An automatic alignment and integrated control system and method for optical crystal arrays

Abstract

This invention discloses an automatic alignment and integrated control system and method for optical crystal arrays, comprising a quantum entanglement-enhanced ultra-precision sensing array, a multi-factor coupling error prediction module, a full-link adaptive compensation execution module, a central control module, and a federated learning collaborative optimization module. The sensing array acquires multi-dimensional error data in real time, including sub-nanometer displacement, Raman spectral deformation, infrared temperature rise, and frictional stress. The prediction module uses an attention mechanism to mine the coupling characteristics between error sources, predicting instantaneous alignment errors and hysteretic deformation errors. The compensation execution module achieves full-link compensation based on the predicted errors through quantum traceability calibration, dynamic composite compensation by a robotic arm, and collaborative adjustment of process parameters. The central control module performs synchronous scheduling and conflict coordination. The federated learning module achieves collaborative optimization of models among multiple devices and privacy protection. This invention achieves ultra-high precision and adaptive intelligent integration of optical crystal arrays.

Description

Technical Field

[0001] This invention relates to the fields of precision optical manufacturing and automated control technology, and in particular to an automatic alignment and integrated control system and method for optical crystal arrays. Background Technology

[0002] Optical crystal arrays, as key functional units in high-precision optical systems, are widely used in fields such as high-energy laser control, precision spectral analysis, quantum information processing, and high-resolution imaging. As related applications develop towards higher integration, higher stability, and higher consistency, optical crystal arrays face increasingly stringent requirements in the integration and manufacturing stage for the relative alignment accuracy, structural consistency, and long-term stability between crystals. Their alignment and integration accuracy have gradually approached the nanometer or even sub-nanometer level.

[0003] In the automated alignment and integration of optical crystal arrays, a complex relationship of high coupling exists between the crystal's inherent characteristics, the state of the mechanical actuation system, and the process environment. On the one hand, the crystal is susceptible to minute deformations due to thermal effects, stress loading, and material inhomogeneity during microscale manipulation. On the other hand, the dynamic response of the robotic arm, the accuracy of sensor feedback, and changes in contact state during execution also have a cumulative impact on the final alignment result. These errors often exhibit multi-source, nonlinear, and time-lag characteristics, making alignment deviations difficult to control effectively through single-dimensional detection and correction methods.

[0004] Existing automatic alignment and integrated control methods typically focus on real-time correction of geometric position deviations, paying insufficient attention to implicit errors such as spectral changes, temperature rise effects, and contact stress changes that occur during the integration process. This makes it difficult to achieve comprehensive perception and prediction of microscale deformation and its evolution trends. At the same time, the lack of a unified modeling and collaborative analysis mechanism for the coupling effects between different error sources leads to compensation control strategies often exhibiting phased and localized adjustments, making it difficult to suppress the propagation and amplification of errors at the system level.

[0005] Furthermore, in high-precision integrated scenarios, alignment control not only relies on feedback correction of instantaneous errors but also requires forward-looking prediction of potential hysteresis deformation and process parameter drift. However, due to factors such as equipment differences, crystal specification diversity, and limited data sharing, the adaptability and stability of relevant prediction models in different application scenarios are still insufficient, making it difficult to balance the needs of accuracy improvement and system generalization.

[0006] Therefore, how to achieve high-precision perception of multi-dimensional micro-errors, effective modeling and prediction of error coupling relationships, and on this basis, complete collaborative compensation and control across execution links during the automatic alignment and integration of optical crystal arrays has become a key technical problem restricting the high-quality manufacturing and application performance improvement of optical crystal arrays. Summary of the Invention

[0007] To address the shortcomings of existing technologies, the present invention aims to provide an automatic alignment and integration control system and method for optical crystal arrays, which solves the problems of insufficient perception of implicit errors, difficulty in modeling error coupling relationships, and localization and lag of compensation strategies in existing technologies, and ultimately achieves adaptive and highly stable integration of optical crystal arrays with nanometer-level precision.

[0008] To achieve the above objectives, the present invention provides the following technical solution:

[0009] An automatic alignment and integrated control system for an optical crystal array includes:

[0010] A quantum entanglement-enhanced ultra-precision sensing array is used to collect multi-dimensional error data in real time during the integration process of optical crystals and robotic arms. The multi-dimensional error data includes sub-nanometer displacement data, Raman spectral deformation data, infrared temperature rise data, and frictional stress data.

[0011] The multi-factor coupling error prediction module is connected to the quantum entanglement-enhanced ultra-precision sensing array. It is used to receive the multi-dimensional error data and mine the coupling characteristics between different error sources based on the attention mechanism, thereby predicting and outputting instantaneous alignment error and hysteretic deformation error.

[0012] A full-link adaptive compensation execution module, connected to the multi-factor coupled error prediction module, includes:

[0013] The quantum traceability calibration unit is used to calibrate the feedback signal of the built-in displacement sensor of the robot arm in real time based on the sub-nanometer displacement data.

[0014] The robot dynamic compensation unit is used to perform composite control compensation on the robot based on the instantaneous alignment error and the hysteretic deformation error.

[0015] A process parameter coordination adjustment unit is used to dynamically adjust the process parameters of multiple process execution devices based on the hysteresis deformation error and the infrared temperature rise data.

[0016] The central control module is connected to the quantum entanglement-enhanced ultra-precise sensing array, the multi-factor coupling error prediction module, and the full-link adaptive compensation execution module, respectively, and is used to generate synchronization trigger signals, schedule the execution timing of the above modules, and monitor the system status.

[0017] The federated learning collaborative optimization module, connected to the multi-factor coupled error prediction module, is used to aggregate and update the prediction model parameters in the multi-factor coupled error prediction module across multiple local devices through federated learning.

[0018] Furthermore, the quantum entanglement-enhanced ultra-precision sensing array includes:

[0019] A quantum entanglement sensing unit is used to generate entangled photon pairs and divide them into a probe light and a reference light. The probe light is irradiated to the interface between the optical crystal and the robotic arm through an anti-interference encapsulation optical path. The reference light is transmitted in a vacuum reference optical path, and the phase difference between the probe light and the reference light is detected to obtain the sub-nanometer displacement data.

[0020] The Raman spectroscopy sensing unit is used to acquire the Raman spectral signal of the region of the optical crystal irradiated by laser, and to obtain the Raman spectral deformation data of the crystal molecular structure by analyzing the spectral shift.

[0021] An infrared thermal imaging unit is used to acquire the infrared temperature rise data of the surface of the optical crystal array and the contact interface of the robotic arm in real time.

[0022] A dynamic friction sensing unit is built into the flexible contact surface of the end effector of the robot, and is used to collect the friction stress data in real time during the relative motion between the robot and the optical crystal.

[0023] The quantum entanglement-enhanced ultra-precision sensing array controls each sensing unit to perform synchronous data acquisition through a synchronous trigger signal issued by the central control module, and sends the output multi-dimensional error data to the multi-factor coupling error prediction module.

[0024] Furthermore, the multi-factor coupled error prediction module includes:

[0025] The data preprocessing unit is used to perform spatiotemporal synchronization calibration, outlier removal and normalization processing on the multi-dimensional error data to form a standardized feature tensor.

[0026] The coupled feature mining unit, connected to the data preprocessing unit, is used to process the feature tensor using a Transformer encoder based on a multi-head attention mechanism. Different attention heads are configured to focus on the first coupling relationship between quantum tracing error and frictional stress, the second coupling relationship between temperature change and manipulator hysteresis characteristics, and the third coupling relationship between temperature change and adhesive curing parameters, respectively, in order to mine hidden correlation features between error sources.

[0027] The error prediction unit, connected to the coupled feature mining unit, is used to use a Transformer decoder to output the first branch prediction result and the second branch prediction result in parallel based on the mined hidden correlation features.

[0028] The first branch prediction result is the instantaneous alignment error, which includes the displacement deviation and attitude angle deviation of the optical crystal in the X, Y, and Z directions; the second branch prediction result is the hysteretic deformation error, which is used to characterize the stress deformation of the optical crystal caused by the adhesive curing process.

[0029] Furthermore, the quantum traceability calibration unit is specifically used for:

[0030] The sub-nanometer displacement data output in real time by the quantum entanglement-enhanced ultra-precision sensing array is received as a reference displacement value;

[0031] Obtain the measured displacement value fed back in real time by the displacement sensor built into the robotic arm;

[0032] Calculate the difference between the reference displacement value and the measured displacement value to generate a displacement error signal;

[0033] Based on the displacement error signal, the control parameters of the manipulator's drive system are compensated in real time to suppress the tracking error caused by the hysteresis nonlinearity of the manipulator's drive system.

[0034] The displacement control commands executed by the drive system of the robotic arm are generated by the central control module based on the real-time alignment error prediction results output by the multi-factor coupling error prediction module.

