A method and system for consistency calibration of multi-source model scores

Abstract

This invention discloses a consistency calibration method and system for multi-source model scoring, specifically relating to the field of distributed calibration technology. The method includes: scoring access package construction, which encapsulates multi-source model scoring in a structured manner and generates a calibration parameter package containing calibration mapping parameters and calibration fingerprints; parameter preloading activation, which achieves consistent switching of calibration parameters among distributed nodes through a two-stage release mechanism and using the calibration version identifier as an anchor point; link version locking, which binds the calibration version identifier at the request entry point and transmits it along the link to ensure that the same request uses the same set of calibration mappings to complete the scoring in a distributed multi-node environment; and consistency monitoring and self-healing, which quantifies consistency risks through multi-granularity monitoring and achieves anomaly detection, automatic loss prevention, and controlled rollback based on the coupled closed-loop control of release convergence credibility and consistency risk index.

Description

Technical Field

[0001] This invention relates to the field of distributed calibration technology, and more specifically, to a consistency calibration method and system for multi-source model scoring. Background Technology

[0002] In online operations such as risk control, recommendation ranking, and content governance, multiple source raw scores for the same target object are typically aggregated through a unified scoring chain. These scores are then converted into a unified calibration score based on calibration mapping, which is used for threshold determination, policy allocation, and subsequent processing. Calibration mapping and its parameters are usually published and loaded in the form of calibration parameter packages in multi-data center and multi-cluster environments, with nodes relying on local caching to quickly generate scores.

[0003] In existing technologies, the distribution and activation of calibration parameter packages often employ a weakly consistent distribution and rolling update approach. When a node times out while fetching a new version from the configuration center and triggers a fallback mechanism, it often continues to use the calibration version identifier from the most recently successfully loaded version. This leads to inconsistent unified calibration scores output for the same request under different entry points or service paths. Such inconsistencies directly propagate to the policy execution layer, causing fluctuations in the allowance and block results for the same object within a short period. Furthermore, due to the lack of strong constraints and traceable records on calibration version identifiers, customer service and operations teams struggle to reproduce the scoring criteria at the time of the test. A / B tests are easily contaminated by different versions, potentially leading to complaints and compliance audit risks in severe cases.

[0004] To address the above problems, this invention proposes a solution. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a consistency calibration method and system for multi-source model scoring to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A consistency calibration method for multi-source model scoring includes the following steps:

[0008] In the unified scoring chain, multi-source model scores corresponding to the same target object are accessed, and the original scores are structured and encapsulated to form scoring event records. Based on the scoring event records, calibration mapping parameters for each scoring source are generated, and the calibration mapping parameters and effective constraints are encapsulated into a calibration parameter package. The calibration fingerprint of the calibration parameter package is calculated, and the calibration version number is bound to the calibration fingerprint to form a calibration version identifier.

[0009] The calibration parameter package is released to the release channel. Through a two-stage release mechanism of preloading and consistent activation, the calibration parameters are switched consistently among distributed scoring nodes using the calibration version identifier as the anchor point. During the canary release, the release convergence credibility is built and monitored. The release convergence credibility is generated based on the statistics of ready coverage, synchronization lag, number of abnormal nodes and consistency risk index within the time window. The progress of unified activation and canary release is controlled according to the release convergence credibility.

[0010] At the entry point of the business request, the calibration version identifier to be used in this request is determined based on the activated calibration parameter package and written into the request context and passed through the call chain; the calibration execution node in the scoring node checks the consistency between the calibration version identifier and the version loaded or currently effective in this node. If they are consistent, the matching calibration mapping parameters are used to calibrate the multi-source original scores to generate a unified calibration score, and if they are inconsistent, the consistency protection path is entered.

[0011] Multi-granularity monitoring is performed on the unified calibration score output in a distributed environment. Based on the monitoring results, a consistency risk index is generated. The consistency risk index is generated based at least on the calibration version inconsistency rate, policy reversal rate, verification failure rate, and tail risk value of score difference. The release convergence credibility and consistency risk index are coupled to form a closed-loop control logic to perform anomaly detection, automatic loss prevention, or controlled rollback operations according to the risk level.

[0012] In a preferred embodiment, during the preloading phase, the distributed scoring nodes pull the calibration parameter package and perform calibration fingerprint consistency verification, then mark it as a ready version. During the consistency activation phase, the distributed scoring nodes are triggered to switch the ready version to the active version.

