Machine learning-based cup labeling equipment fault prediction method and system

Abstract

The invention relates to the technical field of equipment fault prediction, in particular to a cup labeling equipment fault prediction method and system based on machine learning. According to the method, equipment operation state parameters are converted into a multi-mode pulse sequence with a timestamp synchronization characteristic; loading the purified pulse flow to a quantum bit array for entanglement state evolution, and extracting a three-mode entanglement association tensor; carrying out dimensionality reduction projection on the three-mode correlation tensor to an equipment degradation manifold space, and determining quantum tunneling probability density distribution; constructing a time-varying Hamiltonian of an equipment degradation state based on quantum tunneling probability density distribution, and generating a degradation track cluster according to the time-varying Hamiltonian; and performing time sequence convolution processing on the degradation track cluster, performing probability amplitude amplification on a fault critical point in the track cluster by using an energy level splitting characteristic of a time-varying Hamiltonian, and generating a space-time probability cloud picture. The fault evolution law can be visually presented, the accuracy and timeliness of early fault early warning are improved, and a reliable basis is provided for predictive maintenance.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment failure prediction, and in particular to a method and system for predicting cup labeling equipment failure based on machine learning. Background Art

[0002] With the rapid development of social economy, product labeling is becoming increasingly important. Cup labeling equipment is a device that sticks rolls of self-adhesive paper labels (paper or metal foil) on PCBs, products or specified packaging. The stable operation of cup labeling equipment is crucial to production efficiency and product quality. However, traditional fault prediction methods mainly rely on threshold alarms or statistical-based life prediction models, which make it difficult to effectively capture the complex coupling relationship of multimodal parameters (such as mechanical vibration, heat conduction, and visual positioning) during equipment degradation. Existing technologies usually use single-modal signal analysis or shallow machine learning models, which cannot handle the nonlinear time-varying characteristics of equipment operation status and are easily affected by noise, resulting in delayed fault warnings or false alarms. Although some studies have attempted to introduce deep learning, conventional neural networks have limited ability to extract cross-modal spatiotemporal correlation features, especially the lack of sensitive capture of weak fault precursors in the background of quantum noise. In addition, traditional methods fail to effectively integrate the quantum tunneling effect and multi-body entanglement characteristics in the equipment degradation process, resulting in insufficient accuracy in degradation trajectory prediction. In recent years, the intersection of quantum computing and machine learning has provided new ideas for complex system fault prediction. However, existing solutions still have room for optimization in terms of quantum state evolution and multimodal feature fusion. There is an urgent need for a prediction method that can simultaneously process vibration, heat conduction and visual drift characteristics, and accurately amplify the critical point of failure based on the quantum entanglement mechanism, so as to improve the reliability and maintenance level of cup labeling equipment. Summary of the Invention

[0003] The present invention overcomes the deficiencies of the prior art and provides a method and system for predicting cup labeling equipment failures based on machine learning.

[0004] In order to achieve the above-mentioned purpose, the technical solution adopted by the present invention is:

[0005] The first aspect of the present invention discloses a method for predicting cup labeling equipment failure based on machine learning, comprising the following steps:

[0006] Obtaining the equipment operating status parameters of the cup labeling equipment, converting the equipment operating status parameters into a multimodal pulse sequence with time stamp synchronization characteristics, and applying quantum noise suppression filtering to form a purified pulse stream;

[0007] Loading the purified pulse stream into a quantum bit array for entangled state evolution, and extracting a three-modal entangled correlation tensor of vibration phase transition characteristics, heat conduction path distortion characteristics, and visual coordinate drift characteristics;

[0008] Projecting the three-modal correlation tensor into the device degenerate manifold space by dimensionality reduction, and determining the quantum tunneling probability density distribution of the state vector on the manifold surface;

[0009] Constructing a time-varying Hamiltonian of the device degradation state based on the quantum tunneling probability density distribution, and generating a degradation trajectory cluster containing a critical failure point according to the time-varying Hamiltonian;

[0010] The degenerate trajectory cluster is input into the quantum long short-term memory network for temporal convolution, and the energy level splitting characteristics of the time-varying Hamiltonian are used to amplify the probability amplitude of the critical fault point in the trajectory cluster to generate a spatiotemporal probability cloud map with fault precursor characteristics.

[0011] The equipment operation status parameters include the radial runout waveform of the transmission shaft, the temperature gradient change data of the heat dissipation path of the heating module, and the label fitting edge optical flow vector information collected by the visual positioning detector.

[0012] Preferably, the device operating state parameters of the cup labeling device are obtained, the device operating state parameters are converted into a multimodal pulse sequence with time stamp synchronization characteristics, and quantum noise suppression filtering is applied to form a purified pulse stream, specifically:

[0013] Multi-scale time-frequency decomposition of the drive shaft radial runout waveform is performed to generate bidirectional modulated pulses containing time-domain mutation points and frequency-domain resonance peaks. This captures the temperature gradient changes in the heat dissipation path of the heating module and forms a gradient pulse chain with thermodynamic relaxation time calibration.

[0014] A visual positioning detector is used to perform optical flow vector analysis on the label fitting edge, and the position offset is converted into a spatially coded pulse sequence. The bidirectional modulated pulse, gradient pulse chain and spatially coded pulse sequence are then cross-modally time-stamped and aligned using a quantum clock synchronization module to generate a multimodal pulse matrix with spatiotemporal correlation characteristics.

[0015] The multimodal pulse matrix is input into the quantum resonance entanglement mapping layer, the phase coherence of the pulse amplitude is enhanced through the quantum bit rotation gate operation, the time-frequency resonance component in each modal pulse is separated by the quantum state superposition principle, and the incoherent pulse noise is filtered out;

[0016] The phase-enhanced time-frequency resonance component is quantum convolved with the thermal relaxation attenuation component of the gradient pulse chain to construct a noise basis matrix with quantum tunneling suppression characteristics. Basis projection denoising is then performed on the multimodal pulse matrix to output a purified pulse stream with preserved spatiotemporal characteristics.

[0017] Preferably, the purified pulse stream is loaded into a quantum bit array for entangled state evolution, and the three-modal entangled correlation tensor of vibration phase transition characteristics, heat conduction path distortion characteristics, and visual coordinate drift characteristics is extracted, specifically:

[0018] The purified pulse stream is subjected to phase-amplitude dual-domain shaping by a quantum state modulator to generate a pulse cluster carrying nonlinear resonant coding, and the pulse cluster is injected into the resonant cavity coupling node of the quantum bit array in a spatiotemporal modulation manner;

[0019] Constructing a multi-body entangled Hamiltonian in the quantum bit array, driving each quantum bit to produce a cross-relaxation effect, forming a superradiant quantum state vector containing vibrational phase transition characteristics;

[0020] A heat conduction path selection operator is embedded in the quantum bit array to perform topological decomposition on the superradiant quantum state vector and extract the path distortion matrix associated with the thermal gradient of the resonant cavity.

