An artificial intelligence reinforcement learning service platform

Abstract

The application discloses an artificial intelligence reinforcement learning service platform and particularly relates to the field of computer data processing, and is used to solve the problem of semantic drift and non-recoverable training samples caused by the superposition of telemetry backfill disorder and caliber version change in a data center; original record sets are generated by collecting telemetry, double indexes are established, action tickets are generated at a fixed period and backfill time windows are locked, aligned observation packages are formed by extracting observations, missing section items and late section items are labeled, expressions and reward calculation expressions are constructed according to the execution state of caliber version identification, training sample packages are encapsulated, calculation summaries are generated, backfill link completeness indexes and caliber shadow difference risk values are calculated, release judgment values are obtained through a double-axis risk matrix, the training sample packages are written into a sample library or an isolation area, strategy release packages are generated, and an access judgment is completed by using a form interval table, a frozen strategy release package is triggered to output a bottom control instruction and a repair queue is registered.

Description

Technical Field

[0001] This invention relates to the field of computer data processing, and more specifically, to an artificial intelligence reinforcement learning service platform. Background Technology

[0002] In production environments like data centers, enterprises often aim to transform reinforcement learning from a one-off pilot project into a service platform capable of continuously providing capabilities. This platform enables different business systems to perform tasks such as data access, state construction, policy training, performance evaluation, online inference, deployment, and monitoring whenever resource scheduling, load management, capacity control, or operational parameter optimization is required. To ensure policies keep pace with production changes, the platform needs long-term access to operational information from multiple sources. This information comes from different acquisition channels and system perspectives, supporting offline training and playback, online decision-making, and canary deployments, while remaining available and controllable throughout system evolution, adjustments to acquisition rules, and changes in metric meanings.

[0003] In current practices, such platforms often treat multi-source data as a general data processing workflow, directly merging states reported by different systems and then using post-event statistical results to calculate rewards and backfill training records. However, in data center scenarios, there are often delays, omissions, and recalculations between states and results. Furthermore, the meaning and calculation methods of the same metric can change with system upgrades. This leads to the situation where the state seen when an action occurs and the subsequent result do not belong to the same true causal chain. The root cause of the problem lies in the inconsistent temporal semantics of each link, and the platform lacks mechanisms to incorporate time alignment, caliber tracking, reward binding, and replay verification as native platform capabilities. This causes the training phase to treat data that did not occur simultaneously, or even whose meaning has changed, as data from the same moment, thus attributing incorrect results to actions that should not be responsible. Such erroneous attributions can be masked by seemingly normal training convergence, only manifesting as unstable policy output, significant differences in performance of the same policy at different time periods, and discrepancies between offline and online evaluations once the policy is running online. In severe cases, it can make policy deployment and rollback difficult to be explained, audited, and reproducible, thus making it difficult for reinforcement learning service platforms to be stably deployed in data centers in the long term.

[0004] To address the aforementioned problems, a technical solution is provided. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide an artificial intelligence reinforcement learning service platform. This platform generates a raw record set by collecting telemetry data and establishes a dual index. It generates action tickets at fixed intervals and locks the backfill time window. Observations are extracted to form aligned observation packages, and missing and late segment entries are labeled. Training sample packages are encapsulated with expressions for execution status and reward calculation expressions based on caliber version identifiers, and a recalculation summary is generated. The backfill link completeness index and caliber shadow difference risk value are calculated, and a release judgment value is obtained through a dual-axis risk matrix. The training sample packages are written into a sample library or isolation area. A policy release package is generated, and admission is determined using a morphological interval table. The frozen policy release package is triggered, outputting a minimum control command and registering it in the repair queue, thus solving the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: Telemetry Acquisition Module: Acquires event time identifiers and core indicator values ​​during telemetry operations in the data center, generates access time identifiers, writes source identifiers and record sequence numbers, performs time semantic verification and indicator value validity verification, writes verification result identifiers, stores them in the raw stream storage area, forms the raw record set, and establishes an index; Ticket Arrangement Module: Based on the original record set, extract the status summary at fixed intervals and register the trigger point sequence number. When outputting control actions, generate action tickets. Write the action tickets to the action ledger. Bind the action tickets to the trigger point sequence number, backfill time window, caliber version identifier, execution domain identifier, and status summary value. The observation alignment module reads the action ticket, extracts the observation records from the original record set according to the backfill time window, sorts them according to the event time identifier to generate an alignment observation package, marks the missing segments and late segments in the alignment observation package, writes the missing segments into the completion request queue, retains the record sequence number of the late segments and writes the recalculation flag, and stores the alignment observation package in the observation storage area. Sample admission module: Based on the caliber version identifier, the module performs state construction and reward calculation on the aligned observation package, generates training sample package, performs playback consistency verification on the training sample package, outputs release judgment value, and determines whether the training sample package is written to the sample library or the isolation area, and writes the judgment reason identifier. The strategy protection module generates a strategy release package when the release judgment value is in the release state. The strategy release package is bound to the caliber version identifier and the alignment observation package summary. Online inference reads the strategy release package and the latest alignment observation package, checks the backfill integrity and caliber version consistency. If an inconsistency is triggered, the strategy release package is frozen and a backup control command is output and written to the repair queue.

[0007] Furthermore, the telemetry acquisition module includes: reading the event time identifier and core indicator value from the telemetry access terminal, generating an access time identifier, writing the source identifier and record sequence number, performing time semantic verification and indicator value validity verification and generating a verification result identifier, appending the record to the original stream storage area, establishing a main index according to the source identifier and record sequence number, establishing an auxiliary index according to the event time identifier, and forming an original record set.

[0008] Furthermore, the document arrangement module includes: extracting a fixed-period window based on the auxiliary index of the original record set according to the event time identifier; merging the window records into an execution domain window set according to the execution domain mapping table; filtering the valid record set according to the verification result identifier; selecting the most recent valid record according to the event time identifier to obtain the most recent valid value of the core indicator; sorting the core indicators and taking the median valid value to obtain the median valid value of the core indicator; comparing the two and writing the core indicator change direction identifier; counting the abnormal count of the verification result and determining the missing placeholder count; and generating a status summary value.

[0009] Furthermore, the ticket arrangement module also includes: calculating and sorting the arrival delay value according to the source identifier in the historical original record set; determining the upper limit of the source delay based on the position of the largest break between adjacent differences; obtaining the upper limit of the execution domain delay by taking the maximum value of the source delay upper limit in the same execution domain according to the execution domain mapping table; determining the event time identifier of the start of the backfill time window and the event time identifier of the end of the backfill time window; counting the late arrival records that exceed the source delay upper limit and whose event time identifier is within the window range; generating the trigger point sequence number and writing it into the action ticket when outputting the control action; and writing the action ticket into the action ledger.

[0010] Furthermore, the observation alignment module includes: reading action tickets from the action ledger, obtaining the execution domain identifier, trigger point sequence number, backfill time window start event time identifier, backfill time window end event time identifier, extracting observation records from the original record set based on the execution domain allowed set and verification result identifier, generating observation site identifiers according to the source identifier and indicator name, sorting them according to the event time identifier and stabilizing the same order with the record sequence number, and assembling the alignment observation package.

[0011] Furthermore, the observation alignment module also includes: dividing the backfill time window with a fixed period length, constructing a trigger point placeholder string and identifying continuous missing intervals to form missing segment entries, writing the missing segment entries into the alignment observation package and writing them into the completion request queue; maintaining the prefix maximum record number on the event time sorting sequence, recording the record number back as a late record and merging it into a late segment entry, writing the late segment overwritten record into a recalculation flag, generating an observation package identifier and writing it into the observation storage area.

[0012] Furthermore, the sample admission module includes: loading state construction expressions and reward calculation expressions from the caliber library based on the caliber version identifier of the action ticket and parsing them into an expression tree; calculating the state vector and reward value on the aligned observation package after completing the field reference node and operator node verification; performing normalization serialization and generating state summary recalculation value and reward summary recalculation value; and encapsulating the training sample package, which includes the action ticket identifier, observation package identifier, and caliber version identifier.