[0035] Furthermore, the robotic arm dynamic compensation unit is specifically used to execute a composite control strategy, which includes:

[0036] Variable gain control is used to dynamically adjust the control gain of the proportional-integral-derivative controller according to the real-time alignment error, so as to improve the error convergence speed.

[0037] Friction feedforward compensation is used to output a compensation voltage to the drive system of the manipulator in advance based on the dynamic error component caused by frictional stress predicted by the multi-factor coupled error prediction module, so as to counteract the frictional influence during the movement process.

[0038] Pre-alignment offset compensation is used to control the robot to apply a preset offset in the opposite direction to the predicted deformation direction during the alignment stage based on the hysteresis deformation error output by the multi-factor coupling error prediction module, so as to compensate for the curing deformation that will be generated in the subsequent process.

[0039] The robotic arm dynamic compensation unit generates composite control commands by integrating the outputs of the variable gain control, the friction feedforward compensation, and the pre-alignment offset compensation.

[0040] Furthermore, the process execution equipment includes a laser sensing device, a dispensing device, and an adhesive curing control device;

[0041] The process parameter coordinated adjustment unit is specifically used for:

[0042] Based on the infrared temperature rise data, the output power of the laser sensing device is dynamically adjusted to suppress local deformation caused by photothermal effect;

[0043] Based on the predicted results of the hysteresis deformation error, the control parameters of the adhesive curing control equipment during the adhesive curing process are dynamically adjusted, and the control parameters include at least curing temperature and curing time.

[0044] Based on the predicted results of the hysteresis deformation error, the process parameters of the dispensing equipment are adjusted synchronously to minimize the stress introduced by the adhesive while ensuring the bonding quality. The process parameters include at least the dispensing amount and the dispensing rate.

[0045] Furthermore, the central control module is also used to perform dynamic priority scheduling, which specifically includes:

[0046] Monitor adjustment instructions generated in parallel by at least two units in the end-to-end adaptive compensation execution module;

[0047] The real-time impact of each adjustment command on the error to be adjusted is evaluated according to preset rules, and the execution priority is dynamically allocated accordingly.

[0048] Based on the execution priority, the order of adjustment instructions with resource or timing conflicts is coordinated, wherein the weight of the impact on the alignment accuracy of the optical crystal is set to be higher than the weight of the impact on the adjustment of integrated process parameters.

[0049] The central control module ensures that adjustments to core accuracy indicators are executed with priority through dynamic priority scheduling.

[0050] Furthermore, the federated learning collaborative optimization module is specifically used to construct a distributed training architecture, which includes:

[0051] The federated server is used to perform the aggregation and distribution of the global model;

[0052] Multiple local nodes, corresponding to each production device, and each local node deploys a local instance of the multi-factor coupled error prediction module;

[0053] The local node is used to: train the local instance locally using error-process sample data it collects, and generate model parameter gradients; and upload the model parameter gradients with added differential privacy noise to the federated server.

[0054] The federated server is configured to: receive parameter gradients from each of the local nodes; perform weighted aggregation of the parameter gradients based on a federated averaging algorithm to update the global model parameters; and distribute the updated global model parameters to each of the local nodes.

[0055] Each of the local nodes is also used to: update the local instance based on the received global model parameters, and perform personalized fine-tuning in conjunction with local specific optical crystal array specification data.

[0056] Furthermore, the differential privacy processing performed by the local node before uploading the model parameter gradients specifically includes:

[0057] Random noise conforming to a predetermined statistical distribution is added to the gradient of the model parameters;

[0058] The amplitude of the added random noise is configured to ensure the privacy and security of local sample data without destroying the effective information carried by the parameter gradient for global model optimization.

[0059] An automatic alignment and integration control method for an optical crystal array, applied to the automatic alignment and integration control system of the optical crystal array as described above, includes:

[0060] Step S1: The quantum entanglement-enhanced ultra-precision sensing array collects multi-dimensional error data in real time during the integration process of the optical crystal and the robotic arm. The multi-dimensional error data includes sub-nanometer displacement data, Raman spectral deformation data, infrared temperature rise data, and frictional stress data.

[0061] Step S2: The multi-factor coupling error prediction module receives the multi-dimensional error data and mines the coupling characteristics between different error sources based on the attention mechanism, thereby predicting and outputting instantaneous alignment error and hysteretic deformation error.

[0062] Step S3: The quantum traceability calibration unit uses the sub-nanometer displacement data as a reference to calibrate the feedback signal of the robot's built-in displacement sensor in real time; the robot dynamic compensation unit performs composite control compensation on the robot based on the instantaneous alignment error and the hysteretic deformation error; the process parameter collaborative adjustment unit dynamically adjusts the process parameters of multiple process execution devices based on the hysteretic deformation error and the infrared temperature rise data.

[0063] Step S4: The central control module generates a synchronization trigger signal, schedules the execution timing of the above modules, and monitors the system status.

[0064] Step S5: The federated learning collaborative optimization module aggregates and updates the prediction model parameters in the multi-factor coupled error prediction module across multiple local devices using a federated learning approach.

[0065] The beneficial effects of this invention are:

[0066] (i) Achieve multi-dimensional and ultra-precise alignment error perception, significantly improving the upper limit of integration accuracy;

[0067] By constructing a quantum entanglement-enhanced ultra-precision sensing array, the multi-dimensional states such as displacement, spectrum, temperature and contact stress of optical crystals and robotic arms during the integration process can be collected synchronously. This not only acquires traditional geometric position error information, but also reflects the changes in the internal and interface states of the crystal material in real time, thereby achieving high sensitivity to sub-nanometer-level tiny errors. This effectively breaks through the limitations of traditional sensing methods in terms of accuracy and dimensionality, and significantly improves the overall alignment accuracy and consistency of the optical crystal array.

[0068] (ii) Reveal the coupling relationship between multiple source errors and suppress error accumulation and amplification;

[0069] By using a multi-factor coupled error prediction module to jointly analyze multi-dimensional error data, the coupling characteristics between different error sources are explored, and the instantaneous alignment error and hysteretic deformation error are output simultaneously. This enables the system to understand the generation mechanism and evolution trend of errors from a global perspective, avoiding passive correction only for a single error source. This effectively suppresses the transmission, superposition and amplification of errors during polycrystalline integration, and improves the stability and controllability of the array integration process.

[0070] (iii) Achieve end-to-end adaptive compensation to enhance the dynamic response capability of positioning control;

[0071] The quantum traceability calibration unit calibrates the feedback signals of the built-in sensors of the robot in real time. Combined with the robot's dynamic compensation unit, it performs composite control compensation for instantaneous and hysteresis errors, so that the robot can maintain high-precision feedback and execution consistency throughout the entire integration process. This significantly reduces the impact of factors such as sensor drift and execution lag on the alignment results, and improves the system's dynamic response capability and alignment reliability under complex working conditions.

[0072] (iv) Achieve coordinated control of process parameters and structural deformation to reduce the risk of performance degradation after integration;

[0073] By using a process parameter collaborative adjustment unit, hysteresis deformation error and infrared temperature rise data are introduced into the process control decision-making process. The process parameters of multiple process execution devices are dynamically adjusted, transforming structural deformation control from post-process correction to in-process collaborative regulation. This effectively reduces residual stress and thermal deformation risks generated during the integration and curing stage, thereby improving the long-term stability and optical performance consistency of the optical crystal array from the source.

[0074] (v) Improve system-level collaboration efficiency and ensure timing consistency in the high-precision integration process;

[0075] The central control module performs unified scheduling and status monitoring of each functional module, generates synchronous trigger signals, and coordinates the execution timing of each module. This avoids timing misalignment and control conflicts caused by the independent operation of multiple modules, ensuring the synergy and real-time performance of the perception, prediction, and compensation processes at the system level, and improving overall integration efficiency and control accuracy.

[0076] (vi) Enhance the generalization ability of error prediction models to adapt to application scenarios with multiple devices and specifications;

[0077] By introducing a federated learning collaborative optimization mechanism, the error prediction model parameters are aggregated and updated across multiple local devices. Without relying on centralized data sharing, the distributed data characteristics of different devices and optical crystal arrays of different specifications are fully utilized, which effectively improves the generalization ability and prediction accuracy of the prediction model across devices and process scenarios, and enhances the engineering applicability and scalability of the system. Attached Figure Description

[0078] Figure 1 This is a schematic diagram of the automatic alignment and integrated control structure of the optical crystal array in this invention;

[0079] Figure 2 This is a flowchart of the automatic alignment and integrated control method for optical crystal arrays in this invention.

