Intelligent equipment management and control system for high-grade clean laboratory

Abstract

The invention discloses an equipment intelligent management and control system for a high-grade clean laboratory, and the system comprises a multispectral imaging module which comprises a visible light camera and a laser displacement sensor; the quantum edge computing node is integrated with a quantum computing coprocessor; the self-adaptive calibration actuator comprises a nanoscale piezoelectric driving mechanism; the federal learning cloud platform is used for realizing multi-laboratory model cooperative training; and the augmented reality interaction terminal displays the equipment pose deviation in real time. Visible light and laser displacement sensing can be combined, holographic monitoring of the equipment state is achieved, quantum Fourier transform is used for accelerating image processing, and dynamic compensation, multi-laboratory cooperative training supporting and differential privacy protection data security injection are achieved through piezoelectric ceramic driving and a hybrid control algorithm.

Description

Technical Field

[0001] The present invention relates to the field of management and control systems, and in particular to an intelligent equipment management and control system for high-grade clean laboratories. Background Art

[0002] Laboratory management and control systems are centered around information technology, focusing on functions such as equipment monitoring, data collection, and process management. However, they face multiple challenges in demanding scenarios such as high-level cleanroom laboratories:

[0003] Data silos and privacy risks: When multiple laboratories collaborate, data sharing is difficult and sensitive information is easily leaked.

[0004] Insufficient real-time performance and accuracy: Traditional algorithms are difficult to meet the real-time requirements of nano-scale device calibration and high-speed data processing.

[0005] Limitations of human-computer interaction: Reliance on physical contact or fixed interfaces, prone to contamination and low operational flexibility.

[0006] Weak anti-interference ability: Environmental disturbances (such as vibration and electromagnetic noise) can easily lead to measurement errors and system loss of control;

[0007] Therefore, an intelligent equipment management and control system for high-grade clean laboratories is proposed. Summary of the Invention

[0008] The present invention solves the problems existing in the prior art through the following technical solutions, which include:

[0009] Multispectral imaging module, including a visible light camera and a laser displacement sensor;

[0010] Quantum edge computing nodes with integrated quantum computing coprocessors;

[0011] an adaptive calibration actuator, comprising a nanoscale piezoelectric actuator;

[0012] Federated learning cloud platform to enable collaborative training of multi-laboratory models;

[0013] Augmented reality interactive terminal, real-time display of device posture deviation.

[0014] Furthermore, the multispectral imaging module includes:

[0015] a) The visible light channel uses a global shutter CMOS sensor with a resolution of ≥3840×2160;

[0016] b) The laser displacement sensor is based on the principle of triangulation, with a measurement accuracy of ≤0.1μm;

[0017] c) The mounting bracket is equipped with a six-dimensional fine-tuning mechanism and meets the following installation constraints:

[0018]

[0019] where η is the pixel equivalent value, θ is the angle between the camera optical axis and the device normal, is the partial derivative of the pixel equivalent with respect to the angle, representing the pixel equivalent change rate caused by the angle change.

[0020] Furthermore, the quantum edge computing node performs the following process:

[0021] Step 1: Accelerate image feature extraction through quantum Fourier transform:

[0022]

[0023] In the formula, QFT is the quantum Fourier transform operator, |I> is the input image quantum state, encoding the pixel grayscale value as the quantum state amplitude;

[0024] |F> is the quantum state after Fourier transform, representing the frequency domain information;

[0025] |k> is the standard Dirac symbol in quantum computing, representing the ground state of a qubit;

[0026] N is the total number of pixels, and it needs to satisfy N = 2 n (n is the number of qubits);

[0027] j and k are quantum state indices, with the value range 0 ≤ j, k < N, corresponding to the ground state of the qubit;

[0028] is the imaginary unit, e 2πijk / N is the exponential term, representing the phase rotation of the quantum state;

[0029] Step 2: Use a variational quantum circuit to solve the device pose optimization problem:

[0030]

[0031] R(θ) is the rotation matrix, and the parameter θ includes Euler angles or quaternions;

[0032] T(θ) is the translation vector, jointly optimized with the rotation parameter;

[0033] X is the device's three-dimensional point cloud, in millimeters (mm);

[0034] Y is the observed two-dimensional image point, in pixels;

[0035] Proj is the perspective projection function, mapping the three-dimensional points to the two-dimensional image plane.

[0036] Furthermore, the implementation of the quantum Fourier transform includes:

[0037] Construct an n-qubit register, n = log2N;

[0038] Apply a Hadamard gate and a controlled phase gate sequence:

[0039]

[0040] Where QFT is the quantum Fourier transform operator, n is the total number of quantum bits n = log2N, k is the quantum bit number, is the phase rotation factor, |j> represents an integer j encoded by n quantum bits, for example, when =3, =5 corresponds to the quantum state |101>, j / 2 k The binary information of the kth and higher bits is extracted;

[0041] n: number of qubits, n = log2N;

[0042] |j< is the input quantum state.