[0021] Input the path distortion matrix into the visual coordinate calibration module, generate a space-time coordinate compensation operator through non-commutative projection measurement, dynamically correct the relative phase offset between quantum bits based on the space-time coordinate compensation operator, and synchronously capture the visual coordinate drift spectrum;

[0022] The vibration phase transition characteristics, path distortion matrix and coordinate drift spectrum are subjected to tensor product operations, and the multi-body entanglement gate synchronous compression technology is used to eliminate cross-modal redundant information and output the optimized three-modal entanglement correlation tensor.

[0023] Preferably, the three-modal correlation tensor is projected onto the device degenerate manifold space by dimensionality reduction, and the quantum tunneling probability density distribution of the state vector on the manifold surface is determined, specifically:

[0024] The three-modal entanglement correlation tensor is input into the quantum principal component analysis module, and the eigenmode entanglement weights matching the spatial dimension of the device degradation manifold are extracted through the collaborative decomposition of the vibration phase transition characteristics, the path distortion matrix and the coordinate drift spectrum, thereby generating a degradation-sensitive eigensubspace basis;

[0025] A nonlinear degenerate projection operator is constructed based on the eigensubspace basis. The thermal gradient characteristics of the path distortion matrix and the spatiotemporal offset of the coordinate drift spectrum are used to adapt the coupling strength of the quantum bit array to the manifold curvature, and the three-modal characteristics are mapped into cross-modal degradation correlation factors on the manifold surface.

[0026] Construct a tunneling coupling barrier that links vibration phase, heat conduction path, and visual coordinates within the manifold surface, and generate the multi-body constraint tensor of the quantum tunneling channel based on the eigenmode entanglement weights.

[0027] The tunneling path probability amplitude of the state vector on the manifold surface is calculated according to the energy level distribution characteristics of the multi-body constraint tensor, and the spatiotemporal correlation of the cross-modal degradation correlation factors is integrated to perform probability density superposition to generate the quantum tunneling probability density distribution of the state vector on the manifold surface.

[0028] Preferably, a time-varying Hamiltonian of the device degradation state is constructed based on the quantum tunneling probability density distribution, and a degradation trajectory cluster containing a critical point of failure is generated according to the time-varying Hamiltonian, specifically:

[0029] The energy level splitting parameters of each state vector on the manifold surface are extracted based on the quantum tunneling probability density distribution. Combined with the spatiotemporal coupling strength of the cross-modal degradation correlation factor, a time-varying Hamiltonian reflecting the degradation dynamics of the device is constructed.

[0030] Inputting the time-varying Hamiltonian into a non-equilibrium Green's function solver to obtain a time-domain attenuation response of the degradation correlation factor on the manifold surface, and generating an attenuation path spectrum including relaxation time calibration;

[0031] The attenuation path spectrum is optimized by quantum annealing to select mutation points that meet a preset energy level gradient threshold as potential critical failure points, and each critical failure point is connected by a path integral method to form a degradation trajectory cluster;

[0032] By utilizing the backtracking characteristics of the degraded trajectory cluster, quantum Monte Carlo sampling is used to verify its matching degree with the actual operating state of the device. Abnormal trajectories with statistical matching degrees lower than the preset matching threshold are eliminated, and the optimized degraded trajectory cluster is output.

[0033] Preferably, the degenerate trajectory cluster is input into the quantum long short-term memory network for temporal convolution, and the energy level splitting characteristics of the time-varying Hamiltonian are used to amplify the probability amplitude of the critical point of the fault in the trajectory cluster to generate a spatiotemporal probability cloud map with fault precursor characteristics, specifically:

[0034] The time-domain decay paths in the degenerate trajectory cluster are encoded into a time-sequential pulse sequence according to the quantum state superposition principle, and embedded into the memory unit of the quantum long-short-term memory network through quantum gate operation to form a quantum memory state with time correlation.

[0035] The convolution kernel weights are dynamically adjusted based on the energy level splitting parameters of the time-varying Hamiltonian, and multi-scale temporal convolution is performed on the quantum memory state to extract the critical points that meet the preset energy level gradient threshold and generate a quantum characteristic tensor with enhanced critical points.

[0036] The quantum interference effect is used to phase-match the enhanced quantum characteristic tensor of the critical point with the relaxation time calibration parameter of the degenerate trajectory cluster. The quantum probability amplitude of the critical point of the fault is amplified through the Pauli-X gate operation, and the spatiotemporally correlated coherent state probability distribution is output.

[0037] The coherent state probability distribution is subjected to quantum decoherence suppression processing. After filtering out noise interference, a spatiotemporal probability cloud map reflecting the evolution law of fault precursors is generated through projection measurement.

[0038] A second aspect of the present invention discloses a cup labeling equipment fault prediction system based on machine learning. The cup labeling equipment fault prediction system includes a memory and a processor. The memory stores a cup labeling equipment fault prediction method program. When the cup labeling equipment fault prediction method program is executed by the processor, the steps of any one of the cup labeling equipment fault prediction methods are implemented.

[0039] The present invention solves the technical defects existing in the background technology and has the following beneficial effects: obtaining the equipment operating state parameters of the cup labeling equipment, converting the equipment operating state parameters into a multimodal pulse sequence with timestamp synchronization characteristics, and applying quantum noise suppression filtering to form a purified pulse stream; loading the purified pulse stream into a quantum bit array for entangled state evolution, and extracting the three-modal entangled correlation tensor of vibration phase transition characteristics, heat conduction path distortion characteristics, and visual coordinate drift characteristics; projecting the three-modal correlation tensor into the equipment degradation manifold space through dimensionality reduction, and determining the quantum tunneling probability density distribution of the state vector on the manifold surface; constructing a time-varying Hamiltonian of the equipment degradation state based on the quantum tunneling probability density distribution, and generating a degenerate trajectory cluster containing a critical fault point according to the time-varying Hamiltonian; inputting the degenerate trajectory cluster into a quantum long short-term memory network for temporal convolution, and utilizing the energy level splitting characteristics of the time-varying Hamiltonian to amplify the probability amplitude of the critical fault point in the trajectory cluster, thereby generating a spatiotemporal probability cloud map with fault precursor characteristics. It can intuitively present the evolution law of faults, improve the accuracy and timeliness of early fault warnings, and provide a reliable basis for predictive maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, without paying any creative work, they can also obtain drawings of other embodiments based on these drawings.

