Re-training trigger and safe onboarding method and system based on high-confidence blind-state dark stake flip rate determiner

Abstract

The application discloses a method and system for retraining trigger and safe online of a high-confidence blind-state dark pile overthrow rate-based determinator, and belongs to the technical field of digital pathology whole-section image artificial intelligence labeling operation and maintenance, and comprises the following steps: S1, three-barrel type group card and high-confidence dark pile deterministic sampling; S2, blind-state distribution and orthogonal voting collection; S3, two sets of isolation caliber calculation overthrow rate; S4, multi-constraint trigger; S5, forward voting idempotent materialization silver label generation; S6, offline retraining and standardized weight derivation; S7, multi-layer safe online and automatic rollback: sequentially performing weight structure compliance verification, new and old weight parallel shadow scoring, database atomic version switching, gray real-time index monitoring, and automatically rolling back to the old version when deteriorating, and the application has the technical effects of realizing blind area-free performance monitoring, low-interference automatic retraining and standardized safe online.

Description

Technical Field

[0001] This invention belongs to the field of digital pathology whole slide image artificial intelligence annotation and maintenance technology, and particularly relates to a method and system for triggering and safely deploying decision makers based on high confidence blind state dark stake overturning rate. Background Technology

[0002] In AI annotation scenarios for whole-section pathology, after the segmentation model outputs a massive number of candidate regions, a secondary judge identifies and downgrades false positive regions. However, during long-term online operation, the judge experiences performance drift due to changes in the source of the slides and the distribution of lesions, and cannot autonomously perceive its own error rate. Existing model monitoring, automatic retraining, and weight deployment solutions suffer from seven insurmountable technical flaws: Blind spots in high-confidence sample sampling and monitoring: Traditional active learning only extracts samples that the model is uncertain for manual review, completely ignoring high-confidence decision-makers and directly allowing candidate regions without review; in pathological scenarios, the decision-makers are highly confident but misjudge the target lesion, which has the highest risk of high-confidence errors, but existing solutions have no means to collect human feedback on such samples and cannot measure the true error rate of high-confidence segments; offline pre-built gold standard honeypot solutions require the standard answers to be entered in advance, and new batches of slides cannot be quickly deployed and used.

[0003] Model drift monitoring indicators are indirect and lack direct human verification: existing solutions rely on input / output distribution drift statistics to monitor model changes, which can only prove that the data distribution has changed, but cannot intuitively quantify the "true error rate of the decision maker"; medical and pathological scenarios have extremely high requirements for the credibility of results, and pure statistical inference cannot be used as a reliable basis for initiating retraining.

[0004] Automatic retraining triggers poor anti-interference capability: Conventional solutions only set a single threshold, and retraining is initiated when the single statistical value exceeds the limit; when the sample size is only a few dozen, random sample fluctuations can easily trigger invalid training, continuously consuming GPU computing power; lacking minimum sample size, statistical confidence interval, and continuous multi-round hysteresis constraints, the triggering logic is easily driven by noise to run idly.

[0005] The trigger state machine lacks a safety rollback protection mechanism: If the existing state machine reaches the trigger condition due to occasional fluctuations and the subsequent indicators fall back to normal, the system will automatically re-submit the intercepted retraining task, bypassing the safety verification process, which poses a risk of meaningless repeated training.

[0006] The code coupling between the statistical display criteria and the automatic trigger criteria causes pollution: the front-end display page performs read-time downsampling to adapt to pagination, and the downsampling parameters are configured by environment variables; if the automatic retraining trigger logic reuses the same set of sampling and statistical code, the operation and maintenance personnel can implicitly modify the retraining trigger judgment conditions by only adjusting the display pagination parameters, making the fault hidden and difficult to troubleshoot.

[0007] The deployment of new training weights lacks multi-layered security protection: the existing solution directly switches the weights produced by retraining to the production environment, which poses four major risks: missing weight files, disordered label order, mismatched feature dimensions, and silent output of incorrect judgments after loading; no parallel comparison of the effects of old and new weights; the version switching is not an atomic operation, which can easily lead to a business gap where the old version is disabled and the new version is not enabled; the manual review has an inherent lag in the overturn rate, and it takes a long time to discover a large number of missed targets after the poor weights are deployed, making it impossible to stop losses and roll back in advance.