[0013] Furthermore, the sample admission module also includes: the platform extracts observation records from the original record set for playback based on the backfill time window and execution domain allowed set of the action ticket, reconstructs the playback aligned observation package, recalculates the playback status summary and playback reward summary and generates a recalculation summary, generates a completeness profile based on the must-achieve observation list and maps it to obtain the backfill link completeness index, generates a shadow difference feature set based on the previous stable caliber version identifier and calculates the caliber shadow difference risk value, maps it to the backfill confidence level and caliber offset level and generates a release judgment value through a dual-axis risk matrix, and writes the training sample package into the sample library or isolation area according to the release judgment value and writes the judgment reason identifier.

[0014] Furthermore, the sample admission module includes: reading training sample packages from the sample library, filtering training sample packages with release judgment values ​​of release and controlled release, grouping them by caliber version identifier and verifying within each group that the version identifier of the must-reach observation list is consistent with the version identifier of the dual-axis risk matrix, and writing training sample packages with version conflicts into the isolation area; sorting training sample packages by execution domain identifier and trigger point sequence number, reading back the action ledger to obtain control action content, assembling training segments and updating the action value estimation table, generating a policy release package and writing in the caliber version identifier, the version identifier of the must-reach observation list, the version identifier of the dual-axis risk matrix, and the training set summary.

[0015] Furthermore, the strategy escort module also includes: performing standardized serialization on the strategy release package and generating a strategy release package identifier to be written into the release ledger; the online inference entry reads the strategy release package and the latest aligned observation package, verifies that the caliber version identifier, the required observation list version identifier, and the dual-axis risk matrix version identifier are consistent, and outputs control instructions after verifying that the missing segment entries and late segment entries are morphologically compliant according to the morphological interval table; when the admission judgment fails, the strategy release package is frozen and the freeze ledger is registered, the minimum control instruction is output, and the admission failure reason, observation site identifier, and recalculation summary location information are written into the repair queue.

[0016] This invention provides an artificial intelligence reinforcement learning service platform, involving reinforcement learning and deep learning technologies, with the following technical effects and advantages: 1. By locking action tickets in the action ledger and constraining the extraction of the original record set with the backfill time window, and combining the observation site identifier, missing segment entry, late segment entry and recalculation flag of the aligned observation package, the training sample package only absorbs observation facts that are consistent with the same execution domain identifier and the same trigger point sequence number. The recalculation summary locates the differences to the observation site identifier and the expression tree node, so that the replay consistency verification and the repair queue registration have the same evidence chain, reducing the misleading effect of link jitter and out-of-order backfill on policy learning.

[0017] 2. By using the expression tree of the caliber library, the state construction expression and reward calculation expression are solidified and executed under the caliber version identifier. At the same time, the previous stable caliber version identifier is introduced in parallel to generate a shadow difference feature set and calculate the caliber shadow difference risk value. Combined with the must-reach observation list and completeness profile, the backfill link completeness index is generated. Then, the release judgment value is output using a dual-axis risk matrix and the judgment reason identifier is written. This realizes the separation judgment and joint constraint of time direction credibility and caliber direction credibility, so that the sample library maintains semantic consistency and avoids semantic drift brought into training by caliber changes.

[0018] 3. By simultaneously carrying the caliber version identifier, the version identifier of the must-achieve observation list, and the version identifier of the dual-axis risk matrix in the strategy release package, the online inference entry performs version consistency verification and morphological interval table verification on the latest aligned observation package. When the admission judgment fails, the strategy release package is frozen and a backup control instruction is output. The queue entry references the observation site identifier and the recalculation summary location information are repaired to achieve the same source constraint and closed-loop traceability between the training side release judgment and the inference side admission judgment, ensuring that the control instructions remain controllable and auditable in the data center scenario during observation anomalies and caliber switching. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the structure of an artificial intelligence reinforcement learning service platform according to the present invention. Detailed Implementation

[0020] 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.

[0021] Example 1: Figure 1 This invention provides an artificial intelligence reinforcement learning service platform, comprising: Telemetry Acquisition Module: Acquires event time identifiers and core indicator values ​​from telemetry operations in the data center, generates access time identifiers, writes source identifiers and record sequence numbers, performs time semantic verification and indicator value validity verification, writes verification result identifiers, stores them in the raw stream storage area, forms the raw record set, and establishes an index.

[0022] The ticket arrangement module extracts status summaries from the original record set at fixed intervals and registers the trigger point sequence number. When outputting control actions, it generates action tickets, writes the action tickets to the action ledger, binds the trigger point sequence number, binds the backfill time window, and binds the caliber version identifier, execution domain identifier, and status summary value.

[0023] The observation alignment module reads action tickets, extracts observation records from the original record set according to the backfill time window, sorts them by event time identifier to generate an alignment observation package, marks missing segments and late segments in the alignment observation package, writes missing segments into the completion request queue, retains the record sequence number of late segments and writes a recalculation flag, and stores the alignment observation package in the observation storage area.

[0024] Sample admission module: Based on the caliber version identifier, the module performs state construction and reward calculation on the aligned observation package, generates training sample package, performs playback consistency verification on the training sample package simultaneously, outputs release judgment value, and determines whether the training sample package is written to the sample library or the isolation area, and writes the judgment reason identifier.

[0025] The strategy protection module generates a strategy release package when the release judgment value is in the release state. The strategy release package is bound to the caliber version identifier and the alignment observation package summary. Online inference reads the strategy release package and the latest alignment observation package, checks the backfill integrity and caliber version consistency. If an inconsistency is triggered, the strategy release package is frozen and a backup control command is output and written to the repair queue.

[0026] Before the data center reinforcement learning service platform can generate action tickets and perform backfilling extraction, the first problem it encounters is the out-of-order and delayed arrival of multi-source telemetry data. Event time identifiers describe the time when a service occurs, while access time identifiers describe the time when a record arrives. When these two types of time semantics are inconsistent, window truncation and backfilling extraction will concatenate incoherent observations into the same time period, causing semantic shifts in the samples. However, the ticket arrangement module relies on the arrival order structure and event time structure of the original record set to generate state summaries and backfilling time windows. The telemetry acquisition module's processing must ensure that the original record set has traceable order markers and interpretable time verification conclusions.

[0027] S101. Generation of ternary records and writing of fields to disk.

[0028] In multi-source telemetry, the reporting time on the acquisition link and the arrival time at the platform are often inconsistent. The ternary record separates the occurrence time and arrival time, ensuring that the timing relationship can still be interpreted even in out-of-order scenarios. The platform reads the event time identifier and core indicator value from the telemetry access point, generates an access time identifier at the moment of reception, and writes all three into the same record. The record also includes a source identifier and a record sequence number placeholder field. The record is written to an append-only log segment in the raw stream storage area. After writing, a disk write marker is generated and written to the end of the log segment, associated with the log offset position. The log offset position becomes the unified location point for subsequent indexes. The record content corresponds one-to-one with the location point, facilitating subsequent backtracking to the complete record using any index.

[0029] S102. Source Identifier and Record Number Allocation Rules.

[0030] Arrival order is fundamental for determining late and reversed segments. Record sequence numbers monotonically increase within the source identifier range, preserving the arrival sequence without relying on external time. The platform writes the source identifier based on the acquisition link marker from the telemetry access terminal. The source identifier is used to bind the parsing rules and unit affiliation. A sequence number cursor is maintained for the same source identifier. When a new record is received, the sequence number cursor is incremented and written to the record sequence number. Simultaneously, a sequence number check bit is generated. The sequence number check bit is obtained from the record sequence number through a modular ring operation. The modular ring operation maps the sequence number to a finite set, used to detect duplicate writes and skipped number writes. The source identifier and record sequence number are written as the primary index key field in the record header. The primary index key field is bound to the log offset position, ensuring stable record location according to arrival order.

[0031] S103. Temporal semantic verification and temporal offset envelope calculation.