[0041] Figure 1 This is a flow chart of the first method of the cup labeling equipment failure prediction method;

[0042] Figure 2 This is a flow chart of the second method of the cup labeling equipment failure prediction method;

[0043] Figure 3 This is the system block diagram of the cup labeling equipment fault prediction system. DETAILED DESCRIPTION

[0044] In order to more clearly understand the above-mentioned objects, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that, in the absence of conflict, the embodiments of the present application and the features therein can be combined with each other.

[0045] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the scope of protection of the present invention is not limited to the specific embodiments disclosed below.

[0046] like Figure 1 As shown, the first aspect of the present invention discloses a method for predicting cup labeling equipment failure based on machine learning, comprising the following steps:

[0047] S102, obtaining device operating status parameters of the cup labeling device, converting the device operating status parameters into a multimodal pulse sequence with a time stamp synchronization characteristic, and applying quantum noise suppression filtering to form a purified pulse stream;

[0048] S104, loading the purified pulse stream into a quantum bit array for entangled state evolution, and extracting a three-modal entangled correlation tensor of vibration phase transition characteristics, heat conduction path distortion characteristics, and visual coordinate drift characteristics;

[0049] S106. Projecting the three-modal correlation tensor into the device degenerate manifold space by dimension reduction, and determining the quantum tunneling probability density distribution of the state vector on the manifold surface;

[0050] S108. Constructing a time-varying Hamiltonian of the device degradation state based on the quantum tunneling probability density distribution, and generating a degradation trajectory cluster including a critical failure point according to the time-varying Hamiltonian;

[0051] S110, inputting the degenerate trajectory cluster into a quantum long short-term memory network for temporal convolution, utilizing the energy level splitting characteristics of the time-varying Hamiltonian to amplify the probability amplitude of the critical fault point in the trajectory cluster, and generating a spatiotemporal probability cloud map with fault precursor characteristics.

[0052] The equipment operation status parameters include the radial runout waveform of the transmission shaft, the temperature gradient change data of the heat dissipation path of the heating module, and the label fitting edge optical flow vector information collected by the visual positioning detector.

[0053] It should be noted that this invention addresses the difficulties in fusing multi-source heterogeneous data, inaccurate extraction of weak fault features, and insufficient dynamic characterization of degradation processes in cup labeling equipment fault prediction. By using quantum noise suppression and entangled state evolution to achieve high-fidelity fusion of vibration, heat conduction, and visual features, the paper uses quantum tunneling probability density and time-varying Hamiltonian to model the dynamics of equipment degradation. Finally, it enhances fault precursor signals with the help of quantum long-short-term memory networks, thus overcoming the limitations of traditional single-modal analysis and achieving precise quantization of multi-physics field coupling features. The paper also uses spatiotemporal probability cloud maps to intuitively present fault evolution patterns, improving the accuracy and timeliness of early fault warnings and providing a reliable basis for predictive maintenance.

[0054] Preferably, the device operating state parameters of the cup labeling device are obtained, the device operating state parameters are converted into a multimodal pulse sequence with time stamp synchronization characteristics, and quantum noise suppression filtering is applied to form a purified pulse stream, such as Figure 2 As shown, specifically:

[0055] S202, performing multi-scale time-frequency decomposition on the radial runout waveform of the transmission shaft to generate a bidirectional modulated pulse containing a time-domain mutation point and a frequency-domain resonance peak, capturing the temperature gradient change of the heat dissipation path of the heating module, and forming a gradient pulse chain with thermodynamic relaxation time calibration;

[0056] Among them, the gradient pulse chain refers to a pulse signal sequence with time correlation generated by quantifying the temperature gradient change of the heat dissipation path of the heating module (such as the 0.1°C resolution data collected by an infrared thermal imager) and calibrating its attenuation characteristics based on the thermodynamic relaxation time (such as τ = 8.3s). It is used to characterize the dynamic evolution of the thermal conduction state of the equipment.

[0057] S204, using a visual positioning detector to perform optical flow vector analysis on the label fitting edge, converting the position offset into a spatially coded pulse sequence, and performing cross-modal timestamp alignment on the bidirectional modulated pulse, gradient pulse chain, and spatially coded pulse sequence through a quantum clock synchronization module to generate a multimodal pulse matrix with spatiotemporal correlation characteristics;

[0058] S206. Inputting the multimodal pulse matrix into the quantum resonance entanglement mapping layer, performing phase coherence enhancement on the pulse amplitude through a quantum bit rotation gate operation, separating the time-frequency resonance components in each modal pulse using the quantum state superposition principle, and filtering out incoherent pulse noise;

[0059] Among them, the quantum resonance entanglement mapping layer refers to a quantum information processing module that enhances the phase coherence of multimodal pulses through quantum bit rotation gate operations (such as Ry(π / 4) gates), and uses quantum state superposition and Pauli blocking effects to separate time-frequency resonance components and filter out incoherent noise. Its function is to achieve the purification of quantized features and correlation enhancement of cross-modal signals.

[0060] S208. Perform quantum convolution fusion on the phase-enhanced time-frequency resonance component and the thermal relaxation attenuation component of the gradient pulse chain to construct a noise basis matrix with quantum tunneling suppression characteristics, and perform basis projection denoising on the multimodal pulse matrix to output a purified pulse stream with preserved spatiotemporal characteristics.

[0061] Among them, the thermal relaxation attenuation component of the gradient pulse chain refers to the time domain signal component extracted from the temperature gradient data of the heating module and decays exponentially. Its physical essence reflects the thermodynamic relaxation process of the heat dissipation path and quantifies the dynamic attenuation characteristics of temperature changes.

[0062] In a specific embodiment of the present invention, taking the radial runout monitoring of the drive shaft of a cup labeling device as an example, an acceleration sensor is used to collect the radial vibration waveform, which is decomposed into five time-frequency scales through wavelet transform, and the time domain mutation point (such as the amplitude exceeding 0.5 mm / s) is extracted. 2 The method combines the pulses of the three channels (e.g., 125Hz and 250Hz harmonic components) with frequency-domain resonant peaks (e.g., 125Hz and 250Hz harmonic components) to generate a bidirectional modulated pulse train. Simultaneously, an infrared thermal imager monitors the temperature gradient of the heat dissipation path of the heating module, records the temperature decay curve at 1-second intervals, and fits the thermodynamic relaxation time constant (e.g., set to 8.3s) to form a gradient pulse train. A visual positioning detector (frame rate 60fps) performs optical flow tracking of the tag edge and encodes the position offset (e.g., ±0.2mm) as a spatial pulse train. The three pulses are timestamped by a quantum clock synchronization module to generate a 12×12 multimodal pulse matrix. This matrix is input into the quantum resonance entanglement mapping layer, where the pulse amplitude is phase-coherently enhanced using a single-qubit rotation gate (e.g., a Ry(π / 4) gate), and incoherent noise with an amplitude below 0.1V is blocked using a Pauli-Z gate. Finally, the enhanced time-frequency resonance component (such as the 125Hz resonance peak) is quantum convolved with the thermal relaxation decay component of the gradient pulse train to construct the noise basis matrix. After projection and noise reduction, the purified pulse stream is output. The output purified pulse stream example is shown in the following table:

[0063]

[0064] In summary, the present invention converts the original signal into a high-fidelity, spatiotemporally correlated purified pulse stream through quantum noise suppression filtering and cross-modal timestamp alignment, thereby improving the signal-to-noise ratio of multi-source heterogeneous data, enhancing the phase coherence of fault characteristics, and providing high-precision input for subsequent quantum state evolution, thereby improving the accuracy and timeliness of fault prediction.