[0008] The offline retraining process has issues with data leakage and inflated metrics: cross-validation was not performed according to slice groups during retraining, and samples from the same slice flowed into both the training and validation sets at the same time, resulting in inflated AUC and false positive recognition rate in offline evaluation; the operation point threshold was adjusted on the validation set, which led to excellent offline metrics but a significant drop in the actual online recall rate, and the decision maker quickly became inaccurate after going online.

[0009] It is evident that existing technologies cannot simultaneously achieve direct quantifiable performance monitoring covering high confidence segments, automatic retraining triggering against noise, physical isolation of statistical calibers, standardized retraining to prevent leakage, and automatic loss prevention during multi-layered security grayscale deployment. They cannot meet the essential business requirements of high security and high reliability for pathological labeling. Summary of the Invention

[0010] To address the problems existing in the prior art, this invention provides a method and system for triggering and safely deploying decision makers for retraining based on a high-confidence blind-state peg overturning rate. It features blind-state peg-based quantification of model drift, physical isolation of dual statistical calibers, multi-constraint composite triggering, grouped leak prevention training, and a four-layer deployment protection mechanism. This achieves advantages such as blind-zone-free performance monitoring, low-interference automatic retraining, and standardized safe deployment. It solves the problems in existing decision maker maintenance technologies, such as the inability to monitor high-confidence errors, susceptibility to noise-induced false triggering of retraining, mutual interference of statistical calibers, inflated retraining metrics, and lack of multi-layered safety measures for deploying new weights.