[0080] Figure reference numerals: 1. Quantum entanglement enhanced ultra-precision sensing array; 11. Quantum entanglement sensing unit; 12. Raman spectroscopy sensing unit; 13. Infrared thermal imaging unit; 14. Dynamic friction sensing unit; 2. Multi-factor coupling error prediction module; 21. Data preprocessing unit; 22. Coupling feature mining unit; 23. Error prediction unit; 3. End-to-end adaptive compensation execution module; 31. Quantum traceability calibration unit; 32. Robotic arm dynamic compensation unit; 33. Process parameter collaborative adjustment unit; 4. Central control module; 5. Federated learning collaborative optimization module. Detailed Implementation

[0081] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Identical components are denoted by the same reference numerals. It should be noted that the terms "front," "rear," "left," "right," "upper," and "lower" used in the following description refer to directions in the accompanying drawings, and the terms "bottom surface," "top surface," "inner," and "outer" refer to directions toward or away from the geometric center of a specific component, respectively.

[0082] Example 1, refer to Figure 1 This is the first embodiment of the present invention, which provides an automatic alignment and integrated control system for an optical crystal array, comprising:

[0083] The system consists of a quantum entanglement-enhanced ultra-precision sensing array 1, a multi-factor coupling error prediction module 2, a full-link adaptive compensation execution module 3, a central control module 4, and a federated learning collaborative optimization module 5. All modules work collaboratively under the unified scheduling of the central control module 4.

[0084] I. System Overall Structure;

[0085] (a) Quantum entanglement-enhanced ultra-precision sensing array 1;

[0086] The quantum entanglement-enhanced ultra-precision sensing array 1 is used to collect multi-dimensional error data between the optical crystal and the robotic arm in real time during the integration of optical crystal arrays. The multi-dimensional error data includes at least:

[0087] Sub-nanometer displacement data, Raman spectral deformation data, infrared temperature rise data, and frictional stress data.

[0088] In this embodiment, the quantum entanglement-enhanced ultra-precision sensing array 1 is specifically constructed and integrated by the following hardware:

[0089] Quantum entangled photon sensing unit: Utilizing a miniature pulsed quantum entangled photon source with a center wavelength of 780 nm, the entangled photon pair generation efficiency is no less than 1×10⁻⁶. 6 Pairs / second. The photon source is split 1:1 by a single-mode fiber beam splitter to construct the interferometric measurement channel and the reference channel, respectively.

[0090] By leveraging the phase-sensitive properties of quantum entangled photons during interference, ultra-precise measurement of the relative displacement between the end effector of a robotic arm and an optical crystal can be achieved, thereby obtaining sub-nanometer displacement data.

[0091] Displacement measurement and optical path stabilization structure: The optical path employs a phase-shifting interferometer, achieving a measurement accuracy of 0.01 nanometers. To ensure measurement stability, the entire optical path is encapsulated in an aluminum-magnesium alloy shell. An electromagnetic shielding layer is installed inside the shell, with a shielding effectiveness of no less than 80 dB in the 1–10 GHz frequency band. Rubber vibration damping pads are also incorporated, with damping frequencies covering 5–200 Hz, to reduce the impact of environmental vibrations and electromagnetic interference on the quantum measurement results.

[0092] Raman spectroscopy sensing unit 12: A miniature confocal Raman spectrometer with a spectral resolution not exceeding 1 cm⁻¹ is selected. -1 It is used to acquire Raman spectral changes of optical crystals during alignment and curing in real time. By observing Raman peak position shifts and peak shape changes, it reflects the internal stress distribution and microscopic deformation state of the crystal, thereby obtaining Raman spectral deformation data.

[0093] Infrared thermal imaging unit 13: An uncooled focal plane infrared thermal imager with a resolution of 320×256 pixels and a temperature measurement accuracy of ±0.1℃ is used to acquire the real-time temperature distribution of the optical crystal and its surrounding process area, forming infrared temperature rise data to reflect the influence of thermal effects on crystal deformation and alignment accuracy.

[0094] Dynamic friction sensing unit 14: A miniature piezoelectric sensor is integrated inside the flexible contact pad at the end of the robot arm. Its range is 0 to 5 N and its resolution is 0.01 mN. It is used to detect the dynamic friction force changes generated at the interface between the robot arm and the optical crystal and output friction stress data.

[0095] Through the coordinated operation of the aforementioned multiple sensing units, the quantum entanglement-enhanced ultra-precision sensing array 1 can acquire multi-source, multi-dimensional, and high-precision error information on the same time scale, providing a data foundation for subsequent error prediction and compensation.

[0096] II. Multi-factor Coupled Error Prediction Module 2;

[0097] The multi-factor coupling error prediction module 2 is connected to the quantum entanglement enhanced ultra-precision sensing array 1 to receive the above-mentioned multi-dimensional error data, and to mine the coupling characteristics between different error sources based on the attention mechanism, predicting the output: instantaneous alignment error and hysteresis deformation error.

[0098] In this embodiment, the multi-factor coupling error prediction module 2 is implemented based on the Transformer model enhanced by the attention mechanism. Its input is multi-dimensional time series error data, including sub-nanometer displacement data, Raman spectral deformation data, infrared temperature rise data and friction stress data.

[0099] The model uses a self-attention mechanism to weight the correlation of different error sources in the time and feature dimensions, enabling the model to focus on the error factors that have the greatest impact on the current alignment accuracy, thereby achieving high-precision prediction of real-time alignment errors. At the same time, by encoding and decoding historical data sequences, the model can predict hysteretic deformation errors caused by factors such as material solidification and thermal diffusion.

[0100] During model training, the root mean square error (RMSE) of the prediction error is used as the loss function. When the RMSE on the validation set converges and is less than or equal to 0.02 nm, the model is considered to meet the accuracy requirements for engineering applications. This threshold serves as a criterion for judging the effectiveness of the model during system operation, ensuring the reliability of the error prediction results.

[0101] III. End-to-end adaptive compensation execution module 3;

[0102] The end-to-end adaptive compensation execution module 3 is connected to the multi-factor coupled error prediction module 2, and is used to complete adaptive compensation across sensing, execution, and process stages based on the predicted instantaneous alignment error and hysteresis deformation error. This module includes the following units:

[0103] (a) Quantum traceability calibration unit 31;

[0104] The quantum tracing calibration unit 31 uses sub-nanometer displacement data as a benchmark to calibrate the feedback signal of the robot's built-in displacement sensor in real time. By using the quantum entanglement measurement results as a highly reliable reference source, it corrects the systematic deviations caused by factors such as temperature drift and aging of the robot's internal sensors, thereby improving the absolute accuracy of the robot's displacement feedback signal.

[0105] (ii) Dynamic compensation unit 32 for robotic arm;

[0106] The robotic arm is a 6-DOF micro-precision robotic arm with a positioning accuracy of ±0.1 nanometers and a repeatability of ±0.05 nanometers. It adopts a servo motor and piezoelectric ceramic composite drive structure.

[0107] The robot dynamic compensation unit 32 generates composite control commands based on the instantaneous alignment error and the hysteresis deformation error, and adjusts the robot's pose, motion trajectory and micro displacement in real time, so that the robot can dynamically cancel the prediction error during the alignment process, thereby achieving high-precision automatic alignment.

[0108] (iii) Process parameter coordinated adjustment unit 33;

[0109] The process parameter coordination adjustment unit 33 is used to dynamically adjust the process parameters of multiple process execution devices based on hysteresis deformation error and infrared temperature rise data. In this embodiment, the process execution devices include a high-precision multi-dispensing machine and a constant temperature curing chamber: the dispensing accuracy of the dispensing machine is ±1nL; the temperature control accuracy of the constant temperature curing chamber is ±0.5℃.

[0110] When it is predicted that the hysteresis deformation error that may exceed the threshold may occur during the curing stage, the process parameter coordination adjustment unit 33 automatically adjusts the dispensing amount, curing temperature and curing time to make the stress distribution and thermal field distribution during the crystal curing process more uniform, thereby reducing the risk of final deformation.

[0111] IV. Central Control Module 4;

[0112] The central control module 4 is connected to the quantum entanglement-enhanced ultra-precision sensing array 1, the multi-factor coupling error prediction module 2, and the full-link adaptive compensation execution module 3, respectively, and is used to generate synchronous trigger signals, schedule the execution timing of each module, and monitor the system operation status.

[0113] In this embodiment, the central control module 4 adopts an industrial-grade embedded controller with an Intel Core i7-12700E CPU, 32GB of memory, and runs the real-time operating system VxWorks.

[0114] Each sensing unit is connected to the central control module 4 via a PCIe 4.0 interface. The robotic arm, dispensing machine, and constant temperature curing chamber communicate with the central control module 4 via an EtherCAT bus to achieve high-speed, low-latency data transmission and command interaction.

[0115] V. Federated Learning Collaborative Optimization Module 5;

[0116] The federated learning collaborative optimization module 5 is connected to the multi-factor coupled error prediction module 2, and is used to aggregate and update prediction model parameters across multiple local devices through federated learning.