[0032] Data center telemetry exhibits stable latency ranges across different links. Time semantic verification uses time offsets to describe the deviation between the arrival time and the occurrence time, and then uses envelope boundaries to constrain the deviation range, effectively eliminating obviously abnormal timestamp drifts. The platform calculates the time offset for each record by subtracting the event time from the access time identifier, maintaining the time dimension. A time offset reference set is maintained for each source identifier, containing only the most recent time offsets for which the verification result identifier is passed. The reference set is sorted by size, and the segment closer to the bottom of the sorted result is taken as the lower bound of the time offset envelope, while the segment closer to the top is taken as the upper bound. The selection of the lower and upper bounds is defined by fixed rules on the platform to ensure consistent output across different instances.

[0033] The main rhythm interval is calculated based on the frequency of the difference between adjacent event timestamps under the same source identifier. The difference with the highest frequency is selected as the main rhythm interval, which maintains the time dimension. The segment reversal tolerance segment is calculated based on the upper bound of the length of short segments that have recently reversed between the record number and the event timestamp. This upper bound is determined by the segment closest to the highest value after sorting. When determining if the time semantics pass the mark, the validity of the event timestamp format and time zone consistency are checked simultaneously. A time offset falling between the lower and upper bounds of the time offset envelope, and with a distance from the boundary not exceeding the buffer range jointly determined by the main rhythm interval and the segment reversal tolerance segment, is considered a pass condition. The time semantics pass mark is written into the time semantic bit of the verification result identifier. This time semantic bit explicitly records that the verification conclusion originates from the offset envelope and the main rhythm interval, facilitating review and interpretation.

[0034] S104. Verification of the validity of indicator values ​​and determination of consistency of unit attribution.

[0035] Core indicator values ​​originate from different data collection points. Inconsistent parsing methods and unit affiliations can lead to indicators with the same name being mixed with different units, resulting in uninterpretable results in subsequent state construction expressions and reward calculation expressions. The platform uses source identifiers to locate the indicator parsing rule table, which specifies the parsing type and expected unit affiliation for core indicator values. During indicator value validity verification, the core indicator value is first converted from its original representation to a computable representation according to the parsing rules. If the conversion fails, an exception reason code is written, and the valid bit of the indicator is marked as invalid. After successful conversion, the computable representation is checked to ensure it falls within a finite expressible range. If a non-finite form is found, the valid bit of the indicator is marked as invalid, and an exception reason code is written.

[0036] When performing unit attribution consistency determination, the unit attribution flag carried in the record is read and compared with the expected unit attribution in the indicator parsing rule table. If they match, the unit consistency bit is marked as passed; if they do not match, the unit consistency bit is marked as failed and an exception reason code is written. The valid index bit and the unit consistency bit are written into the verification result identifier. The verification result identifier fully reflects the specific reasons for parsing failure, out-of-bounds patterns, and unit conflicts, which facilitates locating the cause of missing segments during the alignment observation package stage.

[0037] S105. Consistency verification of append-only storage and dual-index construction.

[0038] The raw record set must support both replay in arrival order and truncation by event time window. A dual-index structure simultaneously fixes both access paths at the log offset position, preventing inconsistencies for the same record across the two paths. After the platform writes records to the append-only log segments in the raw stream storage area, it constructs a primary index using the source identifier and record sequence number. The primary index maps the primary index key field to the log offset position. A secondary index is constructed using the event time identifier, mapping the event time identifier to a list of log offset positions. This list is arranged in chronological order of the event and retains the corresponding record sequence number, ensuring visibility even in out-of-order conditions.

[0039] During index readback verification, a log offset position is retrieved from the primary index and read back the record. Then, the list of log offset positions corresponding to the same event timestamp is located from the secondary index and read back the records. If the source identifier and record sequence number of the two readback records match the verification result identifier, the index is considered consistent. If an inconsistency is found, an index anomaly flag is written and the write channel corresponding to the source identifier is paused. The index anomaly flag and the log segment position are written to disk together to ensure that the anomaly is traceable and does not pollute the original record set.

[0040] After the telemetry acquisition module completes, the raw stream storage area forms a raw record set. This raw record set includes append-only log segments, a primary index, secondary indexes, and a verification result identifier for each record. The primary index provides a perspective on the arrival order of the source identifier and record sequence number, while the secondary index provides a perspective on the time window of the event time identifier. The verification result identifier embeds the time semantic bits, indicator validity bits, unit consistency bits, and anomaly reason codes within the record. When the ticket arrangement module extracts the window and constructs the status summary, it can directly reference the event time identifier and the verification result identifier. Similarly, when the observation alignment module extracts observation records and labels late segments, it can directly reference the record sequence number and the order information in the secondary index list, ensuring that the action ticket and the backfill time window are semantically based on the same raw record set.

[0041] The original record set has already embedded the event time stamp, access time stamp, source stamp, record sequence number, and verification result stamp into the append-only log and dual-indexed view. The platform understands the path for capturing the window based on the event time stamp, as well as the path for reviewing the arrival order based on the source stamp and record sequence number. However, after a reinforcement learning action is issued, the effect feedback often lags behind for a period of time. The action ticket needs to lock the state summary value and feedback time window at the moment the action is issued to ensure that the feedback extraction boundary remains consistent in interpretation under different disordered data states.

[0042] S201. Window alignment and capture.

[0043] Fixed-period trigger points need to be established based on event timestamps. Access timestamps only reflect arrival times and are not suitable for defining business occurrence windows. The platform uses an auxiliary index to advance the window's end event timestamp from early to late using event timestamps, and uses a fixed-period length to push back the window's start event timestamp. Records with event timestamps between the start and end points are extracted into a window record set. This window record set retains the log offset, source identifier, record sequence number, and verification result identifier. The formation of the window record set ensures that observations within the same time slice fall into the same set, preventing out-of-order arrivals from altering the interpretation of window boundaries.

[0044] S202. Perform domain merging and filter valid records.

[0045] Status summaries and action tickets must fall within the same control boundary, with the execution domain identifier responsible for boundary definition. The platform maps the source identifier of each record in the window record set to an execution domain identifier based on the execution domain mapping table, merging records with the same execution domain identifier into an execution domain window set. Within the execution domain window set, a valid record set is filtered. The filtering criteria for a valid record set require that the time semantic bits of the verification result identifier pass, the indicator validity bits pass, and the unit consistency bits pass. The valid record set excludes noisy records caused by timestamp drift, parsing failures, and unit conflicts, ensuring consistent source for the status summary field, eliminating the need for additional interpretation conditions during subsequent verification.

[0046] S203. State summary generation.

[0047] At the moment a reinforcement learning action is issued, the execution domain state needs to be compressed. The state summary must retain both the core indicator level and the shapes of anomalies and observation gaps. The platform selects the most recent valid record from the valid record set by event time identifier and writes the core indicator value of the most recent valid record into the core indicator's most recent valid value. The core indicator values ​​in the valid record set are sorted from smallest to largest, and the core indicator value corresponding to the middle position after sorting is taken as the core indicator's median valid value. The most recent valid value of the core indicator is compared with the median valid value. If the most recent valid value is greater than the median valid value, an upward direction indicator is written; if the most recent valid value is less than the median valid value, a downward direction indicator is written; if the two are equal, a stationary direction indicator is written.

[0048] Core metrics are used to reflect the critical operational status of the execution domain at the time corresponding to the event time identifier. For example, when the source identifier is a rack switch and the metric name is port packet loss rate, the core metric value records the port packet loss rate. Or when the source identifier is a compute node and the metric name is processor utilization, the core metric value records the processor utilization rate.

[0049] Within the execution domain window set, the number of records that fail any check bit in the verification result is counted and written into the verification result anomaly count. When calculating the missing placeholder count, multiple historical windows with the same execution domain and fixed period length are first selected from the historical original record set. The verification result anomaly count for each historical window is calculated. The historical windows with the smallest anomaly count are used as reference windows. The number of valid records appearing in the reference windows is counted, and the number of valid records with the highest frequency is taken as the expected number of valid records. The missing placeholder count is obtained by subtracting the number of valid records in the current window from the expected number of valid records. If the difference is negative, the missing placeholder count is written as zero. The status summary uniformly writes the level, direction, anomaly, and missing values ​​into structured fields. When the action ticket carries the status summary value, the observation quality and status position at the time the action was issued can be directly reviewed.

[0050] S204. Link delay profile generation and backfill time window determination.