[0011] This invention is implemented as follows: a retraining triggering and safe deployment method for the decision maker based on a high-confidence blind-state hidden stake overturn rate is executed downstream of the secondary decision layer on the output side, forming a complete closed loop of "blind issuance of hidden stake sampling → manual voting collection → dual-caliber overturn rate calculation → multi-constraint automatic retraining triggering → silver standard idempotent materialization → offline leakage prevention retraining → multi-layer safe grayscale deployment", including the following steps: S1, Three-barrel card grouping and high-confidence hidden stake deterministic sampling The system receives all candidate cards from the decision layer and categorizes them into three candidate buckets: in-band scored buckets, unusable buckets, and high-confidence sampling buckets. In-band and unusable buckets are mandatory safety samples and are not discarded. The high-confidence sampling bucket only accepts high-scoring candidates whose confidence level is strictly higher than the upper limit of the decision interval and who are directly approved by the decision-maker without further review. Stable hash deterministic sampling is used to generate hidden stubs. The sampling formula is SHA-256 (slice identifier + coordinate key) digest, with the last two bytes rounded to the nearest integer and modulo 100. A bucket number less than the sampling rate × 100 is selected. A quota limit is set for the high-confidence bucket; a truncation alarm is generated when it overflows. S2, Blinded Distribution and Orthogonal Voting Collection The hash sorting of (task identifier, coordinate key) achieves blind shuffling, making it impossible for reviewers to distinguish between regular samples and hidden samples; during reading, only reading time spectrum sampling is performed without modifying the underlying storage data; Three types of manual votes are collected: target, not target, and uncertain. Each vote generates an independent feedback record line. The feedback record, the machine judgment field, and the manual review status field are orthogonally isolated and do not overwrite each other. For multiple votes on the same coordinate key, only the latest vote record is retained. S3, Overturn rate calculation for two sets of isolation calibers S3.1 Display Scope: Only for front-end manual viewing. When querying, read-time downsampling is performed, and the hidden stake overturning rate is calculated within the sampled subset. Only alarm fields are output, and no background tasks are driven. Alarm threshold configuration is displayed independently. S3.2 Triggering criteria: The automatic triggering module forces aggregation and statistics on the entire candidate set, prohibiting the reuse of display side downsampling code and environment variables; it accumulates the total number of hidden stakes and the number of hidden stakes rejected by manual processing across all ongoing tasks, calculates the aggregation rejection rate, and solves the ratio confidence lower bound based on Wilson confidence level 0.95; it configures an independent automatic triggering threshold, which is not shared with the display threshold. S4, Multi-Constraint Triggering S4.1. The system can enter the waiting state only if all five signals are met. If any one of them is not met, the system will not be triggered: aggregation overturn rate > trigger threshold; total sample size ≥ minimum sample size; confidence interval lower bound synchronously exceeds trigger threshold; number of threshold crossings in consecutive observation rounds ≥ round lower limit; newly added exportable labels since the previous training round ≥ label lower limit; if the threshold crossing in a single round does not meet the hysteresis condition, the system will not enter the waiting state directly. S4.2, Six-State Finite State Machine: Idle → Observation → Pending → Delivered → Cooling → Idle; Two safe backoff edges are set: First, Pending back to Observation state: If the safety gate verification fails or the composite signal falls back, after backoff, the consecutive threshold crossing rounds must be re-accumulated before entering the Pending state again, and the training task will not be automatically reissued; Second, Observation back to Idle state: If the index has completely fallen back and the Pending stage has not been entered. S4.3, Joint verification of four security gates: Retraining tasks can only be automatically delivered if all four pass: The cooldown period since the last delivery has expired; There are no incomplete training tasks in transit; The number of newly added exportable tags has reached the lower limit; The proportion of unusable candidates is lower than the set upper limit. S4.4, Automatic Trigger, Automatic Volume Increase, and Gray-Scale Deployment three-level switches are off by default at the factory. Full-volume weighted volume increase must be manually confirmed. The three-level emergency stop switch can cut off the closed-loop process at any time. S5, positive voting idempotent materialization to generate silver label Only candidates whose latest vote is "target" are materialized into annotation revision records, internally marked as silver, and their status is pending review by two senior experts; negative and uncertain votes are only used as monitoring and training signals and do not modify the original annotations; before materialization, duplicate votes are checked for deduplication based on feedback indicators, and duplicate votes do not add revisions, only incremental processing of new candidates; each batch of votes generates an SHA-256 audit fingerprint for evidence collection and retention; silver-marked revisions are upgraded to gold-marked only after being reviewed and approved by an external senior review panel; S6. Offline Retraining and Standardized Weight Export S6.1 Training Label Export: Reverse deconstruct the 448-pixel bounding box from the silver label revision, perform 7-field self-verification, and directly report and intercept if the geometric dimensions do not meet the contract. S6.2 Feature Extraction: Read the original 448 resolution small image of the candidate center, freeze the pathological encoder to extract features in batches, and directly reject training if the encoder dimension does not match. S6.3 Grouped Leakage Prevention Cross-Validation: Perform 5-fold cross-validation using the slice identifier as the grouping key to generate out-of-bag prediction results; Set up two independent fitting processes for the main caliber and the sensitivity caliber, and only when both calibers meet the criteria can the complete training begin; S6.4 Operation point freeze protocol: The threshold (5th percentile of positive class score) corresponding to the recall of 95% true targets is calculated only from the training set, and a complete recall-threshold curve is generated. No parameter tuning is performed on the test set throughout the process. S6.5 Weight Export Validation: Verify that the feature dimensions are consistent with the metadata declaration; if the validation fails, the export will be rejected. The packaged classifier, label table, and metadata carrying all parameters constitute the weight set. After retraining, only a new "registered, not enabled" version record will be added; it will not automatically switch to the production environment. S7, Multi-layered security deployment and automatic rollback S7.1 Weight structure gate fault closure verification: Before loading, verify that the weight set is complete, the tag set order is consistent, the feature dimension matches, and cross-check with the version record encoder information. If any condition is not met, the loading will be rejected directly, with no fallback default value. S7.2, Shadow Dual Scoring Parallel Audit: New and old weights are used to score the same batch of candidates within the same band simultaneously and retain a complete comparison record. The shadow scoring results are only used for auditing and are not directly put into production. S7.3 Atomic pointer transaction switching: within a single transaction, pessimistic row locks are added to simultaneously disable the old version and enable the new version; if the number of rows affected by the new version update is not equal to 1, the entire transaction is rolled back, and the old version disabling operation is simultaneously undone, eliminating the business window where there is no available arbiter; S7.4, Gray-scale Real-time Guardrail Automatic Loss Prevention: Gray-scale loss is defined based on real-time indicators such as exposure volume, deletion increments in each center, and anomaly count, without relying on manual verification; if the indicator exceeds the limit, it will be automatically rolled back to the old version and an isolation flag will be added; only when the gray-scale indicators are normal can it enter the next volume release stage, and full volume launch requires manual confirmation.