[0013] In a preferred embodiment, when the original scores are structured and encapsulated, discrete grade scores, capped scores, or score sources with insufficient resolution are marked separately as bucket observation sources; and when generating calibration mapping parameters, a bucket-level mapping table is generated for the bucket observation sources based on the bucket-level statistical results.

[0014] In a preferred embodiment, synchronization lag is used to characterize the delay from receiving the release notification to entering the ready or active state of the scoring node, and is weighted according to the proportion of node request processing volume; the number of abnormal nodes is used to count nodes that fail to verify, fail to load, are not ready for a long time, or frequently roll back; the consistency risk index is obtained by averaging the statistics within the time window using a sliding window method; the release convergence credibility is used to control the release progress of unified activation, freezing grayscale expansion, or shrinking grayscale domain.

[0015] In a preferred embodiment, the tail risk value of the scoring difference is generated based on the unified calibration scoring difference set of the main version and the shadow version on the same request, or the unified calibration scoring difference set obtained by cross-entry and cross-path recalculation of the same object, and the high quantile statistic of the difference set is taken; the consistency risk index is generated based on the weighted sum of the calibration version inconsistency rate, policy flip rate, verification failure rate and the tail risk value of the scoring difference.

[0016] In a preferred embodiment, a shadow comparison step is also included, wherein, upon request that the sampling conditions are met, the same multi-source original scores are calibrated in parallel using shadow version mapping parameters to obtain a shadow unified calibration score and record the difference information.

[0017] In a preferred embodiment, multi-granularity monitoring includes cross-node recalculation monitoring, master shadow comparison monitoring, version consistency monitoring, and parameter integrity monitoring.

[0018] In a preferred embodiment, when the consistency risk index exceeds a threshold, a consistency protection path is triggered, including routing the request to a version-consistent node, temporarily downgrading the weight of the source of inconsistency, or outputting a conservative result.

[0019] In a preferred embodiment, abnormal nodes include nodes that fail to verify, fail to load, are not ready for a long time, or frequently roll back, and these nodes are isolated or rate-limited during the release process.

[0020] In a preferred embodiment, the following modules are included:

[0021] The access package module is used to access multi-source model scores corresponding to the same target object in the unified scoring link, and to encapsulate the original scores in a structured manner to form a scoring event record; based on the scoring event record, calibration mapping parameters for each scoring source are generated, and the calibration mapping parameters and effective constraints are encapsulated into a calibration parameter package, the calibration fingerprint of the calibration parameter package is calculated, and the calibration version number is bound to the calibration fingerprint to form a calibration version identifier;

[0022] The distribution activation module is used to publish calibration parameter packages to the release channel. Through a two-stage release mechanism of preloading and consistent activation, it achieves consistent switching of calibration parameters among distributed scoring nodes with calibration version identifier as the anchor point. During the canary release, the release convergence credibility is built and monitored. The release convergence credibility is generated based on the statistics of ready coverage, synchronization lag, number of abnormal nodes, and consistency risk index within the time window. The progress of unified activation and canary release is controlled according to the release convergence credibility.

[0023] The consistency calibration module determines the calibration version identifier to be used in this request based on the activated calibration parameter package at the entry point of the business request, and writes it into the request context and passes it through the call chain. The calibration execution node in the scoring node verifies the consistency between the calibration version identifier and the version that has been loaded or is currently effective in this node. If they are consistent, the matching calibration mapping parameters are used to calibrate the original scores from multiple sources to generate a unified calibration score. If they are inconsistent, the consistency protection path is entered.

[0024] The monitoring and self-healing module is used to perform multi-granular monitoring of the unified calibration score output in a distributed environment. Based on the monitoring results, it quantifies and generates a consistency risk index. The consistency risk index is generated based at least on the calibration version inconsistency rate, policy reversal rate, verification failure rate, and tail risk value of score difference. The release convergence credibility and consistency risk index are coupled to form a closed-loop control logic to perform anomaly detection, automatic loss prevention, or controlled rollback operations according to the risk level.

[0025] The technical effects and advantages of this invention are as follows:

[0026] This invention encapsulates multi-source model scores in a structured manner within a unified scoring chain and generates calibration parameter packages. By binding calibration fingerprints with calibration version numbers to form calibration version identifiers, calibration mapping parameters have verifiable and traceable version anchors. This avoids implicit drift caused by identical version numbers but inconsistent content or parameter truncation, thereby improving the reproducibility and operational controllability of unified calibration scores.

