Real-time energy consumption optimization control system of digital twin for paper cup production line

Abstract

This invention discloses a real-time energy consumption optimization and control system for a digital twin of a paper cup production line, belonging to the field of energy consumption optimization and control technology. The system comprises the following steps: Step 1: Collecting incoming material and environmental fingerprints, semantically aligning them with an asset management shell, and establishing an energy consumption baseline; Step 2: Constructing a quality-energy coupled twin model, integrating infrared and visual methods, and outputting intensity, risk, and unit energy consumption; Step 3: Identifying online and mapping the fingerprints to process boundaries and change rates, and publishing the process window; Step 4: Jointly optimizing within the window, with short-term takeover using reinforcement learning to shield projections during disturbances; Step 5: Securely distributing and storing policy snapshots, and implementing auditing and rectification closed-loop. This achieves multi-source connectivity, energy saving and consumption reduction with controlled quality, peak power suppression, and traceable management; it also includes compressed air supply-distribution-use efficiency evaluation, unified management of boundary versioning and rate limits, safe template rollback for boundary violations, and closed-loop updating of policy parameters.

Description

Technical Field

[0001] This invention relates to the field of energy consumption optimization and control technology, specifically to a real-time energy consumption optimization and control system for a digital twin of a paper cup production line. Background Technology

[0002] Continuous production lines for heat-sealing disposable paper products, such as paper cup sealing stations, are significantly affected by factors such as incoming material moisture content, surface energy, basis weight, and ambient temperature, humidity, and wind speed. Hot air, ultrasound, and line speed are strongly coupled. Common problems include heterogeneous equipment and protocols, inconsistent sampling frequencies and timestamps, and inconsistent dimensions and hysteresis. Existing technologies often aggregate controller-level data acquisition and monitoring systems, lacking a mechanism to abstract multi-source raw signals into a unified data object and bind it to batches, shifts, and time windows, making it difficult to provide a unique anchor point for upper-level modeling and auditing. Although the industry has introduced asset management shells, they mostly remain at the level of static ledgers, failing to form an integrated entry point for fingerprint vectors and semantic alignment vectors. Images and infrared data are mostly used for offline judgment. Optimization control often uses predictive control or experience-based tuning with fixed upper and lower bounds, making it difficult to link with quality risks and requiring repeated tuning when encountering material changes or seasonal disturbances. Energy management often uses averages as a baseline, resulting in inconsistent definitions across systems. The execution layer lacks strategy snapshots and versioned evidence chains, making cross-shift review difficult.

[0003] Based on the above scenarios and current situation, the technical problem to be solved by the present invention is to provide an end-to-end method from data to control in a continuous production process where material and environmental disturbances are frequent, equipment and data sources are heterogeneous, and energy consumption and quality objectives coexist, so that energy consumption optimization, quality constraints and execution constraints can work together under the same interface system.

[0004] The specific manifestations of this problem are as follows: A unified data entry point needs to be established on the data side, where fingerprint vectors are solidified into an asset management shell via semantic alignment vectors and bound to batches, shifts, and time windows to form auditable anchor points. On the model side, a mechanism-data coupled twin needs to be constructed under the conditions of semantic alignment vectors and control vectors, simultaneously providing the corrected state, softening degree, sealing strength, defect risk, and predicted unit energy consumption. On the identification and boundary side, constrained online identification drives the mapping of semantic alignment vectors, energy consumption deviations, and defect risks to process window objects and boundary projection operators, maintaining versioning. On the optimization and execution side, the trajectory is solved within the feasible region of the window and peak power constraints. When the feasible region shrinks, a selected reinforcement learning candidate takes over temporarily, and atomic deployment is achieved through gating and two-phase commit. Simultaneously, a policy snapshot solidifies the context key, fingerprint summary, process window, control sequence, and gating log to support the audit and rectification closed loop. Without the above integrated capabilities, semantic drift, baseline drift, and model mismatch are prone to occur, leading to increased out-of-bounds errors, intensity fluctuations, and downtime, hindering cross-system review and continuous improvement. Summary of the Invention

[0005] (a) Technical problems to be solved To address the shortcomings of existing technologies, this invention provides a real-time energy consumption optimization and control system for a digital twin of a paper cup production line. By constructing a mass-energy coupled twin model, integrating infrared and vision technologies, it outputs intensity, risk, and unit energy consumption. It identifies and maps fingerprints to process boundaries and change rates online, publishing process windows. Within these windows, joint optimization is performed, with short-term takeover via reinforcement learning that shields projections during disturbances. Secure deployment and strategy snapshot storage ensure a closed-loop audit and rectification process. This achieves multi-source connectivity, energy saving and consumption reduction with controlled quality, peak power suppression, and traceable management. Furthermore, it assesses the efficiency of compressed air supply, distribution, and consumption, unifies boundary versioning and rate limits, implements safe template rollback for boundary violations, and updates strategy parameters in a closed loop, thus solving the technical problems described in the background art.

[0006] (II) Technical Solution To achieve the above objectives, the present invention provides the following technical solution: The real-time energy consumption optimization control system of the digital twin of the paper cup production line includes: calibrating fingerprint vectors of multi-source signals, generating semantic alignment vectors with entropy regularization optimal transmission, binding batch / shift / time window, establishing unit energy consumption condition baseline and generating fingerprint summary to register in the asset management shell as the only data entry point; Under the conditions of semantic alignment vector and control vector, a mechanism-data coupling twin model is constructed, a memory kernel is introduced to advance temperature and humidity state, and image feature residual correction is combined to calculate softening degree, sealing strength, defect risk and predict unit energy consumption; The constrained online identification update of the twin parameters is implemented, and the semantic alignment vector, energy consumption deviation and defect risk are mapped to the upper and lower limits and change rates of each component of the control vector, thereby generating the process window object and boundary projection operator; Within the feasible region of the window, with the predicted unit energy consumption as the target and constrained by the defect risk and peak power, the control vector trajectory is obtained by projection method; when the feasible region shrinks, the candidate strategy filtered by the window and safety rules takes over. The trajectory is gated and atomically distributed through a secure channel, generating a policy snapshot containing version information, the fingerprint digest, the process window, and the gated log, which is then stored in the database. Based on the snapshot, audit scores and rectification instructions are output, and the parameters and boundaries are updated by backfeeding.

[0007] Furthermore, at the device layer, raw measurement and context parameters are synchronously acquired through a unified acquisition channel. A fingerprint vector is generated according to the affine calibration matrix and bias term, and timestamp signature is performed using a single clock source, so that the fingerprint vector corresponds one-to-one with batch, shift, and time window.

[0008] Furthermore, the fingerprint vector is mapped to the asset management shell model field via entropy regularization optimal transmission to form a semantic alignment vector; the cost matrix consists of unit conversion and dimension-compatible terms, the row and column edges are balanced with semantic weights, and the semantic alignment vector is registered and archived with context keys.

[0009] Furthermore, a maximum entropy conditional baseline function for energy consumption per unit of qualified product is established based on the semantic alignment vector. This function is parameterized using an exponential family and includes a logarithmic partition function. The natural parameters are determined by the semantic alignment vector. The energy consumption deviation is defined using entropy-type Bregman divergence, and both are used consistently throughout the entire process.

[0010] Furthermore, the semantic alignment vectors within the time window are aggregated into a fingerprint digest by separable time kernel vectors, and aligned with the statistical caliber of energy metering through linear mapping. An infinite norm consistency indicator is calculated, and the fingerprint digest and the indicator are written into the asset management shell field and a version number is established.

[0011] Furthermore, the mechanism sublayer uses discrete-time convolution with a memory kernel to represent the propulsion temperature and humidity state, and the state vector includes equivalent temperature and equivalent humidity; the control vector includes hot air flow rate, hot air temperature setting, linear velocity and ultrasonic power, and the semantic alignment vector is introduced by external mapping to form coupling, and the memory kernel takes the exponential decay family.

[0012] Furthermore, the softening degree is constructed based on a weighted heat and moisture dose, using a weighted kernel that decays monotonically within the pressing window and a smooth hyperbolic response; the sealing strength is obtained by a power-law mapping of the softening degree and the equivalent pressure, and the equivalent pressure is synthesized from mechanical pressing and ultrasonic power through a calibration curve and uniform in diameter.

