin model of the rotating testing fixture. Based on the actual structure of the vacuum cryogenic rotating testing fixture, establish a digital twin model. The rotating testing fixture includes a base, a horizontal carriage, a yaw rotation mechanism, a load platform, a worm gear drive mechanism, a circular grating angle feedback system, a locking mechanism, a limit protection mechanism, a heat insulation ring, multi-layer heat insulation components, a temperature sensor, a heater, and a mounting interface for the camera under test, such as... Figure 2As shown. The digital twin model includes a mechanical structure twin unit, a thermal field twin unit, a motion control twin unit, an error compensation twin unit, and a state assessment twin unit. The mechanical structure twin unit describes the geometric, assembly, and transmission relationships of each component of the rotating test fixture; the thermal field twin unit describes the heat transfer relationships between temperature sensors, heaters, insulation components, and key structural parts; the motion control twin unit describes the motion relationships between the drive mechanism, rotating shaft system, circular grating angle feedback system, and load platform; the error compensation twin unit calculates the angular positioning error based on temperature changes and structural thermal deformation; and the state assessment twin unit determines whether the rotating test fixture's operating state is abnormal, such as... Figure 3 As shown.

[0035] Step Two: Collect physical tooling operation data and synchronize virtual and physical data. Temperature data of key components is collected via temperature sensors; turntable angle data is collected via a circular grating angle feedback system; speed, direction, and operating status data are collected via the motor control unit; heater operating status data is collected via the heating controller; and locking and limit protection mechanism data are collected via locking and limit status data. The collected data can be represented as:

[0036] In the formula, T 1 (k) , T 2 (k) ,... , T n (k) The data are the temperature data of each temperature measuring point at time k. θ g (k) The circular grating feedback angle at time k; ω(k) Let be the angular velocity of the turntable at time k; u h (k) This represents the heating control quantity at time k. S l (k) This represents the limit state at time k. S s (k) This is the locked state at time k; t r (k) This represents the cumulative running time.

[0037] The collected data is input into the digital twin model, and the temperature field state, motion state, heating state, locking state, and limit state in the digital twin model are updated synchronously:

[0038] In the formula,M d (k) The state of the digital twin model at time k; M d (k 1) The state of the digital twin model at the previous moment; D(k) The data collected at time k is the runtime data. U(k) Φ is the control input at time k; Φ is the virtual-real synchronization update function.

[0039] Step 3: Predict Temperature Field Changes and Structural Thermal Deformation. The digital twin model predicts the temperature change trend of key components based on current temperature data, historical temperature data, heater operating status, insulation structure parameters, and vacuum cryogenic environment boundary conditions. It also calculates the thermal deformation of key structural components in the rotating test fixture based on the temperature field prediction results. The thermal balance relationship at the i-th temperature node can be expressed as:

[0040] In the formula, C i Let be the equivalent heat capacity at the i-th temperature node; T i (t) Let i be the temperature of the i-th temperature node; P i (t) Input power for heating; Q i (t) For heat loss; K ij Let be the equivalent thermal conductivity between the i-th temperature node and the j-th temperature node.

[0041] The thermal deformation of the j-th structural component can be expressed as:

[0042] In the formula, ΔL j (k) Let be the thermal deformation of the j-th structural component at time k. α j Let be the coefficient of linear expansion of the j-th structural component; L j Let j be the characteristic length of the j-th structural component; T j (k) Let be the temperature of the j-th structural component at time k; T j0 This is a reference temperature.

[0043] Step 4: Calculate the angular positioning error and generate the compensation amount. Based on the temperature field prediction results and the structural thermal deformation, the digital twin model calculates the angular positioning error caused by thermal deformation and generates the angular compensation amount by combining it with the mechanical transmission error. The angular error caused by thermal deformation can be expressed as:

[0044] In the formula, Δθ T (k) The angle error caused by thermal deformation at time k; F θ This is the mapping function from thermal deformation to angular error.