[0051] The backfill time window needs to cover observations of delayed backfilling, and the link latency profile needs to extract stable boundaries from the original record set to avoid extreme lateness stretching the boundaries. The platform collects a set of records that pass time semantic verification in the historical original record set according to source identifier. For each record, the arrival latency value is calculated, which equals the access time identifier minus the event time identifier. The arrival latency values ​​are sorted from smallest to largest, and the difference between two adjacent arrival latency values ​​is calculated. The break point with the largest difference is found, and the arrival latency value before the break point is taken as the upper bound of the source latency, representing the upper edge of the normal lateness range. The maximum value of multiple source latency upper bounds belonging to the same execution domain under the execution domain mapping table is taken to obtain the execution domain latency upper bound. The starting event time identifier of the backfill time window is determined as the ending event time identifier of the window. The execution domain latency upper bound is added to a fixed period length and then superimposed on the ending event time identifier of the window to obtain the ending event time identifier of the backfill time window.

[0052] When counting late arrival records as placeholders, the arrival delay value is calculated for each record within the execution domain window set. If the arrival delay value exceeds the upper bound of the corresponding source delay and the event time identifier is within the window range, the record is included in the late arrival record placeholder count. The backfill time window and the late arrival record placeholder count share the same upper bound of the source delay, ensuring consistent boundary interpretation and preventing different late arrival conclusions for the same record at different stages.

[0053] S205. Trigger point sequence number generation and action ticket registration.

[0054] Action tickets need to solidify the status summary at the time of action issuance and the backfill time window into auditable credentials, with trigger point numbers used for sorting and location. The platform maintains a trigger point number cursor for each execution domain. When the window's end event time marker reaches the trigger condition, the trigger point number cursor is incremented and written to the trigger point number. The platform reads the caliber version identifier from the caliber library, mapping it to the corresponding reinforcement learning service caliber according to the execution domain. Action tickets are generated when outputting control actions and written to the action ledger. Action ticket fields include the execution domain identifier, trigger point number, caliber version identifier, status summary value, backfill time window start event time marker, and backfill time window end event time marker. The action ledger is indexed by the execution domain identifier and trigger point number, ensuring a stable order for action tickets within the ledger. Backfill extraction can accurately locate the backfill time window without relying on out-of-order access.

[0055] The ticket arrangement module, through window alignment and truncation, execution domain merging, valid record filtering, status summary generation, link latency profiling calculation, backfill time window determination, trigger point sequence number registration, and action ticket posting, generates an action ledger containing a set of action tickets including execution domain identifiers, trigger point sequences, caliber version identifiers, status summary values, and backfill time windows. The window record set and execution domain window set convert the original record set into a time-slice view organized by control boundaries. Late record placeholder counts and missing placeholder counts directly write the observation quality into the status summary. The backfill time window fixes the extraction boundary of effect backfilling to the event time identifier range. When the observation alignment module reads the action ledger, it can directly extract observation records and generate aligned observation packages based on this.

[0056] The original record set has already placed the event timestamp and record sequence number into the original stream storage area, and the action ledger has placed the execution domain identifier, trigger point sequence number, caliber version identifier, status summary value, and backfill time window into action tickets. Aligning the observation package requires extracting observation records from the original record set and aligning them according to the backfill time window defined by the action tickets, while simultaneously creating verifiable annotations for missing and late segments. However, if the extraction and annotation lack a unified judgment standard, subsequent playback consistency verification will result in the same observation being assigned different conclusions at different stages.

[0057] S301. Action Ticket Reading and Observation Extraction Predicate Generation.

[0058] Once the backfill time window is fixed in the action ticket, the observation extraction predicate must be completely derived from the action ticket fields to avoid differences in the extraction boundary at different execution nodes. The platform reads the action ticket from the action ledger, including the execution domain identifier, trigger point number, backfill time window start event time identifier, and backfill time window end event time identifier. It then retrieves the execution domain allowed set corresponding to the execution domain identifier from the execution domain mapping table. The execution domain allowed set contains the set of source identifiers allowed to enter the execution domain. An observation extraction predicate is generated. The observation extraction predicate requires that the event time identifier fall between the backfill time window start event time identifier and the backfill time window end event time identifier. The observation extraction predicate also requires that the source identifier belong to the execution domain allowed set and that the time semantic bit, indicator validity bit, and unit consistency bit of the verification result identifier pass. Once the observation extraction predicate is uniquely determined by the action ticket fields, any replay extraction will yield the same boundary interpretation.

[0059] S302. Extraction of observation records and generation of observation sites.

[0060] Indicators with the same name may have different meanings or belong to different units on different acquisition links. Observation sites need to use both the source identifier and the indicator name as identifiers to avoid records with the same name being mixed in the same sequence. The platform locates the log offset position that meets the conditions on the dual index of the original record set based on the observation extraction predicate, and reads back the log offset position to form an observation record set. For each observation record, the source identifier and indicator name are read, and the source identifier and indicator name are combined to generate an observation site identifier, which serves as the archive key. The observation record set is decomposed into multiple observation record lists according to the observation site identifier. Each observation record list retains the event time identifier, access time identifier, record sequence number, core indicator value, and verification result identifier. Once the observation site identifier is fixed, subsequent difference localization can directly point back to the source identifier and indicator name.

[0061] S303. Event time sorting and alignment of observation packing.

[0062] Alignment observation packages require different observation sites to share a unified time axis. Event timestamps serve as the axis, while record numbers provide stable sorting, preventing order drift caused by duplicate reporting. The platform sorts each observation record list from earliest to latest by event timestamp. When records with the same event timestamp are encountered, they are sorted by record number from smallest to largest. The sorted observation record list is written into the alignment observation package. The header of the alignment observation package includes the execution domain identifier, trigger point number, start event timestamp of the backfill time window, and end event timestamp of the backfill time window. For each observation site, the observation site identifier and the number of record entries are written. With a unique sorting rule, the alignment observation package maintains a consistent record arrangement during recalculation, and the recalculation summary location will not drift due to differences in list order.

[0063] S304. Missing segment identification and completion request generation.

[0064] Missing segment identification relies on trigger point rhythms, which are defined by a fixed cycle length. The fixed cycle length and trigger point sequence number must be consistent within the action ticket system for the missing segment to be aligned and verified with the action ticket. The platform reads the fixed cycle length and, based on the start event time marker of the backfill time window, divides the backfill time window into consecutive trigger point time slices. Each trigger point time slice has a start boundary and an end boundary. The start boundary is the sum of the start event time marker of the backfill time window and the fixed cycle length multiplied by the time slice sequence number; the end boundary is the sum of the start boundary and the fixed cycle length.

[0065] For each observation site, a trigger point placeholder string is constructed, which is arranged according to the time slice number. Within each time slice, the observation record list corresponding to the observation site is searched. If there is at least one record whose event time identifier falls within the time slice boundary, it is marked as arrival; otherwise, it is marked as missing.

[0066] The scan trigger point placeholder string is used to register consecutively missing start and end time slice numbers as missing segment entries, which are then written into the alignment observation package. These missing segment entries are converted into completion requests and written to the completion request queue. Each completion request includes a source identifier, a missing interval, and a set of observation sites. The source identifier is taken from the source identifier in the observation site identifier. Once the fields of the missing segment entries and completion requests match, the completion process can directly locate the gaps according to the missing intervals and verify the completion effect.

[0067] S305. Late segment identification and recalculation flag writing.

[0068] Late segment identification requires preserving the arrival order using record sequence numbers. Record sequence numbers monotonically increase within the source identifier range. A backsliding of record sequence numbers after event time sorting indicates late backfilling or out-of-order arrival. Late segments need to be labeled at the segment level for verification. The platform performs a scan on the event time sorting sequence at each observation site. During the scan, it maintains the maximum prefix record sequence number, which represents the largest record sequence number that appeared before the current position. The record sequence number of the next record is read and compared with the maximum prefix record sequence number. If the record sequence number is less than the maximum prefix record sequence number, the record is marked as a late record. Consecutive late records are merged into late segment entries. Each late segment entry records the start event time identifier, end event time identifier, minimum record sequence number, and maximum record sequence number within the late segment. Late segment entries are written to the aligned observation package. A recalculation flag is written to the observation records covered by the late segments. This recalculation flag is then written to the record-level metadata of the aligned observation package and simultaneously to the index metadata of the observation storage area. This ensures that replay consistency verification can accurately locate the subset of observations that need recalculation. Once the prefix maximum record number scanning rule is fixed, the method for determining the late segment boundary is unique, and late segment entries can be verified using a back-read of the original record set's primary index.