[0013] Furthermore, image feature vectors extracted from infrared thermal imaging and machine vision are constructed and used together with mechanism prediction to form residuals; the state is corrected by minimum intrusion through a symmetric positive definite residual gain matrix, the observation matrix maps the state to surrogate quantities such as temperature moment and gradient, and the gain matrix version information is recorded.

[0014] Furthermore, based on the time-varying hazard rate integral expression of the defect risk defined by the corrected state and softening degree, an integration window and risk upper limit parameter are set; on the energy consumption side, the marginal terms related to hot air, ultrasound and linear velocity, and insufficient softening penalty are superimposed on the energy consumption baseline function to form an analytical expression for predicting unit energy consumption.

[0015] Furthermore, constrained online identification is performed on the twin parameter vectors, using a natural gradient step with an information matrix, wherein the information matrix is ​​quasi-Newtonian and contains a numerical regularization term; the learning rate and direction are gated by a weight matrix composed of the consistency indicator and the defect risk, and after parameter update, the parameters are hard-projected back to the feasible set.

[0016] Furthermore, the semantic alignment vector, the energy consumption deviation, and the defect risk are mapped to candidate upper and lower bounds of control parameters and change rates. The candidates are projected by the rate to obtain the final boundary and encapsulated as a process window object. When the overall violation rate exceeds the set threshold, it falls back to the safety template window and updates the window version number and context key.

[0017] Furthermore, within the feasible region and peak power half-space of the process window object, a rolling target is constructed according to the Bregman term of energy consumption deviation, the logarithmic obstacle of risk, and the smoothing term of action, and solved by the projection gradient method; when the feasible region shrinks, the reinforcement learning candidate that has been shielded and projected is enabled to take over in the short time domain and is issued in a hybrid manner of convex combination and nominal control.

[0018] Furthermore, gating is constructed through boundary projection and rate limiting, and atomic distribution is performed using two-stage submission semantics; the context key, fingerprint digest, process window, control sequence and gating log are packaged into a policy snapshot and a content index key is generated; based on the snapshot, the rectification instruction vector is solved using convex quadratic programming and written back to the asset management shell in a versioned manner.

[0019] (III) Beneficial Effects This invention provides a real-time energy consumption optimization and control system for a digital twin of a paper cup production line, which has the following beneficial effects: A fingerprint vector is generated using a unified acquisition channel, and a semantically aligned vector is generated using entropy regularization optimal transmission and written into the asset management shell. The context key is formed by combining batch, shift, and time window, and a maximum entropy conditional energy consumption baseline, fingerprint summary, and consistency indicator are established to eliminate dimensional and semantic differences. This allows modeling, boundary reconstruction, and auditing to run under the same entry point and caliber. This entry point also supports cross-shift horizontal benchmarking and intra-shift vertical review, ensuring that the retrieval key corresponds one-to-one.

[0020] A mechanism-data coupled twin model is constructed, using a memory kernel to advance temperature and humidity status and introducing image feature residual correction. This allows the corrected state, softening degree, sealing strength, defect risk, and predicted unit energy consumption to be output together within the same model, covering incoming material and environmental disturbance scenarios. This reduces empirical tuning and provides an interpretable and updatable multivariate channel for subsequent optimization and auditing.

[0021] By combining constrained online identification with natural gradients and hard projection, and using consistency indicators and risk gating, semantic alignment vectors, energy consumption deviations and defect risks are mapped to upper and lower bounds and change rates of control vectors. Process window objects and boundary projection operators are generated and versioned, so that the boundaries adaptively shrink or expand according to data confidence, suppressing jitter and ensuring executability.

[0022] Within the feasible region and peak power constraints of the process window object, the projected gradient solution is implemented with energy consumption deviation as the objective and defect risk and action smoothness as constraints. When the feasible region shrinks, the reinforcement learning candidate filtered by the window and safety rules takes over for a short time and mixes with the nominal control output, so that the control sequence takes into account both quality constraints and deployability, and maintains continuous production under disturbance.

[0023] Consistency gating is constructed using boundary projection and rate limiting, and atomic distribution is completed through two-phase commit. At the same time, the context key, fingerprint digest, process window, control sequence and gating log are packaged into a policy snapshot and stored in the database. Then, the parameters and boundaries of the rectification instructions are fed back using audit scoring and path attribution. The semantic alignment vector is used as the global unique entry point to connect the aforementioned layers, so that data, model, optimization, execution and audit can work together in an objectified and versioned framework and can evolve over a long period of time. Attached Figure Description

[0024] Figure 1 This is a schematic diagram of the real-time energy consumption optimization control system of the digital twin of the paper cup production line of the present invention. Detailed Implementation

[0025] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] Please see Figure 1 This invention provides a real-time energy consumption optimization control system for a digital twin of a paper cup production line, comprising: Step 1: Establish a unique data entry point for fingerprinting, semantic alignment, and baseline, unifying multi-source raw signals into a standard data object semantic alignment vector that can be directly called by the upper layer. And bind the energy consumption baseline to the batch / shift / time window. This forms a unified anchor point for subsequent modeling and auditing.

[0027] Transforming end-to-end measurements of future materials and the on-site environment into uniquely mapped fingerprint vectors And complete the asset management shell model Consistent semantic alignment vector This generates computationally achievable and traceable inputs for subsequent twin modeling and online boundary reconstruction.

[0028] Energy consumption and quality behavior of paper cup production line on incoming material moisture content Infrared absorption characteristics Surface energy Weight (G) and ambient temperature ,humidity Wind speed The sampling frequencies, dimensions, and hysteresis of different instruments are strongly coupled and change slowly over time; if these differences are not standardized, they will be amplified into systematic biases in subsequent twin identification. Therefore, it is necessary to publish the channel through a unified acquisition channel of the Industrial Internet at the device level. Complete consistent calibration across devices and dimensions, and implement it at the information layer using an asset management shell model. Solidified into semantic alignment vectors Simultaneously bind batch number b, shift number s, and time window. .

[0029] To ensure that incoming material and environmental measurements are included in the same computational coordinate system, the original measurement vectors are first processed at the equipment level. With context vector (For example, sensor housing temperature, probe contamination level, calibration date) are collected synchronously, and fingerprint vectors are generated through affine calibration with physical prior constraints. : This process incorporates hysteresis compensation, unit normalization, and bias drift into the mapping, ensuring that each component (such as the moisture content of the incoming material) is properly mapped. Infrared absorption characteristics Surface energy Weight (G) and ambient temperature ,humidity Wind speed () Have consistent physical semantics at the same time anchor point t. Through the publishing channel Internally, timestamps are signed using the same clock source to ensure that the causal order of arrival at the information layer from different devices is not disrupted; wherein:

[0030] Where: fingerprint vector The calibrated feature column vector is a compact set composed of the ranges of each physical component. Original measurement vector : The column vector for synchronous sampling by multiple sensors at the device layer, which is The compact set within provides the uncalibrated raw signal; Context vector The set of calibration and hysteresis state parameters is... The calibration matrix and bias are adjusted to achieve environmental adaptability; the calibration matrix... Regarding context vectors The piecewise continuous matrix mapping is as follows: ; bias term Regarding context vectors The vector bias is, This eliminates zero-point drift and installation deviation.

[0031] In use, a single mapping with affine-context linkage eliminates cross-device dimensional and hysteresis differences, ensuring the fingerprint vector... Reproducible at any time, the timing of signatures from the same clock source ensures that the sensitivity of subsequent twin identification to transient correlations is not diluted by time differences. This is because calibration depends on the context vector. It can maintain stable numerical condition numbers under equipment aging and environmental changes.

[0032] Furthermore, the information layer needs to process the fingerprint vector. Mapped to semantic alignment vector And written into the asset management shell model The standard fields allow upper-layer systems to be free from concerns about the heterogeneity of lower-layer devices.

[0033] To ensure the interpretability of the mapping across scenarios, we employ optimal transport alignment with entropy regularization: using unit conversion and dimensional compatibility as the cost matrix C, we find the cross-domain pairing with the minimum cost under the constraints of the transport matrix T and semantic weights P and q, and obtain the semantic alignment vector accordingly. .

