AI black light laboratory asynchronous multi-process cooperation method based on attention mechanism

Abstract

The invention discloses an AI black light laboratory asynchronous multi-process collaboration method based on an attention mechanism, and relates to the technical field of laboratory automation, and the method comprises the steps: taking a strategy snapshot as a single fact source, recording a model version fingerprint, an attention importance vector, a threshold value, an affected sample set and a rollback point, and carrying out the dual writing of a pre-writing-confirmation two-stage atomic transaction between dual systems, when conflicts occur, the read-only freezing is recovered according to a rollback point; a congestion signal for tokenized intersection occupation prediction is introduced as attention offset, total variation steady-state regularization is matched, and an urgent order is triggered by an attention difference threshold value and traces are reserved along with snapshots; performing micro-rearrangement in a time window and a local candidate set without backtracking a key path, and performing anti-fact playback and regret degree auditing; and mapping a penalty term according to uncertainty levels and triggering nearby micro-loop retest, preferentially occupying an empty window under a token interface, unifying congestion thresholds, and forming a scheduling-traffic-quality control closed loop.

Description

Technical Field

[0001] This invention relates to the field of laboratory automation technology, specifically to an AI-based asynchronous multi-process collaborative method for a dark laboratory based on an attention mechanism. Background Technology

[0002] The "lights-out" laboratory operates under conditions of multiple instruments, multiple channels, and multiple shifts. Samples flow asynchronously between processes such as extraction, amplification, instrumentation, quality control, and storage, spanning two business domains: the laboratory information management system and the manufacturing execution system. Existing integrations typically focus on interface mapping and record synchronization, lacking a unified state view based on policy snapshots as the single source of fact. While dispatching based on self-attention or reinforcement learning has emerged on the scheduling side, it does not explicitly inject congestion signals formed by tokenized resource occupancy predictions into attention, nor does it impose total variation steady-state constraints on adjacent decision moments. When urgent order insertions and cross-system submission failures occur, they rely heavily on post-event manual correction, lacking two-phase atomic transactions and read-only freeze-rollback links. Quality retesting and traffic tokens are disconnected, and the retesting sequence is difficult to align with the dispatching rhythm, leading to inconsistencies between records and execution, passive backtracking of critical paths, and incomplete audit trails. Meanwhile, existing time windows and rolling rearrangements of local candidate sets are mostly isolated modules, failing to form an integrated link with two-phase atomic transactions and audit fields; counterfactual replay and regret are mostly at the algorithm level, not solidified by snapshot numbers; the token interface lacks unified specifications in holding, releasing and renewing semantics, and prediction windows and congestion thresholds often exist in static configuration, failing to form a stable binding with threshold fields, affected sample sets and rollback points.

[0003] To adapt to unmanned, asynchronous multi-process, and high-concurrency scenarios, the core technical problem that urgently needs to be solved is:

[0004] In an operational cycle characterized by frequent resource convergence, dynamic congestion changes, and the insertion of urgent orders, how can we drive the isomorphic consistency of scheduling-commitment-audit with a single source of fact using strategy snapshots as the sole source of fact? How can we achieve the synchronization commitment of instructions and states between the laboratory information management system and the manufacturing execution system through two-stage atomic transactions? At the same time, how can we connect technical elements such as congestion signals, total variation steady-state regularization, time windows and micro-rearrangement of local candidate sets, counterfactual replay and regret, token interface and prediction window, uncertainty classification and nearest micro-loop retesting into a traceable link according to field-level relationships?

[0005] This problem typically occurs during the window of concurrent batch testing and incubation, key instrument convergence, cross-shift continuity, and emergency sample intervention. It often results in queue jitter and repeated preemption, partial success of cross-system submissions leading to state drift, decoupling of snapshots and execution causing rollback delays, and conflicts between retest occupancy and production tokens spreading to downstream processes, further amplifying work-in-process inventory retention and path conflicts. Ultimately, this makes it impossible for audit records to correspond one-to-one with model version fingerprints, attention importance vectors, thresholds, affected sample sets, and rollback points, making it difficult to support closed-loop governance and subsequent strategy revisions. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides an asynchronous multi-process collaborative method for AI blackout labs based on an attention mechanism. It uses a policy snapshot as a single source of fact, recording model version fingerprints, attention importance vectors, thresholds, affected sample sets, and rollback points. A two-stage atomic transaction of pre-write and confirmation is used for dual-write between the two systems. In case of conflict, read-only freezing is implemented, and recovery is based on the rollback point. A tokenized congestion signal from intersection occupancy prediction is introduced as an attention bias, combined with total variation steady-state regularization. Urgent orders are triggered by attention differential thresholds and recorded with snapshots. Micro-reordering is performed within the time window and local candidate set without backtracking the critical path. Counterfactual replay and regret are included in the audit. Uncertainty is hierarchically mapped to a penalty term and triggered retesting of the nearest micro-loop. Under the token interface, empty windows are prioritized, and a unified congestion threshold is implemented, thereby solving the technical problems described in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] The AI ​​Blackout Lab asynchronous multi-process collaborative method based on attention mechanism includes: using a policy snapshot as a single source of fact, containing model version fingerprint, attention importance vector, threshold, affected sample set and rollback point; employing a two-stage atomic transaction of write-ahead and confirmation to write to the system; and reading-only freezing and restoring according to the rollback point when cross-system conflicts are detected.

[0009] The congestion signal of the tokenized convergence resource occupancy prediction is explicitly injected into the attention; the total variation steady-state regularization is applied to the attention at adjacent decision times, and urgent orders are triggered by the attention differential threshold and recorded and traced by the threshold of the policy snapshot;

[0010] Micro-reordering is performed only within the time window and local candidate set, without backtracking to lock the critical path; the two-phase atomic transaction is used across systems, and each reordering generates a counterfactual replay and regret score, which are snapshotted into the audit and executed for failure rollback according to the strategy.

[0011] The congestion signal predicted by the tokenized intersection is sinked to the attention weight; the uncertainty is classified and a penalty term is mapped, triggering the retest of the nearest micro-loop and prioritizing the use of the token window; the token interface, prediction window, congestion threshold and retest sequence are defined.

[0012] Furthermore, the two-stage atomic transaction is double-written between the laboratory information management system and the manufacturing execution system. In the pre-write stage, a transaction number, target system identifier, and double-write sequence number are generated, and the model version fingerprint, threshold, field lock order, and rollback point index are written to the temporary area.

[0013] During the confirmation phase, the fingerprints of the current versions of the two systems are compared with those of the model version. If they are inconsistent, the reason code for freezing is recorded, a read-only freeze is executed, and the system is restored according to the rollback point before proceeding to the next round of confirmation.