[0045] The compensated angle control command can be expressed as:

[0046] In the formula, θcmd(k) The compensated angle command is issued to the turntable control system at time k. θ target (k) The original target angle; The error in the predicted thermal deformation angle of the digital twin model; The mechanical transmission error predicted by the digital twin model.

[0047] Step 5: Generate heating control quantities and execute temperature-attitude coordinated control. Based on the target temperature, measured temperature, and temperature predicted by the digital twin model, calculate the temperature control error, and generate the heating control quantities using the Smith predictive control algorithm. The temperature control error is:

[0048] In the formula, e T (k) The temperature control error at time k; T s (k) The target temperature; T(k) This is the measured temperature.

[0049] The Smith estimate of the control output can be expressed as:

[0050] In the formula, u(k) This is for heating control quantity; K p , K i , K d These are the proportional, integral, and differential parameters, respectively. y m(k) This is the output of the Smith predictor.

[0051] The lower-level computer controls the heater to work according to the heating control quantity, and controls the yaw rotation mechanism to move according to the compensated angle control command, so that the rotating test fixture can complete the attitude adjustment while maintaining a stable temperature.

[0052] Step Six: Perform Status Assessment and Fault Early Warning. The digital twin model assesses the operating status of the rotating test fixture based on real-time acquired data, model prediction data, and set thresholds. The assessment objects include temperature status, heater operating status, circular grating angle feedback status, locking status, limit status, motor operating status, and predicted attitude error status. The status assessment function can be expressed as:

[0053] In the formula, R(k) This represents the state risk value at time k. G For state evaluation function; D(k) This is the currently collected data; M d (k) This represents the current state of the digital twin model; Data for predicting future states for digital twin models.

[0054] when R(k)>R lim When the system determines that the rotating test fixture is in an abnormal operating state, it outputs an alarm message and performs actions such as stopping heating, stopping rotation, locking and holding, returning to zero, or emergency stop protection according to the level of abnormality. When the operating state is normal, the system continues to collect data and update the digital twin model to achieve cyclic control.

[0055] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement an intelligent control method for a vacuum cryogenic rotary testing fixture based on digital twins.

[0056] The present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement an intelligent control method for a vacuum cryogenic rotary testing fixture based on digital twins.

[0057] The above description is merely a preferred embodiment of a digital twin-based intelligent control method for vacuum cryogenic rotary testing fixtures. The scope of protection for this digital twin-based intelligent control method is not limited to the above embodiments; all technical solutions falling within this conceptual framework are within the scope of protection of this invention. It should be noted that for those skilled in the art, any improvements and variations made without departing from the principles of this invention should also be considered within the scope of protection of this invention.

Claims

1. A method for intelligent control of vacuum cryogenic rotary testing fixture based on digital twins, characterized by: The method includes the following steps: Step 1: Establish a digital twin model based on the actual structure of the vacuum cryogenic rotary testing fixture; Step 2: Collect multi-source operational data from the physical tooling, preprocess the data, and input it into the digital twin model to achieve synchronous updates between the physical tooling and the digital twin model. Step 3: The digital twin model predicts the temperature field change trend of key components based on current and historical operating data, equipment parameters and environmental boundary conditions, and calculates the thermal deformation of the corresponding structural components; Step 4: Based on the temperature field prediction results and the amount of structural thermal deformation, calculate the angle positioning error caused by thermal deformation, combine it with the mechanical transmission error to generate the angle compensation amount, and obtain the compensated angle control command. Step 5: Based on the target temperature, measured temperature, and temperature predicted by the digital twin model, and combined with the Smith predictive control algorithm, a heating control quantity is generated. The lower-level computer then executes temperature and attitude coordinated control based on the heating control quantity and the compensated angle control command. Step 6: The digital twin model evaluates the operating status of the rotating test fixture based on real-time collected data, model status data, and future prediction data. If the status is determined to be abnormal, an alarm message is output and corresponding protection control is executed. If the status is normal, the next control cycle is entered.