[0117] In this embodiment, the federated learning server is connected to the central control module 4 of each production device via Ethernet. Each local node uploads only the model parameter gradients without uploading the original production data. The server uses a federated averaging algorithm to aggregate the parameters and iteratively update the global model. After at least 100 rounds of federated training, the global model achieves a prediction accuracy of 98% or higher on each node, significantly improving the model's generalization ability across different devices and optical crystal arrays of varying specifications.

[0118] VI. Working principle and technical effects of Example 1;

[0119] During system operation, the quantum entanglement-enhanced ultra-precision sensing array 1 first synchronously senses the multi-dimensional errors in the optical crystal array integration process; then, the multi-factor coupling error prediction module 2 analyzes the error coupling relationship based on the attention mechanism model to predict the instantaneous alignment error and the hysteresis deformation error; the central control module 4 coordinates the full-link adaptive compensation execution module 3 accordingly to coordinate the robot's motion and process parameters; at the same time, the system operation data is continuously used for online fine-tuning of the model, and cross-device model optimization is achieved through the federated learning mechanism.

[0120] Based on the above working principle, this embodiment can achieve sub-nanometer-level alignment accuracy control, effectively suppressing error coupling and accumulation. Performance verification results show that, under the same experimental conditions, the alignment accuracy of the system in this embodiment reaches ±0.45 nanometers, the integration yield is 96.2%, and the single-unit integration time is shortened to 8 minutes. Compared with the traditional system, it achieves significant technical improvements in accuracy, yield, and efficiency.

[0121] Example 2 is the second embodiment of the present invention. Based on the overall system structure of Example 1, the internal structure and working mode of the quantum entanglement enhanced ultra-precision sensing array 1 and the multi-factor coupling error prediction module 2 are further described in detail.

[0122] I. Structure and working principle of quantum entanglement-enhanced ultra-precision sensing array 1;

[0123] In this embodiment, the quantum entanglement-enhanced ultra-precision sensing array 1 includes:

[0124] The quantum entanglement sensing unit 11, the Raman spectroscopy sensing unit 12, the infrared thermal imaging unit 13, and the dynamic friction sensing unit 14 work together under the control of the synchronous trigger signal issued by the central control module 4.

[0125] (a) Quantum entanglement sensing unit 11;

[0126] The quantum entanglement sensing unit 11 includes a miniature quantum entangled photon source, an optical fiber beam splitter, an anti-interference encapsulated optical path, a vacuum reference optical path, and a high-precision interferometer.

[0127] A miniature quantum entangled photon source is used to generate entangled photon pairs, which are then split into probe light and reference light by an optical fiber beam splitter.

[0128] The probe light is irradiated onto the surface of the optical crystal array and the contact interface of the robot end effector through the anti-interference packaged optical path; the anti-interference packaged optical path is equipped with an electromagnetic shielding layer and a vibration damping buffer structure to suppress the influence of environmental electromagnetic interference and mechanical vibration on the phase measurement results.

[0129] The reference light is transmitted in a vacuum reference light path to isolate phase noise caused by air disturbances and temperature fluctuations.

[0130] A high-precision interferometer is used to detect the phase difference between the probe light and the reference light in real time. By utilizing the high coherence characteristics of quantum entangled photon pairs, the phase measurement accuracy breaks through the diffraction limit of traditional interferometry, enabling sub-nanometer-level detection of surface undulations of optical crystals, crystal alignment deviations, and robot displacement errors. In this embodiment, the displacement detection resolution is no less than 0.1 nanometers.

[0131] The displacement measurement results obtained in this way serve as sub-nanometer displacement data and as a benchmark data source for subsequent quantum traceability calibration and error prediction.

[0132] (ii) Raman spectroscopy sensing unit 12;

[0133] The Raman spectroscopy sensing unit 12 integrates a miniature Raman spectroscopy detector and is synchronously triggered with the quantum entanglement sensing unit 11 in time.

[0134] During the integration process, the Raman spectroscopy sensing unit 12 performs real-time Raman spectroscopy acquisition on the optical crystal within the laser irradiation area. By analyzing the frequency shift and peak width changes of the Raman characteristic peaks, it identifies changes in the crystal molecular structure caused by factors such as photothermal coupling and stress loading.

[0135] The Raman spectral offset is converted into quantitative Raman spectral deformation data to reflect the microscopic deformation state inside the crystal. This data is input into the multi-factor coupled error prediction module 2 as an important component of the multi-dimensional error data.

[0136] (iii) Infrared thermal imaging unit 13;

[0137] The infrared thermal imaging unit 13 uses a high-resolution miniature infrared thermal imager to collect temperature distribution information on the surface of the optical crystal array and the contact interface of the robotic arm in real time.

[0138] By continuously acquiring data on temperature field changes over time, the infrared thermal imaging unit 13 can capture local temperature rises generated by photothermal effects, frictional heat generation, and curing heat release processes, thereby generating infrared temperature rise data.

[0139] The infrared temperature rise data is used to characterize the effects of temperature changes on crystal deformation, manipulator hysteresis characteristics, and adhesive curing behavior, and is a key environmental variable in error coupling analysis.

[0140] (iv) Dynamic friction sensing unit 14;

[0141] The dynamic friction sensing unit 14 is built into the flexible contact surface of the end effector of the robot and uses a miniature piezoelectric sensor to collect data on the change of frictional stress in real time during the relative motion between the robot and the optical crystal.

[0142] Friction stress data, combined with crystal surface roughness parameters, is used to reflect changes in the contact interface state, providing fundamental data support for modeling friction error and its coupling relationship with displacement error.

[0143] (v) Synchronization triggering and data transmission mechanism;

[0144] The quantum entanglement-enhanced ultra-precision sensing array 1 achieves synchronous data acquisition of each sensing unit under the control of the synchronous trigger signal issued by the central control module 4.

[0145] The timing error of the synchronous trigger signal is controlled within microseconds, thereby ensuring the consistency of multi-source data in the time dimension.

[0146] The multi-dimensional error data collected by each sensing unit is transmitted to the multi-factor coupled error prediction module 2 via a high-speed interface for subsequent unified processing and modeling.

[0147] II. Structure and working principle of multi-factor coupled error prediction module 2;

[0148] The multi-factor coupling error prediction module 2 is used to perform full-link coupling analysis and prediction on multi-source error data. It includes: a data preprocessing unit 21, a coupling feature mining unit 22, and an error prediction unit 23.

[0149] (a) Data preprocessing unit 21;

[0150] The data preprocessing unit 21 receives multi-source data from the quantum entanglement-enhanced ultra-precision sensing array 1, including sub-nanometer displacement data, Raman spectral deformation data, infrared temperature rise data, and frictional stress data; at the same time, it also obtains the hysteresis characteristic parameters of the robot arm, the curing parameters of the adhesive, and the specification parameters of the optical crystal array through the device interface.

[0151] The data preprocessing unit 21 first performs spatiotemporal synchronization calibration on the multi-source data based on the synchronization trigger timestamp, then removes outliers, and uses a normalization method to map data of different dimensions to a unified numerical range, ultimately forming a standardized high-dimensional feature tensor.

[0152] (ii) Coupled Feature Mining Unit 22;

[0153] The coupled feature mining unit 22 uses a Transformer encoder based on a multi-head attention mechanism to process high-dimensional feature tensors.

[0154] The different attention heads are configured to focus on the following coupling relationships:

[0155] The first coupling relationship between quantum traceability error and frictional stress;

[0156] The second coupling relationship between temperature change and the hysteresis characteristics of the manipulator;

[0157] A third coupling relationship between temperature change and adhesive curing parameters.

[0158] Through the multi-head attention mechanism, the model can adaptively allocate weights in the feature dimension and time dimension, highlighting the error correlation factors that have the greatest impact on alignment accuracy, thereby uncovering the hidden correlation features between various error sources.

[0159] (iii) Error prediction unit 23;

[0160] Error prediction unit 23 adopts a Transformer decoder structure and outputs dual-branch prediction results in parallel based on the hidden correlation features output by coupled feature mining unit 22.

[0161] Among them, the prediction result of the first branch is the instantaneous alignment error, which is used to characterize the displacement deviation and attitude angle deviation of the optical crystal in the X, Y, and Z directions.

[0162] The second branch prediction result is the hysteresis deformation error, which is used to characterize the stress deformation generated during the adhesive curing process.

[0163] In this embodiment, when the root mean square error of the prediction model on the validation dataset is no greater than 0.02 nanometers, the prediction result is considered to meet the system control accuracy requirements. This threshold is used as the basis for judging the effectiveness of the model during system operation.

[0164] (iv) Dataset construction and training methods for multi-factor coupled error prediction models;

[0165] To ensure that the multi-factor coupling error prediction module 2 has stable and reliable prediction capabilities under different optical crystal specifications and complex working conditions, this embodiment systematically constructed and trained the prediction model with a dataset during the system development phase.