[0069] S306. Generation and storage of observation package identifiers.

[0070] Alignment observation packages require stable identifiers for storage. These stable identifiers must be generated from defined fields in the action ticket to avoid duplication or mismatches during concurrent writes. The platform standardizes the execution domain identifier, trigger point sequence number, backfill time window start event time identifier, and backfill time window end event time identifier by encoding them in a fixed field order. This standardization unifies the time format to the same time zone and precision, and the encoded result is used as the observation package identifier. The aligned observation package is written to the observation storage area along with an observation package identifier index, which points to the storage location of the aligned observation package. Once the observation package identifiers are stable, training sample packages can form a unique reference between the action ticket identifier and the observation package identifier, ensuring a clear backtracking path for recalculation summaries.

[0071] After the observation alignment module is completed, the aligned observation package contains observation site identifiers, event time sequence, missing segment entries, late segment entries, recalculation flags, and observation package identifiers. The aligned observation package forms a stable storage object in the observation storage area. The missing segment entries compress the observation gaps into verifiable trigger point intervals, the late segment entries compress out-of-order and late entries into verifiable event time intervals and record sequence number intervals, and the recalculation flag explicitly marks the observation subsets that need to be verified. When the sample admission module executes the state construction expression and reward calculation expression, it can directly align the observation package to perform consistency verification and locate the causes of differences.

[0072] The alignment observation package has already embedded the observation site identifier, missing segment entry, late segment entry, recalculation flag, and observation package identifier into the observation storage area. The action ticket has already embedded the execution domain identifier, trigger point sequence number, caliber version identifier, status summary value, and backfill time window into the action ledger. If the reinforcement learning training sample package misinterprets missing segments and late segments as real feedback, the policy will learn link jitter as a pattern; if it misinterprets caliber version switching as environmental change, the policy will learn semantic drift as a pattern. However, the sample admission module bears the dual responsibility of generating training sample packages and releasing judgment values. The sample admission module must summarize consistency verification, backfill link completeness index, and caliber shadow difference risk value into the judgment reason identifier to ensure consistent interpretation of sample entry into the database.

[0073] S401. Expression tree loading and expression tree verification.

[0074] The caliber version identifier determines the observation site identifiers and operator semantics referenced in the state construction expression and reward calculation expression. Performing expression tree verification before expression loading separates expression errors from data errors. The platform reads the caliber version identifier based on the action ticket, retrieves the state construction expression and reward calculation expression from the caliber library, and parses the two sets of expressions into an expression tree. The expression tree node list includes field reference nodes, time alignment operator nodes, missing data handling operator nodes, discretization operator nodes, and boundary pruning operator nodes. Each field reference node is verified to have a corresponding observation site identifier; each operator node is verified to be in the allowed operator table of the caliber library; and the segment boundaries of the boundary pruning nodes are verified to exist and their order is valid. Nodes failing verification are written to the expression exception field of the recalculation summary, and sample generation is terminated. After successful expression tree verification, the expression tree structure is stable, the expression tree calculation path is unique, and the calculation results are interpreted consistently.

[0075] S402. State Vector Generation and State Summary Recalculation Generation.

[0076] Using state vectors directly for training without summarizing and solidifying them leads to consistency checks requiring field-by-field comparisons and amplifying minor format differences. State summary recalculation compresses the state vectors into stable summaries for easier verification. The platform reads aligned observation packets from the observation storage area, recursively calculates state construction expressions from the root node of the expression tree, and the time alignment operator locates the observation record to the corresponding trigger point time slice according to the event time identifier. The missing data handling operator triggers missing branches based on missing segment entries and writes missing placeholder values. The discretization operator maps continuous values ​​to interval markers according to the discretization rules of the caliber library, and the boundary pruning operator prunes out-of-bounds values ​​to boundary markers according to boundary rules. The output of the root node of the expression tree is summarized to form the state vector. Normalization and serialization are performed to write the state vector in a fixed field order, writing the unit affiliation marker with the unified code of the caliber library, writing empty fields as fixed missing data markers, and writing numerical fields in a fixed precision format. A fixed-length hash digest operation is performed on the normalized serialization result to obtain the recalculated state digest value. The recalculated state digest value is completely consistent when the field order is consistent, and will inevitably change when the field content changes. Consistency verification can stably locate differences.

[0077] S403. Reward value generation and reward summary recalculation value generation and packaging of training sample packages.

[0078] The reward value is derived from the reward calculation expression. Semantic consistency of the samples is guaranteed only when the reward value and state vector originate from the same aligned observation package and the same caliber version identifier. The platform performs reward calculation expression tree computation on the same aligned observation package. The time alignment operator node and the missing data handling operator node reuse the alignment and missing data rules used during state vector generation to obtain the reward value, which is then written into the training sample package. Normalized serialization is performed on the reward value, writing it in a fixed-precision format and adding a unit attribution marker. A fixed-length hash digest operation is then performed on the normalized serialization result to obtain the reward digest recalculated value. The action ticket identifier, observation package identifier, caliber version identifier, state vector, reward value, state digest recalculated value, and reward digest recalculated value are encapsulated into a training sample package. The field order of the training sample package is fixed, and the training sample package possesses auditable and verifiable unified semantics.

[0079] S404. Original record set playback extraction and playback alignment observation packaging.

[0080] The observation storage area has completed the extraction and sorting of aligned observation packages. Replay consistency verification requires reverting to the original record set to reconstruct the observation process. The reconstruction process reuses the action ticket boundary and the execution domain's allowed set to ensure consistent recalculation boundaries. The platform reads the start and end event time identifiers of the backfill time window based on the action ticket, and reads the execution domain's allowed set based on the execution domain identifier. Observation records are extracted from the original record set, with extraction conditions simultaneously satisfying the following: the event time identifier is within the backfill time window range, the source identifier belongs to the execution domain's allowed set, the verification result identifier's time semantic bits pass, the indicator's legality bits pass, and the unit consistency bits pass. The extracted observation records are combined with the source identifier and indicator name to generate observation point identifiers. These are then archived according to the observation point identifiers to form an observation record list. Each observation record list is sorted by event time identifier and its sequence number is used to stabilize the order. A replay aligned observation package is assembled and written with the same header fields as the aligned observation package. The replay aligned observation package reconstruction process can be verified using the primary index.

[0081] S405. Replay recalculation and recalculation summary generation.

[0082] The recalculation summary needs to cover both state differences and reward differences, and also needs to provide the difference source path. This path, which associates expression tree nodes with observation site identifiers, missing segment entries, and late segment entries, is essential for guiding the repair process. The platform executes state construction and reward calculation expressions on the replay-aligned observation package, generating a replay state vector and replay reward value. These are then processed using normalized serialization and hash summarization consistent with the training sample package to obtain the replay state summary and replay reward summary. The recalculated state summary value is compared with the replay state summary, and the recalculated reward summary value is compared with the replay reward summary. If any comparison is inconsistent, node-level recalculation is initiated. This node-level recalculation performs normalized serialization and hash summarization on the output of each node in the expression tree, locating the first set of nodes with differences and writing it into a difference node list. This list also records the observation site identifier, missing segment entry number, late segment entry number, and recalculation flag position. The consistency flag, difference node list, and difference propagation path are then summarized to generate the recalculation summary, which provides a traceable link back to the original record set log offset position.

[0083] S406. Maintenance of the Must-Reach Observation List and Export of Criticality Levels.

[0084] The backfill link completeness index requires first determining the guaranteed observation points within the execution domain. These guaranteed observation points are drawn from stable operating intervals, not arbitrary historical intervals, and the criticality level is derived from control surface dependencies rather than human experience. The platform filters training sample packages from the sample library that have passed the release judgment value and whose recalculated summaries match. It then reads the corresponding aligned observation packages and generates a trigger point placeholder string for each observation point. The trigger point placeholder string records the three states of arrival, absence, and lateness according to the trigger point time slice.