[0012] The system for triggering and safely launching a decision maker retraining based on a high-confidence blind-state hidden stake overturning rate is applicable to the aforementioned method for triggering and safely launching a decision maker retraining based on a high-confidence blind-state hidden stake overturning rate, including: an output-side secondary decision layer, a human-machine feedback closed-loop device, an external senior review gate, a three-level emergency stop switch, and a telemetry interface. The output-side secondary judgment layer is an upstream module used to output the "retention / false positive" judgment results and target probabilities for each candidate; the external senior review gate is an independent dual-person review process used for upgrading from silver to gold certification; the three-level emergency stop switch and telemetry interface are used to hierarchically cut off the automatic triggering, automatic volume expansion, and grayscale deployment processes, and are completely shut down at the factory; the human-machine feedback closed-loop device is the core of this invention and includes eight linkage modules: Card grouping module: realizes three-bucket candidate assembly, high-confidence hash deterministic sampling, and quota truncation alarm; Blind transmission module: Completes hash blind state shuffling, read time spectrum sampling, and hides the source identifier of the hidden stake; Voting collection module: Orthogonally stores three types of manual votes, and only retains the latest votes with the same coordinate key; Measurement module (display caliber): Independently calculates and displays the rejection rate during read downsampling, only outputs alarms, and does not drive tasks; Automatic triggering module (triggering caliber): physically isolated from the measurement module code and configuration, it aggregates the total overturn rate and confidence lower bound, and performs five-signal composite judgment, six-state machine transition, and four-fold safety gate verification; Materialization module: Only generate silver label revisions by making the "is target" vote idempotent, and generate audit fingerprints; Retraining pipeline module: Implements label export verification, frozen encoding, slice grouping anti-leakage cross-validation, dual-caliber admission, and weighted three-piece set verification export; Online pipeline module: sequentially executes weight structure gate verification, shadow double scoring, atomic transaction version switching, grayscale real-time guardrail monitoring and automatic rollback.

[0013] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention achieves direct quantification of decision drift and eliminates blind spots in high-confidence sample monitoring: It extracts high-confidence release samples of the decision-maker through stable hashing as blind stakes, which can be deployed in new slice batches without the need for pre-installed offline gold standards; it uses the proportion of manual true rejection as direct evidence of decision-maker inaccuracy, which is different from the indirect statistics of traditional distribution drift. It specifically covers the high-confidence misjudgment area that active learning cannot monitor, and perfectly adapts to the needs of pathological safety management.

[0014] Two sets of statistical standards are physically isolated to prevent hidden contamination of operation and maintenance parameters: The two sets of flip-flop calculation logic, code, and configuration for display and automatic triggering are completely independent. The automatic triggering module forces the use of full candidate statistics and does not reuse the display pagination downsampling logic; adjustments to the front-end display parameters by operation and maintenance will not change the retraining trigger conditions, eliminating hidden fault paths.

[0015] A multi-constraint composite triggering mechanism completely avoids random noise-induced idle training: It adopts a triple anti-noise design of five-signal joint judgment, continuous round hysteresis constraint, and Wilson confidence interval lower bound verification, so that a single round of accidental threshold exceedance will not trigger retraining; combined with cooling period, minimum sample size, and new label lower bound constraint, it greatly reduces the invalid GPU training overhead; the state machine is set with a safe rollback mechanism, so that the training task will not be automatically reissued when the index falls.

[0016] Human-machine feedback data is orthogonally isolated, and idempotent silver label sedimentation does not contaminate the labeled true value: the fields of manual voting, machine judgment, and original manual review are stored independently and do not overwrite each other; only positive target vote increments generate silver label revisions, and negative votes are only used as monitoring signals; duplicate votes are automatically deduplicated, audit traceability is clear, and human-machine divergence rate statistics and label consistency verification are not contaminated.

[0017] Offline retraining prevents leakage and standardizes offline metrics to match real-world online performance: sliced ​​grouping cross-validation is used to prevent leakage of training and validation set samples; dual-caliber independent fitting allows filtering of inflated models; operation point thresholds are determined and frozen only by the training set and are not tuned on the test set; feature dimensions are forcibly verified before export to avoid online judgment distortion from the source.