[0166] 1. Dataset construction;

[0167] In this embodiment, data acquisition is performed under a constant experimental environment, which includes:

[0168] The temperature control condition is 25±0.5℃;

[0169] The vibration reduction condition is that the vibration acceleration is not higher than 0.01g.

[0170] Under the above environmental conditions, various optical crystal arrays of different specifications were selected as experimental subjects, including but not limited to:

[0171] Lithium niobate (LiNbO3) optical crystal with dimensions of 1mm × 1mm × 0.5mm;

[0172] Bismuth germanium oxide (BGO) optical crystal with dimensions of 0.8mm × 0.8mm × 0.3mm.

[0173] Under different integrated operating conditions, the following multi-source data are collected through the quantum entanglement-enhanced ultra-precision sensing array 1 and system interface: quantum tracing error data (sub-nanometer displacement data); Raman spectroscopy data (crystal microstructure deformation information); infrared temperature rise data; dynamic frictional stress data; hysteresis characteristic parameters of the manipulator; adhesive curing parameters; actual measured alignment error data; and actual measured curing deformation error data.

[0174] By collecting data under different crystal specifications, different combinations of process parameters, and different operating conditions, a dataset containing over 100,000 samples was finally constructed. This dataset covers the main error distribution ranges in the automatic alignment and integration process of optical crystal arrays, providing a sufficient data foundation for the model to learn the multi-factor error coupling relationship.

[0175] 2. Predictive model training methods;

[0176] In this embodiment, the prediction model in the multi-factor coupling error prediction module 2 is implemented based on the Transformer network structure enhanced by the attention mechanism and trained using the PyTorch deep learning framework.

[0177] The dataset is divided into training, validation, and test sets in a 7:2:1 ratio.

[0178] During model training, the root mean square error (RMSE) between the predicted result and the actual measurement error is used as the loss function, and its mathematical expression is: ;

[0179] Where: N represents the number of samples; This represents the error value predicted by the model; This represents the actual measurement error value for the corresponding sample. This represents the root mean square error.

[0180] The loss function described above is used to simultaneously constrain the instantaneous alignment error prediction branch and the hysteresis deformation error prediction branch, so that the model as a whole takes into account both spatial alignment accuracy and time-lag deformation prediction accuracy.

[0181] The model training uses the Adam adaptive moment estimation optimization algorithm for parameter updates, with an initial learning rate set to 1×10⁻⁶. -4 And dynamically adjust according to the following rules during training:

[0182] The learning rate decays by 10% every 10 training epochs.

[0183] When the model's loss function converges on the validation set and the RMSE is no greater than 0.02 nm, the prediction model is considered to have reached the accuracy threshold required for system operation. This RMSE ≤ 0.02 nm serves as the accuracy judgment threshold for the multi-factor coupled error prediction module, used during system operation to determine whether the model's prediction results can be used to drive the subsequent end-to-end adaptive compensation execution module.

[0184] (v) Explanation of the predictive model structure formula and its technical function;

[0185] After data preprocessing is completed, the normalized feature tensor output by the data preprocessing unit It can be represented as:

[0186] ;

[0187] in, Indicates at time step The following is a multidimensional feature vector composed of sub-nanometer displacement data, Raman spectral deformation data, infrared temperature rise data, frictional stress data, magnetic hysteresis parameters, and curing parameters.

[0188] The coupled feature mining unit 22 encodes the feature tensor based on a multi-head attention mechanism. The calculation form of a single attention head is as follows: ;

[0189] Where Q, K, and V are the query vector, key vector, and value vector obtained by mapping the input feature tensor, respectively; is the dimension of the key vector.

[0190] By setting up multiple attention heads, different attention heads focus on modeling the coupling relationships between quantum tracing errors and frictional stress, temperature changes and manipulator hysteresis characteristics, and temperature changes and adhesive curing parameters in the feature space, thereby forming a hidden feature representation that reflects the inherent correlation of multi-source errors.

[0191] Based on the aforementioned hidden features, the error prediction unit 23 outputs the prediction result through the Transformer decoder, and its output can be expressed as: ;

[0192] in: The real-time alignment error prediction results include the displacement deviation and attitude angle deviation of the optical crystal in the X, Y, and Z directions;

[0193] The results of the hysteresis deformation error prediction are used to characterize the stress deformation generated during the curing process of the adhesive.

[0194] (vi) The role of model formulas in solving technical problems;

[0195] Through the above model design, the multi-factor coupled error prediction module 2 can simultaneously learn the nonlinear coupling relationship between multi-source error data within a unified network framework, and realize parallel prediction of instantaneous alignment error and hysteretic deformation error.

[0196] Among them, the multi-head attention mechanism enables the model to automatically identify the error factors that have the greatest impact on the current integration accuracy, avoiding the problem that traditional empirical models cannot characterize complex error coupling; the introduction of the RMSE threshold ensures that the model prediction accuracy meets the sub-nanometer alignment control requirements, thereby providing a reliable error input basis for the end-to-end adaptive compensation execution module 3.

[0197] Therefore, the multi-factor coupling error prediction module 2 in this embodiment effectively solves the problems of difficulty in modeling multi-source errors and difficulty in predicting hysteresis deformation in advance during the automatic alignment and integration of optical crystal arrays, providing core technical support for the system to achieve high-precision and high-stability automatic integrated control.

[0198] III. Technical Effects of Embodiment 2;

[0199] Through the structure and operation of Embodiment 2, the system of the present invention can realize the synchronous perception and coupled modeling of multi-dimensional errors during the alignment and integration process. It can not only accurately obtain the real-time alignment error, but also predict the hysteresis deformation error that may occur in the curing stage in advance.

[0200] Compared to control methods that rely solely on a single sensor or static model, this embodiment improves the accuracy of error tracing by using a quantum entanglement-enhanced ultra-precision sensing array 1 and enhances the accuracy and foresight of error prediction by using a multi-factor coupled error prediction module 2. This provides a reliable basis for subsequent end-to-end adaptive compensation, ultimately significantly improving the accuracy, stability, and product consistency of the automatic alignment and integration process of the optical crystal array.

[0201] Example 3 is the third embodiment of the present invention. Unlike the previous embodiment, this embodiment further describes in detail the structure, control logic and collaborative compensation principle of the full-link adaptive compensation execution module 3 based on Example 1 and Example 2.

[0202] I. System composition of Example 3;

[0203] Based on the output of the multi-factor coupled error prediction module 2, the end-to-end adaptive compensation execution module 3 constructs a closed-loop collaborative compensation mechanism of "quantum traceability calibration - robot dynamic compensation - process parameter collaborative adjustment" to suppress and proactively offset errors in real time throughout the alignment and integration process of the optical crystal array.

[0204] This module includes: a quantum traceability calibration unit 31; a robotic arm dynamic compensation unit 32; and a process parameter coordinated adjustment unit 33; and operates in coordination under the unified scheduling of the central control module 4.

[0205] II. Structure and working principle of quantum traceability calibration unit 31;

[0206] The quantum traceability calibration unit 31 is used to perform high-precision real-time calibration of the robot arm's displacement feedback signal. Its specific working process includes the following steps:

[0207] Reference displacement acquisition: The quantum traceability calibration unit 31 receives sub-nanometer displacement data output in real time by the quantum entanglement enhanced ultra-precision sensing array 1, and uses this data as the reference displacement value.

[0208] The reference displacement value is based on the highly coherent measurement results of the quantum entanglement sensing unit 11, and its displacement resolution accuracy is not less than 0.1 nanometers.

[0209] Displacement Measurement Acquisition: Simultaneously, the quantum traceability calibration unit 31 acquires the measured displacement value fed back in real time by the displacement sensor built into the robot arm. This measured displacement value originates from the closed-loop feedback within the robot arm's drive system and is used to characterize the robot arm's current actual displacement.

[0210] Displacement error calculation: The quantum traceability calibration unit 31 calculates the difference between the reference displacement value and the measured displacement value, generating a displacement error signal. This displacement error signal reflects the tracking error caused by factors such as the nonlinear characteristics of the manipulator's drive system, temperature drift, or load changes.

[0211] Real-time compensation of drive parameters: Based on the displacement error signal, the quantum traceability calibration unit 31 performs real-time compensation on the control parameters of the robot's drive system, corrects the hysteresis nonlinearity effect in the servo motor and piezoelectric ceramic composite drive structure, and ensures that the actual displacement of the robot is highly consistent with the control command.

[0212] The displacement control command executed by the drive system of the robotic arm is generated by the central control module 4. The displacement control command is determined based on the real-time alignment error prediction result output by the multi-factor coupling error prediction module 2, thereby achieving a high consistency mapping between the prediction result and the actual execution.