[0043] Furthermore, the adaptive calibration actuator includes:

[0044] Piezoelectric ceramic drive unit, displacement resolution meets:

[0045]

[0046] Where δx is the displacement resolution, in nanometers (nm), and the target value is ≤10nm;

[0047] V max is the maximum driving voltage, 100 volts (V);

[0048] G is the piezoelectric ceramic gauge factor, 105V / m (volts / meter);

[0049] N ADC The number of bits of the analog-to-digital converter, 16 bits (2 16 =65536 levels);

[0050] Closed-loop control algorithm implementation:

[0051]

[0052] u k is the kth control quantity, unit volt (V);

[0053] e k is the displacement error, e k =Δ d目标 -Δ d实际 , unit nanometer (nm);

[0054] K pis the nonlinear gain coefficient, which is taken as 0.8 (dimensionless).

[0055] Furthermore, the closed-loop control algorithm further includes:

[0056] When the quantum computing node outputs confidence C q When it is less than 0.9, it switches to the classic PID control mode:

[0057]

[0058] Where K p ,K i ,K d are the proportional, integral, and differential gains, determined by quantum annealing optimization;

[0059] e(t′) refers to the displacement error at time t′;

[0060] e(t) is the real-time error, in nanometers (nm);

[0061] Control mode switching conditions are met:

[0062]

[0063] Where: Cq is the quantum calculation confidence, ranging from [0,1], calculated by the statistical variance of quantum state measurement;

[0064] Δt is the time window, in seconds (s).

[0065] Furthermore, the federated learning cloud platform is used to:

[0066] Differential privacy parameter injection:

[0067]

[0068] θ global is the global model parameter, the dimension is related to the neural network structure, and I is the unit matrix;

[0069] |Di| is the data volume of the i-th laboratory, the unit sample number, and N refers to the Gaussian distribution;

[0070] σ is the standard deviation of Gaussian noise, which is 0.01;

[0071] M is the number of participating nodes, M is required to be ≥ 5;

[0072] Model update verification mechanism: requirements Otherwise, rollback protection is triggered;

[0073] ||θnew-θold||2 is the Euclidean norm, which does not exceed the preset threshold ∈=0.1 and measures the magnitude of parameter change.

[0074] Furthermore, the augmented reality interactive terminal realizes visualization projection of posture deviation:

[0075] ΔP AR =K·[R|T]·(ΔX,ΔY,ΔZ,1) T ;

[0076] Where: K is the camera intrinsic parameter matrix, a 3×3 matrix containing focal length and principal point parameters;

[0077] [R|T] is the external parameter matrix, 3×4, rotation and translation transformation;

[0078] (ΔX, ΔY, ΔZ) is the equipment displacement deviation, in millimeters (mm);

[0079] Gesture control command mapping:

[0080]

[0081] MLP is the abbreviation of multi-layer perceptron;

[0082] Represents the tensor splicing (or feature fusion) operation, that is, the hand joint point coordinates J hand and gesture semantic encoding S gesture Concatenate by dimension to form a new input feature vector for subsequent model processing;

[0083] J hand ∈R 21×3 is the hand joint coordinate, S gesture Encoding the semantics of gestures.

[0084] Furthermore, the process of generating the gesture semantic code includes:

[0085] Constructing a spatiotemporal graph convolutional network:

[0086]

[0087] Where: W adj is the adjacency matrix weight, reflecting the connection relationship of the joint points;

[0088] X t (v): Feature vector of joint point v in frame t;

[0089] W conv is the trainable convolution kernel weight;

[0090] Training with contrastive loss:

[0091] Where: s pis the positive sample similarity, cosine similarity calculation;

[0092] s n is the negative sample similarity, obtained by random sampling;

[0093] τ is the temperature coefficient, which is 0.07.

[0094] Compared with the existing technology, the present invention has the following advantages: the intelligent equipment management and control system for high-level clean laboratories realizes all-round and accurate monitoring of equipment status through a multi-spectral imaging module combined with visible light and laser displacement sensing technology, effectively improving environmental adaptability and measurement reliability. The quantum edge computing node uses quantum algorithms to accelerate data processing, significantly optimizes the efficiency of image feature extraction and posture calculation, and enhances the system's real-time response capability. The actuator based on nanoscale piezoelectric drive supports real-time adjustment to ensure the continuous stability of the equipment posture and reduce the need for manual intervention. The federated learning cloud platform uses multi-laboratory model joint training to improve the generalization ability of the global model while adopting a differential privacy mechanism to ensure data security and achieve a balance between knowledge sharing and privacy protection. The augmented reality terminal visualizes the device posture deviation and combines gesture semantic recognition technology to provide an intuitive operation interface, improving user control convenience and interactive experience. The quantum-classical hybrid control mode dynamically switches according to the confidence level to ensure stable operation in extreme scenarios, enhance the reliability and anti-interference ability of the system, and make the system more worthy of promotion and use. BRIEF DESCRIPTION OF THE DRAWINGS

[0095] Figure 1 It is a system block diagram of the present invention. DETAILED DESCRIPTION

[0096] The following is a detailed description of an embodiment of the present invention. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process. However, the protection scope of the present invention is not limited to the following embodiment.