[0014] Furthermore, the cross-system state conflict detection compares the affected sample set field by field based on the field lock order, and the comparison order follows the field priority of the model version fingerprint, threshold, rollback point index and the affected sample set.

[0015] When a difference is detected, the write path is frozen and the start and end times of the freeze window are marked. During the freeze, only reading is allowed, and within the window, the rollback point is rolled back to the most recently successfully committed snapshot version. After the freeze is lifted, the confirmation phase is retried in the original order and the dual write is completed.

[0016] Furthermore, the congestion signal is added to the query-key calculation with a bias term from the attention scoring. The bias term is generated by linear mapping from the convergence resource occupancy prediction and associated with the threshold.

[0017] The total variation steady-state regularization is a weighted sum of the attention importance vectors at adjacent decision times based on the absolute differences of the elements. The upper and lower bounds of the regularization coefficients are recorded by the policy snapshot and are shared during the training and inference phases.

[0018] Furthermore, the urgent order insertion trigger is based on comparing the maximum absolute difference between the attention importance vector corresponding to the target process at adjacent decision times and the attention difference threshold. If the difference is exceeded, an urgent order entry is generated. The entry includes the process identifier, trigger time, and difference value. The threshold value is bound to the threshold field of the strategy snapshot and written into the audit stream along with the transaction number and timestamp for subsequent retrieval.

[0019] Furthermore, the time window for micro-reordering is bounded by the start and end of the rolling interval and the step size recorded in the strategy snapshot. The local candidate set is limited to the set of processes with available resources within the window that are not locked by the critical path, and a sorting key is set for concurrent candidates of the same resource. Reordering does not backtrack the critical path, cross-system submissions use the two-stage atomic transactions, and if the submission fails, it rolls back according to the rollback point.

[0020] Furthermore, each counterfactual replay generated by the reordering includes the original state index, the list of actions taken and unselected actions, the constraint context, and the corresponding resource identifier. The regret entry records the cost difference and time index between the selected action and the unselected action. Both are stored in the audit partition along with the policy snapshot, and are automatically rolled back and the failure reason code is added when the submission fails.

[0021] Furthermore, the token interface includes three types of operations: holding, releasing, and renewing, and the interface call order is marked by an event sequence number; the prediction window and congestion threshold are uniformly configured by the policy snapshot and propagated with transactions;

[0022] After the congestion signal is incorporated into the attention weight, the retest task is preferentially allocated within the token window according to the renewal strategy, aligned with the prediction window, and synchronously recorded in the laboratory information management system and manufacturing execution system.

[0023] Furthermore, the uncertainty classification is mapped to a penalty term according to the interval boundary given by the policy snapshot, and the classification label is recorded together with the model version fingerprint of the policy snapshot;

[0024] When triggering the nearest micro-loop retest, the retest task determines the timing based on the holding and releasing events of the token interface. The retest entries are bound to hierarchical tags, prioritize occupying token windows, and are aligned with the prediction window and time window.

[0025] Furthermore, the audit fields include at least the strategy snapshot number, model version fingerprint, attention difference threshold, time window boundary, local candidate set identifier, regret entry, and token interface event number. These fields are stored in alignment with transaction numbers between the laboratory information management system and the manufacturing execution system, and after rollback, supplementary records are generated in the audit partition with the same number to maintain continuity.

[0026] This invention provides an asynchronous multi-process collaborative method for an AI-powered "lights-out" lab based on an attention mechanism, which has the following beneficial effects:

[0027] Using the strategy snapshot as the single source of fact and employing a two-stage atomic transaction dual-write approach of write-ahead and confirmation between the laboratory information management system and the manufacturing execution system, the strategy snapshot includes model version fingerprint, attention importance vector, threshold, affected sample set and rollback point, and is configured with cross-system conflict read-only freeze and recovery by rollback point, thereby forming field and version consistency and traceability control;

[0028] The congestion signal obtained from the tokenized convergence resource occupancy prediction is injected into the attention as an explicit bias term and bound to the threshold field in the strategy snapshot. The bias is generated by linear mapping before query and key calculation, so that the attention weight of high occupancy process is subject to interpretable constraint during work assignment, so that the decision reason corresponds to the audit field.

[0029] A total variation steady-state regularization is applied to the attention importance vector at adjacent decision moments. The steady-state regularization coefficients are recorded by the policy snapshot and used in training and push, so that attention can maintain a smooth transition in time and avoid scheduling instruction jitter, thereby keeping the key and subsequent process instructions stable under rolling decision.

[0030] The urgent order insertion is triggered by the attention difference threshold, and the urgent order entries are recorded along with the policy snapshot threshold field, transaction number and timestamp. The urgent order insertion is only performed in slots that do not conflict with the critical path, and the affected sample set is updated in a timely manner after insertion, so as to achieve the determinism of the conditions, scope and order of urgent order insertion.

[0031] Micro-reordering is performed within the time window and local candidate set. The local candidate set is the set of processes with available resources within the window that are not locked by the critical path. Locked critical paths are not backtracked. Cross-system submissions use two-phase atomic transactions. If the submission fails, it is rolled back according to the rollback point. Counterfactual replays and regret scores are generated during reordering and are entered into the audit along with the strategy snapshot for offline retraining and strategy revision. Attached Figure Description

[0032] Figure 1 This is a schematic diagram of the asynchronous multi-process collaborative method for AI-powered darkroom labs according to the present invention. Detailed Implementation

[0033] 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.

[0034] Please see Figure 1 This invention provides an asynchronous multi-process collaborative method for an AI-powered "lights-out" lab based on an attention mechanism, including:

[0035] Step 1: Take a policy snapshot To ensure a single source of fact, a two-phase write-ahead transaction is used to atomically write to both the Laboratory Information Management System and the Manufacturing Execution System; when a cross-system conflict is detected, a metric is applied. If the threshold is exceeded, immediately freeze in read-only mode and press the rollback point. Verifiable recovery creates a unified timeline that is replayable, auditable, and constrained, providing a consistent anchor point for subsequent attention assignment and runtime micro-reordering.

[0036] With strategy snapshot As a single source of fact, in the spatiotemporal graph data surface The above completes the atomic dual-write disk placement and consistency reinforcement, making the attention importance vector Threshold vector Affected sample set With rollback point Strict alignment between the two types of business systems.

[0037] The "lights-out" laboratory environment involves asynchronous concurrency across equipment, processes, and transportation links. Relying solely on post-audit results in a time drift between attention decisions and the actual state of material flow. On one hand, the composition ratio vector of the sample materials... With process window vector It can change in a short period of time due to differences between batches; on the other hand, the spatiotemporal graph data surface The confluence of resource nodes in the process may be contested by concurrent processes, causing critical path drift.