[0213] Technical effect: By introducing the quantum traceability calibration unit 31, this embodiment can significantly suppress the displacement tracking error caused by the hysteresis nonlinearity of the manipulator's drive system, providing a reliable displacement reference for subsequent high-precision dynamic compensation, and ensuring the stability and repeatability of the alignment control.

[0214] III. Structure and Composite Control Principle of Dynamic Compensation Unit 32 for Robotic Arm;

[0215] The robot dynamic compensation unit 32 is used to suppress dynamic errors in the robot's execution process in real time. It adopts a composite control strategy, which includes: variable gain control; friction feedforward compensation; and pre-alignment offset compensation.

[0216] The robot dynamic compensation unit 32 integrates the outputs of the above three compensation strategies to generate a composite control command, and sends the composite control command to the robot's drive system.

[0217] (a) Variable gain control principle;

[0218] Variable gain control is used to dynamically adjust the control gain of the proportional-integral-derivative controller based on the real-time alignment error.

[0219] When the instantaneous alignment error is large, increase the proportional and derivative gains to accelerate the error convergence speed; when the error gradually decreases, decrease the proportional gain and enhance the integral action to reduce overshoot and steady-state error.

[0220] This variable gain control mechanism enables the robot to maintain good dynamic response characteristics during both the initial alignment stage and the fine adjustment stage.

[0221] (ii) Friction feedforward compensation principle;

[0222] Friction feedforward compensation is based on the dynamic error components caused by frictional stress predicted by the multi-factor coupled error prediction module 2. Before the actual movement of the robot, it outputs a compensation voltage to the drive system of the robot in advance.

[0223] This compensation voltage is used to counteract the dynamic friction generated during the contact between the end effector of the robot and the optical crystal, thereby avoiding the adverse effects of sudden changes in frictional force on alignment accuracy.

[0224] (III) Principle of pre-alignment offset compensation;

[0225] The pre-alignment offset compensation is based on the hysteresis deformation error prediction result output by the multi-factor coupled error prediction module 2. During the alignment stage, the robot arm is controlled to apply a preset offset in the opposite direction to the predicted deformation direction.

[0226] The magnitude and direction of this preset offset are determined by the predicted curing stress deformation error and are used to offset the structural deformation that will occur during subsequent curing processes.

[0227] Technical effect: By organically integrating variable gain control, friction feedforward compensation and pre-alignment offset compensation, this embodiment can simultaneously suppress instantaneous errors and compensate for hysteresis errors in the alignment stage, thereby significantly improving alignment accuracy and reducing the risk of residual deformation after integration.

[0228] IV. Working principle of process parameter collaborative adjustment unit 33;

[0229] The process parameter coordination adjustment unit 33 is used to dynamically adjust the process parameters of the integrated process execution equipment.

[0230] In this embodiment, the process execution equipment includes: a laser sensing device; a dispensing device; and an adhesive curing control device.

[0231] (a) Power adjustment of laser sensing equipment;

[0232] The process parameter coordination adjustment unit 33 dynamically adjusts the output power of the laser sensing device based on infrared temperature rise data.

[0233] When a local temperature rise is detected to exceed a preset threshold, the laser output power is reduced to suppress local crystal deformation caused by photothermal effect.

[0234] (ii) Adjustment of adhesive curing parameters;

[0235] Based on the prediction results of hysteresis deformation error, the process parameter coordination adjustment unit 33 dynamically adjusts the control parameters of the adhesive curing control equipment during the curing process. The control parameters include at least curing temperature and curing time.

[0236] The curing temperature is controlled with an accuracy of ±0.5℃. By optimizing the temperature and time curves, the risk of stress concentration and deformation accumulation during the curing process is reduced.

[0237] (iii) Adjustment of dispensing process parameters;

[0238] Meanwhile, the process parameter coordination adjustment unit 33 adjusts the dispensing quantity and dispensing rate of the dispensing equipment synchronously based on the prediction results of the hysteresis deformation error.

[0239] While ensuring bonding quality, the additional stress introduced by the adhesive is minimized by reducing excess adhesive usage and optimizing the dispensing process rhythm.

[0240] Technical effect: By coordinating the adjustment of laser power, dispensing parameters and curing parameters, this embodiment achieves active suppression of structural deformation at the process level, extending error control from simple mechanical compensation to the process control level, and significantly improving the structural stability and optical consistency of the integrated optical crystal array.

[0241] V. Overall technical effects of Example 3;

[0242] Through the full-link adaptive compensation execution mechanism in Example 3, the system of the present invention realizes multi-level collaborative control from quantum-level displacement tracing, mechanical execution compensation to dynamic adjustment of process parameters, effectively solving the problem that instantaneous error and hysteresis error are difficult to suppress simultaneously during the automatic alignment and integration of optical crystal arrays.

[0243] This embodiment significantly improves alignment accuracy, reduces the risk of curing deformation, and enhances the consistency and reliability of the integrated finished product, providing a complete and feasible technical solution for the large-scale automated manufacturing of high-precision optical crystal arrays.

[0244] I. Overall Design of Example 4;

[0245] Example 4 is the fourth embodiment of the present invention. This embodiment addresses engineering problems such as the parallel execution of multiple compensation actions, limited control resources, and the need for continuous cross-device optimization of the model during the automatic alignment and integration of optical crystal arrays. Based on the previous embodiments, it further introduces the following:

[0246] The dynamic priority scheduling mechanism of the central control module 4 is used to ensure core accuracy indicators when multiple types of compensation instructions are executed concurrently.

[0247] The distributed training and differential privacy mechanism of the Federated Learning Collaborative Optimization Module 5 is used to improve the generalization ability of error prediction models and protect local data privacy in multi-device scenarios.

[0248] Through the above mechanisms, the system can simultaneously possess real-time control stability, precision-priority assurance capabilities, and cross-device continuous evolution capabilities in complex production environments.

[0249] II. Dynamic priority scheduling mechanism of central control module 4;

[0250] (a) Hardware and basic functions of central control module 4;

[0251] In this embodiment, the central control module 4 adopts an industrial-grade embedded controller with a built-in real-time operating system, and is used as the core scheduling and coordination unit of the system.

[0252] The central control module 4 maintains real-time communication connections with the quantum entanglement-enhanced ultra-precise sensing array 1, the multi-factor coupling error prediction module 2, the full-link adaptive compensation execution module 3, and the federated learning collaborative optimization module 5.

[0253] The basic functions of the central control module 4 include: generating high-precision synchronous trigger signals; uniformly scheduling the execution sequence of each module; and real-time monitoring of the system's operating status and executing safety controls.

[0254] (ii) Triggering and monitoring mechanism for dynamic priority scheduling;

[0255] During system operation, the quantum traceability calibration unit 31, the robotic arm dynamic compensation unit 32, and the process parameter collaborative adjustment unit 33 in the full-link adaptive compensation execution module 3 may generate multiple adjustment commands in parallel.

[0256] The central control module 4 monitors the adjustment commands generated in parallel from at least two of the above units in real time, and obtains the target error type and error magnitude information corresponding to each adjustment command.

[0257] (III) Assessment of the impact of errors and priority allocation;

[0258] The central control module 4 evaluates the real-time impact of the error to be adjusted corresponding to each adjustment command according to preset rules.

[0259] The evaluation rules should consider at least the following factors: the degree of impact of the error on the alignment accuracy of the optical crystal; the degree of impact of the error on the stability of subsequent integration processes; and whether the error has the risk of irreversibility or amplification.

[0260] During the priority allocation process, the system explicitly sets:

[0261] The weight of the influence on the alignment accuracy of optical crystals is higher than the weight of the influence on the adjustment of integrated process parameters.

[0262] That is, when there is a resource or timing conflict between the alignment accuracy compensation command and the process parameter adjustment command, the compensation action related to sub-nanometer alignment accuracy is executed first.

[0263] (iv) Conflict coordination and command execution;

[0264] Based on the above priority allocation results, the central control module 4 coordinates adjustment instructions that have resource or timing conflicts, dynamically adjusts their execution order, and ensures that compensation actions related to core accuracy are completed first.

[0265] Through a dynamic priority scheduling mechanism, the central control module 4 avoids control oscillations or execution conflicts that may be caused by the disorderly superposition of multiple compensation actions.

[0266] (v) Status monitoring and anomaly handling;

[0267] The central control module 4 also continuously monitors the system's operating status, including the stability of the quantum entangled state, the integrity of sensor data, and the operating status of the robotic arm. When quantum state inaccuracy, errors exceeding preset thresholds, or abnormal equipment operation are detected, an alarm is immediately triggered and the system is put into a safe shutdown state to avoid damage to the optical crystal or equipment.

[0268] Technical effect: By introducing a dynamic priority scheduling mechanism, this embodiment can always prioritize the core alignment accuracy of the optical crystal array in complex control scenarios with multiple compensation actions in parallel, significantly improving the stability and reliability of system operation.