[0027] This invention adopts a two-stage release mechanism of preloading and consistent activation. During the canary release, the release convergence credibility is constrained to promote unified activation and canary expansion. The consistency risk index is included in the convergence criterion, so that the node side only enters the ready and effective switching after completing fingerprint verification and structural verification. This significantly reduces the risk of cross-node inconsistency caused by some nodes being effective and some nodes still being the old version under distributed rolling updates. At the same time, it supports the identification, isolation and loss prevention of abnormal nodes.

[0028] This invention locks the link version of business requests at the entry orchestration layer and transmits the calibration version identifier along the call link. The calibration execution node takes version consistency verification as a prerequisite to ensure that the same request uses the same set of calibration mapping to complete the output under multiple nodes and multiple paths. When version inconsistency is detected, it enters the consistency protection path and combines shadow comparison and multi-granularity monitoring to quantify the difference. Furthermore, it triggers anomaly detection, automatic loss prevention and controlled rollback by coupling the release convergence credibility and consistency risk index in a closed loop, thereby suppressing the amplified impact of policy reversal and result fluctuation on business, and improving system stability and compliance audit support capabilities. Attached Figure Description

[0029] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings;

[0030] Figure 1 This is a flowchart illustrating the consistency calibration method for multi-source model scoring according to the present invention.

[0031] Figure 2 This is a schematic diagram of the structure of a consistency calibration system for multi-source model scoring according to the present invention. Detailed Implementation

[0032] 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.

[0033] Example 1: A consistency calibration method for multi-source model scoring according to the present invention, such as... Figure 1 As shown, it includes the following steps:

[0034] Step 1: Create a package for integrating the scoring system;

[0035] The system integrates multi-source model scores corresponding to the same target object within a unified scoring chain. The target object can be any identifiable entity defined by the transaction, user, content, device, or business side. To ensure consistent calibration and traceability of scores from different sources, the system establishes a source file for each scoring source and manages its registration. This source file includes at least a source identifier, model or rule version identifier, score value range and unit, score meaning declaration, and the score's position in the business decision-making chain. The system uniformly assigns a request identifier that can be transparently transmitted throughout the entire scoring chain to each incoming scoring request and records the score generation time, request context identifier, and target object identifier. This ensures that scores generated for the same object at different nodes or service paths have the basic conditions for alignment and recalculation.

[0036] Furthermore, to avoid semantic bias caused by directly mixing scores with the same name but different meanings or different units, the system structurally encapsulates the original scores at the access layer, forming a unified score event record. This score event record includes at least: target object identifier, source identifier, model version identifier, original score value, score range declaration, and necessary quality marker fields. The quality marker fields preferably include: whether it is a catch-all output, whether it triggers truncation or capping, whether it is on a missing feature path, and whether the key inputs used for score calculation are complete, so as to isolate abnormal paths during subsequent calibration mapping construction. For discrete level scores, capped scores, or score sources with significant resolution deficiencies, the system preferably labels them separately as bucketed observation sources to avoid introducing irreversible calibration errors by using the same fitting strategy as continuous score sources.

[0037] During offline calibration or periodic updates, the system extracts a sample set for generating calibration mappings from the aforementioned scoring event records and performs alignment and deduplication on the samples. Preferably, the system associates the original scores with result feedback events using request identifiers or business event identifiers. These result feedback events can be rejection results, manual review conclusions, valid complaints, repair reports, or other business closed-loop signals that can characterize the true results. When the same target object has multiple scores within the same decision-making cycle, the system selects the score closest to the decision time according to preset rules, or selects representative scores using strategies such as maximum risk or minimum quality, to ensure the temporal consistency of calibration samples. To improve the usability and stability of calibration mappings, the system preferably performs segmented coverage checks on the samples: the sample size and event occurrence rate are statistically analyzed within each scoring segment. If some segments have sparse samples, adjacent segments are merged or smoothing constraints are introduced to avoid abrupt jumps in the calibration mapping within sparse intervals.