[0065] Preferably, the purified pulse stream is loaded into a quantum bit array for entangled state evolution, and the three-modal entangled correlation tensor of vibration phase transition characteristics, heat conduction path distortion characteristics, and visual coordinate drift characteristics is extracted, specifically:

[0066] The purified pulse stream is subjected to phase-amplitude dual-domain shaping by a quantum state modulator to generate a pulse cluster carrying nonlinear resonant coding, and the pulse cluster is injected into the resonant cavity coupling node of the quantum bit array in a spatiotemporal modulation manner;

[0067] Among them, the above-mentioned quantum state modulator is a quantum signal processing unit that shapes quantum pulses through phase rotation and amplitude scaling to produce nonlinear resonant coding; the resonant cavity coupling node is a microwave resonant cavity connection structure in the quantum bit array used to realize pulse energy transfer and quantum state control; the quantum bit array is a physical system composed of multiple quantum bits (such as superconducting quantum bits) for quantum information processing and entangled state evolution.

[0068] Constructing a multi-body entangled Hamiltonian in the quantum bit array, driving each quantum bit to produce a cross-relaxation effect, forming a superradiant quantum state vector containing vibrational phase transition characteristics;

[0069] Among them, the above-mentioned multi-body entanglement Hamiltonian is a physical quantity operator that describes the interaction between quantum bits (such as XX-type coupling) to drive the system to produce entangled evolution; the superradiant state quantum state vector is a quantum state with collective radiation enhancement characteristics formed by a multi-qubit system under the action of cooperative radiation.

[0070] A heat conduction path selection operator is embedded in the quantum bit array to perform topological decomposition on the superradiant quantum state vector and extract the path distortion matrix associated with the thermal gradient of the resonant cavity.

[0071] The heat conduction path selection operator is a diagonal matrix operator that extracts features associated with the thermal gradient from the quantum state based on a diagonalization operation.

[0072] Input the path distortion matrix into the visual coordinate calibration module, generate a space-time coordinate compensation operator through non-commutative projection measurement, dynamically correct the relative phase offset between quantum bits based on the space-time coordinate compensation operator, and synchronously capture the visual coordinate drift spectrum;

[0073] The vibration phase transition characteristics, path distortion matrix and coordinate drift spectrum are subjected to tensor product operations, and the multi-body entanglement gate synchronous compression technology is used to eliminate cross-modal redundant information and output the optimized three-modal entanglement correlation tensor.

[0074] In a specific embodiment of the present invention, taking an array of five superconducting qubits as an example, the generated purified pulse stream (such as a synchronous pulse of 0.35V in vibration mode, 0.25V in thermal conduction mode, and 0.09V in visual mode at a timestamp of 3600μs) is input into a quantum state modulator, and a nonlinear resonantly encoded pulse cluster (such as a microwave pulse sequence with a frequency of 4.8GHz and a pulse width of 20ns) is generated by applying a π / 2 phase rotation and amplitude scaling (scale factor 1.2). The pulse cluster is injected into the qubit array through a resonant cavity coupling node (coupling strength 15MHz), constructing a multi-body entangled Hamiltonian containing XX-type interactions (strength 12MHz), driving the qubits to produce cross relaxation (decoherence time T2 = 50μs), and forming a superradiant quantum state vector. By embedding a heat conduction path selection operator (e.g., the diagonal elements [0.3, 0, 0, 0.7, 0]), a topological decomposition of the superradiant quantum state vector is performed, extracting the path distortion matrix (a 3×3 real symmetric matrix with a maximum eigenvalue of 0.82). This matrix is then fed into the visual coordinate calibration module, where a compensation operator (e.g., a phase offset Δφ = 0.15π) is generated through non-commutative projection measurement. After dynamically correcting the inter-qubit phase, the coordinate drift spectrum (frequency shift of ±5 MHz) is captured. Finally, a tensor product operation is performed on the vibrational phase transition signature (amplitude 0.42ej0.3π), the principal eigenmodes of the path distortion matrix (0.82, 0.12, 0.08), and the coordinate drift spectrum (5 MHz, -3 MHz). After compression via a CZ gate, a three-modal entanglement correlation tensor (dimensions 3×3×2, Frobenius norm 1.24) is output.

[0075] In summary, the present invention utilizes the quantum many-body entanglement mechanism to achieve deep coupling of cross-modal features, synchronously extract the quantum correlations of vibration phase transitions, thermal conduction distortion, and visual drift, and generate a three-modal entangled tensor with clear physical meaning. This provides high-fidelity, low-redundancy quantum state feature input for subsequent degradation trajectory prediction, thereby improving the sensitivity of critical fault point detection.

[0076] Preferably, the three-modal correlation tensor is projected onto the device degenerate manifold space by dimensionality reduction, and the quantum tunneling probability density distribution of the state vector on the manifold surface is determined, specifically:

[0077] The three-modal entanglement correlation tensor is input into the quantum principal component analysis module, and the eigenmode entanglement weights matching the spatial dimension of the device degradation manifold are extracted through the collaborative decomposition of the vibration phase transition characteristics, the path distortion matrix and the coordinate drift spectrum, thereby generating a degradation-sensitive eigensubspace basis;

[0078] Among them, the above-mentioned principal component analysis module is a quantum computing unit that extracts the main components of multimodal data through feature decomposition to construct a low-dimensional degenerate feature space.

[0079] Construct a non - linear degenerate projection operator based on the eigen - subspace basis, and use the thermal gradient characteristics of the path distortion matrix and the spatio - temporal offset of the coordinate drift spectrum to perform manifold curvature adaptation on the coupling strength of the qubit array, mapping the three - mode features into cross - modal degenerate correlation factors on the manifold surface;

[0080] Among them, the thermal gradient characteristics of the above - mentioned path distortion matrix refer to the matrix parameters that reflect the temperature change rate and spatial distribution characteristics of the abnormal thermal conduction path of the device; the spatio - temporal offset of the coordinate drift spectrum is the frequency - domain characterization of the coordinate deviation generated by the visual positioning system in the time and space dimensions; the manifold curvature adaptation is an operation to make the feature projection match the geometric characteristics of the device degradation manifold by adjusting the qubit coupling strength.