[0269] III. Structure and working principle of Federated Learning Collaborative Optimization Module 5;

[0270] (a) Construction of distributed training architecture;

[0271] The Federated Learning Collaborative Optimization Module 5 is used to build a distributed training architecture, which includes: a federated server; multiple local nodes, each corresponding to a different production device.

[0272] Each local node deploys a local instance of the multi-factor coupled error prediction module 2 and retains the error-process sample data collected during the production process.

[0273] (ii) Local model training and gradient generation;

[0274] During system operation, each local node uses the error-process sample data it collects to train the local instance and generate model parameter gradients.

[0275] The training process is completed locally, and the original sample data does not leave the local device, thus avoiding the risks of centralized storage and transmission of sensitive production data.

[0276] (iii) Differential privacy processing mechanism;

[0277] Before uploading the model parameter gradients, each local node performs differential privacy processing on the parameter gradients.

[0278] Specifically, random noise conforming to a predetermined statistical distribution is added to the gradient of the model parameters, and the amplitude of the added random noise is configured as follows:

[0279] While ensuring the privacy and security of local sample data, the effective information in the parameter gradient used for global model optimization is not destroyed.

[0280] This configuration ensures that the model can still converge effectively, while significantly reducing the risk of back-engineering the original data through gradients.

[0281] (iv) Global model aggregation and distribution;

[0282] The federated server receives parameter gradients from each local node after differential privacy processing, and performs weighted aggregation of parameter gradients based on the federated averaging algorithm to update the global model parameters.

[0283] The updated global model parameters are then distributed to each local node as the base model for a new round of local training and inference.

[0284] (v) Local model personalization and fine-tuning;

[0285] After receiving the updated global model parameters, each local node performs personalized fine-tuning based on its own specific optical crystal array specifications to form a dedicated prediction model adapted to the characteristics of the local device and the crystal specifications.

[0286] This process allows the model to retain its ability to adapt to local differences while sharing cross-device error patterns.

[0287] Technical effect: Through the federated learning collaborative optimization module 5, this embodiment achieves collaborative learning of error features among multiple devices without sharing the original data, which significantly improves the generalization ability of the multi-factor coupled error prediction module 2 in different devices and crystal specifications, while meeting the requirements of industrial scenarios for data privacy and security.

[0288] (vi) Federated learning initialization and global model iterative optimization process;

[0289] In this embodiment, to ensure that the multi-factor coupling error prediction module 2 has high prediction accuracy and stability before the system is put into actual operation, the federated learning collaborative optimization module 5 executes the federated learning initialization process before the formal operation phase.

[0290] 1. Basic model deployment and global model initialization;

[0291] During the system initialization phase, the basic prediction model, trained using centralized data, is first deployed to each local node.

[0292] The basic prediction model is a Transformer model enhanced with an attention mechanism, which has the basic ability to jointly model multi-source error data.

[0293] At the same time, the federated server initializes the global model parameters to ensure that the global model parameters are consistent with the parameters of the base prediction model, thereby providing a unified initial model state for subsequent distributed collaborative training.

[0294] 2. Local node model fine-tuning and gradient generation;

[0295] After the basic model deployment is completed, each local node uses sample data of niche-specification optical crystal arrays collected by its own production equipment during actual operation to fine-tune and train the locally deployed prediction model.

[0296] The sample data for niche-specification optical crystal arrays includes locally unique error-process data distribution characteristics to enhance the model's adaptability to local equipment and crystal specification differences.

[0297] After completing local fine-tuning training, each local node generates the corresponding model parameter gradient and uses this model parameter gradient as the upload object for federated learning.

[0298] 3. Parameter gradient upload and federated average aggregation;

[0299] Before uploading the model parameter gradient, each local node performs differential privacy processing on the parameter gradient, adding random noise that conforms to a predetermined statistical distribution to the parameter gradient, so as to retain the effective information in the gradient that plays a decisive role in the global model optimization while ensuring the privacy and security of local sample data.

[0300] The gradients of the model parameters after differential privacy processing are uploaded to the federated server.

[0301] After receiving parameter gradients from multiple local nodes, the federated server uses a federated averaging algorithm to weight and aggregate the parameter gradients, and then updates the global model parameters.

[0302] 4. Global model iterative update and convergence determination;

[0303] After completing a parameter aggregation and updating the global model parameters, the federated server distributes the updated global model parameters to each local node for the next round of local model training and inference.

[0304] The aforementioned federated learning process of "local fine-tuning - gradient uploading - global aggregation - parameter distribution" is repeated for no less than 100 rounds.

[0305] During the iteration process, the central control module 4 or the federated server evaluates the prediction performance of the global model on each local node. When the prediction accuracy of the global model on each local node reaches or exceeds 98%, it is determined that the global model meets the system operation requirements and the federated learning initialization phase ends.

[0306] Among them, a prediction accuracy of ≥98% is used as the model performance threshold in the federated learning initialization phase, which is clearly stated in the instruction manual to ensure the reliability of error prediction results during subsequent system operation.

[0307] Technical effect: By introducing the above-mentioned federated learning initialization and multi-round global model iterative optimization mechanism before the system is officially run, this embodiment can fully integrate the error feature information of different devices and optical crystal arrays of different specifications without centrally sharing the original production data, so that the multi-factor coupled error prediction module 2 has high prediction accuracy and generalization ability in the initial running stage.

[0308] This mechanism effectively avoids the prediction bias problem caused by model cold start, and provides stable and reliable error prediction input for the dynamic priority scheduling of the central control module 4 and the full-link adaptive compensation execution module 3, thereby further improving the overall accuracy consistency and engineering applicability of the automatic alignment and integrated control system for optical crystal arrays in multi-device and multi-process scenarios.

[0309] IV. Overall technical effects of Example 4;

[0310] Through the dynamic priority scheduling and federated learning collaborative optimization mechanism in Example 4, the system of the present invention achieves an organic combination of precision priority guarantee at the control execution level and continuous evolution optimization at the model level in complex production environments.

[0311] This embodiment effectively solves problems such as conflicting multiple compensation actions, insufficient cross-device adaptability of the model, and limited data privacy, and further improves the stability, accuracy consistency, and scalability of the automatic alignment and integrated control system for optical crystal arrays in industrial applications.

[0312] An automatic alignment and integrated control method for an optical crystal array is proposed, applied to the automatic alignment and integrated control system of the aforementioned optical crystal array, with reference to... Figure 2 ,include:

[0313] Step S1: The quantum entanglement-enhanced ultra-precision sensing array 1 collects multi-dimensional error data of the optical crystal and the robotic arm in real time during the integration process. The multi-dimensional error data includes sub-nanometer displacement data, Raman spectral deformation data, infrared temperature rise data and frictional stress data.

[0314] Step S2: The multi-factor coupling error prediction module 2 receives multi-dimensional error data and mines the coupling characteristics between different error sources based on the attention mechanism, thereby predicting and outputting instantaneous alignment error and hysteretic deformation error.

[0315] In step S3, the quantum traceability calibration unit 31 uses sub-nanometer displacement data as a benchmark to calibrate the feedback signal of the built-in displacement sensor of the robot in real time; the robot dynamic compensation unit 32 performs composite control compensation for the robot based on the instantaneous alignment error and the hysteresis deformation error; and the process parameter collaborative adjustment unit 33 dynamically adjusts the process parameters of multiple process execution devices based on the hysteresis deformation error and infrared temperature rise data.

[0316] In step S4, the central control module 4 generates a synchronization trigger signal, schedules the execution timing of the above modules, and monitors the system status.

[0317] In step S5, the federated learning collaborative optimization module 5 aggregates and updates the prediction model parameters in the multi-factor coupling error prediction module 2 across multiple local devices using a federated learning approach.

[0318] The above are merely preferred embodiments of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principle of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. An automatic alignment and integrated control system for an optical crystal array, characterized in that, include: A quantum entanglement-enhanced ultra-precision sensing array is used to collect multi-dimensional error data in real time during the integration process of optical crystals and robotic arms. The multi-dimensional error data includes sub-nanometer displacement data, Raman spectral deformation data, infrared temperature rise data, and frictional stress data. The multi-factor coupling error prediction module is connected to the quantum entanglement-enhanced ultra-precision sensing array. It is used to receive the multi-dimensional error data and mine the coupling characteristics between different error sources based on the attention mechanism, thereby predicting and outputting instantaneous alignment error and hysteretic deformation error. A full-link adaptive compensation execution module, connected to the multi-factor coupled error prediction module, includes: The quantum traceability calibration unit is used to calibrate the feedback signal of the built-in displacement sensor of the robot arm in real time based on the sub-nanometer displacement data. The robot dynamic compensation unit is used to perform composite control compensation on the robot based on the instantaneous alignment error and the hysteretic deformation error. A process parameter coordination adjustment unit is used to dynamically adjust the process parameters of multiple process execution devices based on the hysteresis deformation error and the infrared temperature rise data. The central control module is connected to the quantum entanglement-enhanced ultra-precise sensing array, the multi-factor coupling error prediction module, and the full-link adaptive compensation execution module, respectively, and is used to generate synchronization trigger signals, schedule the execution timing of the above modules, and monitor the system status. The federated learning collaborative optimization module, connected to the multi-factor coupled error prediction module, is used to aggregate and update the prediction model parameters in the multi-factor coupled error prediction module across multiple local devices through federated learning.