[0034] At the same time, semantic alignment vector Forming a context key with batch number b, shift number s, and time window Δt In the asset management shell model The key is registered in the database to ensure that subsequent searches and audits can directly achieve unambiguous associations based on it, including:

[0035] Where: optimal transmission matrix : Dimension m × n, non-negative, its function is to transfer the "source distribution" to the "target distribution" according to cost regularization; Transfer matrix T: the coupling weight matrix from fingerprint components to asset management shell fields, which is... ; Feasible region 𝛱(𝐩,𝐪): A set of edge constraints that ensures the weights of each row and column match the semantic weights p and q, and takes the value of a set of non-negative matrices satisfying the edge conditions; Semantic weights p and q: Discrete weights of the fingerprint domain and the asset management shell field domain, respectively, and are probabilistic simplexes; Cost matrix C: A non-negative matrix constructed by unit conversion, dimensional compatibility, and physical coupling penalty, and is... ; Entropy regularity coefficient Positive scalar, balancing sparse matching and numerical stability; semantic alignment vector The standard data object in the asset management shell field sequence is... As the only callable data for upper-level models and optimizations, fingerprint vectors : Here, it serves as the source domain input for optimal transmission; inner product Frobenius inner product, cumulative cost.

[0036] When used, the semantic alignment vector is optimized through structural alignment during transmission. Each dimension corresponds to an asset management shell model. The defined fields eliminate ambiguity caused by multiple devices and protocols; the entropy regularization term ensures the stability and rapid convergence of online calculations; and the context key binding allows subsequent batch-level playback and shift-level auditing to be directly implemented. To enable zero-loss retrieval for the index.

[0037] In semantic alignment vector Under the conditions of [condition], construct unit qualified product energy consumption Maximum entropy conditional baseline function And generate fingerprint digest and cross-system consistency indicators This tightly binds energy consumption management elements to batches, shifts, and time windows, forming a closed-loop management system oriented towards auditing and continuous improvement.

[0038] Energy consumption audits require horizontal benchmarking and vertical improvement based on reproducible experimental evidence. This is due to the energy consumption per unit of qualified product. The distribution is affected by the fingerprint vector semantic alignment vector Due to the combined constraints of factors, directly using empirical means would mask operator differences and environmental disturbances. By employing the maximum entropy principle to construct the least biased conditional distribution under given statistical constraints, a robust and interpretable energy consumption baseline function can be obtained. Then use the fingerprint digest within the time window. With energy metering Perform consistency checks to ensure consistent closure between the information layer and the energy layer.

[0039] Given a semantic alignment vector Under these conditions, the energy consumption per unit of qualified product is described using an exponential family of forms. The conditional distribution is obtained, and the energy consumption baseline function is derived from it. Deviation assessment uses entropy-based Bregman divergence to avoid interference from scale and heteroscedasticity, where: Conditional distribution In semantic alignment vectors The probability density function of unit energy consumption under given conditions is the set of density functions for positive real numbers; the unit energy consumption scalar e represents the energy consumption observation of a single qualified product. Natural parameter function Depend on The scalar mapping determined is an open set that makes the logarithmic partition function finite; Logarithmic partition function Given by the logarithmic Laplace transform of the reference measure, it is a convex function that ensures density normalization and generates the moment properties of the exponential family.

[0040] Natural parameters Negative values ​​ensure normalization; determined by parameters. Calibration; Logarithmic allocation Standard form of the family of exponents; derivative For conditional expectations, energy consumption baseline : Positive real number, which can be reused in subsequent steps.

[0041] Furthermore, construct the deviation. :

[0042] Wherein: Deviation Asymmetric divergence relative to the baseline, Characterizes the degree of deviation of current energy consumption from the baseline; unit energy consumption observation Energy consumption of qualified products under time index t Energy consumption baseline function The baseline of conditional expectation takes the value of It provides a benchmark for auditing and control; Using maximum entropy / exponential family modeling, we can map to natural parameters and obtain conditional expectations:

[0043] Natural parameters : Negative scalar, ensuring normalization; parameter : Defined and versioned using historical samples; Baseline Positive scalar quantity, with units of "kWh / thousand" or "kJ / piece"; Potential function : We choose an entropy-type potential function that is strictly convex, forming a family of differentiable convex functions with respect to positive real numbers, and define the Bregman divergence; it can be:

[0044] Domain Its function is to generate entropy-type Bregman deviations, with reference energy consumption. Positive real numbers, used as anchor points for measurement; derivatives The first derivative of the potential function, and the slope of the tangent line at the linearized base point.

[0045] When using it, the energy consumption baseline function obtained by maximum entropy modeling is adopted. Insensitive to unobserved perturbations and aligned with semantically related vectors One-to-one correspondence facilitates horizontal comparisons across different shifts and material arrival conditions; Bregman deviation

[0046] As an asymmetric metric, it is easier to capture abnormally high-energy-consuming transients and issue constraint signals to the optimizer; this characterization is naturally compatible with residual correction and risk modeling of subsequent twin layers.

[0047] To achieve reproducibility for auditing and fast retrieval for optimization, it is necessary to align the semantic vectors within the time window. Aggregated into fingerprint digest and the shift metering of the energy metering system Perform consistency checks to ensure closed-loop consistency between the information layer and the energy layer. Employ a separable time kernel. Differential weighting is applied to the memory length of different components to achieve higher temporal resolution for energy-sensitive components, while applying longer smoothing to slowly drifting components, wherein:

[0048] Among them: fingerprint digest Aggregated fingerprints of shift-batch-time window are used for... This serves as a compressed representation of auditing and benchmarking; Start time The left endpoint of the time window represents a specific moment on the timeline, defining the window; Hadamard multiplication... Component-wise multiplication achieves component-level weighting; Time window Δt: Length of the continuous interval, a positive real number, defining the aggregation span; Time kernel : A non-negative kernel function vector that can be separable by components is Assign differentiated memory weights to different components;

[0049] Time core The components can be separated; Assign weights to components (e.g., give greater weight to "incoming material moisture content" and "ambient humidity").

[0050] Furthermore, construct consistency indicators :

[0051] Among them: consistency indication The value is {0,1}, used to determine whether the information layer aggregation and energy layer metering are consistent within the tolerance; mapping matrix. A linear mapping from fingerprint digests to the econometric space, with values ​​ranging from 1 to 1. fingerprint summary Conversion to metering standards; energy metering vector : Shift-batch measurement reading, for Infinite norm The norm of the largest absolute component of a vector, characterizing the error of the worst-case component; tolerance. Positive scalar The tolerance band for combining measurement uncertainty and mapping error; indicator function : A Boolean mapping to {0,1} that provides a binary judgment of whether a statement is valid or not.

[0052] When using, fingerprint summary High-frequency features within a time window are compressed into comparable low-dimensional representations, while preserving the temporal characteristics of energy-sensitive components; consistency indication. An automated reconciliation mechanism is established across systems, which can trigger process review or equipment inspection if any discrepancies are found; this provides a traceable and quantifiable anchor point for subsequent strategy audits and continuous improvement.

[0053] Step 2: Constructing a twin model of mechanism-data coupling In the semantic alignment vector With control vector Under the condition, jointly generate the corrected state. Softness Sealing strength Defect risk Compared with predicted unit energy consumption It provides predictable and constrained multiple output channels.

[0054] semantic alignment vector With control vector As input, a mechanistic sublayer was established that can explicitly characterize heat transfer, moisture migration, and softening kinetics. Output state vector With softening and sealing strength Provides a physical base.

[0055] Under the combined effects of hot air and ultrasonic coupling, paper cup preforms are influenced by the moisture content of the incoming material, surface energy, basis weight, and ambient wind speed. Their equivalent temperature and moisture content exhibit time-varying coupling, nonlinearly modulating the softening threshold and adhesion diffusion rate. Without a heat-mass transfer representation with memory and hysteresis, it is impossible to robustly predict sealing strength during shift changes or seasonal humidity fluctuations. Consequently, it becomes difficult to simultaneously apply the goals of energy reduction and defect risk mitigation to an executable multivariate trajectory.

[0056] Therefore, a state evolution and softening representation that is both interpretable and updatable is needed so that the upper-level optimizer can both predict and directly tune parameters using physical quantities.