[0038] Furthermore, the system constructs calibration mapping parameters from the original score to the unified calibration score for each scoring source. The unified calibration score is a comparable score under a unified scale, preferably a risk probability scale or a unified standard subscale, ensuring consistent semantics for calibrated scores from different sources. The calibration mapping parameters can be implemented using a segmented mapping table: the system divides the original score into several intervals according to preset segments, such as equal-width segments or equal-frequency segments, calculates the event occurrence rate or target statistic within each interval, and smooths the interval statistics while maintaining monotonicity, resulting in a mapping table from the original segments to the unified calibration segments; the smoothing correction can use a moving average method or a method based on monotonic spline regression. For example, for a segmented event rate sequence... The smoothed event rate can be calculated by taking a moving average with a window size of 3. Furthermore, special handling is applied to boundary values ​​to ensure the monotonically non-decreasing nature of the entire sequence; among which, The smoothed event rate; For the i-th Event rate of a single segment; The event rate observed within the i-th original score segment; Let be the event rate of the (i+1)th segment. For sources marked as bucketed observation sources, the system preferably generates a bucket-level mapping table directly from the bucket-level statistical results, and records the uncertainty range within the bucket in the mapping annotation field, so that the source can be automatically downweighted or conservatively handled during subsequent fusion or strategy execution.

[0039] After completing the source calibration mapping calculations, the mapping parameters and effective constraints for each source are combined into a calibration parameter package. This package includes at least the source identifier, model version identifier, calibration version number, mapping parameter entity, applicable scope, effective time window, and rollback compatibility. For the calibration fingerprint calculation of the calibration parameter package, the calibration fingerprint preferably points to the summary value of the content within the parameter package. The calibration version number and calibration fingerprint are then bound to form a calibration version identifier. By choosing to sign or affix a verification code to this identifier, it can be published as a release list. Therefore, any node can complete integrity and consistency checks, avoiding issues such as cache retention, inconsistent configuration distribution, or parameter truncation resulting in different content for the same version name.

[0040] To support subsequent canary releases and consistency monitoring, the calibration parameter package reserves status and audit fields related to release control. These status and audit fields preferably include: release batch number, canary domain identifier, minimum ready coverage threshold, and the specified calibration version identifier field name for link pass-through. Simultaneously, the generation process of the calibration parameter package and key statistical information are written into the audit log. The key statistical information preferably includes the sample size of each segment, segment event rate, smoothing correction magnitude, and sparse segment merging status. This allows for the reproduction of parameter sources and generation basis based on the same calibration version identifier in case of subsequent consistency disputes or the need for post-mortem analysis.

[0041] Step 2: Parameter preloading activation;

[0042] After generating the calibration parameter package as described in step one, the system publishes the calibration parameter package to a publication channel used for distributed node synchronization. This publication channel can be implemented using a combination of a configuration center, object storage, and a message publishing component: the object storage stores the full content of the calibration parameter package and its calibration version identifier; the message publishing component broadcasts notifications of the new version's availability to scoring nodes in each data center and cluster; and the configuration center carries publication control information such as grayscale selection, activation timing, and rollback strategies. To avoid situations where some nodes are already using the new version while others remain with the old version due to reliance on weak consistency, the system employs a two-stage publication mechanism of pre-loading and consistent activation for the calibration parameter package, using the calibration version identifier as the unique anchor point for cross-node consistency.

[0043] During the preloading phase, upon receiving a new version notification, each scoring node does not directly switch the calibration mapping used for online scoring. Instead, it first pulls the corresponding calibration parameter package based on the calibration version identifier carried in the notification. After the node pulls the parameter package, it preferably writes it to local persistent media and establishes a read-only cache copy. Simultaneously, it performs integrity and consistency checks on the calibration parameter package. These checks include at least a matching check between the calibration version number and the calibration fingerprint, as well as a structural and length check of key fields in the calibration parameter package, to prevent abnormalities such as truncated parameters, missing fields, or incompatible formats. In an optional implementation, the node also performs signature or checksum verification on the calibration version identifier to prevent unauthorized replacement of the parameter package. Only when the verification passes is the node marked as a ready version and loaded into the switching area in memory. If the verification or pull fails, the node continues to use the currently effective version and reports the reason for the failure and the node identifier to the release controller for subsequent abnormal node identification and isolation.

[0044] To improve the feasibility and maintainability of the two-phase mechanism, the node side preferably manages the lifecycle of the calibration version using a defined version state machine. This state machine includes at least three states: the currently active version, the ready version, and the rollback backup version. When a node enters the ready state, it records the calibration version number, calibration fingerprint, pull time, and verification result corresponding to that ready version, and periodically reports this information to the release controller. This allows the release controller to monitor the ready coverage of each grayscale region and the synchronization lag of tail nodes in real time. For nodes that repeatedly pull the same version multiple times within a short period or experience frequent ready failures, the system preferably marks them as abnormal nodes and takes measures such as rate limiting, removal, or prohibition of entry into critical traffic domains to prevent abnormal nodes from introducing inconsistent output risks during the activation phase.