[0038] To avoid the common distortions caused by executing first and then verifying, the model version fingerprint must be included. Attention importance vector Threshold vector Affected sample set With rollback point Unified convergence as a policy snapshot It employs a two-stage transaction to perform atomic double writes between the laboratory information management system and the manufacturing execution system, thereby enabling the decision-execution-audit process to close on the same time reference.

[0039] The orchestration hub first pulls atomic data from the device, transport, and storage nodes to form a spatiotemporal graph data surface. Transform the component ratio vector of the sample materials (Record solvent ratio, metal ion concentration, etc.) and process window vector (Record temperature, stirring rate, centrifugation acceleration, etc.) Based on the affected sample set Aggregate and generate attention importance vectors using an attention model. With threshold vector .

[0040] Then construct a policy snapshot. During the pre-write phase, pre-write logs and verification metadata are registered for the two types of business systems respectively. Only after all are confirmed will the confirmation phase begin and the data be written to disk. If any end receives an abnormal receipt, a read-only freeze will be triggered and the system will be rolled back to the rollback point. .

[0041] To ensure policy snapshot It is replayable, verifiable, and strictly aligned with subsequent micro-rearrangements, deterministically encoding the snapshot content and calculating the model version fingerprint at the moment of generation. This makes it strongly bound to the attention importance vector. Threshold vector Affected sample set With rollback point To prevent duplicate values ​​with the same name.

[0042] Therefore, the model version fingerprint Defined as a join hash of the snapshot's key fields:

[0043]

[0044] Among them, hash function For encrypted collision-resistant mapping, the output value space is... Model version vector A version summary representing the training weights, which is a real-valued vector with dimension 1. Fixed during model development; attention importance vector , for dimension Normalized weights, ;

[0045] Threshold vector , is a non-negative real vector; the set of affected samples , which is the set of sample identifiers, defining the sample domain affected by this strategy;

[0046] Set encoding operator For deterministic serialization mapping, samples are first monotonically sorted by identifier, then stably concatenated and intermediate summaries are calculated. The output is a bit string. The following deterministic definition is given:

[0047]

[0048] Where: length Number of affected samples (non-negative integer); sample sequence For the affected sample set 64-bit unsigned big-endian encoding after ascending order (value range) ;

[0049] constant Field separation constant (fixed bit string); concatenation operator Byte-level concatenation; hash function Fixed as BLAKE2b-256, output bit width 256 (value space) ); Rollback point The time index of the previously confirmed snapshot; symbol This indicates a byte-level connection.

[0050] When using it, use the model version fingerprint. By strongly binding five key fields, any unauthorized modification of a single field will be detected instantly at the hash level; set encoding operator This ensures that the representation of the sample set is insensitive to the order of elements, thereby reducing false positives in concurrent convergence; the snapshot, after being encoded in this way, has unified verifiability across systems, providing a solid anchor point for subsequent dual-write consistency and conflict detection.

[0051] To avoid inconsistencies caused by success only in a single system, a two-phase write-to-acknowledge transaction is adopted, and dual writes are performed on both the laboratory information management system and the manufacturing execution system.

[0052] In the pre-writing stage, first take a snapshot of the strategy. Metadata (including model version fingerprint) Attention importance vector Threshold vector Affected sample set Rollback point The registration is done in the candidate areas at both ends, and the final disk disposal is only performed during the confirmation phase when both parties return a commit flag. This is indicated by a transaction commitment quantity. Formal atomic conditions:

[0053]

[0054] Among them, the commitment indication quantity , indicating a transaction Whether the submission was successful is represented by a binary set; the business system set. Fixed as two types of systems, defining dual write domains; write-ahead acknowledgment function. , indicating system Accept write-ahead; Confirmation acknowledgment function , indicating system Whether the write operation is completed during the confirmation phase is the criterion for the disk write operation in the second phase; indicator function It takes the value 1 when the condition is true and 0 otherwise. Its function is to transform Boolean conditions into scalar multiplication that is easy to conjunct.

[0055] When using, the indicated quantity is guaranteed. The product paradigm transforms both ends into strict atomic conditions simultaneously; failure at either end leads to overall failure. The consistent effectiveness of the double-written data on the spatiotemporal graph ensures the attention importance vector... With threshold vector The explanation and audit path can be aligned; the failed path will then naturally enter the freeze and rollback mechanism, avoiding the implicit drift caused by half-commit.

[0056] When cross-system state divergences are detected, quantitative conflict measurement is used. The driver is frozen in read-only mode, and based on the rollback point... Perform certifiable recovery to ensure that the scheduling and compliance trajectories are re-entrant in both time and space dimensions.

[0057] While dual-write avoids unilateral commits, it still needs to address issues such as network jitter, concurrent queueing, and secondary changes from external systems. For example, the manufacturing execution system might accept manual priority adjustments after confirmation, leading to changes in the attention importance vector. With threshold vector The valid values ​​are inconsistent with the laboratory information management system; for example, the affected sample set... Members are replaced during transport due to container damage, causing offsets at the aggregation level. Without a unified conflict metric, freezing and rollback lack criteria, potentially leading to accidental freezing or allowing errors to spread unchecked.

[0058] Therefore, a model version fingerprint is required. Under the anchoring, the snapshots at both ends are projected onto a unified vector space and the difference is measured. If the difference exceeds a threshold, the process is immediately frozen and rolled back to the most recently provable rollback point. .

[0059] The orchestration hub subscribes to both ends of the snapshot confirmation stream and the secondary change stream, continuously calculating conflict metrics. , where, when conflict measurement When the freeze threshold is exceeded, the spatiotemporal graph data plane enters a read-only freeze window, prohibiting any new writes and allowing only writes based on rollback points. The process involves read-play and recovery replay. After recovery, the two-phase transaction is re-entered, forming a closed loop of conflict-freeze-rollback-recommit.

[0060] Fingerprint the model version The projection is a vector on the probability simplex, then interpolated with the threshold vector. With attention importance vector Cross-system difference synthesis conflict measurement The metric captures both version inconsistencies and reflects shifts in decision weights. The conflict metric is defined as:

[0061]

[0062] Among them, conflict measurement , is a non-negative real number; Bregman divergence Defined by the generating function, it models the asymmetric differences in version projection;

[0063] Version fingerprint projection operator Normalize the bit fingerprint blocks to a probabilistic simplex, and then... A positive probability vector, whose purpose is to... Introduce a differentiable space;

[0064]

[0065] Where: probability vector (Each component is positive and sums to 1); block value Smoothing term Prevent all zeros; Selected based on implementation; threshold difference weight Weights based on attention difference It is a non-negative real number; for Norm;

[0066] Discrete total variation The sum of the absolute values ​​of the first-order differences of the input vectors is a non-negative real number. In the edge space... The specified order of specifications First sort by resource cluster number, then sort by edge ID in ascending order, and give the following:

[0067]

[0068] Where: vector Standardized order For fixed replacement, the version fingerprint is used during model solidification. Record.