[0097] like Figure 1 As shown, this embodiment provides a technical solution: an intelligent equipment management and control system for high-grade clean laboratories, including:

[0098] Multispectral imaging module, including a visible light camera and a laser displacement sensor;

[0099] Quantum edge computing nodes with integrated quantum computing coprocessors;

[0100] an adaptive calibration actuator, comprising a nanoscale piezoelectric actuator;

[0101] Federated learning cloud platform to enable collaborative training of multi-laboratory models;

[0102] Augmented reality interactive terminal, real-time display of device posture deviation.

[0103] Furthermore, the multispectral imaging module includes:

[0104] a) The visible light channel uses a global shutter CMOS sensor with a resolution of ≥3840×2160;

[0105] b) The laser displacement sensor is based on the principle of triangulation, with a measurement accuracy of ≤0.1μm;

[0106] c) The mounting bracket is equipped with a six-dimensional fine-tuning mechanism and meets the following installation constraints:

[0107]

[0108] Where η is the pixel equivalent value, θ is the angle between the camera optical axis and the device normal, is the partial derivative of the pixel equivalent with respect to the angle, which indicates the rate of change of the pixel equivalent caused by the change of the angle;

[0109] The global shutter CMOS sensor clearly captures microscopic deformation or displacement of equipment in clean laboratories, avoiding motion blur and improving the ability to restore detailed information in monitoring data. The laser displacement sensor, based on the principle of triangulation, achieves non-contact, high-precision displacement monitoring, meeting the laboratory's stringent requirements for equipment stability. The six-dimensional fine-tuning mechanism uses mathematical constraints to ensure pixel equivalent stability when the camera installation angle changes, reducing the impact of laboratory vibration or airflow disturbances on measurement accuracy.

[0110] In the laboratory, the stage position of microelectromechanical systems (MEMS) manufacturing equipment needs to be calibrated in real time to ensure nanometer-level processing accuracy.

[0111] The global shutter CMOS sensor captures real-time images of the MEMS device platform at 4K resolution, capturing micron-level deformations caused by temperature fluctuations or mechanical vibrations (such as slight warping at the platform edge).

[0112] The laser displacement sensor measures the distance change between the platform and the laser reference surface with an accuracy of ≤0.1μm and provides real-time feedback to the control system to ensure that the alignment error between the processing tool and the platform is controlled within the nanometer range.

[0113] When the laboratory clean air conditioning system causes airflow disturbances, causing the angle between the camera optical axis and the platform normal to shift to θ = 8°, the six-dimensional fine-tuning mechanism automatically adjusts the bracket angle so that the pixel equivalent change rate is always less than 0.01mm / °, avoiding distortion of the measurement data due to changes in the installation angle.

[0114] Through the synergistic effect of the multispectral imaging module, the system can real-time correct the device position deviation, ensure the nanoscale processing accuracy of MEMS devices, reduce the scrap rate caused by environmental interference, and improve the automation control efficiency of the clean laboratory.

[0115] The quantum edge computing node performs the following process:

[0116] Step 1: Accelerate image feature extraction through quantum Fourier transform:

[0117]

[0118] In the formula, QFT is the quantum Fourier transform operator, |I> is the input image quantum state, encoding the pixel gray value as the quantum state amplitude;

[0119] |F> is the quantum state after Fourier transform, representing the frequency domain information;

[0120] |k> is the standard Dirac symbol in quantum computing, representing the ground state of a qubit;

[0121] N is the total number of pixels, and it needs to satisfy N = 2 n (n is the number of qubits);

[0122] j and k are quantum state indices, with the value range of 0 ≤ j, k < N, corresponding to the ground state of the qubit;

[0123] is the imaginary unit, e 2πijk / N is the exponential term, representing the phase rotation of the quantum state;

[0124] Step 2: Use the variational quantum circuit to solve the device pose optimization problem:

[0125]

[0126] R(θ) is the rotation matrix, and the parameter θ includes Euler angles or quaternions;

[0127] T(θ) is the translation vector, jointly optimized with the rotation parameter;

[0128] X is the three-dimensional point cloud of the device, in millimeters (mm);

[0129] Y is the observed two-dimensional image point, in pixels;

[0130] Proj is the perspective projection function, mapping the three-dimensional points to the two-dimensional image plane;

[0131] Ultra-high-speed feature extraction: Accelerate the image frequency domain analysis through the quantum Fourier transform (QFT), reducing the time complexity of the traditional Fourier transform from O(N 2) is reduced to O(NlogN), significantly improving the feature extraction efficiency of high-resolution images (such as 4K images).

[0132] Precise pose optimization: The variational quantum circuit (VQC) combines classic optimization algorithms to efficiently solve the joint optimization problem of the device rotation matrix (R) and translation vector (T), breaking through the local optimal limitations of traditional iterative algorithms and improving pose calibration accuracy.