2. The method according to claim 1, characterized in that: The digital twin model includes mechanical structure twin units, thermal field twin units, motion control twin units, error compensation twin units, and state assessment twin units; Mechanical twin units are used to describe the geometric, assembly, and transmission relationships of the components of a rotating test fixture; Thermal twins are used to describe the heat transfer relationships between temperature sensors, heaters, insulation components, and key structural components; The motion control twin is used to describe the motion relationship between the drive mechanism, the rotary axis system, the circular grating angle feedback system, and the load platform; The error-compensating twin unit is used to calculate the angular positioning error based on temperature changes and structural thermal deformation; The status assessment twin unit is used to determine whether the operating status of the rotating test fixture is abnormal.

3. The method according to claim 2, characterized in that: Temperature data of key components is collected via temperature sensors; turntable angle data is collected via a circular grating angle feedback system; speed, direction, and operating status data are collected via a motor control unit; heater operating status data is collected via a heating controller; and locking and limit protection mechanism data are collected via locking and limit protection mechanisms. The collected data is represented as follows: in, T 1 (k) , T 2 (k) ,... , T n (k) The data are the temperature data of each temperature measuring point at time k. θ g (k) The circular grating feedback angle at time k; ω(k) Let be the angular velocity of the turntable at time k; u h (k) This represents the heating control quantity at time k. S l (k) This represents the limit state at time k. S s (k) This is the locked state at time k; t r (k) Cumulative running time; The collected data is input into the digital twin model, and the temperature field state, motion state, heating state, locking state, and limit state in the digital twin model are updated synchronously: in, M d (k) The state of the digital twin model at time k; M d (k 1) The state of the digital twin model at the previous moment; D(k) This refers to the runtime data collected at time k. U(k) Φ is the control input at time k; Φ is the virtual-real synchronization update function.

4. The method according to claim 3, characterized in that: The thermal balance relationship at the i-th temperature node is expressed as: in, C i Let be the equivalent heat capacity at the i-th temperature node; T i (t) Let i be the temperature of the i-th temperature node; P i (t) Input power for heating; Q i (t) For heat loss; K ij The equivalent thermal conductivity between the i-th temperature node and the j-th temperature node; The thermal deformation of the j-th structural component is expressed as: in, ΔL j (k) Let be the thermal deformation of the j-th structural component at time k. α j Let be the coefficient of linear expansion of the j-th structural component; L j Let j be the characteristic length of the j-th structural component; T j (k) Let be the temperature of the j-th structural component at time k; T j0 This is a reference temperature.

5. The method according to claim 4, characterized in that: The angular error caused by thermal deformation is expressed as: in, Δθ T (k) The angle error caused by thermal deformation at time k; F θ This is the mapping function from thermal deformation to angular error; The compensated angle control command is expressed as follows: in, θcmd(k) The compensated angle command is issued to the turntable control system at time k. θ target (k) The original target angle; The error in the predicted thermal deformation angle of the digital twin model; The mechanical transmission error predicted by the digital twin model.

6. The method according to claim 5, characterized in that: Temperature control error is: in, e T (k) The temperature control error at time k; T s (k) The target temperature; T(k) This is the measured temperature; The Smith estimate of the control output is expressed as: in, u(k) This is for heating control quantity; K p , K i , K d These are the proportional, integral, and differential parameters, respectively. y m (k) For the Smith predictor output; The lower-level computer controls the heater to work according to the heating control quantity, and controls the yaw rotation mechanism to move according to the compensated angle control command, so that the rotating test fixture can complete the attitude adjustment while maintaining a stable temperature.

7. The method according to claim 6, characterized in that: The state evaluation function is expressed as: in, R(k) This represents the state risk value at time k. G For state evaluation function; D(k) This is the currently collected data; M d (k) This represents the current state of the digital twin model; Data for predicting future states for digital twin models.

8. The method according to claim 7, characterized in that: when R(k)>R lim When the system determines that the rotating test fixture is in an abnormal operating state, it outputs an alarm message and performs actions such as stopping heating, stopping rotation, locking and holding, returning to zero, or emergency stop protection according to the level of abnormality. When the operating state is normal, the system continues to collect data and update the digital twin model to achieve cyclic control.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the method as claimed in any one of claims 1-8.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the method of any one of claims 1-8.

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