[0069] To ensure sufficient disclosure, the Bregman divergence is taken in its general form:

[0070]

[0071] Among them, the generating function exist Strictly convex at the top, its function is to induce relative entropy-type divergence; gradient For component-wise partial derivatives, inner product This is the Euclidean inner product.

[0072] When used, conflict measurement By combining version inconsistency threshold drift and attention jitter on the same scale, isolated alarms are avoided; the asymmetry of Bregman divergence makes the direction of which covers which clear, which is beneficial for choosing master and slave during rollback; discrete total variation amplifies structural breakpoints rather than uniform micro-variables, reducing sensitivity to noise.

[0073] When conflict measurement When the freeze threshold is exceeded, the system enters a read-only freeze window, where any write operations are blocked. Subsequently, the optimal rollback anchor point is selected based on the rollback point set, and replay is performed. (This includes model version fingerprints.) Attention importance vector With threshold vector The overall consistency criterion for selecting the rollback point is as follows:

[0074]

[0075] Among them, the optimal rollback point , is the target index; candidate set , is the set of confirmed historical snapshot indexes, which is a non-empty finite set; , For model version fingerprint; , The steady-state attention importance vector represents the relative importance of the process / resource edge at that time; the threshold vector... , A non-negative threshold vector consistent with the edge dimension (including safety components, difference threshold components, etc.); consistency weights It is a non-negative real number, balancing the matching degree between attention and threshold dimensions;

[0076] In terms of execution, the central scheduling is based on... Roll back the effective views of both business systems and replay them in read-only mode. up to the current legitimate event sequence, up to the conflict metric The freeze will be lifted and the two-stage transaction will resume only when the price drops below the freeze threshold.

[0077] When in use, the rollback point selection based on the multi-dimensional consistency criterion avoids the oldest feasible or the most recent empirical rollback, reducing the probability of oscillation again after recovery; the read-only freeze window makes pending writes naturally queued, and together with replay, it can ensure that the spatiotemporal graph data surface converges synchronously in terms of topology and timing; the whole process closes the conflict detection-freeze-rollback-replay into one, reducing the long-term accumulation of cross-system drift.

[0078] Step 2: Convert the tokenized resource occupancy prediction into a congestion signal. Attention is explicitly injected and generated, and the steady-state attention importance vector is obtained by combining the steady-state regularization of total variation across time steps. Attention difference threshold Triggering Urgent Order Gating Constructing the critical path probability distribution for congestion awareness With critical path locking domain It outputs an interpretable and auditable dispatch baseline.

[0079] Congestion signal Injecting attention generation as an explicit term makes the dispatch weights interpretable and traceable in relation to the competitive pressure of converging resources, and obtains a continuous and consistent attention importance vector across time steps through steady-state regularization. .

[0080] In the parallel advancement of asynchronous multi-process operations, the space-time graph data surface in the "lights-out" laboratory... Short-term congestion peaks may occur at convergence resources (such as robot transfer intersections, shared buffer points for centrifugation-pipette transfer, and cold chain-room temperature switching nodes). If attention is generated solely from process characteristics and order priorities, weights may be tilted towards high-congestion edges, leading to critical path drift and equipment idling. Therefore, it is necessary to translate tokenized convergence resource occupancy predictions into quantified congestion signals. Furthermore, attention is injected in an interpretable penalty manner during attention generation; simultaneously, attention across decision moments should maintain a steady-state transition to avoid jitter due to instantaneous observation noise. This is based on a policy snapshot from step one. ,by Based on the threshold vector Using the threshold, construct the attention function after congestion correction and steady-state regularization. .

[0081] The orchestration hub incrementally pulls the affected sample set from the laboratory information management system and the manufacturing execution system. The process progress, resource queue, and token inventory are combined with the sample composition ratio vector. With process window vector Real-time changes are observed, the dwell time and resource occupancy curve of each sample on the candidate edge are calculated, and the data are fed into the intersection occupancy predictor to obtain the occupancy rate vector, which is then normalized and mapped to a congestion signal. Subsequently, a mapping matrix is ​​used in the edge-level dimensions of the spacetime graph. Alignment is performed to obtain a congestion projection consistent with the attention dimension. This is done using the attention from the policy snapshot. As a priori, congestion injection-entropy regularization is performed first, followed by cross-time steady-state regularization, and finally output. And it will be stored on disk with the next version of the strategy snapshot.

[0082] To transform the impact of traffic congestion on dispatching from an implicit correlation to an explicit constraint, an entropy regularization projection with a linear congestion penalty is introduced into the attention generation process; the goal is to maintain the baseline attention as much as possible. At the same time, the weights of the corresponding edges are reduced according to the congestion intensity, and interpretability and normalization are guaranteed by probabilistic simplex constraints:

[0083]

[0084] in,

[0085]

[0086] Where: mapping matrix ;element If and only if the edge Resource consumption Congestion signal ,

[0087] Congestion Injection Post-Attention The probability vector to be determined is... 3D simplex; baseline attention The model, without considering traffic congestion, uses the sample composition vector. Process window vector Attention priors generated with process context; relative entropy , used to measure deviation from the baseline, is a non-negative real number; congestion weight , where is a non-negative real number; mapping matrix ,Bundle Alignment of resource congestion signals at dimensional intersections to 3D edge space;

[0088] Candidate attention vectors , It is the edge-level probability weight vector, with dimension [missing information]. It equals the number of edges in the current spacetime graph. Component meaning: The first... Each component Indicates the first The relative importance of each process step (dimensionless, auditable, sum to 1).

[0089] Congestion signal vector , where represents the dimensionless congestion intensity of each converging resource at the moment of decision-making. Inner product It is an Euclidean inner product, which accumulates the linear effect of congestion on attention.

[0090] In practice, this entropy regularization projection applies soft suppression to congested resources while maintaining semantic stability, avoiding the uninterpretable breakpoints caused by simply pruning attention to zero; the linear penalty is applied through the mapping matrix. The sparse structure accurately sinks resource-level congestion to the most competitive edges; the optimization objective has a unique minimum value on the convex domain, which facilitates real-time solution and disk write within the transaction time limit.