[0133] Quantum-classical collaborative computing: By encoding image information through quantum states (such as grayscale value amplitude encoding), we can fully utilize quantum parallelism to accelerate complex computing tasks while retaining the adaptability of classical algorithms to physical parameters.

[0134] Calibration of precision optical equipment in high-grade cleanroom laboratories:

[0135] During the installation and debugging of a laser interferometer or lithography machine, the three-dimensional posture (position + angle) of the device needs to be calculated in real time to ensure the accuracy of the beam path or exposure focal plane.

[0136] The 4K images captured by the multispectral imaging module (such as the real-time image of the lithography machine lens group) are encoded into quantum states (|I>) through the quantum edge node, and the frequency domain features (|F>) are quickly extracted through QFT to identify tiny offsets or distortions of the lens group (such as nanometer-level grating misalignment).

[0137] Traditional algorithms take several seconds to complete Fourier analysis, but quantum coprocessors only take milliseconds to complete, meeting real-time monitoring needs.

[0138] The device's three-dimensional point cloud (X) and the observed image point (Y) are input into the variational quantum circuit, and the Euler angle θ is iteratively optimized through a parameterized quantum gate (such as a rotation gate) to minimize the projection error (i.e., minimize |Proj(RX+T)-Y|2).

[0139] Compared with the classical gradient descent method, which is prone to falling into local optimality, quantum optimization can globally search for the optimal solution, ensuring that the six-degree-of-freedom posture calibration error of the lithography machine lens group is ≤10 nanometers.

[0140] Quantum edge computing nodes shorten the device posture calculation time by 9%, while improving the calibration accuracy to the nanometer level, ensuring the efficient and stable operation of optical equipment in clean laboratories and avoiding process defects caused by calibration delays or errors.

[0141] The implementation of the quantum Fourier transform includes:

[0142] Construct an n-qubit register, n = log2N;

[0143] Apply a Hadamard gate and a controlled phase gate sequence:

[0144]

[0145] Where QFT is the quantum Fourier transform operator, n is the total number of quantum bits n = log2N, k is the quantum bit number, is the phase rotation factor, |j> represents an integer j encoded by n quantum bits, for example, when =3, =5 corresponds to the quantum state |101>, j / 2 k The binary information of the kth and higher bits is extracted;

[0146] n: number of qubits, n = log2N;

[0147] |j> is the input quantum state;

[0148] |j> is a quantum state vector consisting of n quantum bits, where j is a non-negative integer with a value range of 0≤j <N(N=2 n , n is the number of quantum bits).

[0149] j1j2j3……j n is the binary representation of j, which corresponds to the high to low bits of the quantum bit from left to right (i.e. j1 is the highest bit quantum bit, j n is the lowest qubit).

[0150] Efficient use of quantum computing resources: By constructing n=log2N quantum bit registers, the number of physical quantum bits can be significantly reduced, adapting to the hardware limitations of current medium-sized quantum devices and improving feasibility.

[0151] Based on the sequential operation of Hadamard gates and controlled phase gates, quantum parallelism is fully utilized to further reduce the computational complexity of Fourier transform from O in the classical algorithm, achieving ultra-high-speed frequency domain feature extraction.

[0152] Precise quantum state mapping: Binary encoding of the input quantum state |j> ensures a one-to-one correspondence between image pixel grayscale values ​​and quantum state amplitudes, preventing information loss and ensuring the accuracy of subsequent quantum calculations.

[0153] Real-time monitoring of the surface morphology of optical components in high-grade clean laboratories:

[0154] Detect nano-level scratches or contamination on high-precision lens surfaces to ensure optical system performance.

[0155] A microscopic image of the lens surface with a resolution of 4096×4096 (N=16,777,216 pixels) is collected through a multispectral imaging module, and a 24-qubit register is constructed according to n=log2N (an approximate simplification can be used in practice).

[0156] The image grayscale value is encoded as quantum state amplitude, and the superposition state is generated through the Hadamard gate. The controlled phase gate is superimposed to achieve frequency domain phase adjustment and quickly complete the QFT.

[0157] Traditional FFT takes several seconds to complete calculations, but the quantum coprocessor can output frequency domain features (such as defect signals corresponding to high-frequency noise) within milliseconds.

[0158] The frequency domain characteristics reveal the abnormal frequency components of tiny scratches on the surface (such as 10nm level). The system triggers the adaptive calibration actuator to adjust the position of the cleaning robot arm to accurately remove contamination.

[0159] Quantum Fourier transform increases the speed of surface defect detection in optical components while achieving nanometer-level sensitivity, avoiding the delays and errors of manual visual inspection and ensuring zero-defect production standards for clean laboratory optical equipment.