[0085] The dominant patterns of trigger point placeholder strings are grouped into rhythm tags. Rhythm tags include three categories: continuous arrival, intermittent arrival, and event arrival. Continuous arrival corresponds to placeholder strings that do not show any missing or late arrivals for a long period of time. Intermittent arrival corresponds to placeholder strings that have missing segments, but the length of the missing segments is controlled. Event arrival corresponds to placeholder strings that only appear stably in the time slices near the occurrence of the action.

[0086] Read the control plane dependency table, which records the set of observation site identifiers referenced by the control action generation rule and the set of observation site identifiers referenced by the reward calculation expression. Mark the observation site identifiers that appear in both the control action generation rule and reward calculation expression reference sets as high criticality level, mark the observation site identifiers that appear only in the control action generation rule reference set as medium criticality level, and mark the observation site identifiers that appear only in the state construction expression reference set as low criticality level.

[0087] The observation site identifier, rhythm label, and criticality level are written into the mandatory observation list, and the version of the mandatory observation list is written into the caliber database. The generation process of the mandatory observation list has auditable evidence.

[0088] S407. Completeness profile generation and backfill link completeness index calculation.

[0089] The backfilling of the link completeness index requires converting missing and late segment entries into a unified profile representation. This profile representation retains factual details while being compressed into an index, and the two-layer representation facilitates consistency judgment and verification. The platform reads the guaranteed observation list and locks its version. It processes the observation site identifiers in the guaranteed observation list one by one on the current aligned observation package, generating arrival morphology codes according to the trigger point time slice. The arrival morphology code records the three states of arrival, missing, and late at each trigger point time slice.

[0090] Segment compression is performed on the arriving morphology codes. Segment compression merges consecutive missing segments into a list of consecutive missing segments and consecutive late segments into a list of late segments. Simultaneously, the overlap between the event time marker reversed segment and the record number reversed segment is extracted from the late segment entries to generate a list of sequence-reversed segments. The relative position level of the missing segment to the action occurrence point is calculated. The relative position level is obtained by dividing the time distance between the event time marker of the missing segment's starting point and the event time marker of the window's ending point by a fixed period length and then rounding down.

[0091] The lists of consecutive missing segments, reversed sequence segments, and relative position levels are summarized into a completeness profile based on the observation site identifiers. The segments with the largest and second-largest penalties are retained in the completeness profile according to their criticality level. The backfill index level table is then read, mapping the criticality level, missing segment length level, relative position level, and reversed sequence overlap level to a backfill link completeness index.

[0092] The method for obtaining the backfill link completeness index is to first transform the completeness profile into a set of enumerable level labels, and then use the backfill index level table to look up the results: The platform first reads the list of consecutive missing segments and the list of sequential reversal segments at each observation point covered by the guaranteed observation list. It takes the longest missing segment in the consecutive missing segments to obtain the missing segment length level, takes the segment of the longest missing segment that is closest to the action occurrence point to obtain the relative position level, and takes the segment with the strongest overlap between the reversed event time identifier segment and the reversed record number segment in the sequential reversal segment list to obtain the sequential reversal overlap level. At the same time, it reads the criticality level in the guaranteed observation list. The platform combines the criticality level and the three levels in a fixed order to form a level key. Then, it uses the level key to locate the corresponding index level in the backfill index level table and outputs it as the backfill link completeness index. The lookup hit item is synchronously written with the judgment reason identifier for review.

[0093] Based on this, the backfill link completeness index is output and the mapped hit items are written into the judgment reason identifier. The backfill link completeness index is calculated to make the link verifiable and interpretable.

[0094] S408. Generation of shadow difference feature set and calculation of caliber shadow difference risk value.

[0095] The risk value of caliber shadow difference needs to isolate the semantic shift caused by caliber changes. The shadow difference feature set is obtained by parallel computation of two sets of caliber results from the same aligned observation package to avoid confusion between environmental changes and caliber changes. The platform reads the previous stable caliber version identifier based on the caliber library's stable marker field. The platform executes the expression tree corresponding to the caliber version identifier and the expression tree corresponding to the previous stable caliber version identifier in parallel on the same aligned observation package to obtain two sets of state summaries and two sets of reward summaries.

[0096] The platform generates a symbol flip list, a sort flip list, and a range crossing list. The symbol flip list records the flipping of reward direction and the flipping of key state field direction. The sort flip list encodes the relative sorting changes of key state fields within the execution domain as ranking consistency fragments. The range crossing list records the number of times key state fields cross boundary trimming segment boundaries and the trigger point locations. The platform encodes these three lists into a shadow difference feature set. Each feature in the shadow difference feature set is first binned, with binning boundaries derived from the feature binning table stored in the caliber database. The feature binning table is generated and versioned from historical processing records. The caliber risk model loads model parameters in logistic regression form, with model parameters stored by feature binning item. The parameters of the hit binning items are accumulated one by one to obtain a risk score. The risk score is converted into a caliber shadow difference risk value through a monotonic probability mapping function. The hit binning items and risk scores are written into the judgment cause identifier, making the source of the caliber shadow difference risk value transparent and suppressing spurious patterns caused by caliber changes before entering the sample database.

[0097] S409. Grade mapping dual-axis risk matrix determination and sample allocation.

[0098] The release decision value needs to be discretized in both the time and caliber directions before being recombined. A fixed discretization boundary version is necessary to ensure consistent decisions for the same input. The platform reads the backfill confidence level boundary table, which sorts and segments the backfill link completeness index into three segments: complete, questionable, and damaged. This boundary table is generated from historical release samples sorted and written to the caliber library version. The platform also reads the caliber offset level boundary table, which sorts and segments the caliber shadow difference risk value into three segments: stable, offset, and high-risk. This boundary table is also versioned and saved.

[0099] The method for disclosing the backfill trust level boundary table is as follows: The platform summarizes historical training sample packages according to the execution domain identifier and the version identifier of the guaranteed observation list, and extracts two types of samples respectively. One type comes from the training sample packages in the sample library whose release judgment value is released and whose repair queue has not generated backfill. The other type comes from the training sample packages marked as needing repair in the isolation area or repair queue. The platform takes the maximum value of the backfill link completeness index in the samples that need repair and the minimum value of the backfill link completeness index in the stable samples and writes it into the boundary table. When the maximum value is less than the minimum value, it is mapped to being less than or equal to the maximum value as broken, and greater than or equal to the minimum value as complete. The value between the two is mapped to being questionable. When the maximum value is greater than or equal to the minimum value, it is mapped to being less than or equal to the minimum value as broken, and greater than or equal to the maximum value as complete. The value between the two is mapped to being questionable.

[0100] The method for disclosing the caliber offset level boundary table is as follows: The platform summarizes historical training sample packages according to the execution domain identifier and caliber version identifier, and extracts two types of samples respectively. One type comes from training sample packages in the sample library whose release judgment value is released and whose repair queue has not generated backfilling. The other type comes from training sample packages marked as needing repair in the isolation area or repair queue. The platform takes the maximum value of the caliber shadow difference risk value in the stable samples and the minimum value of the caliber shadow difference risk value in the samples that need repair and writes it into the boundary table. When the maximum value is less than the minimum value, it is mapped to stable if it is less than or equal to the maximum value, and to high risk if it is greater than or equal to the minimum value. The value between the two is mapped to offset. When the maximum value is greater than or equal to the minimum value, it is mapped to stable if it is less than or equal to the minimum value, and to high risk if it is greater than or equal to the maximum value. The value between the two is mapped to offset.

[0101] Based on two boundary tables, the backfill link completeness index is mapped to the backfill confidence level, and the caliber shadow difference risk value is mapped to the caliber offset level. A release judgment value is output based on a dual-axis risk matrix. The matrix rules are fixed: a broken backfill confidence level corresponds to isolation; a high-risk caliber offset level and a questionable backfill confidence level correspond to isolation; a high-risk caliber offset level and a complete backfill confidence level correspond to controlled release; a stable caliber offset level and a complete backfill confidence level correspond to release; and other combinations correspond to controlled release. The platform writes the recalculated summary, backfill link completeness index, caliber shadow difference risk value, backfill confidence level, caliber offset level, and release judgment value into a judgment reason identifier. The platform writes training sample packages with release judgment values ​​of release and controlled release into the sample library and adds a controlled tag; the platform writes training sample packages with release judgment values ​​of isolation into the isolation area and adds an isolation reason field. The sample diversion results have stable basis and a traceable evidence chain.