[0081] Construct a tunneling coupling barrier that links the vibration phase - thermal conduction path - visual coordinate within the manifold surface, and generate a many - body constraint tensor for the quantum tunneling channel according to the eigen - mode entanglement weight;

[0082] Among them, the tunneling coupling barrier is a virtual energy barrier constructed to quantify the transition probability of the state vector on the manifold surface.

[0083] Calculate the tunneling path probability amplitude of the state vector on the manifold surface according to the energy level distribution characteristics of the many - body constraint tensor, and perform probability density superposition by integrating the spatio - temporal correlation of the cross - modal degenerate correlation factor to generate the quantum tunneling probability density distribution of the state vector on the manifold surface.

[0084] Among them, the calculation formula for the above - mentioned tunneling path probability amplitude is:

[0085]

[0086] In the formula, ψ is the tunneling path probability amplitude; A is the normalization coefficient (related to the eigen - mode weight of the many - body constraint tensor); B is the barrier shape adjustment factor (dimensionless, with a value range of 0 < B ≤ 1); ΔE is the energy level splitting difference; d is the tunneling path length, representing the transition distance of the state vector on the manifold; h is the reduced Planck constant.

[0087] In a specific embodiment of the present invention, the generated three-modal entangled correlation tensor (dimension 3×3×2, Frobenius norm 1.24) is taken as an example, and it is input into the quantum principal component analysis module. By collaboratively decomposing the vibration phase transition characteristics (amplitude 0.42ej0.3π), the path distortion matrix (principal eigenvalue 0.82) and the coordinate drift spectrum (±5MHz), the three eigenmode entanglement weights ([0.45, 0.35, 0.20]) are extracted, and a three-dimensional degeneration sensitive basis is constructed (the basis vector orthogonality error is <0.01). Based on this basis, a nonlinear projection operator (Jacobi matrix condition number 2.1) was constructed. Combining the thermal gradient characteristics (temperature decay slope 0.12℃ / ms) with the coordinate drift space-time offset (X / Y axis offset ratio 1.5:1), the coupling strength of the quantum bit array was adjusted (12MHz→15MHz) to achieve manifold curvature adaptation (curvature radius 0.8m), and three cross-modal degradation correlation factors were generated (vibration-thermal conduction factor 0.62, thermal conduction-visual factor 0.55, vibration-visual factor 0.48). A tunneling barrier (height 8meV, width 2nm) was set within the manifold surface, and a multi-body constraint tensor (3×3×3, main diagonal elements [0.45, 0.33, 0.22]) was generated based on the eigenmode weights. The state vector (such as

[0088] [0.6, 0.3, 0.1]) has a tunneling path probability amplitude (0.38ejπ / 4), and after superimposing the spatiotemporal correlation, the quantum tunneling probability density distribution (peak probability density 0.28, distribution half-height width 0.15nm) is output.

[0089] In summary, this method uses an improved quantum principal component analysis and manifold projection method to transform the complex coupling relationship between vibration, heat conduction, and visual characteristics into quantifiable degradation correlation factors on the manifold surface. By constructing a multi-body constraint tensor, quantum tunneling probability modeling of cross-modal degradation characteristics is achieved, thereby intuitively presenting the dynamic evolution law of the equipment degradation process and providing a high-precision quantum state degradation characterization basis for fault prediction.

[0090] Preferably, a time-varying Hamiltonian of the device degradation state is constructed based on the quantum tunneling probability density distribution, and a degradation trajectory cluster containing a critical point of failure is generated according to the time-varying Hamiltonian, specifically:

[0091] The energy level splitting parameters of each state vector on the manifold surface are extracted based on the quantum tunneling probability density distribution. Combined with the spatiotemporal coupling strength of the cross-modal degradation correlation factor, a time-varying Hamiltonian reflecting the degradation dynamics of the device is constructed.

[0092] Inputting the time-varying Hamiltonian into a non-equilibrium Green's function solver to obtain a time-domain attenuation response of the degradation correlation factor on the manifold surface, and generating an attenuation path spectrum including relaxation time calibration;

[0093] The attenuation path spectrum is optimized by quantum annealing to select mutation points that meet a preset energy level gradient threshold as potential critical failure points, and each critical failure point is connected by a path integral method to form a degradation trajectory cluster;

[0094] By utilizing the backtracking characteristics of the degraded trajectory cluster, quantum Monte Carlo sampling is used to verify its matching degree with the actual operating state of the device. Abnormal trajectories with statistical matching degrees lower than the preset matching threshold are eliminated, and the optimized degraded trajectory cluster is output.

[0095] In a specific embodiment of the present invention, taking the obtained quantum tunneling probability density distribution (peak value 0.28, half-height width 0.15nm) as an example, three characteristic state vectors ([0.6, 0.3, 0.1],

[0096] The energy level splitting parameters (ΔE = 1.2 meV, 0.8 meV, and 1.5 meV) for the modal [0.4, 0.5, 0.1] and [0.2, 0.7, 0.1]) were combined with the spatiotemporal coupling strength (time decay constant τ = 8.3 s, spatial correlation length 2.1 mm) of the cross-modal degradation correlation factors (such as the vibration-thermal conductivity factor of 0.62). A time-varying Hamiltonian (matrix dimension 3×3, maximum off-diagonal element amplitude 0.45 meV) was constructed. This Hamiltonian was input into a non-equilibrium Green's function solver to obtain the decay response curve (time resolution 1 μs), and the decay path spectrum (bandwidth 5 MHz) with calibrated relaxation times (τ1 = 12 ms, τ2 = 18 ms) was generated. Quantum annealing (annealing rate 0.05 K / ns) was used to screen for energy gradients exceeding a threshold of 0.3 meV / nm as potential critical failure points (e.g., ΔE' = 0.42 meV at t = 23 ms). A degenerate trajectory cluster (trajectory length 8.7 nm) containing three critical failure points was formed through path integration. Quantum Monte Carlo sampling (5000 times) was used to verify the matching degree. After eliminating anomalous trajectories with a matching degree of less than 85%, the optimized trajectory cluster was output.

[0097] In summary, this method maps the quantum tunneling probability density into a time-varying Hamiltonian and combines the solution of non-equilibrium Green's function with quantum annealing optimization to achieve accurate quantification of key state transitions in the degradation process, generating a critical point distribution and evolution trajectory with clear physical meaning.