[0160] The adaptive calibration actuator contains:

[0161] Piezoelectric ceramic drive unit, displacement resolution meets:

[0162]

[0163] Where δx is the displacement resolution, in nanometers (nm), and the target value is ≤10nm;

[0164] V max is the maximum driving voltage, 100 volts (V);

[0165] G is the piezoelectric ceramic gauge factor, 105V / m (volts / meter);

[0166] N ADC The number of bits of the analog-to-digital converter, 16 bits (2 16 =65536 levels);

[0167] Closed-loop control algorithm implementation:

[0168]

[0169] u k is the kth control quantity, unit volt (V);

[0170] e k is the displacement error, e k =Δ d目标 -Δ d实际 , unit nanometer (nm);

[0171] K p is the nonlinear gain coefficient, which is taken as 0.8 (dimensionless);

[0172] The piezoelectric ceramic drive unit achieves sub-micron real-time calibration of ultra-precision equipment through high-resolution displacement control (target ≤10nm), meeting the ultimate requirements of clean laboratories for equipment stability. The nonlinear closed-loop control algorithm combines quantum computing confidence feedback to adaptively adjust the drive voltage to ensure rapid convergence and stable compensation in complex environments. When the quantum computing confidence is insufficient, it seamlessly switches to the classical PID control mode (parameters optimized by quantum annealing) to avoid system loss of control due to quantum computing fluctuations and ensure continuous and reliable operation.

[0173] Real-time positioning calibration of atomic force microscope (AFM) probes in clean labs:

[0174] The AFM probe needs to scan the sample surface with nanometer-level precision, but environmental vibration or thermal drift can easily cause the probe to shift, requiring dynamic calibration of its position.

[0175] The piezoelectric ceramic drive unit generates a corresponding driving voltage based on the probe offset data output by the quantum edge computing node, and adjusts the probe to the target position with a displacement resolution of ≤10nm.

[0176] When sudden electromagnetic interference in the laboratory causes the quantum computing confidence C q When it drops to 0.85, the system automatically switches to the classical PID mode and continues to control the piezoelectric ceramic based on the parameters pre-optimized by quantum annealing to maintain the stability of the probe.

[0177] The probe position deviation is always controlled within ±5nm, and even when quantum computing is abnormal, it can still seamlessly transition to classical control, ensuring the atomic-level imaging accuracy of AFM for biomolecules or semiconductor structures, and avoiding experimental interruptions or data distortion due to calibration failure.

[0178] The closed-loop control algorithm also includes:

[0179] When the quantum computing node outputs confidence C q When it is less than 0.9, it switches to the classic PID control mode:

[0180]

[0181] Where K p ,K i ,K d are the proportional, integral, and differential gains, determined by quantum annealing optimization;

[0182] e(t′) refers to the displacement error at time t′;

[0183] e(t) is the real-time error, in nanometers (nm);

[0184] Control mode switching conditions are met:

[0185]

[0186] Where: Cq is the quantum calculation confidence, ranging from [0,1], calculated by the statistical variance of quantum state measurement;

[0187] Δt is the time window, in seconds (s);

[0188] Real-time judgment of system reliability based on quantum computing confidence. q When the value is less than 0.9, it automatically switches to the classic PID control mode to avoid calibration failure caused by quantum computing anomalies and ensure system stability in extreme scenarios.

[0189] Global parameter optimization: Optimize PID parameters through the quantum annealing algorithm, breaking through the limitations of traditional trial-and-error methods, ensuring that control parameters adapt to complex nonlinear systems, and improving the response speed and accuracy of classic control modes.

[0190] Control mode switching conditions (such as C q Combined with the smooth transition logic, the system oscillation during the switching process is reduced and the continuous stability of the device posture is maintained.

[0191] Focus system calibration of electron beam lithography tools in clean labs:

[0192] Electron beam lithography needs to maintain nanometer-level focusing accuracy, but quantum computing nodes may experience confidence fluctuations due to environmental noise (such as electromagnetic interference), and dynamic switching of control modes is required to ensure stable focusing.

[0193] Quantum annealing optimizes PID parameters:

[0194] The optimal PID parameters (such as Kp=0.7, Ki=0.15, Kd=0.05) are solved in advance through the quantum annealing algorithm to make the classical control mode adapt to the nonlinear response characteristics of the electromagnetic lens of the lithography machine.

[0195] When strong electromagnetic interference suddenly occurs in the laboratory and the output confidence level Cq of the quantum computing node drops to 0.85 (Δt = 2 seconds), the system immediately switches to the classical PID mode, adjusts the electromagnetic lens current based on the optimized parameters, and compensates for the electron beam focusing offset.

[0196] During the switching process, the PID controller inherits the current error value (e(t) = 12nm) output by the quantum calculation, converges quickly according to the optimized parameters, and controls the focusing deviation within ±5nm to avoid distortion of the lithography pattern.

[0197] The electron beam lithography machine can still maintain nanometer-level focusing accuracy when quantum computing is abnormal, ensuring the writing quality of integrated circuit masks, reducing wafer scrapping due to system loss of control, and improving the all-weather reliability of high-end manufacturing equipment in clean laboratories.