[0102] After the sample admission module is completed, the training sample package is formed under the constraint of the caliber version identifier. The recalculated summary solidifies the consistency verification facts, the backfilled link completeness index compresses missing and late segment entries into decisionable indicators, the caliber shadow difference risk value compresses the semantic offset of caliber changes into decisionable indicators, and the dual-axis risk matrix combines the two types of indicators into a release judgment value and writes it into the judgment reason identifier. The sample library and the isolation zone each form a stable set indexed by action ticket identifier and observation package identifier, and the release training and online verification can verify the sample source with the same evidence chain.

[0103] After the alignment observation package is split by the release judgment value, it enters the sample library. The training sample package has been bound to the action ticket identifier, observation package identifier, caliber version identifier, and judgment reason identifier. The semantic boundaries of the samples have been fixed in the caliber library and action ledger records. However, policy training and online inference still face two types of easily confused risks. One is semantic drift caused by the inconsistency between the caliber version identifier and the version identifier of the must-achieve observation list. The other is observation distortion caused by the out-of-bounds form of missing segment entries and late segment entries. The policy escort module needs to transform the consistency verification conclusion into hard rules for training admission and inference admission.

[0104] S501. Sample library selection and training set grouping.

[0105] The version identifier determines how the state vector and reward value are interpreted, while the version identifiers for the guaranteed observation list and the dual-axis risk matrix determine the criteria for judging observation quality. Using all three types of version identifiers together will place incompatible samples in the same training set, weakening policy stability. Training set grouping can be achieved through hard filtering using built-in fields in the training sample package.

[0106] The platform reads training sample packages from the sample library, retaining only those with release or controlled release criteria. It reads the version identifier of each training sample package, the version identifier of the mandatory observation list, the version identifier of the dual-axis risk matrix, and a consistency marker in the recalculation summary. First, it groups the samples by version identifier, then within each group, it filters for consistency based on the version identifiers of the mandatory observation list and the dual-axis risk matrix. Training sample packages with inconsistent versions are written to an isolation area, and the source of the version conflict is added to the judgment reason marker. After filtering, the version boundaries within each group are clear, allowing the version field to be used to directly reproduce the training data range during audit review.

[0107] S502. Strategy training data assembly and strategy update.

[0108] The training sample package stores state vectors and reward values. Action ticket identifiers point to action ledger records, which contain control action content. Missing control action content will result in policy updates lacking action dimensions, thus preventing the formation of action value estimates. Action fields can be read from the action ledger and aligned with the training sample package.

[0109] Within each training set, the platform sorts training sample packets by execution domain identifier and trigger point number. Two training sample packets corresponding to adjacent trigger point numbers are concatenated into a training segment. Each training segment includes the current state vector, control action content, reward value, and next state vector. Control action content is read back from the action ledger by action ticket identifier and written into the training segment. Only training sample packets with identical recalculation digest markers are accepted for training segment generation. An action value estimation table is maintained, indexed by the state vector and control action content to an estimated value. For each training segment, a temporal difference residual is calculated. The temporal difference residual equals the reward value plus the maximum action value estimate of the next state vector across all candidate control action contents, minus the action value estimate corresponding to the current state vector and control action content.

[0110] For each training segment, the temporal difference residual is first calculated in a fixed order: first, the reward value is taken, then the maximum action value estimate of the next state vector on the candidate control action content is taken, and the two are added together and then the action value estimate corresponding to the current state vector and control action content is subtracted. The platform decomposes the temporal difference residual into two parts: sign and amplitude. The sign is distinguished only by positive and negative, and the amplitude is taken as the absolute value. The platform uses the amplitude to find the hit interval in the segmented interval table. The segmented interval table is arranged in ascending order according to the amplitude boundary. Each interval is bound to an update step value and written into the caliber library and versioned with the caliber version identifier. The platform obtains the update step value based on the hit interval, determines the direction of increase or decrease based on the sign, adds the update step value to the estimated value of the corresponding item in the action value estimation table according to the direction, and uses the estimated value boundary table to restrict the updated estimated value from going out of bounds. The update record is written to the policy training log and associated with the action ticket identifier and observation package identifier. When the amplitude falls into a lower interval, the step value is smaller, thereby reducing the fluctuations introduced by the controlled release training sample package. When the amplitude continues to fall into a higher interval, the step value is larger, thereby accelerating the response to the stable reward trend.

[0111] S503. Strategy release package generation and strategy release package identifier fixation.

[0112] Online inference requires stable reference strategies and version metadata. The lack of a fixed identifier in the strategy release package can cause difficulties in release rollback and auditing. Stable identifiers can be generated for the strategy release package through normalized serialization and hash digest operations.

[0113] After policy training is completed for each training set, a policy release package is generated. The policy release package includes an entry-level version identifier, a guaranteed-to-reach observation list version identifier, and a dual-axis risk matrix version identifier, and is also written to the training set summary. The training set summary includes the covered execution domain identifier range, trigger point sequence number range, observation site identifier summary, and morphological interval table version number. Normalized serialization is performed on the policy release package, fixing the field order, time format and time zone expression, empty field syntax, numerical field precision expression, and list field sorting rules. A hash digest operation is performed on the normalized serialization result to obtain the policy release package identifier. The policy release package identifier and the policy release package are written together to the release storage area and registered in the release ledger. With the policy release package identifier fixed, online inference and offline review can use the same identifier to locate the same release content, significantly reducing the risk of version drift.

[0114] S504. Verification and Admission Determination of Online Reasoning Entry Points.

[0115] When reading the latest aligned observation package during online inference, missing segment entries and late segment entries exceeding their morphological boundaries can cause structural gaps in the state vector. Inconsistent version identifiers can lead to mismatches between policy meaning and input meaning. The inference entry point can solidify version consistency and morphological compliance as admission criteria.

[0116] At the inference entry point, the strategy release package and the latest aligned observation package are read simultaneously. The version identifier of the aligned observation package is checked to ensure it matches the version identifier of the strategy release package. The version identifier of the required observation list referenced by the aligned observation package is also checked to ensure it matches the version identifier of the required observation list referenced by the strategy release package. Finally, the version identifier of the dual-axis risk matrix referenced by the aligned observation package is checked to ensure it matches the version identifier of the dual-axis risk matrix referenced by the strategy release package. Missing segment entries and late segment entries from the aligned observation package are read. The morphological interval table is retrieved from the caliber library and its version number is locked. The missing segment length level and relative position level of each missing segment entry are compared item by item with the morphological interval table. The inversion segment level and recalculation flag coverage ratio level of each late segment entry are also compared item by item with the morphological interval table. The three version consistency conclusions are combined with the morphological comparison conclusion to form an admission decision. If the admission decision passes, the strategy inference process begins and control commands are output. After the admission decision is formed, the online inference input boundary remains consistent with the offline training boundary, significantly reducing the probability of abnormal observations entering the strategy inference path.

[0117] S505. Freeze policy release package and repair queue registration.

[0118] Failure to pass the access assessment indicates version inconsistency or out-of-bounds morphology. Directly using the policy release package to output control commands will amplify observed anomalies into control anomalies. The platform can use freeze and backup control commands to isolate risks and structure and store remediation information. When the access assessment fails, the platform freezes the policy release package, and the freeze record is written to the freeze ledger. The freeze record includes the policy release package identifier, execution domain identifier, trigger point sequence number, and freeze time identifier. Backup control commands are output. These commands come from a preset safety action template in the control plane. The safety action template is bound to the execution domain identifier and written to the control command channel. The reason for the access failure is written to the remediation queue. The remediation queue entries include the execution domain identifier, observation site identifier, summary of missing segment entries, summary of late segment entries, caliber offset level, and recalculation summary location information. At the same time, the entry caliber version identifier verification result, the required observation list version identifier verification result, the dual-axis risk matrix version identifier verification result, and the morphology interval table comparison hit items are also written. Once the frozen ledger and repair queue are established, the online control output enters the safety boundary, enabling repair personnel to quickly locate the root cause and restore the release by following the observation site markers and recalculation summary location information.