[0018] A four-layered security defense system mitigates the risk of missed diagnoses in batches through real-time gray-scale mitigation: a weight structure gate intercepts incomplete, mismatched, or incorrectly labeled inferior weights; shadow dual scoring retains audit records of both old and new effects; atomic transaction version switching eliminates business gaps; and gray-scale real-time guardrails rely on real-time business indicators to identify model degradation in advance and automatically roll back to the old version before the delayed manual overturning rate feedback arrives, completely resolving the fatal risk of batch accidental deletion of real lesion targets in pathological scenarios.

[0019] The entire process is controllable and compatible with grayscale iteration: automatic triggering, automatic volume increase, and grayscale launch switch are turned off by default at the factory. All full volume increase operations must be manually confirmed. It is equipped with multi-level emergency stop switches to support rapid shut-off and closed-loop failure. The entire system is seamlessly adapted to the upstream and downstream secondary screening decision device on the output side of the previous full slice, and follows the pathological safety constraints of "only downgrading without physical deletion of annotations and prioritizing the recall of 95% of real targets" throughout the process. Attached Figure Description

[0020] Figure 1 This is a closed-loop block diagram of the verification backflow and adaptive retraining provided in the embodiments of the present invention; Figure 2 This is a schematic diagram of a retraining automatically triggered state machine and access control system provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the new weighting and grayscale rollback pipeline provided in an embodiment of the present invention. Detailed Implementation

[0021] To further understand the invention's content, features, and effects, the following embodiments are provided, and detailed descriptions are given in conjunction with the accompanying drawings.

[0022] Example 1: End-to-end Example S1, Three-Barrel Card Grouping and High-Confidence Hash Blind Stake Sampling The system receives all candidate cards produced by the secondary decision layer on the upstream output side and divides them into three candidate buckets according to the confidence interval: in-band scored bucket (confidence [0.30, 0.90], secondary scoring completed), unusable bucket (in-band interval but decision-maker loading / threshold parsing failed), and high-confidence sampling bucket (confidence strictly > 0.90, decision-maker directly allows, no review process). In-band bucket and unusable bucket are security must-see samples and are never discarded under any circumstances; the high-confidence sampling bucket is configured with a quota limit of 5000 entries, and a truncation alarm is generated if the quota is exceeded. For high-scoring candidates, SHA-256 (slice identifier + coordinate key) stable hash sampling is used: the last 2 bytes of the hash digest are taken as big-endian integers modulo 100, and if the value is less than 8, they are selected as blind stubs; the sampling results of the same slice + coordinate key combination are fixed and reproducible, and there is no need for offline pre-built honeypot gold labels.

[0023] S2, Blind Distribution and Orthogonal Sparse Voting Collection The blind sorting module shuffles the card order by hashing (task identifier, coordinate key), making it impossible for reviewing doctors to distinguish between ordinary candidates and hidden candidates; when the page is displayed, the reading spectrum is compressed to 200 cards, and all unusable cards are forcibly retained, while the underlying original candidate data is not modified; Reviewers can cast three types of votes: target, not target, and uncertain. Each vote generates a separate feedback record line. The feedback record, machine judgment dictionary, and manual review status field are completely orthogonally isolated and do not overwrite each other. When multiple votes are cast for the same coordinate key, the system only retains the latest vote result.

[0024] S3. Calculate the overturning rate separately for the two independent calibers. Display metrics: When querying on the front-end page, the hidden stake overturn rate is calculated based on the card set after downsampling during read time. Only an alarm prompt is output on the page, and no retraining task is triggered in the background. An independent display alarm threshold configuration is used; when the operation and maintenance adjusts the pagination sampling parameters, it only affects the page display effect. Automatic aggregation of trigger criteria: The automatic trigger module strictly prohibits calling the display-side downsampling code and does not read display-related environment variables. It reads all high-scoring candidate raw data every 3600 seconds. It accumulates the total number of hidden stakes and the number of manually rejected hidden stakes across all ongoing tasks, calculates the aggregation overturning rate, and solves the ratio lower bound at the 95% confidence level based on Wilson's formula. It has an independent automatic trigger threshold, which is configured separately from the display threshold and is not shared between them.