[0198] The federated learning cloud platform is used to:

[0199] Differential privacy parameter injection:

[0200]

[0201] θ global is the global model parameter, the dimension is related to the neural network structure, and I is the unit matrix;

[0202] |Di| is the data volume of the i-th laboratory, the unit sample number, and N refers to the Gaussian distribution;

[0203] σ is the standard deviation of Gaussian noise, which is 0.01;

[0204] M is the number of participating nodes, M is required to be ≥ 5;

[0205] Model update verification mechanism: requirements Otherwise, rollback protection is triggered;

[0206] ||θnew-θold||2 is the Euclidean norm, which does not exceed the preset threshold ∈=0.1 and measures the magnitude of parameter change;

[0207] Through differential privacy technology (such as injecting Gaussian noise σ = 0.01), the original data of each laboratory is protected during the federated learning process, sensitive information leakage is prevented, and the high standards of clean laboratories for data security are met.

[0208] Based on the Euclidean norm, the parameter change verification mechanism (requires ||θ new -θ old ||2≤threshold), automatically intercepting abnormal model updates to avoid global model performance degradation caused by single-node errors or malicious attacks.

[0209] It supports joint training of models by multiple laboratories (M≥5), breaks down data silos, and uses distributed data to improve model generalization capabilities without sharing original data.

[0210] Multi-laboratory joint optimization clean room environmental control model:

[0211] Multiple high-grade clean laboratories need to jointly optimize the prediction models of temperature, humidity, and particle concentration. However, the data of each laboratory involves confidential process parameters and requires privacy protection.

[0212] Labs A, B, and C train local models and upload model parameters θ local To the federal cloud platform.

[0213] The cloud platform has a global parameter θ globalInject Gaussian noise (σ=0.01) and aggregate to generate a new model θ new , ensuring that the original data of each laboratory (such as the temperature and humidity curve of a specific process) cannot be reversed.

[0214] If laboratory B submits abnormal parameters (such as ||θ new -θ old ||2 exceeds the threshold), the cloud platform triggers the rollback mechanism, rejects the update, and reverts to the previous version θ_old to prevent contamination of the global model.

[0215] After multiple rounds of security aggregation, the global model learns the common laws of each laboratory's environmental data (such as the nonlinear relationship between particle concentration and airflow velocity), and is eventually deployed to each laboratory to achieve more precise environmental control.

[0216] Under the premise of zero data sharing, each laboratory jointly trained a high-precision environmental control model to improve the stability of the clean room environment while eliminating the risk of leakage of sensitive process parameters, ensuring the safety and efficiency of cross-laboratory collaboration.

[0217] The augmented reality interactive terminal realizes the visualization projection of posture deviation:

[0218] ΔP AR =K·[R|T]·(ΔX,ΔY,ΔZ,1) T ;

[0219] Where: K is the camera intrinsic parameter matrix, a 3×3 matrix containing focal length and principal point parameters;

[0220] [R|T] is the external parameter matrix, 3×4, rotation and translation transformation;

[0221] (ΔX, ΔY, ΔZ) is the equipment displacement deviation, in millimeters (mm);

[0222] Gesture control command mapping:

[0223]

[0224] MLP is the abbreviation of Multi-Layer Perceptron, which is a classic feedforward neural network structure. It is used to map the spliced ​​feature vectors into specific control commands, realizing the semantic conversion of gestures to device operations.

[0225] Represents the tensor splicing (or feature fusion) operation, that is, the hand joint point coordinates J hand and gesture semantic encoding S gesture Concatenate by dimension to form a new input feature vector for subsequent model processing;

[0226] J hand ∈R 21×3 is the hand joint coordinate, S gesture Encode the semantics of gestures;

[0227] The device posture deviation (ΔX, ΔY, ΔZ) is projected into the physical space in real time through the augmented reality (AR) terminal. The camera intrinsic parameter (K) and extrinsic parameter matrix are combined to achieve millimeter-level precision three-dimensional deviation visualization, reduce human interpretation errors, and gesture control command mapping technology (based on the hand joint coordinates J hand With semantic encoding S gesture ), allowing operators to remotely adjust equipment through natural gestures, avoiding physical contact and contamination of the clean environment.

[0228] Dynamically adapt to complex scenarios: The spatiotemporal graph convolutional network (ST-GCN) accurately identifies multi-joint movements (such as fist clenching and sliding) by analyzing the spatiotemporal characteristics of gestures, improving the recognition robustness of complex operation instructions.

[0229] Calibration of wafer transfer robot in high-grade cleanroom:

[0230] Wafer transfer robots need to accurately position wafer carriers in a dust-free environment, but traditional manual calibration is prone to particle contamination and is inefficient.

[0231] AR visualization of deviation projection:

[0232] The augmented reality terminal obtains the real-time posture of the robotic arm through the multispectral imaging module and calculates its deviation from the target position (such as ΔX = 0.5mm, ΔY = -0.3mm, ΔZ = 1.2mm) in combination with the external parameter matrix.