[0098] Preferably, the degenerate trajectory cluster is input into the quantum long short-term memory network for temporal convolution, and the energy level splitting characteristics of the time-varying Hamiltonian are used to amplify the probability amplitude of the critical point of the fault in the trajectory cluster to generate a spatiotemporal probability cloud map with fault precursor characteristics, specifically:

[0099] The time-domain decay paths in the degenerate trajectory cluster are encoded into a time-sequential pulse sequence according to the quantum state superposition principle, and embedded into the memory unit of the quantum long-short-term memory network through quantum gate operation to form a quantum memory state with time correlation.

[0100] Among them, the time domain attenuation path in the above-mentioned degradation trajectory cluster refers to the energy dissipation trajectory of each state vector evolving over time during the device degradation process; the quantum state superposition principle is the linear combination characteristic that a quantum system can be in multiple states at the same time; the quantum gate operation is the basic logical unit for controlling the quantum state through unitary transformation (such as the Ry gate rotating the phase of the quantum state).

[0101] The convolution kernel weights are dynamically adjusted based on the energy level splitting parameters of the time-varying Hamiltonian, and multi-scale temporal convolution is performed on the quantum memory state to extract the critical points that meet the preset energy level gradient threshold and generate a quantum characteristic tensor with enhanced critical points.

[0102] The quantum interference effect is used to phase-match the enhanced quantum characteristic tensor of the critical point with the relaxation time calibration parameter of the degenerate trajectory cluster. The quantum probability amplitude of the critical point of the fault is amplified through the Pauli-X gate operation, and the spatiotemporally correlated coherent state probability distribution is output.

[0103] Among them, the quantum interference effect is a wave phenomenon that uses the phase difference of quantum states to achieve probability amplitude enhancement or cancellation; the Pauli-X gate operation is a single-qubit logic gate that rotates the quantum state by π radians on the X-axis of the Bloch sphere.

[0104] The coherent state probability distribution is subjected to quantum decoherence suppression processing. After filtering out noise interference, a spatiotemporal probability cloud map reflecting the evolution law of fault precursors is generated through projection measurement.

[0105] Among them, projection measurement is the observation process of collapsing the quantum state to a specific basis vector through a quantum operator (such as Pauli-Z).

[0106] In a specific embodiment of the present invention, taking the optimized degenerate trajectory cluster (2 trajectories, average matching degree 92%) as an example, the time domain decay path (time resolution 1μs) is encoded into a time-sequential pulse sequence (pulse width 15ns, amplitude 0.8V) through the Ry(π / 3) quantum gate, and input into a 5-qubit long short-term memory network (memory unit coupling strength 20MHz) to form a quantum memory state (such as |ψ m>=0.7|010>+0.3|101>). Based on the energy level splitting parameter of the time-varying Hamiltonian (ΔE=1.2meV), the weights of the three-layer convolution kernel (size 3×3, step size 2) are dynamically adjusted, and the quantum memory state is subjected to multi-scale temporal convolution (feature extraction rate 95%) to generate a critical point enhanced quantum feature tensor (dimension 4×4×2, Frobenius norm 1.8). A quantum interferometer (phase matching accuracy ±0.05π) is used to match the feature tensor with the relaxation time parameter (τ=12ms / 18ms), and a Pauli-X gate (rotation angle π / 2) is applied to amplify the critical point probability amplitude from 0.3 to 0.65, and output the coherent state probability distribution (coherence time 50μs). Finally, through decoherence suppression (decoherence error <5%) and projection measurement, a spatiotemporal probability cloud map is generated (time span 30ms, spatial resolution 0.1mm 2 ).

[0107] In summary, in order to solve the problem that it is difficult to extract effective precursor features from complex degradation trajectories in traditional fault prediction, the present invention combines quantum long-short-term memory networks with quantum interference effects to process degradation trajectory clusters, and uses quantum state superposition and energy level splitting characteristics to enhance the signal characteristics of critical fault points. Through multi-scale time series convolution and probability amplitude amplification, the detection sensitivity of weak fault precursors is improved. The final generated space-time probability cloud map can intuitively display the key space-time correlation features in the fault evolution process, and realize accurate visual prediction of early faults. Specifically, the space-time probability cloud map can accurately locate the critical time and location of potential equipment failures by visualizing the space-time evolution laws of fault precursors, thereby providing an intuitive decision-making basis for predictive maintenance, thereby guiding targeted maintenance and avoiding unplanned downtime.

[0108] In this embodiment, the cup labeling equipment fault prediction method may further include the following steps:

[0109] The degradation trajectory cluster of a single device (including critical failure points with spatiotemporal correlation) is encoded as quantum state information, where the spatiotemporal coordinates and failure probability amplitude of each trajectory are mapped into a super-entangled state vector through quantum bit phase-amplitude coupling;

[0110] A quantum channel based on Bell state entangled pairs is established between the central server and each labeling device, and the quantum state information is decomposed into classical correlation bits and quantum teleportation parameter packets through the quantum teleportation protocol;

[0111] The receiving device recovers the shared degenerate trajectory cluster through quantum state recombination and uses quantum joint measurement to extract the degradation correlation factors between devices (integrating the cooperative distortion characteristics of vibration phase transition and heat conduction path distortion) to generate a non-local correlation tensor.

[0112] Inputting the non-local correlation tensor into a graph embedding module, dynamically allocating device node weights in the graph embedding module, eliminating phase mismatches between nodes through a multi-body decoherence compensation operator, and forming a quantum topological graph with degenerate correlation strength as edge weights and device nodes as vertices;

[0113] A quantum graph convolution operation is performed on the quantum topological graph to aggregate the degradation correlation factors of adjacent nodes to generate a cross-device collaborative degradation vector. The vector phase is adjusted through Pauli-Y gate rotation, and finally a collaborative warning signal containing the probability distribution of the fault propagation path and the spatiotemporal correlation strength is output.

[0114] In a specific embodiment of the present invention, taking the coordinated early warning of three labeling devices (devices A / B / C) as an example, the degradation trajectory cluster of device A (containing two trajectories, with a critical failure probability amplitude of 0.65) is encoded into a super-entangled state vector (dimensions 4×4, Frobenius norm 1.2) using five superconducting quantum bits. The space-time coordinates are mapped to quantum phases (for example, an X-axis offset corresponds to a phase Δφ=0.2π), and the failure probability amplitude is coupled to the amplitude (amplitude 0.8V). A Bell state entanglement channel (fidelity 98%) is established between the central server and devices B / C, decomposing the super-entangled state into classical correlation bits (256 bits) and quantum parameter packets (entanglement parameters α=0.6, β=0.8). The receiving device B recovers the trajectory cluster through quantum state reorganization (phase compensation error <0.05π). Quantum joint measurement (Bell-base projection) extracts the cross-device degradation correlation factor (vibration-heat conduction synergistic distortion characteristic covariance of 0.75), generating a non-local correlation tensor (dimensions 3×3×3, trace norm 2.1). This tensor is input into the graph embedding module, dynamically assigning node weights (device A / B / C weight ratio 1.2:1.0:0.8), and using a multi-body decoherence compensation operator (decoherence error compensation rate 92%) to generate a quantum topological graph (edge weight range 0.3-0.7, vertex degree distribution [2,3,2]). Quantum graph convolution is performed on the topological graph (convolution kernel size 2×2, coupling strength 15MHz), and a collaborative degradation vector (dimension 3×1, principal component amplitude 0.91) is aggregated and generated. After adjusting the phase through Pauli-Y gate rotation (θ=π / 3), a collaborative warning signal is output (fault propagation path probability peak value 0.82, spatiotemporal correlation strength 0.68).