[0025] S4, five-signal composite judgment and state machine with safety return, quadruple delivery safety gate Observe the five sets of trigger signals for synchronous verification within the loop. Only when all of them are satisfied can the "waiting" state be entered. If the single round of indicators exceeds the threshold and does not meet the hysteresis condition, it will be skipped directly: aggregation overturn rate > 0.15; cumulative sampling of hidden stakes ≥ 100; confidence interval lower bound synchronously higher than 0.15; number of consecutive observation rounds exceeding the threshold ≥ 2; new derivable silver labels added since the last training ≥ 20. The system operates a six-state finite state machine: Idle → Observation → Pending → Delivered → Cooling. Two safe backoff logics are set: when the safety gate verification fails or the indicator drops, it returns from the Pending state to the Observation state, the accumulated consecutive round count is cleared, and the pending delivery task will not be automatically resent. The consecutive threshold-crossing rounds need to be re-accumulated; when the indicator drops completely and has not entered the Pending stage, it directly returns to the Idle state. In the pending state, a retraining task will be automatically delivered only if it passes four security gate checks: 72 hours have passed since the last delivery, there are no ongoing training tasks, ≥20 new tags have been added, and the percentage of unusable candidates is less than 0.5. When the main switch is automatically turned off, only statistical data is recorded, and no training task is initiated.

[0026] S5, Only Positive Voting Idempotent Materialization Generates Silver Label Revision The system only generates annotation revision records for candidates whose latest vote is "is the target", internally marks them as silver and places them in a state pending review by senior double experts; the two types of votes, "is not the target" and "uncertain", are only stored in the feedback library as monitoring and training signals and do not modify the original pathological annotations. Before materialization, duplicate voting is checked according to the feedback identifier. Duplicate voting will not add new revision records, achieving idempotent processing. Each batch of voting data is used to calculate SHA-256 audit fingerprints for evidence collection and retention. Silver-label revisions are pushed to external senior reviewers. Only after double review and approval can it be upgraded to a gold-label sample.

[0027] S6, Offline Leakage Prevention Retraining and Standardized Export of Weight Set (Three-Piece Set) Training label export: reverse engineer 448-pixel bounding boxes from silver / gold labels, perform 7-field self-validation, and directly intercept and report errors if the geometric dimensions do not conform to the rules; Feature extraction: The candidate center reads the original 448×448 small image and sends it to the frozen pathology encoder for batch feature extraction. If the encoder dimension does not match the configuration, the training is directly rejected. Grouped leakage prevention verification: 5-fold cross-validation is performed using the single slice ID as the grouping key. Two independent out-of-bag fittings are run separately for the main classification caliber and the sensitivity caliber. Only when both calibers meet the criteria can complete training be allowed. Operation point freezing protocol: Calculate the threshold (5th percentile of positive class score) corresponding to the 95% true target recall based only on the training set samples, generate a complete recall-threshold curve, and use it directly to freeze the test paper after freezing. No parameter tuning is performed on the validation set throughout the process. Weight export verification: Before exporting, verify that the actual output dimension of the encoder is completely consistent with the metadata declaration. If the verification fails, the export is rejected. The packaged classifier, label table, and metadata with complete parameters form a weight set. The metadata is fixed and the judgment rules are irreversible. After retraining, only the "registered, not enabled" version record is added. The online production judge will not be automatically switched.

[0028] S7, four-layer security grayscale deployment, and automatic rollback of real-time metrics. Weighted Structure Gate (Fault Closure): Before loading, the three-piece verification set of files is complete, the label order is fixed [normal,tumor], the feature dimension is matched, and the information is cross-checked with the information of the historical version encoder. If any condition is not met, the loading is directly rejected, and a fallback probability of 0.5 is not used. Shadow dual scoring: New and old weights score the same batch of candidates within the band in parallel and retain audit data. Shadow scoring is only used for offline comparison and is not integrated into the online judgment process. The underlying logic of this mechanism has been implemented, but it has not yet been integrated into automatic online orchestration. Atomic pointer transaction switching: The database enables pessimistic row locks, and a single transaction synchronously disables the old version and enables the new version; if the number of rows affected by the new version update is not 1, the entire transaction is rolled back, the old version disabling operation is undone, and business gaps without a decision maker are eliminated; this step has been integrated into production orchestration. Gray-scale real-time guardrail: Gray-scale loss is defined by real-time indicators such as exposure volume, deletion of incremental data from each collection center, and anomaly feedback count, without relying on manual verification; if the indicator exceeds the limit, it will be automatically rolled back to the old version and an isolation flag will be added; the gray-scale phase indicators can only enter the next volume release level when they are stable, and full volume release must be confirmed manually; the underlying logic of this mechanism has been implemented, but it has not yet been integrated into automatic online orchestration.