[0233] The AR interface superimposes the deviation on the real robotic arm as colored vector arrows, allowing the operator to intuitively view the direction and magnitude of adjustment required.

[0234] The operator wears AR glasses and sends a "horizontal fine-tuning" command through gestures (such as spreading five fingers and sliding to the right).

[0235] The spatiotemporal graph convolutional network analyzes the coordinates of the 21 hand joints in real time and generates a semantic code S gesture = Move right 0.5mm, triggering the adaptive calibration actuator to drive the robotic arm.

[0236] After the robotic arm adjusts according to the instructions, the AR terminal updates the projection deviation in real time until ΔX, ΔY, and ΔZ are all <0.1mm. The system automatically locks the position and prompts that the calibration is complete.

[0237] The process of generating the gesture semantic code includes:

[0238] Constructing a spatiotemporal graph convolutional network:

[0239]

[0240] Where: W adj is the adjacency matrix weight, reflecting the connection relationship of the joint points;

[0241] X t (v): Feature vector of joint point v in frame t;

[0242] W conv is the trainable convolution kernel weight;

[0243] Training with contrastive loss:

[0244] Where: s p is the positive sample similarity, cosine similarity calculation;

[0245] s n is the negative sample similarity, obtained by random sampling;

[0246] τ is the temperature coefficient, which is 0.07;

[0247] The spatiotemporal graph convolutional network (ST-GCN) accurately identifies multi-joint coordinated actions (such as pinching and rotation) by analyzing the spatial topological relationship and temporal dynamic changes of hand joints, improving the semantic encoding accuracy of complex gestures. Contrastive loss training (such as positive and negative sample similarity optimization) enhances the model's adaptability to lighting changes and partial occlusion, ensuring the stability of gesture recognition in complex clean laboratory environments. The local feature extraction mechanism of the spatiotemporal graph convolution is combined with trainable weight optimization to reduce the number of model parameters, adapt to the real-time requirements of edge computing devices, and avoid operation delays.

[0248] Remote control of sterile operating tables in clean laboratories:

[0249] Experimenters need to remotely control precision instruments (such as microfluidic chip assembly machines) under non-contact conditions to avoid the risk of contamination caused by operating physical interfaces while wearing protective clothing.

[0250] Gesture motion capture:

[0251] The AR interactive terminal uses a depth camera to collect the three-dimensional coordinates of the 21 joints of the experimenter's hand in real time and construct a spatiotemporal graph sequence (e.g., 30 frames per second).

[0252] Spatiotemporal graph convolution feature extraction:

[0253] ST-GCN is based on the adjacency matrix (W adj ) encodes the joint connection relationship (such as the strong association between the thumb and the index finger), through the convolution kernel (W conv) to extract the spatiotemporal features of gestures (such as the continuous trajectory of the “pinch-release” action).

[0254] Contrastive loss optimization and semantic mapping:

[0255] During model training, the contrastive loss function forces the semantic encoding difference between positive samples (such as the standard "rotation" gesture) and negative samples (such as random actions) to be maximized, thereby improving classification robustness.

[0256] Instruction execution and feedback:

[0257] The experimenter makes a “pinch and rotate clockwise” gesture, and ST-GCN outputs the semantic code S gesture = Rotate 30°, triggering the assembly machine to precisely rotate the microfluidic chip to the target angle, and the AR interface displays the operation results in real time.

[0258] The system operation method includes the steps of:

[0259] a) Obtain the initial value P0 of the device posture through quantum-classical hybrid calculation;

[0260] b) Federated learning optimizes model parameters θ;

[0261] c) adaptive calibration of the actuator to compensate for displacement deviation ΔP;

[0262] d) Stop the iteration when the following convergence conditions are met:

[0263]

[0264] And Cq>0.95.

[0265] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "plurality" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0266] In the description of this specification, the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0267] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. An intelligent equipment management and control system for high-grade clean laboratories, characterized in that: Comprising: A multi - spectral imaging module, including a visible - light camera and a laser displacement sensor; A quantum edge - computing node, integrated with a quantum - computing co - processor; An adaptive calibration actuator, including a nano - scale piezoelectric driving mechanism; A federated learning cloud platform, realizing collaborative training of multi - laboratory models; An augmented - reality interaction terminal, displaying the pose deviation of the device in real - time.

2. The intelligent equipment management and control system for high-grade clean laboratories according to claim 1 is characterized by: The multi - spectral imaging module includes: a): The visible - light channel uses a global - shutter CMOS sensor; b): The laser displacement sensor is based on the triangulation principle; c): The mounting bracket is configured with a six - dimensional fine - tuning mechanism, meeting the following installation constraint conditions: Where η is the pixel equivalent value, θ is the angle between the camera optical axis and the device normal, It is the partial derivative of the pixel equivalent with respect to the angle, which indicates the rate of change of the pixel equivalent caused by the change of the angle.