[0045] During the unified activation phase, the release controller does not solely rely on whether any node has pulled the new version. Instead, it combines grayscale domain configuration and readiness status for unified activation control. Specifically, the release controller first determines the target grayscale domain for this release. This grayscale domain can be defined by data center, cluster, service group, or traffic group. Then, based on conditions such as the readiness coverage rate of nodes within the grayscale domain reaching a preset threshold, the synchronization lag of tail nodes not exceeding a preset upper limit, and the number of abnormal nodes not exceeding a preset proportion, a unified activation command is issued to the target grayscale domain. After receiving the unified activation command, the node atomically switches its ready version to the currently effective version and records the switch time and calibration version identifier at the switch point. To avoid intermediate state errors caused by service restarts, thread concurrency, or local caching, the node side preferably adopts a read-write isolation switching method. That is, the old version calibration mapping and the new version calibration mapping coexist in memory, and the atomic switch is completed by updating the version pointer or configuration reference in one go, thereby ensuring that any request will only hit a specific calibration version at any given time.

[0046] Furthermore, to prevent end-to-end version inconsistencies caused by upstream activation being outdated and downstream activation being non-activated due to cross-data center links or cross-cluster calls, it is preferable to distribute the unified activation command in layers according to the call link topology: first activate the ingress orchestration layer and calibration execution layer, and then activate the downstream dependent policy execution components, or adopt a reverse order to ensure that the calibration version of the critical path is consistent; and after activation, the release controller can trigger a lightweight health check, so that each node reports its currently effective calibration version identifier. If nodes with inconsistent version identifiers are found in the grayscale domain, a forced reload check is performed on the node or it is removed from the traffic pool to ensure that the same calibration version identifier corresponds to the same calibration parameter content in the grayscale domain.

[0047] In a further implementation, to ensure that activation and grayscale expansion have quantifiable convergence criteria, a release convergence confidence level is constructed during the grayscale release process. And it is used to constrain the release process. The release convergence reliability... Can be done in time window The content is generated as follows:

[0048] ;

[0049] in, Readiness coverage is used to characterize the percentage of nodes that have completed preloading and passed calibration fingerprint verification; in one feasible implementation, Indicates time window Within the target grayscale range, the proportion of service instances that have completed preloading and passed calibration fingerprint verification is defined as follows: [Insert proportion here]. Preferably, the proportion calculation excludes instances that have been marked as abnormal nodes to ensure the accuracy of the coverage calculation.

[0050] Synchronization lag is a statistical measure used to characterize the time delay from the start of deployment to the node entering a ready or active state; the synchronization lag... The calculation is as follows: First, for each node i within the target grayscale domain, record the delay from receiving the publication notification to entering the ready state. Subsequently, the node's position within the statistical time window is calculated. Percentage of requests processed within The time window It can be set to sixty seconds; ultimately, That is, the average synchronization delay weighted by the number of requests;

[0051] This refers to the number of abnormal nodes, used to count the number of nodes that failed verification, failed loading, were not ready for a long time, or frequently rolled back. In the time window Within the specified number of service instances that meet any of the following conditions: fingerprint calibration fails more than 3 times consecutively, loading fails more than 2 times, fails to enter the ready state after a preset time (e.g., 60 seconds) after the notification is published, or experiences frequent rollbacks after becoming ready, such as more than 2 rollbacks within 1 minute.

[0052] The consistency risk index is a statistical measure within a time window, used to reflect the feedback of the consistency risk of actual scores in the grayscale domain on the convergence of the release. The construction method of the consistency risk index is described in step three below; specifically, the consistency risk index is a statistical measure within a time window. Based on the consistency risk index defined in step three Calculated. In one feasible implementation, In the time window Within this process, the flow rate of the newly calibrated version activated in the target grayscale domain is sampled, and a series of results are calculated. A statistical measure of the value, such as the arithmetic mean, sliding window average, or exponentially weighted moving average; as a preferred embodiment, the statistical measure... The update formula is calculated using the exponentially weighted moving average: Smoothing factor The value range is from 0 to 1, for example, taking =0.1, to balance the current risk value with historical trends; this statistic is used to macroscopically reflect the overall consistency risk level of effective traffic at the release control level.