[0091] To suppress attention jitter between adjacent decision moments, attention is injected after congestion. Based on this, a combination of first-order and second-order time difference is introduced. Steady-state regularization ensures that attention is both close to the result after the current congestion injection and smoothly transitions along the time axis:

[0092]

[0093] Among them, steady-state attention This is the optimized output; historical steady-state attention. Provides a temporal prior for the confirmed results at the previous and previous decision points; steady-state weights With smoothing weights The value is a non-negative real number, derived from the threshold vector in the policy snapshot. Within a limited scope; for The norm serves to facilitate sparse and interpretable adjustments; to ensure feasibility, it can be set at the initial time. .

[0094] When using, composite Regularization suppresses sudden jumps and constrains acceleration terms, ensuring that attention follows a piecewise constant or gradually changing structure along the time axis; similar to policy snapshots. Range binding enables auditable and replayable steady-state strength; outputs steady-state attention. It significantly improves dispatch stability without sacrificing congestion sensitivity.

[0095] The urgent order insertion is triggered by the attention difference threshold and recorded in the policy snapshot. At the same time, the critical path distribution with congestion awareness is constructed to provide a quantifiable basis for path locking for subsequent small window micro-rearrangement.

[0096] Under high concurrency conditions, uncontrolled, temporarily inserted urgent orders can easily lead to widespread reordering of existing dispatching, thereby compromising critical path locking and cross-system consistency. An intrinsic criterion driven by attention itself is needed: when congestion injection and steady-state regularization work together, if the edge-level change in attention reaches the differential threshold recorded in the policy snapshot in a local area sensitive to urgent orders, then urgent order insertion is triggered only in that local area; otherwise, the original decision is maintained and the evaluation waits for the next time window. Simultaneously, critical path estimation should explicitly consider congestion costs, quantifying the interaction between traffic, scheduling, and auditing at the path level.

[0097] The central focus of programming is on steady-state attention. and The differential metric measures structural changes and, combined with a threshold component in the policy snapshot, selects whether to trigger an urgent order insertion; once triggered, the system analyzes the affected sample set. The local candidate set is expanded to include rapidly unilateral paths, which are then incorporated into the path set for critical path estimation. The critical path distribution is calculated using congestion-aware path energy. Triggering events and parameter values ​​are saved to disk along with a new policy snapshot for micro-rearrangement and audit reproduction in step three.

[0098] To avoid untraceable jumps caused by direct overriding of human priorities, a weighted infinity norm is used to measure the change in attention on edges sensitive to urgent orders, and this is compared with a policy snapshot threshold to determine triggering.

[0099]

[0100] Among them, the difference amplitude , a non-negative real number, represents the maximum weighted change of attention on the acute single-related edge; acute single sensitivity vector A non-negative vector, pre-configured by process safety boundaries and regulatory compliance constraints; Hadamard product. Used for component-wise weighting; infinite norm It takes the largest absolute component and is sensitive to the most prominent local changes;

[0101] Trigger gating A binary variable, used to control whether urgent orders are inserted; attention difference threshold. , for strategy snapshot The dedicated component is limited by the compliant template.

[0102] Once triggered, the sample identifiers of urgent orders are incorporated into the affected sample set. The local extended set, and put Along with model version fingerprint Record a new policy snapshot.

[0103] When in use, gating triggering avoids global reordering, ensuring that insertion only occurs in the most necessary local areas; threshold binding with snapshots makes the trigger-execution-audit closed loop reproducible; sensitivity vectors ensure priority protection for regulatory-sensitive process edges.

[0104] To unify congestion costs and attention weights at the path level, a distribution is established on the candidate path set using soft shortest paths, ensuring that critical paths both favor high-weight edges and avoid highly congested resources; temperature is taken. Controlling the steepness of the distribution:

[0105]

[0106] Among them, path probability , as candidate paths The selection probability is used for critical path identification and risk assessment; candidate path set. , from the affected sample set The process diagram is derived, limiting the search space; edge set elements , for path On the process edge; placeholder path variable During normalization, the candidate path set is traversed. The index path;

[0107] Steady-state attention components , representing edge weights, which are used to reward high-value edges; a positive constant. , is a numerically stable term; congestion component , is the result of the mapping matrix Congestion intensity aligned to the edge level; congestion weight , is a non-negative real number; temperature coefficient Controlling the sharpness of the distribution serves to transition from soft integration to approximate minimization; based on path probability The maximum posterior path is selected as the current critical path and written into the critical path locking field of the policy snapshot.

[0108] In practice, the soft selection at the path level integrates edge-level attention and resource congestion under a unified energy model, avoiding path breakage caused by relying solely on local weights; temperature control enables the system to select a more conservative path during steady-state periods and quickly converge to a more decisive path during emergencies; the critical path locking domain provides precise boundaries for step three, which involves only micro-rearranging within a small window without backtracking the critical path.

[0109] Step 3: Lock the domain without backtracking the critical path. Under the premise of only a small time window With local candidate set Internal solution for minimum perturbation assignment matrix It uses two-phase atomic transactions to consistently persist data across systems; if data goes out of bounds or the commit fails, it is automatically frozen and rolled back based on the rollback point. Recovery; simultaneously, counterfactual evaluation of alternative solutions within the same domain is performed to generate regret scores. With diagnostic logging, closed-loop rearrangement of protocols - compliance - learning cycle.

[0110] Locking the domain without backtracking the critical path Under the premise of time window only With local candidate set The dispatching process within the system performs atomic-level micro-reordering and uses two-phase transactions to ensure consistent disk write-to-disk operation across systems and automatic rollback in case of failure.

[0111] In a lights-out laboratory operating under high concurrency, the component ratio vector of sample materials... With process window vector This can change over a short timescale due to batch discrepancies or intermediate measurement corrections, causing the feasible domain and risk boundary of individual processes to drift over time. If the global work assignment is significantly rearranged, it will inevitably disrupt the critical path locked domain formed in step two. This can lead to inconsistencies across systems.

[0112] Therefore, micro-rearrangements are limited to only... Internal, only for The edge-resource mapping on the surface is modified with minimum provable value, and the steady-state attention importance vector is used as the basis for the modification. With congestion signals The runtime cost is jointly constructed to ensure the interpretability, continuity, and minimal disturbance of existing decisions. The orchestration hub is first based on the critical path probability distribution output in step two. Select the path with the largest a posteriori and write it to disk in the critical path locking domain of the policy snapshot; denote this as the critical path locking domain. Subsequently, according to the urgent order gate control With the affected sample set The process safety boundary (based on the process window vector) Provided) and material proportioning constraints (given by the component proportioning vector) Provides) cropping out a local candidate set And construct time windows in the current time slot and its preceding and following boundaries. .