[0057] To avoid simply linearizing heat transfer along the line, boundary layer heat transfer, and latent heat of vaporization, a discrete-time-continuous convolutional representation with a memory kernel is adopted, embedding the linear velocity-driven residence time effect and coating moisture hysteresis into the state update. Thus, the state vector... With control vector via semantic alignment vector The combined effect of external disturbances completes the explainable advancement of the temperature and humidity field, as detailed below:

[0058] Where: state vector : From the equivalent temperature scalar Equivalent moisture content standard The column vector formed is A bounded closed set representing the equivalent thermal-mass state of the material in the sealing region; time step Δ: a positive real number, the discrete advance interval, defined by the linear velocity and the camera synchronization clock; state matrix The constant (piecewise) matrix describes the linear dominant term of the coupling between heat conduction, diffusion and self-heating / evaporation, and its value ensures that the spectral radius is bounded to ensure numerical stability. Control Matrix The coupling matrix will determine the hot air flow rate. Hot air temperature setting Linear speed Ultrasonic power Mapped as heat-mass injection and equivalent residence time modulation; external matrix The mapping matrix introduces semantic alignment vectors. The static / slow variable bias of the state is influenced by factors such as the moisture content of the incoming material, infrared absorption characteristics, surface energy, basis weight, ambient temperature, humidity, and wind speed. memory core The nonnegative matrix kernel, The latent heat of vaporization and the hysteresis memory of coating migration are introduced; an exponential decay family of nuclei is selected and calibrated by shift to ensure feasibility and updability.

[0059]

[0060] memory core Non-negative, integrable For temperature / humidity memory duration; For memory gain, For the memory core to backtrack time, Fast time within the current period; control vector : A compact set within the device's rated range, adjustable input. Semantic alignment vector. Standard data objects, for It carries the fingerprints of incoming materials and the environment.

[0061] When in use, through memory kernel embedding, the state evolution naturally includes residence-evaporation-boundary layer hysteresis, and dynamic response consistent with linear velocity and humidity mutation can be obtained without high-dimensional discretization; the separation of B and G makes the control and external influences orthogonal to each other, which facilitates the subsequent domain management of process boundary and environmental disturbance; integral propulsion maintains the conservation of physical quantities when the sampling frequency changes, thereby improving multi-shift reproducibility.

[0062] In fiber-based coating composite systems, material softening is not solely triggered by temperature but is controlled by a viscoelastic-diffusion synergistic effect induced by moisture content. To capture this threshold-saturation dual characteristic on an equivalent dimension, a weighted heat-moisture dosage is introduced, and a softening degree is constructed using an S-shaped response. Subsequently, a calibrable power-law mapping is used to the sealing strength, facilitating alignment with tensile peel or burst pressure testing standards at the production end, thus establishing the softening degree. :

[0063] Among them: softening degree The dimensionless scalar on the surface describes the proportion of the transition from glass to fusion; contact window Real number, the effective thermo-mechanical duration determined by the linear velocity and the compression trajectory; weight.

[0064] The integrable kernel function emphasizes the dose contribution near the compression moment, and selects a family of realizable kernels that monotonically decay with time, such as: Weight kernel W(S): Monotonically decaying ; and contact window Consistent; Temperature scalar : From the state vector The first component is determined by the material's temperature resistance; the moisture content is also considered. : From the state vector The second component is the material's moisture safety region; parameter The regularized calibration parameters are in the finite positive real number domain and their functions are dose scale, temperature sensitivity, humidity sensitivity gain, and threshold shift, respectively.

[0065] Furthermore, construct sealing strength :

[0066] Among them: sealing strength : Positive real number, used as a direct physical quantity for quality targets and risk calculations; maximum strength Positive real number, the upper limit achievable by the material-coating system under ideal softening and pressing conditions, given by type testing; power exponent Positive real number, which adjusts the softening sensitivity and the intensity of the equivalent pressure. Equivalent pressure A positive real number, representing the pressure index that combines mechanical pressing pressure and ultrasonic power equivalently. The ultrasonic power is mapped to an equivalent compression contribution according to the equipment calibration curve and weighted and synthesized with the mechanical pressure. This represents the mechanical pressing pressure. With ultrasonic power Synthesized scalar; Determined by offline calibration (fitted to the peel / burst test caliber).

[0067] In use, through dose integration and S-shaped compression, the softening degree exhibits reasonable zero-saturation limits at both ends of the low-temperature-short-time and high-temperature-long-time ranges, avoiding overfitting; the power-law intensity mapping compresses the complex microscopic bonding and diffusion process into a calibrable family of three parameters, adaptable to production tests and facilitating online inversion; based on the softening degree... With equivalent pressure The separation can be finely tuned by adjusting the pressing or ultrasonication without changing the hot air strategy, thus reducing energy consumption coupling.

[0068] At the mechanism sub-layer Based on this, a residual sublayer is introduced. By absorbing online features from infrared thermal imaging and machine vision, consistency correction is performed on the status and output, and defect risk is generated. Energy consumption per unit of qualified product as predicted .

[0069] Mechanistic models exhibit systematic biases due to factors such as incoming fiber orientation, coating thickness unevenness, and nozzle temperature field drift; while infrared thermography and vision can provide direct evidence of temperature distribution and geometric continuity in the sealing area. If these two are not incorporated into a unified correction, the intensity-risk prediction will collectively mismatch in edge scenarios, forcing the optimizer to operate conservatively. Therefore, it is necessary to construct a residual correction driven by the image feature-mechanistic prediction difference, ensuring that the state and output converge on a rolling basis in each shift, and align with the energy consumption baseline function from step one. This forms a ternary closed loop of energy consumption, quality, and fingerprint.

[0070] The features extracted from infrared thermal images and visual observations within the sealed window are aggregated into an image feature vector. Its components include the zero / first / second moments of the temperature distribution, the extreme values ​​of the boundary temperature gradient, and the closure degree of the sealing profile; simultaneously, the mechanistic sublayer generates observation predictions within the same window. A self-consistent residual is constructed, and a regularized information gain is used as the gain matrix to achieve minimal intrusion correction of the state, ensuring an engineering balance between speed and stability. The corrected state is then constructed. :

[0071] Wherein: corrected state : The column vectors within the original state serve as the immediate state for subsequent output calculations and boundary reconstruction; the original state... Mechanism sublayer output; residual gain matrix The symmetric positive definite matrix projects the residuals onto the state space according to the principle of information consistency; it can be implemented by a regularized inverse mapping composed of a quasi-Newton information matrix and feature weights, and its calibration snapshot is recorded in the asset management shell version field for audit reproduction;

[0072] weight matrix Configuration based on image feature confidence; Ridge regularization coefficient For Tikhonov regularization, the observation matrix Linear operators that map the state to the same metric space as the image features (such as temperature moments and gradient surrogates); identity matrix ; Image feature vector The column vector represents the statistics jointly extracted from the infrared and visual data of the sealed area, with values ​​obtained after semantic alignment in step one. Maintain consistency in standards.

[0073] When in use, the calibrated state Without disrupting the underlying mechanism, it absorbs the unmodelable errors of the local field, ensuring that the predictions of intensity and risk remain convergent even during material replacement and sudden environmental shifts; the residual gain matrix... The versioning record improves auditability and provides a quantitative basis for triggering out-of-bounds rollback in step three.

[0074] After obtaining the corrected state With softening Subsequently, a risk characterization of time-varying hazard rate-integral failure probability is constructed, and a predictable expression of unit qualified product energy consumption is constructed using baseline-marginal energy consumption.

[0075] The risk side is driven by softening degree, temperature gradient, sealing strength, and contour closure; the energy consumption side is driven by the energy consumption baseline function. Anchor points are introduced, along with control margins and penalties for insufficient softening, to ensure a consistent optimization interface that saves energy without compromising quality, further addressing defect risks. :

[0076] Among them: defect risk The scalar value serves as the direct input to the hard / soft constraints in step four; contact dwell. Positive real number, risk integral window, and contact window Coordination and consistency; intercept Real number, baseline hazard rate; coefficient vector: The column vector characterizes the exponential impact of each feature on the hazard rate, and its values ​​are obtained through shift-level calibration. Feature vector Column vector containing softening degree Sealing strength Temperature gradient surcharge, profile closure and equivalent pressure etc., with and Combination and composition.

[0077] Further constructing predictive unit energy consumption :

[0078] Among them: predicted unit energy consumption : Positive real number, energy consumption output directly related to the optimization objective; energy consumption baseline function The conditional baseline from step one provides the least biased reference under the fingerprint conditions.