3. The intelligent equipment management and control system for high-grade clean laboratories according to claim 1 is characterized in that: The quantum edge - computing node performs the following processes: Step 1: Accelerate image feature extraction through quantum Fourier transform: In the formula, QFT is the quantum Fourier transform operator, |I> is the input - image quantum state, encoding the pixel gray - scale value as the amplitude of the quantum state; |F> is the quantum state after Fourier transform, representing frequency - domain information; |k> is the frequency - domain basis state after Fourier transform, and each basis state corresponds to a frequency component of the input image in the frequency domain; N is the total number of pixels, which must satisfy N=2 n ; j and k are quantum - state indices, with the value range of 0 ≤ j, k < N, corresponding to the basis states of quantum bits; is the imaginary unit, e 2πijk / N is an exponential term, representing the phase rotation of the quantum state; Step 2: Use a variational quantum circuit to solve the device - pose optimization problem: R(θ) is the rotation matrix, and the parameter θ includes Euler angles or quaternions; T(θ) is the translation vector, jointly optimized with the rotation parameter; X is the three - dimensional point cloud of the device; Y is the observed two - dimensional image point; Proj is the perspective - projection function, mapping the three - dimensional point to the two - dimensional image plane.

4. The intelligent equipment management and control system for high-grade clean laboratories according to claim 3 is characterized by: The implementation of the quantum Fourier transform includes: Construct an n - qubit register, where n = log2N; Apply a sequence of Hadamard gates and controlled - phase gates: Where QFT is the quantum Fourier transform operator, n is the total number of quantum bits n = log2N, k is the quantum bit number, is the phase rotation factor, |j> represents an integer j encoded by n quantum bits, j / 2 k is the extracted binary information of the kth and higher bits.

5. The intelligent equipment management and control system for high-grade clean laboratories according to claim 1 is characterized in that: The adaptive calibration actuator includes: A piezoelectric ceramic driving unit, with the displacement resolution satisfying: In the formula, δx is the displacement resolution; V max is the maximum driving voltage; G is the piezoelectric ceramic strain coefficient; N ADC is the number of bits of the analog-to-digital converter; Realize through a closed - loop control algorithm: u k is the kth control quantity; e k is the displacement error, e k =Δ d目标 -Δ d实际 ; K p is the nonlinear gain coefficient.

6. The intelligent equipment management and control system for high-grade clean laboratories according to claim 5 is characterized by: The closed - loop control algorithm also includes: When the quantum computing node outputs confidence C q When it is less than 0.9, it switches to the classic PID control mode: Where K p ,K i ,K d are the proportional, integral, and differential gains, determined by quantum annealing optimization; e(t′) refers to the displacement error at time t′; e(t) is the real - time error; The control - mode switching condition is satisfied: In the formula: Cq is the quantum - computing confidence level, with the range [0,1], calculated from the statistical variance of quantum - state measurement; Δt is the time window.

7. The intelligent equipment management and control system for high-grade clean laboratories according to claim 1 is characterized in that: The federated learning cloud platform is used for: Differential - privacy parameter injection: θ global is the global model parameter, the dimension is related to the neural network structure, and I is the unit matrix; ∣Di∣ is the data volume of the i - th laboratory, in unit samples, and N refers to the Gaussian distribution; σ is the standard deviation of Gaussian noise, taking 0.01; M is the number of participating nodes, and it is required that M ≥ 5; Model update verification mechanism: requirements Otherwise, rollback protection is triggered; ||θnew - θold||2 is the Euclidean norm, not exceeding the preset threshold ∈ = 0.1, measuring the parameter - change amplitude.

8. The intelligent equipment management and control system for high-grade clean laboratories according to claim 7, characterized in that: The augmented - reality interaction terminal realizes visual projection of pose deviation: ΔP AR =K·[R|T]·(Δx,ΔY,ΔZ,1) T ; In the formula: K is the camera intrinsic - parameter matrix, a 3×3 matrix, including focal - length and principal - point parameters; [R∣T] is the extrinsic - parameter matrix, 3×4, for rotation and translation transformation; (ΔX,ΔY,ΔZ) is the device - displacement deviation; Gesture - control instruction mapping: MLP is the abbreviation of multi - layer perceptron; Represents the tensor splicing operation, that is, the hand joint coordinates J hand and gesture semantic encoding S gesture Concatenate by dimension to form a new input feature vector for subsequent model processing; J hand ∈R 21×3 is the hand joint coordinate, S gesture Encoding the semantics of gestures.

9. The intelligent equipment management and control system for high-grade clean laboratories according to claim 8, characterized in that: The process of generating the gesture semantic encoding includes: Construct a spatio - temporal graph convolutional network: Where: W adj is the adjacency matrix weight, reflecting the connection relationship of the joint points; X t (v): Feature vector of joint point v in frame t; W conv is the trainable convolution kernel weight; Training with contrastive loss: Where: s p is the positive sample similarity, cosine similarity calculation; s n is the negative sample similarity, obtained by random sampling; τ is the temperature coefficient.

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