[0115] In summary, to address the problems of warning lag and misjudgment caused by data silos and insufficient cross-device correlation modeling in multi-device collaborative fault warning, this method achieves lossless synchronization of degradation trajectory clusters across devices through a quantum teleportation protocol. It uses non-local correlation tensors to accurately capture the collaborative distortion patterns of degradation characteristics between devices. Combining dynamic weight allocation and graph convolution aggregation of quantum topological graphs, it generates a probability distribution of fault propagation paths with spatiotemporal correlation, thereby improving the sensitivity of multi-device collaborative degradation analysis and the interpretability of warning signals.

[0116] In this embodiment, the cup labeling equipment fault prediction method may further include the following steps:

[0117] The interference fringe region with periodic phase modulation is separated from the spatiotemporal probability cloud map, and the fringe spacing, inclination angle and contrast characteristics are extracted to form the interference feature vector that characterizes the spatiotemporal distribution of fault precursors.

[0118] Matching the interference eigenvector with the path topology in the quantum tunneling probability density distribution, and establishing an association weight matrix between the fault mode (e.g., bearing wear, label offset) and the specific tunneling path through quantum mutual information; wherein the path topology in the quantum tunneling probability density distribution refers to the networked connection relationship formed by the possible transition paths of the quantum state on the manifold surface during the device degradation process, and its geometric characteristics (e.g., branch points, loop paths) reflect the evolution channels of different fault modes;

[0119] Encoding the correlation weight matrix into the Ising Hamiltonian of the quantum annealing model, optimizing the matrix elements by adjusting the coupler bias voltage, eliminating redundant correlations and enhancing the tunneling path weights of critical failure modes;

[0120] Based on the optimized correlation matrix, the quantum tunneling path is used as the branch node and the fault mode is used as the leaf node. The branch threshold is determined by the energy level splitting parameter, and a multi-level decision tree is constructed to reflect the dependency relationship between the fault evolution stage and the path.

[0121] The quantum probability amplitude feedback of nodes at each level in the decision tree is used to adaptively adjust the parameters of the interference fringe feature extraction module to form a closed-loop optimized fault evolution analysis architecture.

[0122] It should be noted that in fault prediction, the weak correlation between fault modes and quantum evolution paths, as well as the reliance on empirical thresholds for evolution path modeling, results in insufficient decision-making accuracy. This method accurately maps fault modes to physical evolution mechanisms by correlating the mutual information between interference fringe features and quantum tunneling paths. Quantum annealing optimization dynamically screens critical paths and constructs an energy-level-driven decision tree, improving the interpretability of fault evolution stage division and path dependency. Furthermore, a closed-loop feedback mechanism is incorporated to adaptively calibrate feature extraction parameters, enabling continuous optimization of the fault evolution analysis system and enhancing the robustness of the prediction model.

[0123] like Figure 3 As shown, the second aspect of the present invention discloses a cup labeling equipment fault prediction system 8 based on machine learning. The cup labeling equipment fault prediction system includes a memory 60 and a processor 80. The memory 60 stores a cup labeling equipment fault prediction method program. When the cup labeling equipment fault prediction method program is executed by the processor 80, any step of the cup labeling equipment fault prediction method is implemented.

[0124] Those skilled in the art will appreciate that all or part of the steps of the above-mentioned method embodiments may be implemented by hardware associated with program instructions, and the aforementioned program may be stored in a computer-readable storage medium. When the program is executed, the program executes the steps of the above-mentioned method embodiments. The aforementioned storage medium includes various media that can store program codes, such as mobile storage devices, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0125] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods of each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

[0126] The above are only specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed by the present invention, which should be covered by the scope of protection of the present invention.

Claims

1. A cup labeling equipment fault prediction method based on machine learning, characterized in that: The following steps are involved: Obtaining the equipment operating status parameters of the cup labeling equipment, converting the equipment operating status parameters into a multimodal pulse sequence with time stamp synchronization characteristics, and applying quantum noise suppression filtering to form a purified pulse stream; Loading the purified pulse stream into a quantum bit array for entangled state evolution, and extracting a three-modal entangled correlation tensor of vibration phase transition characteristics, heat conduction path distortion characteristics, and visual coordinate drift characteristics; Projecting the three-modal correlation tensor into the device degenerate manifold space by dimensionality reduction, and determining the quantum tunneling probability density distribution of the state vector on the manifold surface; constructing a time-varying Hamiltonian of the device degradation state based on the quantum tunneling probability density distribution, and generating a degradation trajectory cluster containing a critical failure point according to the time-varying Hamiltonian; The degenerate trajectory cluster is input into the quantum long short-term memory network for temporal convolution, and the energy level splitting characteristics of the time-varying Hamiltonian are used to amplify the probability amplitude of the critical fault point in the trajectory cluster to generate a spatiotemporal probability cloud map with fault precursor characteristics.

2. The method for predicting cup labeling equipment failure based on machine learning according to claim 1, characterized in that: The equipment operating status parameters of the cup labeling equipment are obtained, converted into a multimodal pulse sequence with time stamp synchronization characteristics, and quantum noise suppression filtering is applied to form a purified pulse stream. Specifically: Multi-scale time-frequency decomposition of the drive shaft radial runout waveform is performed to generate bidirectional modulated pulses containing time-domain mutation points and frequency-domain resonance peaks. This captures the temperature gradient changes in the heat dissipation path of the heating module and forms a gradient pulse chain with thermodynamic relaxation time calibration. A visual positioning detector is used to perform optical flow vector analysis on the label fitting edge, and the position offset is converted into a spatially coded pulse sequence. The bidirectional modulated pulse, gradient pulse chain and spatially coded pulse sequence are then cross-modally time-stamped and aligned using a quantum clock synchronization module to generate a multimodal pulse matrix with spatiotemporal correlation characteristics. The multimodal pulse matrix is input into the quantum resonance entanglement mapping layer, the phase coherence of the pulse amplitude is enhanced through the quantum bit rotation gate operation, the time-frequency resonance component in each modal pulse is separated by the quantum state superposition principle, and the incoherent pulse noise is filtered out; The phase-enhanced time-frequency resonance component is quantum convolved with the thermal relaxation attenuation component of the gradient pulse chain to construct a noise basis matrix with quantum tunneling suppression characteristics. Basis projection denoising is then performed on the multimodal pulse matrix to output a purified pulse stream with preserved spatiotemporal characteristics.