[0079] coefficient Positive real numbers, representing the marginal energy consumption weights of hot air flow, ultrasonic power, reciprocal of linear velocity (dwelling enhancement), and insufficient softening penalty, respectively, are calibrated through in-shift regression and consistent with the power specifications on the equipment nameplate; control components The main regulating factors of energy consumption and quality; In practice, the risk representation, expressed as an exponential hazard rate with time integral, is sensitive to short-term high gradients and locally closed defects, thus providing early warnings for edge scenarios without sacrificing average strength. The energy consumption representation unifies the baseline-edge-softening notch onto a differentiable curve, facilitating subsequent solver processing and resolving the conflict between reducing energy consumption and maintaining strength through coefficients. The balance is explicitly adjustable.

[0080] Step 3: Implement constrained online identification to update twin parameters. The semantic alignment vector, energy consumption deviation, and defect risk are mapped to the dynamic upper and lower limits and change rates of hot air flow, hot air temperature setting, linear velocity, and ultrasonic power, and a versioned release process window object is generated and versioned. With boundary projection calculation .

[0081] parameter vectors of twin models under continuous production conditions Constrained online identification is performed, and fingerprint-residual consistency-gated updates are used to ensure identification convergence and engineering feasibility, thereby providing a basis for process boundary set. Provides robust and traceable physical-data support. With fluctuations in incoming material moisture content and seasonal humidity transitions, the aforementioned mechanistic sublayer-residual sublayer's temperature-humidity coupling matrix A, control coupling matrix B, external mapping matrix G, and softening kinetic parameters... marginal coefficient of energy consumption Gradual shifts will occur; if offline calibration is relied upon alone, the model will systematically underestimate defect spikes or overestimate energy-saving potential.

[0082] Therefore, an online identification law is needed that integrates image features, energy consumption observations, and mechanism prediction differences, and is consistent with fingerprint indicators. Collaborative triggering ensures that identification proceeds only when the data is reliable and the risk is controllable, thus avoiding drift amplification. Therefore, parameter updates are constructed using constrained natural gradient-information regularization, and the feasible region of the parameters is defined. With hard projection operator It is made public to ensure that those skilled in the art can reproduce and implement it.

[0083] With observation vector With prediction vector The residuals between them serve as the driving force, and the natural gradient step with an information matrix is ​​used for advancement, with fingerprint consistency as the indicator. and defect risk The weight matrix adjusts the learning rate and direction to ensure robust updates when the quality-energy-fingerprint ternary parameters are consistent. To prevent parameters from crossing the physically feasible region, the updated parameters are passed through a hard projection operator. Return to feasible set The specific method is as follows:

[0084] Wherein: twin model parameter vector : A column vector containing the vectorized coefficients of the temperature and humidity coupling matrix, softening kinetic parameters, and energy consumption marginal coefficients, representing the feasible set of parameters. The objects identified online; Hard projection operator : An operator that truncates any vector element-wise to the boundary of a feasible set, specifically by squeezing each component with upper and lower bounds; refers to a mapping that directly pushes any vector back onto a specified feasible set, for non-empty closed convex sets. , The Euclidean second norm is defined as follows:

[0085] in, Map any X to The closest point in; For a non-empty closed convex set, vector The projection point and candidate points have the same dimension and control / parameter space; L2 norm. : Euclidean distance metric; Dimension d: a positive integer, determined by the number of specific variables.

[0086] Step scalar : Positive real number, (0,1], time-adaptive learning rate, which can be scheduled by a class stability strategy; information matrix Positive definite matrix Take specific ( (for numerical regularization); Jacobian matrix The matrix is ​​the set of partial derivatives of the prediction vector with respect to the parameters, whose values ​​are explicitly obtained from the twin model; the weight matrix is ​​also included. A diagonal nonnegative matrix, fused fingerprint consistency indicator With defect risk

[0087] The weighting of the importance of the observation residuals is updated on a rolling basis with each shift; Observation vector : From image feature vectors With unit energy consumption observation The concatenated column vector is Reference for identification; prediction vector The observation matrix H acts on the corrected state. Predictive characteristics and predicted unit energy consumption The concatenated column vector is The residuals are formed by comparing with the observations; Observation matrix A matrix maps states to a space with the same dimensions as image features; the corrected state Column vectors represent the immediate state after data consistency; image feature vectors. Column vectors, infrared-visual features; unit energy consumption observation Positive real number, real-time energy consumption measurement during the shift; regularization parameter Positive real numbers, avoid pathological inversions.

[0088] When in use, the update law converges stably under the dual constraints of the information matrix and hard projection, and can suppress parameter oscillations when noise disturbances and short-term deviations exist. The gating weight incorporates risk and consistency into the identification, so that the parameters are updated only in a reliable and safe period of time, thereby improving the reliability and traceability of subsequent boundary reconstruction.

[0089] Obtaining the new twin model parameter vector Next, the real-time semantic alignment vector needs to be... The energy consumption-risk deviation is jointly mapped to the upper and lower bound candidates and the rate of change of the control parameters. To ensure interpretability and implementability, a composite mapping of linear basis and saturated nonlinearity is adopted, through a saturated mapping function. Limit the boundary offset range and explicitly deviate the energy consumption from the limit. With defect risk Incorporate the boundary correction term to form candidate boundaries. With the rate of change vector The specific method is as follows:

[0090]

[0091] Where: candidate lower bound Candidate upper bound The column vectors correspond to the hot air flow rates, respectively. Hot air temperature setting Linear speed Ultrasonic power Candidate upper and lower limits; intermediate quantities for boundary reconstruction; lower bound of the benchmark. upper limit of the benchmark Column vectors, device nameplates, and conservative templates given by empirical rules; scale matrix. The matrix maps the correction values ​​in the feature space to the upper and lower bounds of the four control parameters. Saturation mapping function : The element-wise tanh function, i.e. Limit the boundary correction range and ensure numerical stability; fingerprint mapping matrix Matrix, from semantically aligned vectors Linear principal components for generating boundary offsets; energy consumption offset weights. Vector, deviating energy consumption Injection correction amount; risk weight Vector, defect risk Inject correction amount; rate of change vector Column vectors, four control parameters, allowable maximum rate of change, used for rate constraints; minimum / maximum rate. Column vector, rate lower / upper template; fingerprint increment Column vector, The time difference is used to characterize the intensity of the mutation; the rate mapping matrix. A matrix maps fingerprint increments to the rate scheduling space; element-wise multiplication... Element-level weighting; In use, the mapping structurally guarantees the unidirectionality and interpretability of fingerprint change-boundary correction-rate scheduling; the saturation function suppresses boundary jumps caused by extreme values; and the explicit coupling of energy consumption and risk enables the boundary to tighten towards energy saving and to quickly converge to the conservative domain when the risk increases.

[0092] candidate boundary In the rate of change vector Under constraints, it is tuned to the process boundary set. and form a process window object. Published online; in the event of a risk-energy consumption-consistency violation, it will revert to the safety template window based on the quantified threshold. And versioned records.

[0093] Directly using candidate boundaries can lead to frequent jitter in the execution layer due to rate jumps and local anomalies, and may even cause peak power out-of-bounds errors and quality fluctuations. A three-layer mechanism of model confidence, rate projection, and out-of-bounds backoff is needed to stabilize the boundaries, and a process window object should be used for this purpose. As the sole external interface (including upper and lower bounds, rate of change, and confidence level), it is defined as a context key within the asset management shell. Versioning and solidification align optimization and auditing. Therefore, based on a computable confidence metric, the final executable boundary is generated by rate projection and hard boundary assembly, and the trigger quantity and action primitive for out-of-bounds fallback are defined.

[0094] First, a model confidence score is constructed using residual energy-information weights to adjust the intensity of boundary changes. Then, a rate projection operator is applied to the candidate boundaries. This generates smooth and executable final boundaries; the higher the confidence level, the closer the boundary is to the candidate. As the confidence level decreases, the boundary variation is compressed. Specifically:

[0095] Among them: window confidence Scalar, measures the degree of agreement between the current model and observations; weighted norm. Defined as With weight matrix Measure residual energy.

[0096]

[0097] Wherein: final lower bound Ultimate Upper Realm Column vectors form the process boundary set Executable upper and lower limits; previous time-bound boundary Column vectors provide the base points for rate projection; rate projection operator : Element-wise rate limiting operator, specifically Where Δ>0 is the time step; the boundary changes are clipped to the allowable rate range; Candidate lower / upper bound rate of change vector Time step Δ: Defined in sub-step 301, reused here. Process boundary set :Depend on The defined set of hyperrectangular constraints is the feasible region that can be invoked by the optimization and execution layers.