[0113] Based on this, establish a minimum perturbation edge-resource-time slot allocation variable. (Action Assignment Matrix), for The task-resource bindings within the system are slightly rearranged, and any unauthorized access is prohibited. The operation, while the penalty is relative to the previously confirmed allocation. The switching process. Reordering yields the optimal allocation matrix. Then, a two-phase cross-system dual-write transaction is initiated; if any end experiences an abnormal retrieval or the security measure exceeds the limit, the system will automatically freeze and roll back to the rollback point. .

[0114] To strictly limit micro-reordering to a small visible range and to uniformly characterize runtime costs using attention-congestion-switching costs, the following optimization is constructed:

[0115]

[0116] Wherein, the optimal allocation matrix For relaxed binary allocation of task-resource-time slots, provide an executable mapping within the window; feasible region By time window Local candidate set With critical path locking domain The commonly defined constraint set takes values ​​that satisfy capacity, pre- and post-constraints, and material and process constraints.

[0117] Runtime cost matrix The steady-state attention importance vector With congestion signals Generate by combination, with non-negative values; switch weights. , is a non-negative real number, suppressing the allocation of already confirmed assignments. Significant changes; critical path barrier weights , where is a non-negative real number, strengthens the locking field constraint;

[0118] Barrier function In the action assignment matrix Do not touch the critical path locking domain Take 0 if it is true, otherwise take 0. ;

[0119]

[0120] Inner product and norm Measure the cumulative cost and the switching scale separately.

[0121] When used, this optimization can quickly find the minimum solution in the simplex relaxation domain and improve the non-backtrackable critical path from a soft constraint to a hard constraint through the barrier function; the switching term ensures minimal disturbance to the existing plan; the runtime cost unifies attention and congestion into an interpretable cost, keeping the semantics of micro-reordering and step two continuous.

[0122] The micro-reordering results must be atomically consistent between the laboratory information management system and the manufacturing execution system to take effect. Simultaneously, a submission gating is applied to the window-candidate-path locking rules, and the micro-reordering transaction commitment indicator is defined:

[0123]

[0124] with safety margin Define out-of-bounds indicator:

[0125]

[0126] Among them, the micro-reordering transaction commitment indication This is used to determine whether to proceed to confirm disk write-to-control. A value of 1 indicates that both ends have successfully written to disk and confirmed, and that the window and lock field constraints are satisfied.

[0127] Business System Collection Fixed; pre-written acknowledgment function With confirmation receipt function Returns two binary values, representing two-stage receipts;

[0128]

[0129] Among them: business systems ; transactions Strategy Snapshot Window optimal allocation matrix A receipt value of 1 indicates a successful stage; set containment relationship. This indicates that the set of time slots involved in the allocation matrix falls entirely within the time window; safety margin. , is a non-negative real number, used to determine whether read-only freeze and rollback are triggered;

[0130] Security Sensitivity Matrix The vector of process hazard and sample composition ratio The upper limit offset is combined with the device state, and the value is non-negative; safety threshold , for strategy snapshot The safe component.

[0131] If the micro-reordering transaction commitment indicator or safety margin The system enters read-only freeze and rolls back to the rollback point. At the same time, the reasons for failure and the out-of-bounds components are written into the audit domain.

[0132] When in use, this control will connect atomic double write, window gating, path locking, and security threshold in parallel into the same commitment variable. If any link is not satisfied, the entire submission will be rejected. The security margin mechanism can automatically freeze and roll back without relying on manual intervention, ensuring the safety and compliance boundaries of the device during operation.

[0133] The execution and non-execution of each micro-rearrangement within the window are compared counterfactually on the same time basis, and the regret degree is used as the core diagnostic metric to be incorporated into the audit and offline retraining library, forming a closed-loop upgrade of rearrangement-audit-learning.

[0134] Even if micro-rearrangements are strictly limited to a time window With local candidate set Internally, its long-term impact still depends on the adequacy of the understanding of the possible solution space; without systematic counterfactual evaluation, the learner will find it difficult to extract better solutions from the operational data under the current congestion-attention-process boundaries. Therefore, it is necessary to construct a counterfactual distribution with an energy model as its core, without disrupting the atomic transaction disk, to sample and evaluate alternative allocations within the same window, and to use regret and distribution drift metrics to drive the priority of training samples.

[0135] The central organization is based on For the already executed plan, in the same Constructing counterfactual energy under constraints Energy is synthesized at the cost of congestion-attention-switching and generated into a Gibbs-type distribution via a temperature coefficient. Alternative solutions are finitely enumerated or sampled on an offline thread. The runtime cost is calculated for each sample and compared with... Cost comparison to obtain regret level Then, regret rate, energy gradient, and key events (such as rush order triggering) will be considered. (Freeze and rollback) are written together to the diagnostic field of the policy snapshot, and simultaneously weighted. Push the data into the retraining data queue and prioritize learning from samples with high regret and high confidence.

[0136] To evaluate alternatives within the same domain as the original decision, we define the counterfactual distribution and regret:

[0137]

[0138] Among them, the counterfactual distribution Defined in the same feasible region Above, assign energy-consistent sampling probabilities to candidate alternatives;

[0139] Energy function Integrating with the path energy of step two, attention, congestion, and switching costs are unified into a differentiable kernel;

[0140] temperature Control the exploration degree of sampling; switch the penalty weight. Nonnegative scalar; regret degree For real numbers, it measures the superiority or inferiority of the implemented solution relative to the expected alternative; the numerical stability term. prevent Expectation operator Take the expectation under the counterfactual distribution.

[0141] When used, the regret value obtained under the same-domain energy constraint avoids the bias of out-of-domain comparison and ensures consistency with the constraints of atomic transactions; temperature regulation makes the system focus more on exploration in the data sparse stage and converge to the near-minimum alternative in the stable stage; unified energy kernel ensures the consistency between diagnostic quantities and runtime optimization objectives and improves interpretability.

[0142] To translate regret and distribution drift into priorities for retrained samples, a weight mapping is defined:

[0143]

[0144] Among them, sample weights A real number controls the sampling priority of retraining data and the scaling of the learning rate; the Sigmoid function. Compress the input to The values ​​are continuous to avoid extreme weights; regret weight. With distribution drift weight It is a non-negative real number; relative entropy It measures the counterfactual distribution drift between two consecutive windows and takes a non-negative value.

[0145] Weights, regret, energy gradient, and triggering events Commitment Indication Quantity Safety margin All are treated as diagnostic fields according to the policy.

[0146] When used, this mapping incorporates short-term performance degradation and distribution shift into the learning objective simultaneously, preventing the learner from overfitting to only one type of anomaly; the weights are bounded, which helps to maintain training stability during the distribution shift phase; the atomic disking of the trace field allows any model retraining to be traced back to a specific window, specific candidate set, and specific constraints.