[0119] After the strategy escort module was corrected, the sample library screening stage solidified the caliber version identifier, the must-achieve observation list version identifier, and the dual-axis risk matrix version identifier as the training set boundary. In the strategy training stage, the action content was locked from the action ledger and the action value estimation table was updated with temporal difference residuals. In the strategy release stage, the strategy release package identifier was solidified with normalized serialization and hash digest operation. In the online inference stage, the admission judgment was completed with version consistency and morphological interval table. Admission failure triggered the freezing of the ledger, the minimum control instruction, and the registration of the repair queue. The training and inference behavior boundaries formed a unified and auditable link.

[0120] Specifically, the above are merely preferred embodiments of this application and are not intended to limit this application.

[0121] In the description of this specification, references to terms such as "an embodiment," "example," and "specific example" indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0122] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. An artificial intelligence reinforcement learning service platform, characterized in that, include: Telemetry Acquisition Module: Acquires event time identifiers and core indicator values ​​during telemetry operations in the data center, generates access time identifiers, writes source identifiers and record sequence numbers, performs time semantic verification and indicator value validity verification, writes verification result identifiers, stores them in the raw stream storage area, forms the raw record set, and establishes an index; Ticket Arrangement Module: Based on the original record set, extract the status summary at fixed intervals and register the trigger point sequence number. When outputting control actions, generate action tickets. Write the action tickets to the action ledger. Bind the action tickets to the trigger point sequence number, backfill time window, caliber version identifier, execution domain identifier, and status summary value. The observation alignment module reads the action ticket, extracts the observation records from the original record set according to the backfill time window, sorts them according to the event time identifier to generate an alignment observation package, marks the missing segments and late segments in the alignment observation package, writes the missing segments into the completion request queue, retains the record sequence number of the late segments and writes the recalculation flag, and stores the alignment observation package in the observation storage area. Sample admission module: Based on the caliber version identifier, the module performs state construction and reward calculation on the aligned observation package, generates training sample package, performs playback consistency verification on the training sample package, outputs release judgment value, and determines whether the training sample package is written to the sample library or the isolation area, and writes the judgment reason identifier. The strategy protection module generates a strategy release package when the release judgment value is in the release state. The strategy release package is bound to the caliber version identifier and the alignment observation package summary. Online inference reads the strategy release package and the latest alignment observation package, checks the backfill integrity and caliber version consistency. If an inconsistency is triggered, the strategy release package is frozen and a backup control command is output and written to the repair queue.

2. The artificial intelligence reinforcement learning service platform according to claim 1, characterized in that: The telemetry acquisition module includes: reading event time identifiers and core indicator values ​​from the telemetry access terminal, generating access time identifiers, writing source identifiers and record sequence numbers, performing time semantic verification and indicator value validity verification and generating verification result identifiers, appending records to the raw stream storage area, establishing a main index according to the source identifier and record sequence number, establishing an auxiliary index according to the event time identifier, and forming a raw record set.

3. The artificial intelligence reinforcement learning service platform according to claim 2, characterized in that: The document arrangement module includes: extracting fixed-period windows based on the event time identifier using the auxiliary index of the original record set; merging window records into execution domain window sets according to the execution domain mapping table; filtering the set of valid records based on the verification result identifier; selecting the most recent valid record by the event time identifier to obtain the most recent valid value of the core indicator; sorting by the core indicator and taking the median valid value to obtain the median valid value of the core indicator; comparing the two and writing the core indicator change direction identifier; counting the abnormal count of the verification result and determining the missing placeholder count; and generating the status summary value.

4. The artificial intelligence reinforcement learning service platform according to claim 3, characterized in that: The ticket arrangement module also includes: calculating and sorting arrival delay values ​​according to source identifiers in the historical original record set; determining the upper limit of source delay based on the location of the largest break between adjacent differences; obtaining the upper limit of execution domain delay by taking the maximum value of the upper limit of source delay in the same execution domain according to the execution domain mapping table; determining the event time identifier of the start of the backfill time window and the event time identifier of the end of the backfill time window; counting late arrival records based on arrival delay values ​​exceeding the upper limit of source delay and event time identifiers being within the window range; generating trigger point sequence numbers and writing them into action tickets when outputting control actions; and writing action tickets into the action ledger.

5. The artificial intelligence reinforcement learning service platform according to claim 4, characterized in that: The observation alignment module includes: reading action tickets from the action ledger, obtaining the execution domain identifier, trigger point sequence number, backfill time window start event time identifier, and backfill time window end event time identifier; extracting observation records from the original record set based on the execution domain allowed set and verification result identifier; generating observation site identifiers according to the source identifier and indicator name; sorting by event time identifier and stabilizing the order of the records according to the record sequence number; and assembling the alignment observation package.

6. The artificial intelligence reinforcement learning service platform according to claim 5, characterized in that: The observation alignment module also includes: dividing the backfill time window with a fixed period length, constructing a trigger point placeholder string and identifying continuous missing intervals to form missing segment entries, writing the missing segment entries into the alignment observation package and writing them into the completion request queue; maintaining the prefix maximum record number on the event time sorting sequence, recording the record number back as a late record and merging it into a late segment entry, writing the late segment overwritten record into a recalculation flag, generating an observation package identifier and writing it into the observation storage area.

7. The artificial intelligence reinforcement learning service platform according to claim 6, characterized in that: The sample admission module includes: loading state construction expressions and reward calculation expressions from the caliber library based on the caliber version identifier of the action ticket and parsing them into an expression tree; calculating the state vector and reward value on the aligned observation package after completing the field reference node and operator node verification; performing normalization serialization and generating state summary recalculation value and reward summary recalculation value; and encapsulating the training sample package, which includes the action ticket identifier, observation package identifier, and caliber version identifier.

8. The artificial intelligence reinforcement learning service platform according to claim 7, characterized in that: The sample admission module also includes: the platform extracts observation records from the original record set based on the backfill time window and execution domain allowed set of the action ticket, reconstructs the replay aligned observation package, recalculates the replay status summary and replay reward summary and generates a recalculation summary, generates a completeness profile based on the must-achieve observation list and maps it to obtain the backfill link completeness index, generates a shadow difference feature set based on the previous stable caliber version identifier and calculates the caliber shadow difference risk value, maps it to the backfill confidence level and caliber offset level and generates a release judgment value through a dual-axis risk matrix, and writes the training sample package into the sample library or isolation area according to the release judgment value and writes the judgment reason identifier.

9. The artificial intelligence reinforcement learning service platform according to claim 8, characterized in that: The sample admission module includes: reading training sample packages from the sample library, filtering training sample packages with release judgment values ​​of release and controlled release, grouping them by caliber version identifier and verifying within each group that the version identifier of the must-reach observation list is consistent with the version identifier of the dual-axis risk matrix, and writing training sample packages with version conflicts into the isolation area; sorting training sample packages by execution domain identifier and trigger point sequence number, reading back the action ledger to obtain control action content, assembling training fragments and updating the action value estimation table, generating a policy release package and writing in the caliber version identifier, the version identifier of the must-reach observation list, the version identifier of the dual-axis risk matrix, and the training set summary.

10. An artificial intelligence reinforcement learning service platform according to claim 9, characterized in that: The strategy escort module also includes: performing standardized serialization on the strategy release package and generating a strategy release package identifier to write into the release ledger; the online inference entry reads the strategy release package and the latest aligned observation package, verifies the consistency of the caliber version identifier, the required observation list version identifier, and the dual-axis risk matrix version identifier; and outputs control instructions after verifying the morphological compliance of missing segment entries and late segment entries according to the morphological interval table. When the admission judgment fails, the strategy release package is frozen and the freeze ledger is registered. The minimum control instruction is output and the admission failure reason, observation site identifier, and recalculation summary location information are written into the repair queue.

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