[0098] In use, the two-level mechanism of confidence-projection causes the boundary to automatically shrink or expand with the data confidence, significantly reducing upper and lower bound jitter and frequent parameter tuning at the execution layer; the rate projection clearly controls the physical rate of boundary changes, avoiding thermo-mechanical coupling impact on the hot air system and ultrasonic transducer.

[0099] When the comprehensive index of quality risk, energy consumption deviation, and feasibility projection exceeds the threshold, immediately revert to the safety template window. Update the window version number and define the boundary projection operator. This serves as a unified external feasibility filtering interface to ensure that subsequent optimizations and candidate control trajectories are rigidly pruned.

[0100]

[0101] Among them: Overall violation rate : Non-negative scalar, a single criterion for triggering a backoff; weighting coefficient Positive real numbers; the importance of balancing the three types of violations: quality, energy consumption, and feasibility; control vectors. Column vector containing hot air flow rate Hot air temperature setting Linear speed Ultrasonic power ; Infinite norm : Maximum absolute component of a vector, characterizing the worst-case feasibility deviation; Minimum / maximum element-wise operator : Defines the explicit form of the boundary projection operator Cut arbitrary control vectors into .

[0102] When used, the violation rate aggregates the three types of risk sources in the form of a single scalar, which facilitates the formulation of a unified threshold; the explicit closed expression of the boundary projection ensures that subsequent optimization and candidate trajectories can be hard-constrained and pruned at the interface layer; versioned rollback, together with asset management shell records, can directly serve audit review and root cause tracing.

[0103] Step 4: In the process window object Within the feasible region, the solution is used to predict unit energy consumption. The optimal control sequence is defined as the objective and constrained by defect risk and peak power. When the feasible region shrinks, a short-term takeover is performed using reinforcement learning candidates filtered by the window and safety rules, outputting a directly executable control vector trajectory.

[0104] In the process window object Within the defined feasible domain, a joint objective of energy consumption, quality, and execution smoothness is constructed based on a mechanism-data coupled twin model. Perform rolling prediction and solve for the optimal control sequence To obtain an executable and perturbation-robust energy-optimal trajectory.

[0105] Choosing the optimal energy consumption solely based on instantaneous energy consumption amplifies short-term fluctuations, triggers hot air and ultrasonic power spikes, and easily sacrifices sealing strength and edge safety; while prioritizing quality redundancy for stability leads to long-term energy consumption failures. Actual production requires establishing a continuously differentiable and adjustable trade-off between deviation from the baseline in unit energy consumption and defect risk, while respecting process window objects. Given upper and lower bounds and rate constraints.

[0106] Therefore, on predictable multivariate paths, a unified objective of Bregman deviation-log barrier-rate penalty is used to generate a smooth trajectory that is friendly to the execution layer, and all previously defined interface quantities are explicitly coupled to ensure horizontal consistency and vertical traceability.

[0107] A rolling window is established with an optimized time domain length H, using the potential function from step one. Define the Bregman deviation of unit energy consumption relative to the baseline By superimposing the logarithmic risk barrier and the action smoothing term, a total objective function spanning the entire time domain is obtained. Through this construction, energy consumption, quality, and execution smoothing are embedded into a differentiable curve, facilitating integrated solution and sensitivity analysis. The total objective function is constructed first. The details are as follows:

[0108] Among them: Overall goal : Non-negative scalar, measures the combined cost of energy consumption, risk, and smoothing within the optimization time domain; Time domain length H: Positive integer, number of rolling prediction steps, with values ​​configured according to tick time and execution delay; Bregman deviated : Using the same potential function in step one Forecast unit energy consumption Relative energy consumption baseline function The deviation measure is Key indicators for energy consumption optimization; Risk penalty Logarithmic barrier function, varying with risk ceiling Return to oneness, for Defect risk push away from the upper limit; If prediction Then force in the inner iteration And trigger a fallback to the feasible region. ; Risk weight Positive real numbers, balancing energy consumption and risk; smoothing weights Positive real numbers, suppressing execution jitter; weighted norm 1 Where the weight vector Smoothing intensity is allocated based on actuator lifespan; control vector , : These represent the controls for the current and previous cycles, respectively, and their values ​​are constrained by the process window.

[0109] Predicted unit energy consumption Semantic alignment vector Defect risk All are derived from the twin outputs / inputs of step two, with a maximum risk. : Positive real number, quality red line, time index t, k: t is the current scrolling time, This is the index for the prediction step; the sampling period is Δ>0.

[0110] In use, the objective function unifies energy consumption deviation, risk approach and execution smoothness into a differentiable combination, enabling the solver to weigh the factors within the same sensitivity framework; the logarithmic barrier amplifies exponentially when the risk approaches the upper limit, forming an adaptive soft hardening effect; the weighted norm quantitatively suppresses abrupt changes, significantly reducing the probability of peak power and thermal shock.

[0111] Based on the goal The rolling prediction of the twin model employs a joint projection-gradient iterative solution with an embedded process window object. The feasible set consists of the peak power half-space. This process performs gradient step propagation on the full-time domain control sequence at each step, followed by feasible region projection, ensuring that the generated trajectory can be directly deployed without secondary adjustments. Specifically:

[0112] Wherein: control sequence Column vector, optimization variables; iteration index j: non-negative integer, inner layer iteration count; step size. : Positive real number, gradient step size, which can be set according to the Armijo criterion or a fixed value; gradient The analytical gradient of the objective function is obtained by following the chain rule from step two. Explicit export.

[0113] feasible region projection Projecting onto a set The operator satisfies upper and lower bounds, rate, and peak power limits; where is the equipment power equivalence factor, is the upper limit of peak power; time step Δ:: a positive real number, the time scale for rate limiting.

[0114] When used, this iteration follows the principle of projection first and execution later, ensuring that each candidate solution is in the physically feasible region, greatly reducing the overhead of secondary verification before distribution; the introduction of peak power half space makes energy consumption optimization naturally take into account electrical shock safety; the gradient is provided analytically by the twin model, avoiding noise amplification and convergence lag caused by numerical difference.

[0115] When large perturbations or a decrease in model confidence lead to a shrinking of the feasible region for conventional optimization, a reinforcement learning candidate strategy with only short-term intervention is introduced. Before distribution, a shielded-projection safety filter performs a conservative check on constraint consistency and quality-energy consumption linearization to generate safety controls. The output is mixed with the nominal decomposition, and all trigger-toggle-rollback events are written to the policy snapshot immediately.

[0116] Sudden disturbances (such as material moisture transition or partial nozzle blockage) can cause [damage] within a short window. Reduce and As risk increases, the feasible region of conventional prediction optimization may be squeezed into infeasibility by risk constraints. At this point, a complete shutdown or forced rollback would lead to capacity loss, while unconstrained activation of reinforcement learning carries the risk of unknown actions exceeding limits and quality issues. Therefore, a two-layer screening structure combining linearized safety constraints and process window projection is needed to... Candidate actions are subjected to computationally computable and traceable strong constraint filtering, allowing them to take over only within a very short time domain and ensuring that all issued actions meet the hard constraints.

[0117] In the current corrected state With nominal control At this point, a first-order sensitivity approximation is used to determine the upper limit of risk. With lower limit of strength Linearize into convex constraints, and use least squares distance to... Candidate actions are trimmed to the intersection of the linearized safety set and the process window to obtain safety control. :

[0118] Among them: security control Column vectors, which can be sent out in a single-cycle control after masking-projection; candidate control Column vectors, generated by reinforcement learning strategies Candidate actions generated; nominal control Column vector, outputting the optimal first move; risk gradient The column vector, representing the sensitivity of defect risk to control, is analytically obtained from the hazard rate integral form in step two using the chain rule. Intensity gradient The column vector represents the sensitivity of the sealing strength to control; the upper risk limit is obtained analytically from the power-law mapping in step two. Intensity lower limit Positive real numbers, quality red line and strength protection threshold; process boundary set :Depend on The defined feasible region comes from step three; rate of change Time step Δ, previous beat control All are defined above. In use, linearization masking locally convexes the nonlinear quality constraint at the current operating point, ensuring the solver can solve it quickly within one clock cycle; and... The intersection projection ensures that the hard boundary and rate limit are not exceeded; the target adopts the minimum displacement principle, so that... The exploration was preserved with minimal modifications, balancing adaptability and security.