[0147] Step 4: Aggregate future occupancy forecasts into risk-adjusted congestion. Detect token empty window indicator As a time-series hard gate; scoring of quality uncertainty Injecting runtime costs and retest priorities through convex mapping Ensure that the retest prioritizes the use of the token window without disrupting the test. This will enable a three-tiered collaborative closed loop of quality, transportation, and scheduling to steadily improve the compliance and capacity of the entire chain.

[0148] Transform tokenized resource forecasting and transportation management into calculable risk-adjusted congestion signals. With token empty window indicator This allows for low-intrusion constraints on traffic consistency between dispatching and route selection.

[0149] In a darkened laboratory, resources such as automated material handling and retrieval systems, and centrifugation-pipette sharing nodes exhibit strong short-term competition; relying solely on current congestion signals... When making decisions, the response to short-term peaks and delayed declines is insufficient, easily leading to alternating oscillations of congestion at the tail of dispatch and equipment idleness. Therefore, it is necessary to read the occupancy prediction of future time windows from the token interface, construct a risk-sensitive multi-timescale aggregated congestion degree, and on this basis, identify token windows that can be safely preempted; then, the constraint of this window is passed to dispatching and micro-reordering, so that the traffic dimension becomes a priori condition for attention weighting and path locking rather than a post-event correction.

[0150] The orchestration center obtains the time-series occupancy prediction sequences of each converging resource from the token interface of the transportation subsystem, forming a two-dimensional resource-time slot view; and uses policy snapshots. Threshold vector in We provide congestion and resource occupancy thresholds, and aggregate multi-step predictions in a risk-averse manner to obtain risk-adjusted congestion signals. ;

[0151] Then, on the discrete sub-window, segments with maximum occupancy not exceeding the threshold and global risk under control are detected, and the token empty window indicator is output. The above quantities, along with the steady-state attention importance vector, Write back to runtime cost and critical path estimation to make traffic priors → attention-path → execution constraints a single chain.

[0152] To transform multi-step occupancy prediction into single-moment congestion levels available for dispatch, exponential risk aggregation combined with Sigmoid compression is employed to construct risk-adjusted congestion signals on a resource-by-resource basis.

[0153]

[0154] Among them: risk-adjusted congestion signals Sigmoid function Risk aversion coefficient Controlling sensitivity to high occupancy tails; predicting weights and Prediction window length Predicted occupancy rate For resources In the future The token occupancy rate of each step will exponentially increase and compress high-risk future occupancy rates to a minimum. As a transportation term in dispatching and path energy.

[0155] In practice, risk aggregation is more sensitive to low-probability, high-occupancy peaks, thus suppressing potential bottlenecks in advance; Sigmoid compression avoids abnormal peaks causing complete attention collapse, maintaining adjustable soft constraints; prediction weights... The configurability allows the system to dynamically balance near-term and long-term congestion.

[0156] To provide securely preemptible transport slots during execution, two thresholds—resource occupancy and global risk—are applied simultaneously to discrete sub-windows, resulting in a token window indicator:

[0157]

[0158] Among them: token empty window indicator quantity Indicator functions Take value 1 when the condition is met:

[0159]

[0160] Among them: Time Sub-Window Index Set For the first A detection sub-window with a fixed width. Sliding window division , , ;

[0161] Resource usage threshold , derived from threshold vector Resource components; congestion threshold Derived from threshold vector The congestion component, of which the output resource In the sub-window A binary criterion for whether a test can be prioritized for retesting.

[0162] In use, the dual-threshold structure ensures both low local occupancy and controlled global risk, preventing the overlooking of impending systemic congestion based solely on local gaps; the adjustable sub-window granularity allows for both forward-looking and overly conservative retest insertion; and the token window indicator... Its duality facilitates direct combination with runtime transaction gating.

[0163] Quality uncertainty score Non-linear mapping is used to penalize and inject runtime costs and priorities, while indicating the amount in the token empty window. Under time constraints, priority is given to retesting the nearest micro-loop, and a quality-policy closed loop is constructed.

[0164] In high-throughput detection chains, cross-contamination during pipetting, reagent drift, and sensor drift can easily introduce uncertainty into the results. If this uncertainty is only used for downstream manual verification, the scheduling system will accumulate defective samples over a long period. Conversely, if uncertainty is directly equated to mandatory rework, it will trigger cascading blockages in high-congestion segments. Therefore, it is necessary to transform quality uncertainty into a continuously adjustable penalty term, dynamically amplifying or discounting the runtime cost, and triggering priority occupancy for retesting only when the token window indicator is true, in order to ensure consistency between the dual objectives of quality control improvement and traffic steady-state.

[0165] The orchestration center receives the quality uncertainty score vector for each sample from the detection unit, and forms the quality uncertainty score after calibration with a compliance template. ; Snapshot of the strategy threshold vector The minimum retest threshold and penalty weight range are given; then, convex gain mapping is used to... The penalty magnitude is converted and located to the edge set of the retested micro-loop, and the runtime cost matrix is ​​incrementally updated. Based on this, a retest priority score is defined, requiring that a transport window can only be occupied when the token empty window indicator is true, and without touching the critical path locking domain. .

[0166] To achieve continuous control where high-uncertainty samples are prioritized for retesting and low-uncertainty samples do not disturb the main path, an exponentially normalized mapping is used for the quality uncertainty score, and runtime costs are injected in an incremental manner at the edge level.

[0167]

[0168] Incremental cost Penalty weight Mapping function Convex gain function; quality uncertainty score Retesting the edge set of the micro-loop From the sample Generation of the nearest retest path; indicator function ; Side marker Effect: Only adds cost to retest-related edges, making the optimizer inclined to perform retests during token-free windows. To ensure sufficient exposure, the mapping function is taken in exponentially normalized form:

[0169]

[0170] Mapping function Shape factor .

[0171] When the shape factor As the value increases, it accelerates upward in the high uncertainty range, achieving a convex response of strong amplification and weak suppression.

[0172] When used, incremental injection avoids modifying the global cost matrix. The destructive rewrite maintains the steady-state attention importance vector. Semantic continuity; exponentially normalized mappings provide a smooth adjustment knob from mild preferences to mandatory retesting, facilitating auditing and playback; and retesting micro-loop edge sets. The binding ensures that penalties only apply where necessary, reducing interference with the main path.

[0173] To ensure that retest insertion both complies with traffic token constraints and respects existing critical path locking domains, a retest priority score is constructed at the resource-subwindow dimension and triggered using a gating method:

[0174]

[0175] Among them: retest priority score Steady-state attention components Indicates sample In resources Attention to the retested edge; token window indicator quantity ; Retest priority gain coefficient To control the extent to which uncertainty increases priority; mapping function Re-measurement of edge markings Determined by sample-resource mapping; retested micro-loop edge set Indicator functions .