[0029] Example 2: Implementation of Unified Verification of Boundaries and Anomalies The card grouping and overturn rate calculation logic are both purely deterministic functions with no side effects. During execution, the original input data will not be modified in place. If the number of high-confidence sampling cards is 0, the system will directly assign the overturn rate to 0 and set the alarm status to false, without triggering division-by-zero errors or false alarms for empty samples.

[0030] When the same coordinate key corresponds to multiple manual votes, the "latest vote takes effect" rule applies; entries with empty coordinate keys in the feedback record are directly ignored in the overturn rate measurement and silver label materialization stages to prevent empty key data from polluting the statistical results and annotation revision library.

[0031] The materialization process for the silver label uses the unique feedback identifier as the basis for idempotent deduplication. If the bounding box geometry carried by the manual feedback is distorted (e.g., the coordinates contain null values ​​or illegal values), the system defensively reverts to resolving the standard bounding box using the coordinate key. If the coordinate key also fails to resolve the valid geometry, the materialization process for this feedback is skipped, and only an alarm log is output, which will not cause the materialization process to crash or be interrupted.

[0032] The offline retraining process is configured with two layers of verification and interception: when reading the historical encoding cache, if the encoder name and feature dimension recorded in the cache do not match the expected parameters for the current run, training will be refused to start directly; during the weight export stage, if the actual encoded feature dimension is inconsistent with the metadata declaration dimension, the complete set of weights will be refused to be exported.

[0033] Multiple exception handling mechanisms are in place during the new weight deployment process: if any check item of the weight structure gate fails to meet the requirements, loading will be directly rejected, and false judgments will not be generated with a fallback probability of 0.5; in atomic version switching transactions, if the number of rows affected by the new version update is 0, the entire transaction will be rolled back; when the real-time business reading in the grayscale exceeds the safety threshold, it will automatically roll back to the old version judge and add a traffic isolation flag.

[0034] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0035] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for triggering retraining and ensuring safe online deployment of a decision maker based on a high-confidence blind-state peg overturning rate, characterized in that, Includes the following steps: S1. Three-bucket grouping and high-confidence blind stake deterministic sampling: The output candidates of the decision layer are divided into three types of buckets and collected. Stable hash deterministic sampling is used to generate blind stakes for high-scoring direct-pass candidates. Quota limits are set for high-confidence buckets. S2, Blind Distribution and Orthogonal Voting Collection: Hash shuffles candidate order to hide the identity of the hidden stakes, three types of manual votes are stored independently, and only the latest voting record is retained for the same coordinate key; S3. Two sets of isolated calculation methods for overturn rate: The display method and the automatic triggering method are distinguished. The two sets of code and configuration are completely independent and isolated. The overturn rate of hidden stubs is calculated separately. The triggering method is based on the full candidate statistics and solves the confidence lower bound. S4. Multi-constraint triggering: It enters the ready-to-deliver state only when five indicators are met simultaneously. It is equipped with a six-state flow mechanism with safe return, and verifies four safety gates before delivery. The automatic control switch is turned off by default at the factory. S5. Positive voting idempotent materialization to generate silver labels: only the manually generated "is the target" silver labels are revised, duplicate votes are automatically removed, and the remaining votes are only used as monitoring signals without changing the original labels; S6. Offline retraining and standardized weight export: Read the silver label to construct training samples, use slice grouping cross-validation and dual-caliber admission verification, generate a fixed recall threshold only from the training set, export the weight set after verifying the dimensions, and only register the new version without automatically switching to online. S7, Multi-layered security deployment and automatic rollback: Sequentially execute weight structure compliance verification, parallel shadow scoring of old and new weights, atomic version switching of the database, and real-time grayscale indicator monitoring. When degradation occurs, it automatically rolls back to the old version.

2. The method for triggering retraining and secure deployment of the decision maker based on high-confidence blind-state peg overturning rate as described in claim 1, characterized in that, Step S1 specifically includes: dividing candidates into in-band scored buckets, unusable buckets, and high-confidence sampling buckets. The first two categories are mandatory samples and are not discarded. Sampling is completed by taking the last 2 bytes of the SHA-256 (slice identifier + coordinate key) digest as a modulo of 100. The sampling rate is configurable. A truncation alarm is generated when the high-confidence bucket exceeds its quota. The sampling results for the same coordinate key are fixed and reproducible.