[0119] To avoid pattern jitter, a window-based confidence level is used. With overall violation rate Hysteresis switching logic; within the permissible intervention period, convex combination hybrid safety control. With nominal control And package all trigger quantities and control versions into a policy snapshot and write it to the asset management shell:

[0120] Among them: control distribution Column vector, the final execution quantity after masking and blending; blending coefficient. A scalar determines whether to employ safety controls; in implementation, a hysteresis threshold is used. and Set intervention threshold Exit threshold With intervention threshold Exit threshold Avoid frequent switching; Window confidence Scalar, derived from the residual energy metric in step three; overall violation rate. : A non-negative scalar, derived from the quality-energy consumption-feasibility aggregate index in step three; indicator function Boolean to The mapping provides a mode selection.

[0121] When in use, hysteresis switching effectively suppresses oscillations and back-and-forth interventions near the boundary; convex mixing can be implemented at the interface layer without modifying the underlying controller; policy snapshots version and solidify the mixing ratio, trigger threshold, and context key, forming a reproducible experimental-level evidence chain.

[0122] Step 5: Send the control vector trajectory to the controller through a secure channel, and package the policy, parameter version, fingerprint digest, process window and gate log into a policy snapshot and store it in the database; perform audit scoring based on the snapshot and generate rectification instructions to drive the continuous updating and versioning of parameters and boundaries.

[0123] The output of step four will be sent down to control In the safety passage Internally complete consistency gating and atomic distribution, and use policy snapshots. Version and solidify all key objects and triggering events during the shift to ensure that upper-level audits and subsequent reviews can be re-enacted.

[0124] In the production execution layer, field clock fluctuations, network jitter, and controller heterogeneity can introduce timing misalignments at the millisecond level. If boundary-rate-clock triple consistency gating is not implemented at the interface layer, the issued control may misalign with the process window object in terms of differential constraints. Disconnection can lead to loss of control over peak power or sealing strength. On the other hand, continuous improvement requires that the causal chain of each control deployment – ​​quality result – energy consumption metering – gating trigger be fixed as a policy snapshot in an irrefutable form and indexed for retrieval. Based on this, a computable consistency criterion is first defined for gating deployment, and then a structured snapshot object and content index are constructed to ensure that policy deployment and evidence collection are synchronized.

[0125] In the safety passage In the middle, the boundary projection operator With rate limiting Control over distribution Gating is implemented, and consistency deviations are defined to trigger release or rollback. To ensure that actions reach multiple controller nodes indivisibly, an atomic framing-consistency acknowledgment strategy is adopted: a single-frame atomic packet is formed and broadcast only when all gating quantities meet the threshold; if any acknowledgment is missing, the entire process rolls back to the safe template window. The nominal control is implemented in the following ways:

[0126] Among them: execution consistency deviation : A non-negative scalar, used to determine whether to allow the issuance of data. ;Issue control The column vector represents the control variables to be issued, with components being hot air flow rate, in order. Hot air temperature setting Linear speed Ultrasonic power Values ​​are subject to With rate limiting; Boundary projection operator : Cut arbitrary control into the process boundary set Operators that guarantee upper and lower bound constraints; infinite norm The maximum absolute component norm of a vector measures the worst deviation; the amplitude-limiting norm. This represents the norm of the maximum value taken for the excess rate of each component, and checks whether the instantaneous rate exceeds the limit. Previous beat control Column vector, rate base point; rate of change vector Column vectors, from Rate limiting; time step Δ: a positive real number, the time scale for rate limiting; process boundary set. Upper and lower boundaries All of these originate from the process window object in step three. (Security Channel) The atomic acknowledgment uses a two-phase commit semantic: the frame contains a boundary hash and a clock stamp; a commit is only made when all controllers return ACKs; otherwise, a rollback occurs. Controlled in name only.

[0127] When used, in a single scalar Aggregating boundary and rate violations greatly simplifies the release criteria; atomic distribution avoids half-state execution caused by asynchronous multiple controllers; gating in advance ensures that any out-of-bounds control is absorbed at the interface layer, reducing the probability of compounding execution layer exceptions.

[0128] To support cross-period benchmarking and external auditing, the input, calculation, output, gating, and result of the current shift strategy are packaged into a strategy snapshot according to a unified semantics. It generates deterministic index keys for fast retrieval and verification.

[0129] The core fields of a snapshot include: context key fingerprint summary Process Window Object Nominal optimal sequence Safety control sequence, risk and energy consumption curve Trigger count and threshold Information includes compressed air chain metering and caliber, as well as equipment and algorithm version information. To ensure lightweight and collision resistance, the index key uses a content digest encoded with random projection-symbol quantization-readable coding, as follows:

[0130] Where: index key : String, primary key for snapshot retrieval, value is a fixed-length encoded string; encoding operator Will Encoding operators that map to readable strings (such as Base32 style) facilitate manual comparison and system indexing; symbolic functions Element-level symbolic quantization converts a real vector into a symbolic representation of a real vector. ; Random projection matrix A matrix that generates a compressed summary from a content vector, its seed being a context key. The values ​​are fixed and follow a sub-Gaussian distribution with each row being independent and having a mean of zero.

[0131] Where: Gaussian matrix G: is determined by the context key Generated deterministic pseudo-random sequence; content vector A column vector, formed by concatenating the core fields of the snapshot in a predefined order and with their dimensions normalized. Content length L, dimension. Positive integers, representing the summary length and content dimensions.

[0132] In use, structured snapshots ensure that evidence of who issued which controls, when, and under what boundaries, and what results were available in a single instance; random projection indexes offer low collision and reproducibility, facilitating rapid location within large-scale snapshot libraries across systems and shifts; and context keys... The coupling ensures that the content summary of the same window is stable and consistent.

[0133] Based on policy snapshot Construct a differentiable audit scoring function It also performs cross-domain attribution to solve for the rectification instruction vector. It also updates the asset management shell in a closed loop to achieve continuous improvement in energy consumption and quality.

[0134] Audits must benchmark against different shifts / batch horizontally and assess improvement trends under the same working conditions vertically. Relying solely on experience-based scoring or single-indicator thresholds makes it difficult to reflect the least biased baseline and systemic losses in the utility chain under fingerprint conditions. Therefore, the energy consumption baseline function from step one... As an anchor, the defect risk of step two is superimposed. Together with the trigger counts and window confidence in steps three / four, a differentiable audit target is formed; then, an attribution vector is generated using path integral sensitivity, and the deviation is distributed to the four domains of fingerprint, control, equipment and utilities. Finally, executable rectification instructions are generated through convex quadratic programming and fed back into the system version.

[0135] For time window Energy consumption, risk, and utility efficiency within the system are uniformly measured to generate audit scores that can be linked to batches / shifts. Simultaneously, the audit-driven feature vector q is attributed based on path integral sensitivity to identify areas for improvement. The specific methods are as follows:

[0136] Among them: audit score : Non-negative scalar, the comprehensive audit target for this shift / batch window; the smaller the value, the better; start and end time. , +Δt: Real number, defining the audit time window; weight : Positive real number, a tradeoff coefficient for energy consumption, risk, utilities, and trigger penalties; Bregman deviation Δ Potential function from step one Defined energy consumption deviation from baseline; risk penalty : Logarithmic barrier function from step four; Compressed air equivalent efficiency Scalar, overall efficiency of the supply-transmission-distribution-consumption gas chain (obtained from shift metering and caliber conversion); trigger count : A non-negative integer representing the cumulative number of times out-of-bounds rollback / masking has been triggered.

[0137] Furthermore, attribution coefficients are constructed. :

[0138] Where: Attribution coefficient : Real number, quantifying the i-th audit-driven feature pair Marginal contribution; audit-driven feature vector Column vectors containing fingerprint summary components, control statistics (such as average / extreme control values ​​and start-up / shutdown ratios), equipment health indicators, and key parameters of the compressed air chain; values ​​are semantically aligned and normalized; baseline points. Column vector, the reference path starting point for the same working condition (given by a conservative template or historical best window); path parameters Continuous scalar on, path integral dummy variable; partial derivative The sensitivity of the audit target to the i-th feature is calculated from the above formula according to the chain rule.