[0176] Scores are assigned based on retest priority within the small window. Descending order organization of retest slots, and critical path locking domains The barrier constraints work together, only when Only when there is a time gap in the transportation schedule can the transportation window be occupied.

[0177] When used, the retest priority score integrates quality uncertainty, attention, and traffic gaps in the same domain, avoiding unauditable disturbances caused by experience-based queue jumping; the gating structure ensures that retesting will not crowd out critical transport during peak congestion periods; and the parallel constraints with the critical path locking domain limit the impact of retesting insertion on the main production chain to the minimum necessary range.

[0178] 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 implementations should not be considered beyond the scope of this invention.

[0179] 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.

[0180] In the embodiments provided by this invention, 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.

[0181] 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.

[0182] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. An asynchronous multi-process collaborative method for an AI-powered "lights-out" lab based on an attention mechanism, characterized by: include, Using the policy snapshot as a single source of fact, including model version fingerprint, attention importance vector, threshold, affected sample set and rollback point, a two-stage atomic transaction of write-ahead and confirmation is used to write to the system; when cross-system conflicts are detected, read-only freezing is performed and recovery is carried out according to the rollback point. Inject attention into the explicit terms of congestion signals predicted by tokenized convergence resource occupancy; Total variation steady-state regularization is applied to attention at adjacent decision moments. Urgent orders are triggered by attention difference thresholds and are recorded and traced by the threshold of the policy snapshot. Micro-reordering is performed only within the time window and local candidate set, without backtracking to lock the critical path; the two-phase atomic transaction is used across systems, and each reordering generates a counterfactual replay and regret score, which are snapshotted into the audit and executed for failure rollback according to the strategy. The congestion signal predicted by the tokenized intersection is sinked to the attention weight; the uncertainty is classified and a penalty term is mapped, triggering the retest of the nearest micro-loop and prioritizing the use of the token window; the token interface, prediction window, congestion threshold and retest sequence are defined.

2. The asynchronous multi-process collaborative method for AI-powered darkroom laboratories according to claim 1, characterized in that: The two-stage atomic transaction is written twice between the laboratory information management system and the manufacturing execution system. In the pre-write stage, the transaction number, target system identifier and double write sequence number are generated, and the model version fingerprint, threshold, field lock order and rollback point index are written to the temporary area. During the confirmation phase, the fingerprints of the current versions of the two systems are compared with those of the model version. If they are inconsistent, the reason code for freezing is recorded, a read-only freeze is executed, and the system is restored according to the rollback point before proceeding to the next round of confirmation.

3. The asynchronous multi-process collaborative method for AI-powered darkroom laboratories according to claim 2, characterized in that: The cross-system state conflict detection is based on the field lock order, comparing the affected sample set field by field. The comparison order follows the field priority of the model version fingerprint, threshold, rollback point index and the affected sample set. When a difference is detected, the write path is frozen and the start and end times of the freeze window are marked. During the freeze, only reading is allowed, and within the window, the rollback point is rolled back to the most recently successfully committed snapshot version. After the freeze is lifted, the confirmation phase is retried in the original order and the dual write is completed.

4. The AI-powered, light-out laboratory asynchronous multi-process collaborative method according to claim 1, characterized in that: The congestion signal is calculated by adding a bias term from the attention scoring to the query-key. The bias term is generated by linear mapping from the convergence resource occupancy prediction and associated with the threshold. The total variation steady-state regularization is a weighted sum of the attention importance vectors at adjacent decision times based on the absolute differences of the elements. The upper and lower bounds of the regularization coefficients are recorded by the policy snapshot and are shared during the training and inference phases.

5. The AI-powered, light-out laboratory asynchronous multi-process collaborative method according to claim 4, characterized in that: The urgent order insertion trigger is based on comparing the maximum absolute difference between the attention importance vector corresponding to the target process at adjacent decision times and the attention difference threshold. If the difference is exceeded, an urgent order entry is generated. The entry includes the process identifier, the trigger time, and the difference. The threshold is bound to the threshold field of the strategy snapshot and written into the audit stream along with the transaction number and timestamp for subsequent retrieval.

6. The AI-powered, light-out laboratory asynchronous multi-process collaborative method according to claim 1, characterized in that: The time window for micro-reordering is bounded by the start and end of the rolling interval and the step size recorded in the strategy snapshot. The local candidate set is limited to the set of processes with available resources within the window that are not locked by the critical path, and a sorting key is set for concurrent candidates with the same resource. Reordering does not backtrack the critical path, and cross-system submissions use the two-stage atomic transactions. If the submission fails, it will roll back according to the rollback point.

7. The AI-powered, light-out laboratory asynchronous multi-process collaborative method according to claim 6, characterized in that: Each rearrangement generates a counterfactual replay containing the original state index, a list of actions taken and unselected actions, a constraint context, and a corresponding resource identifier. The regret entry records the cost difference and time index between the selected and unselected actions. Both are stored in the audit partition along with the policy snapshot and are automatically rolled back and the failure reason code is added when the submission fails.

8. The asynchronous multi-process collaborative method for AI-powered darkroom laboratories according to claim 1, characterized in that: The token interface includes three types of operations: holding, releasing, and renewing, and the order of interface calls is marked by an event sequence number; the prediction window and congestion threshold are uniformly configured by the policy snapshot and propagated with transactions. After the congestion signal is incorporated into the attention weight, the retest task is preferentially allocated within the token window according to the renewal strategy, aligned with the prediction window, and synchronously recorded in the laboratory information management system and manufacturing execution system.

9. The AI-powered, light-out laboratory asynchronous multi-process collaborative method according to claim 8, characterized in that: The uncertainty classification is mapped to a penalty term based on the interval boundary given by the policy snapshot, and the classification label is recorded together with the model version fingerprint of the policy snapshot; When triggering the nearest micro-loop retest, the retest task determines the timing based on the holding and releasing events of the token interface. The retest entries are bound to hierarchical tags, prioritize occupying token windows, and are aligned with the prediction window and time window.

10. The AI-powered, light-out laboratory asynchronous multi-process collaborative method according to any one of claims 7-9, characterized in that: The audit fields include at least the strategy snapshot number, model version fingerprint, attention difference threshold, time window boundary, local candidate set identifier, regret entry and token interface event number. These fields are stored in alignment with transaction numbers between the laboratory information management system and the manufacturing execution system, and after rollback, supplementary records are generated in the audit partition with the same number to maintain continuity.

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