3. The method for triggering retraining and secure deployment of the decision maker based on high-confidence blind-state peg overturning rate as described in claim 1, characterized in that, In step S2, the voting storage rules are as follows: feedback records, machine judgment fields, and manual review status fields interact positively without overlapping; voting types include three categories: target, not target, and uncertain; the page only displays the reading spectrum sampling, and the underlying original data is completely preserved without modification.

4. The method for triggering retraining and secure deployment of the decision maker based on high-confidence blind-state peg overturning rate as described in claim 1, characterized in that, In step S3, the two sets of caliber configurations are independent of each other and do not share threshold parameters; when displaying caliber queries, read-time downsampling is performed, and only visual alarms are output without initiating tasks; the caliber is triggered to prohibit reuse of display-side sampling code, aggregate data across all in-process tasks, and calculate the lower bound of Wilson ratio confidence based on a 0.95 confidence level.

5. The method for triggering retraining and secure deployment of the decision maker based on high-confidence blind-state peg overturning rate as described in claim 1, characterized in that, In step S4, the detailed constraints are as follows: five signals must be met simultaneously: overturn rate exceeding threshold, sample size meeting standard, confidence lower bound simultaneously exceeding threshold, multiple consecutive rounds exceeding threshold, and sufficient number of newly added labels; the state machine contains six nodes: idle, observation, waiting to be sent, delivered, and cooling down. After a task is returned from waiting to observation, the consecutive rounds must be re-accumulated, and the task will not be automatically resent; four safety gates respectively verify the cooling down period, no tasks in transit, sufficient number of labels, and controllable proportion of unusable tasks; Full weighted volume release must be manually confirmed.

6. The method for triggering retraining and secure deployment of the decision maker based on high-confidence blind-state peg overturning rate as described in claim 1, characterized in that, In step S5, the materialization rules are as follows: when generating a silver label, a batch audit fingerprint of SHA-256 is generated simultaneously; the silver label status is marked as pending review by senior double reviewers, and it is upgraded to a gold label only after passing external review; idempotency is achieved by checking for duplicates based on feedback identifiers, and repeated calls do not add new annotation revisions.

7. The method for triggering retraining and secure deployment of the decision maker based on high-confidence blind-state peg overturning rate as described in claim 1, characterized in that, In step S6, the retraining process is as follows: read the candidate 448-size bounding boxes and perform field self-checks, freeze the pathology encoder to extract features in batches; Five-fold cross-validation is performed using slice identifiers as grouping keys. Only when both the primary and sensitivity metrics meet the requirements can the training be completed. Metadata is exported to solidify irreversible decision rules. After verifying that the feature dimensions are consistent, the weight set is packaged.

8. The method for triggering retraining and secure deployment of the decision maker based on high-confidence blind-state peg overturning rate as described in claim 1, characterized in that, In step S7, the four layers of online protection are as follows: fault-closed weighted structure gate, shadow double scoring audit, pessimistic locking atomic transaction switching, and grayscale real-time guardrail. If the number of rows updated by an atomic switch is not 1, the entire transaction is rolled back; the underlying logic of shadow scoring and grayscale guardrail has been implemented, and the weight verification and atomic switch modules have been integrated into production orchestration.

9. A decision maker retraining triggering and secure online system based on high-confidence blind-state peg overturning rate, characterized in that, The method for triggering and safely launching a decision maker based on a high-confidence blind-state overturning rate, applicable to any one of claims 1-8, includes an output-side secondary decision layer, a human-machine feedback closed-loop device, an external senior review gate, a three-level emergency stop switch, and a telemetry interface; the human-machine feedback closed-loop device is the core processing unit, comprising eight linkage modules: card grouping module, blind firing module, voting collection module, measurement module, automatic triggering module, materialization module, retraining pipeline module, and launch pipeline module.

10. The decision maker retraining triggering and secure online system based on high-confidence blind-state peg overturning rate as described in claim 9, characterized in that, The system is only used to correct the accuracy of false positive identification of the decision maker. It does not inject negative samples into the upstream segmentation model, does not compensate for missed detections, and does not physically delete annotations. It is configured with a global master switch, automatic triggering, and automatic volume increase three-level switches, all of which are turned off at the factory. It supports hierarchical disconnection of the entire automatic closed loop.

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