[0139] When used, the audit score unifies energy consumption, risk, and utility efficiency in a differentiable form, facilitating sensitivity analysis and cross-window benchmarking; path integral attribution avoids the bias of local linear approximation, can stably identify the feature dimensions that have the greatest impact on audit objectives, and provides a focused direction for rectification; and the inclusion of trigger counts in the objectives makes the system sensitive to robust degradation phenomena such as frequent rollbacks.

[0140] By mapping the gradient of audit scores and attribution results to the policy and maintenance executable parameter space, a quadratic programming problem with positive definite priors is constructed to solve for the rectification instruction vector. And it is constrained within the budget and boundaries; subsequently, the updated results are written back to the asset management shell in a versioned manner, becoming the prerequisite for subsequent shifts, as follows:

[0141] Among them: rectification instruction vector Vectors, incremental suggestions for executable parameters such as strategy / threshold / maintenance (e.g., risk weights). Smoothing weights Risk ceiling Intensity lower limit (Speed ​​scale coefficient, compressed air supply pressure setting, etc.), the values ​​are subject to a set of constraints. Constraints; Feasible set : Convex set, a set of budget, boundary, and compatibility constraints (including upper and lower bounds for each parameter and the total change range); feasible set Enforceable constraints:

[0142] upper and lower bounds of parameters : From safety and regulatory red lines. Maximum change amount Avoid making too large a change at once. Budget constraints. The costs of maintenance, manpower, and downtime are converted into linear resource constraints.

[0143] Audit Target Gradient Column vector, The gradient of the executable parameters (obtained through sensitivity propagation of the objective and constraints in step four); Hessian matrix. A symmetric positive definite matrix provides a quadratic prior for the stability and directional regularity of the solution; the attribution coefficient vector Column vector; Mapping matrix A matrix that projects the attribution dimension onto the executable parameter dimension (calibrated by offline sensitivity testing or device characteristics). Attribution weights. : Positive real number, balancing gradient-based direct descent and attribution-based directional adjustment.

[0144] When used, the minimum cost rectification vector is directly given in the executable parameter space through quadratic programming, avoiding blind tuning of the weights inside the original optimizer; positive definite priors suppress system oscillations caused by over-correction; after embedding the attribution into the target, the rectification priority is aligned with the actual source of deviation, improving the efficiency of improvement under limited resources; versioned backfeeding ensures that each rectification can be traced to a specific audit window and evidence chain.

[0145] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0146] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0147] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0148] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0149] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A real-time energy consumption optimization control system for a digital twin of a paper cup production line, characterized in that: include, Fingerprint vectors are obtained by calibrating multi-source signals. Semantic alignment vectors are generated by entropy regularization optimal transmission. These vectors are bound to batches / shifts / time windows, and a unit energy consumption condition baseline is established. Fingerprint summaries are generated and registered in the asset management shell as the sole data entry point. Under the conditions of the semantic alignment vector and control vector, a mechanism-data coupled twin model is constructed. A memory kernel is introduced to advance the temperature and humidity state. Combined with image feature residual correction, the softening degree, sealing strength, defect risk and predicted unit energy consumption are calculated. The mechanism sub-layer uses discrete-time convolution with a memory kernel to represent the advancing temperature and humidity state. The state vector includes equivalent temperature and equivalent humidity. The control vector includes hot air flow rate, hot air temperature setting, linear velocity and ultrasonic power. It is coupled by introducing the semantic alignment vector with an external mapping. The memory kernel takes an exponential decay family. The constrained online identification update of the twin parameters is implemented, and the semantic alignment vector, energy consumption deviation and defect risk are mapped to the upper and lower limits and change rates of each component of the control vector, thereby generating the process window object and boundary projection operator; Within the feasible region of the window, with the predicted unit energy consumption as the objective and constrained by the defect risk and peak power, the control vector trajectory is obtained by projection method. When the feasible region shrinks, the candidate strategy filtered by the window and security rules takes over; The trajectory is gated and atomically distributed through a secure channel, generating a policy snapshot containing version information, the fingerprint digest, the process window, and the gated log, which is then stored in the database. Based on the snapshot, audit scores and rectification instructions are output, and the parameters and boundaries are updated by backfeeding.

2. The real-time energy consumption optimization control system according to claim 1, characterized in that: At the device layer, raw measurement and context parameters are synchronously acquired through a unified acquisition channel. A fingerprint vector is generated according to the affine calibration matrix and bias term, and timestamp signature is performed using a single clock source, so that the fingerprint vector corresponds one-to-one with batch, shift, and time window.

3. The real-time energy consumption optimization control system according to claim 2, characterized in that: The fingerprint vector is mapped to the asset management shell model field through entropy regularization optimal transmission to form a semantic alignment vector; the cost matrix consists of unit conversion and dimension compatibility terms, the row and column edges are balanced with semantic weights, and the semantic alignment vector is registered and archived with context keys.

4. The real-time energy consumption optimization control system according to claim 3, characterized in that: Based on the semantic alignment vector, a maximum entropy conditional baseline function for energy consumption per unit of qualified product is established. It is parameterized using an exponential family and includes a logarithmic partition function. The natural parameters are determined by the semantic alignment vector. The energy consumption deviation is defined using entropy-type Bregman divergence, and both are used in a unified manner throughout the entire process.

5. The real-time energy consumption optimization control system according to claim 3 or 4, characterized in that: The semantic alignment vectors within the time window are aggregated into a fingerprint digest by separable time kernel vectors, and aligned with the statistical caliber of energy metering through linear mapping. An infinite norm consistency indicator is calculated, and the fingerprint digest and the indicator are written into the asset management shell field and a version number is established.

6. The real-time energy consumption optimization control system according to claim 5, characterized in that: The softening degree is constructed based on a weighted heat and moisture dose, using a weighted kernel that decays monotonically within the pressing window and a smooth hyperbolic response; the sealing strength is obtained by a power-law mapping of the softening degree and the equivalent pressure, which is synthesized from mechanical pressing and ultrasonic power through a calibration curve and has a unified caliber.

7. The real-time energy consumption optimization control system according to claim 6, characterized in that: Image feature vectors extracted from infrared thermal imaging and machine vision are constructed and used together with mechanism prediction to form residuals. The state is corrected by minimum intrusion through a symmetric positive definite residual gain matrix. The observation matrix maps the state to surrogate quantities such as temperature moment and gradient, and records the version information of the gain matrix.

8. The real-time energy consumption optimization control system according to claim 7, characterized in that: Based on the time-varying hazard rate integral expression of the defect risk defined by the corrected state and softening degree, the integration window and risk upper limit parameters are set; on the energy consumption side, the marginal terms related to hot air, ultrasound and linear velocity and the insufficient softening penalty are superimposed on the energy consumption baseline function to form an analytical expression for predicting unit energy consumption.

9. The real-time energy consumption optimization control system according to claim 1, characterized in that: Constrained online identification is performed on the twin parameter vectors, using a natural gradient step with an information matrix, wherein the information matrix is ​​quasi-Newtonian and contains a numerical regularization term; the learning rate and direction are gated by a weight matrix composed of the consistency indicator and the defect risk, and after parameter update, the parameters are hard-projected back to the feasible set.

10. The real-time energy consumption optimization control system according to claim 9, characterized in that: The semantic alignment vector, the energy consumption deviation, and the defect risk are mapped to upper and lower bound candidates and change rates of control parameters. The candidates are projected by the rate to obtain the final boundary and encapsulated as a process window object. When the overall violation rate exceeds the set threshold, it falls back to the safety template window and updates the window version number and context key.

11. The real-time energy consumption optimization control system according to claim 10, characterized in that: Within the feasible region and peak power half-space of the process window object, a rolling target is constructed according to the Bregman term of energy consumption deviation, the logarithmic obstacle of risk and the motion smoothing term, and solved by the projection gradient method. When the feasible domain shrinks, the reinforcement learning candidates that have been shielded and projected are enabled to take over in the short time domain and are distributed in a hybrid manner of convex combination and nominal control.

12. The real-time energy consumption optimization control system according to claim 11, characterized in that: Gating is constructed by boundary projection and rate limiting, and atomic delivery is performed using two-stage submission semantics; The context key, fingerprint digest, process window, control sequence, and gating log are packaged into a policy snapshot and a content index key is generated. Based on the snapshot, the rectification instruction vector is solved using convex quadratic programming and written back to the asset management shell in a versioned manner.

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