2. The automatic alignment and integrated control system for optical crystal arrays according to claim 1, characterized in that, The quantum entanglement-enhanced ultra-precision sensing array includes: A quantum entanglement sensing unit is used to generate entangled photon pairs and divide them into a probe light and a reference light. The probe light is irradiated to the interface between the optical crystal and the robotic arm through an anti-interference encapsulation optical path. The reference light is transmitted in a vacuum reference optical path, and the phase difference between the probe light and the reference light is detected to obtain the sub-nanometer displacement data. The Raman spectroscopy sensing unit is used to acquire the Raman spectral signal of the region of the optical crystal irradiated by laser, and to obtain the Raman spectral deformation data of the crystal molecular structure by analyzing the spectral shift. An infrared thermal imaging unit is used to acquire the infrared temperature rise data of the surface of the optical crystal array and the contact interface of the robotic arm in real time. A dynamic friction sensing unit is built into the flexible contact surface of the end effector of the robot, and is used to collect the friction stress data in real time during the relative motion between the robot and the optical crystal. The quantum entanglement-enhanced ultra-precision sensing array controls each sensing unit to perform synchronous data acquisition through a synchronous trigger signal issued by the central control module, and sends the output multi-dimensional error data to the multi-factor coupling error prediction module.

3. The automatic alignment and integrated control system for optical crystal arrays according to claim 1, characterized in that, The multi-factor coupled error prediction module includes: The data preprocessing unit is used to perform spatiotemporal synchronization calibration, outlier removal and normalization processing on the multi-dimensional error data to form a standardized feature tensor. The coupled feature mining unit, connected to the data preprocessing unit, is used to process the feature tensor using a Transformer encoder based on a multi-head attention mechanism. Different attention heads are configured to focus on the first coupling relationship between quantum tracing error and frictional stress, the second coupling relationship between temperature change and manipulator hysteresis characteristics, and the third coupling relationship between temperature change and adhesive curing parameters, respectively, in order to mine hidden correlation features between error sources. The error prediction unit, connected to the coupled feature mining unit, is used to use a Transformer decoder to output the first branch prediction result and the second branch prediction result in parallel based on the mined hidden correlation features. The first branch prediction result is the instantaneous alignment error, which includes the displacement deviation and attitude angle deviation of the optical crystal in the X, Y, and Z directions; the second branch prediction result is the hysteretic deformation error, which is used to characterize the stress deformation of the optical crystal caused by the adhesive curing process.

4. The automatic alignment and integrated control system for optical crystal arrays according to claim 1, characterized in that, The quantum traceability calibration unit is specifically used for: The sub-nanometer displacement data output in real time by the quantum entanglement-enhanced ultra-precision sensing array is received as a reference displacement value; Obtain the measured displacement value fed back in real time by the displacement sensor built into the robotic arm; Calculate the difference between the reference displacement value and the measured displacement value to generate a displacement error signal; Based on the displacement error signal, the control parameters of the manipulator's drive system are compensated in real time to suppress the tracking error caused by the hysteresis nonlinearity of the manipulator's drive system. The displacement control commands executed by the drive system of the robotic arm are generated by the central control module based on the real-time alignment error prediction results output by the multi-factor coupling error prediction module.

5. The automatic alignment and integrated control system for optical crystal arrays according to claim 1, characterized in that, The robotic arm dynamic compensation unit is specifically used to execute a composite control strategy, which includes: Variable gain control is used to dynamically adjust the control gain of the proportional-integral-derivative controller according to the real-time alignment error, so as to improve the error convergence speed. Friction feedforward compensation is used to output a compensation voltage to the drive system of the manipulator in advance based on the dynamic error component caused by frictional stress predicted by the multi-factor coupled error prediction module, so as to counteract the frictional influence during the movement process. Pre-alignment offset compensation is used to control the robot to apply a preset offset in the opposite direction to the predicted deformation direction during the alignment stage based on the hysteresis deformation error output by the multi-factor coupling error prediction module, so as to compensate for the curing deformation that will be generated in the subsequent process. The robotic arm dynamic compensation unit generates composite control commands by integrating the outputs of the variable gain control, the friction feedforward compensation, and the pre-alignment offset compensation.

6. The automatic alignment and integrated control system for optical crystal arrays according to claim 1, characterized in that, The process execution equipment includes laser sensing equipment, dispensing equipment, and adhesive curing control equipment; The process parameter coordinated adjustment unit is specifically used for: Based on the infrared temperature rise data, the output power of the laser sensing device is dynamically adjusted to suppress local deformation caused by photothermal effect; Based on the predicted results of the hysteresis deformation error, the control parameters of the adhesive curing control equipment during the adhesive curing process are dynamically adjusted, and the control parameters include at least curing temperature and curing time. Based on the predicted results of the hysteresis deformation error, the process parameters of the dispensing equipment are adjusted synchronously to minimize the stress introduced by the adhesive while ensuring the bonding quality. The process parameters include at least the dispensing amount and the dispensing rate.

7. The automatic alignment and integrated control system for optical crystal arrays according to claim 1, characterized in that, The central control module is also used to perform dynamic priority scheduling, which specifically includes: Monitor adjustment instructions generated in parallel by at least two units in the end-to-end adaptive compensation execution module; The real-time impact of each adjustment command on the error to be adjusted is evaluated according to preset rules, and the execution priority is dynamically allocated accordingly. Based on the execution priority, the order of adjustment instructions with resource or timing conflicts is coordinated, wherein the weight of the impact on the alignment accuracy of the optical crystal is set to be higher than the weight of the impact on the adjustment of integrated process parameters. The central control module ensures that adjustments to core accuracy indicators are executed with priority through dynamic priority scheduling.

8. The automatic alignment and integrated control system for optical crystal arrays according to claim 1, characterized in that, The federated learning collaborative optimization module is specifically used to construct a distributed training architecture, which includes: The federated server is used to perform the aggregation and distribution of the global model; Multiple local nodes, corresponding to each production device, and each local node deploys a local instance of the multi-factor coupled error prediction module; The local node is used to: train the local instance locally using error-process sample data it collects, and generate model parameter gradients; and upload the model parameter gradients with added differential privacy noise to the federated server. The federated server is configured to: receive parameter gradients from each of the local nodes; perform weighted aggregation of the parameter gradients based on a federated averaging algorithm to update the global model parameters; and distribute the updated global model parameters to each of the local nodes. Each of the local nodes is also used to: update the local instance based on the received global model parameters, and perform personalized fine-tuning in conjunction with local specific optical crystal array specification data.

9. The automatic alignment and integrated control system for an optical crystal array according to claim 8, characterized in that, The differential privacy processing performed by the local node before uploading the model parameter gradients specifically includes: Random noise conforming to a predetermined statistical distribution is added to the gradient of the model parameters; The amplitude of the added random noise is configured to ensure the privacy and security of local sample data without destroying the effective information carried by the parameter gradient for global model optimization.

10. An automatic alignment and integrated control method for an optical crystal array, applied to the automatic alignment and integrated control system of the optical crystal array according to any one of claims 1-9, characterized in that, include: Step S1: The quantum entanglement-enhanced ultra-precision sensing array collects multi-dimensional error data in real time during the integration process of the optical crystal and the robotic arm. The multi-dimensional error data includes sub-nanometer displacement data, Raman spectral deformation data, infrared temperature rise data, and frictional stress data. Step S2: The multi-factor coupling error prediction module receives the multi-dimensional error data and mines the coupling characteristics between different error sources based on the attention mechanism, thereby predicting and outputting instantaneous alignment error and hysteretic deformation error. Step S3: The quantum traceability calibration unit uses the sub-nanometer displacement data as a reference to calibrate the feedback signal of the robot's built-in displacement sensor in real time; the robot dynamic compensation unit performs composite control compensation on the robot based on the instantaneous alignment error and the hysteretic deformation error; the process parameter collaborative adjustment unit dynamically adjusts the process parameters of multiple process execution devices based on the hysteretic deformation error and the infrared temperature rise data. Step S4: The central control module generates a synchronization trigger signal, schedules the execution timing of the above modules, and monitors the system status. Step S5: The federated learning collaborative optimization module aggregates and updates the prediction model parameters in the multi-factor coupled error prediction module across multiple local devices using a federated learning approach.

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