3. The cup labeling equipment fault prediction method based on machine learning according to claim 1, characterized in that: The purified pulse stream is loaded into the quantum bit array for entangled state evolution, and the three-modal entangled correlation tensor of vibration phase transition characteristics, heat conduction path distortion characteristics and visual coordinate drift characteristics is extracted, specifically: The purified pulse stream is subjected to phase-amplitude dual-domain shaping by a quantum state modulator to generate a pulse cluster carrying nonlinear resonant coding, and the pulse cluster is injected into the resonant cavity coupling node of the quantum bit array in a spatiotemporal modulation manner; Constructing a multi-body entangled Hamiltonian in the quantum bit array, driving each quantum bit to produce a cross-relaxation effect, forming a superradiant quantum state vector containing vibrational phase transition characteristics; A heat conduction path selection operator is embedded in the quantum bit array to perform topological decomposition on the superradiant quantum state vector and extract the path distortion matrix associated with the thermal gradient of the resonant cavity. Input the path distortion matrix into the visual coordinate calibration module, generate a space-time coordinate compensation operator through non-commutative projection measurement, dynamically correct the relative phase offset between quantum bits based on the space-time coordinate compensation operator, and synchronously capture the visual coordinate drift spectrum; The vibration phase transition characteristics, path distortion matrix and coordinate drift spectrum are subjected to tensor product operations, and the multi-body entanglement gate synchronous compression technology is used to eliminate cross-modal redundant information and output the optimized three-modal entanglement correlation tensor.

4. The cup labeling equipment fault prediction method based on machine learning according to claim 1 is characterized in that: The three-modal correlation tensor is projected onto the device degenerate manifold space by dimensionality reduction, and the quantum tunneling probability density distribution of the state vector on the manifold surface is determined, specifically: The three-modal entanglement correlation tensor is input into the quantum principal component analysis module, and the eigenmode entanglement weights matching the spatial dimension of the device degradation manifold are extracted through the collaborative decomposition of the vibration phase transition characteristics, the path distortion matrix and the coordinate drift spectrum, thereby generating a degradation-sensitive eigensubspace basis; A nonlinear degenerate projection operator is constructed based on the eigensubspace basis. The thermal gradient characteristics of the path distortion matrix and the spatiotemporal offset of the coordinate drift spectrum are used to adapt the coupling strength of the quantum bit array to the manifold curvature, and the three-modal characteristics are mapped into cross-modal degradation correlation factors on the manifold surface. A tunneling coupling barrier that links vibration phase, heat conduction path, and visual coordinates is constructed within the manifold surface, and the multi-body constraint tensor of the quantum tunneling channel is generated based on the eigenmode entanglement weights. The tunneling path probability amplitude of the state vector on the manifold surface is calculated according to the energy level distribution characteristics of the multi-body constraint tensor, and the spatiotemporal correlation of the cross-modal degradation correlation factors is integrated to perform probability density superposition to generate the quantum tunneling probability density distribution of the state vector on the manifold surface.

5. The method for predicting cup labeling equipment failure based on machine learning according to claim 1, characterized in that: A time-varying Hamiltonian of the device degradation state is constructed based on the quantum tunneling probability density distribution, and a degradation trajectory cluster containing a critical failure point is generated according to the time-varying Hamiltonian, specifically: The energy level splitting parameters of each state vector on the manifold surface are extracted based on the quantum tunneling probability density distribution. Combined with the spatiotemporal coupling strength of the cross-modal degradation correlation factor, a time-varying Hamiltonian reflecting the degradation dynamics of the device is constructed. Inputting the time-varying Hamiltonian into a non-equilibrium Green's function solver to obtain a time-domain attenuation response of the degradation correlation factor on the manifold surface, and generating an attenuation path spectrum including relaxation time calibration; The attenuation path spectrum is optimized by quantum annealing to select mutation points that meet a preset energy level gradient threshold as potential critical failure points, and each critical failure point is connected by a path integral method to form a degradation trajectory cluster; By utilizing the backtracking characteristics of the degraded trajectory cluster, quantum Monte Carlo sampling is used to verify its matching degree with the actual operating state of the device. Abnormal trajectories with statistical matching degrees lower than the preset matching threshold are eliminated, and the optimized degraded trajectory cluster is output.

6. The cup labeling equipment fault prediction method based on machine learning according to claim 1, characterized in that: The degenerate trajectory cluster is input into the quantum long short-term memory network for temporal convolution. The energy level splitting characteristics of the time-varying Hamiltonian are used to amplify the probability amplitude of the critical point of the fault in the trajectory cluster, and a spatiotemporal probability cloud map with fault precursor characteristics is generated. Specifically, The time-domain decay paths in the degenerate trajectory cluster are encoded into a time-sequential pulse sequence according to the quantum state superposition principle, and embedded into the memory unit of the quantum long-short-term memory network through quantum gate operation to form a quantum memory state with time correlation. The convolution kernel weights are dynamically adjusted based on the energy level splitting parameters of the time-varying Hamiltonian, and multi-scale temporal convolution is performed on the quantum memory state to extract the critical points that meet the preset energy level gradient threshold and generate a quantum characteristic tensor with enhanced critical points. The quantum interference effect is used to phase-match the enhanced quantum characteristic tensor of the critical point with the relaxation time calibration parameter of the degenerate trajectory cluster. The quantum probability amplitude of the critical point of the fault is amplified through the Pauli-X gate operation, and the spatiotemporally correlated coherent state probability distribution is output. The coherent state probability distribution is subjected to quantum decoherence suppression processing. After filtering out noise interference, a spatiotemporal probability cloud map reflecting the evolution law of fault precursors is generated through projection measurement.

7. The method for predicting cup labeling equipment failure based on machine learning according to claim 1, characterized in that: The equipment operating status parameters include the radial runout waveform of the transmission shaft, the temperature gradient change data of the heat dissipation path of the heating module, and the label fitting edge optical flow vector information collected by the visual positioning detector.

8. A machine learning-based cup labeling equipment fault prediction system, characterized in that: The cup labeling equipment fault prediction system includes a memory and a processor. The memory stores a cup labeling equipment fault prediction method program. When the cup labeling equipment fault prediction method program is executed by the processor, the steps of the cup labeling equipment fault prediction method according to any one of claims 1 to 7 are implemented.

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