[0053] to The fusion weights; to It can be trained based on business risk tolerance and historical release data. In one exemplary implementation, it can be adopted... , , , It also allows for dynamic adjustments based on actual release results.

[0054] The system preferably sets a trusted release threshold, when If the threshold is not reached, the unified activation is frozen or further grayscale expansion is paused, and preferably the grayscale range is shrunk or abnormal nodes are isolated; when Only when the threshold is reached and the system remains stable will it allow the next stage of gray-scale diffusion or full activation to be advanced, thereby reducing the risk of version inconsistency and inconsistent scores caused by diffusion before convergence.

[0055] Step 3: Link version locking;

[0056] When a business request enters the unified scoring chain, the system performs version binding for consistent calibration at the entry orchestration layer to ensure that the same request uses the same calibration mapping to achieve unified scoring under distributed multi-node, multi-path call conditions. Specifically, after receiving a request, the entry orchestration layer first parses the business domain, channel, grayscale domain identifier, and target object identifier of the request, and determines the calibration version identifier to be used for this request based on the calibration parameter package that has been consistently activated in step two. The calibration version identifier includes at least a calibration version number and a calibration fingerprint, and corresponds one-to-one with the content of the calibration parameter package. The entry orchestration layer writes the calibration version identifier into the request context and carries it as a link pass-through field, preferably also writing a full-link tracing identifier, so that the use of a particular calibration version can be reproduced in the logs later.

[0057] Before performing unified calibration on the multi-source raw scores, the calibration execution node first checks the consistency between the calibration version identifier carried in the request and the currently effective version of the node. If the consistency check passes, the calibration execution node reads the calibration mapping parameters matching the calibration version identifier from its local memory, performs mapping conversion on the multi-source raw scores one by one according to the source identifier, generates the calibrated scores for each source, and further obtains a unified calibration score according to a preset fusion strategy, which is used for downstream sorting, threshold determination, or strategy execution. To ensure that the same request is not affected by concurrent switching during execution, the calibration execution node preferably binds the calibration version identifier and the mapping parameter handle to the request-level context when the request begins processing, and keeps it unchanged throughout the lifecycle of the request. Even if the node switches versions during processing, it will not affect the calibration mapping consistency of the request, thereby avoiding the unreproducible situation where the same request uses the old version in the first half and the new version in the second half within a single link.

[0058] Furthermore, if the calibration execution node finds that the calibration version identifier carried in the request is inconsistent with the current effective version of the node, the node will not use calibration mappings of other local versions to replace the score, so as to avoid the same object obtaining contradictory unified calibration scores under different nodes or different service paths. At this time, the node enters the consistency protection path, preferably first performing a version availability judgment: if the node has preloaded but has not yet activated the calibration version, then under the condition of meeting the latency constraint, the node will complete the controlled switch from the ready version to the effective version according to the release control policy before performing calibration; if the node does not hold the version, the node will forward the request to the node that has loaded the calibration version or the dedicated calibration service to perform the score output through the routing component; if the routing cannot be completed due to network, load or latency constraints, the node will output a conservative result with a consistency mark and trigger a degradation strategy. The degradation strategy preferably includes: using only the confirmed consistent partial sources for calibration fusion, temporarily downgrading the weight of sources with inconsistent versions, or only outputting the risk level rather than the fine score, so as to ensure that the decision-making link can still run under abnormal conditions and reduce the risk of misjudgment or omission.

[0059] Without altering online decision-making, users can utilize shadow calibration. Under predefined sampling conditions, the calibration execution node provides a unified calibration score for the main version, while the background uses shadow version mapping parameters to perform shadow calibration on the same multi-source original scores in parallel, obtaining a unified shadow calibration score. The shadow does not participate in the execution of the actual policy. The differences between the main and shadow versions, the hit segment information, and the possible policy reversal results are written into the shadow calibration record and shared with the end-to-end tracing identifier of the request. Using shadow calibration, the system can continuously observe the different patterns of the old and new versions in real traffic during the gray-scale diffusion process, providing a basis for subsequent risk analysis and rollback decisions.

[0060] Furthermore, to ensure that consistency protection and shadow comparison have a unified quantitative triggering basis, the system preferably selects a time window... Internal Building Consistency Risk Index This is used to comprehensively characterize the risk levels of version inconsistency, score differences, and strategy reversal. The consistency risk index... It can be generated as follows

[0061]

[0062] in, The calibration version inconsistency rate is used to characterize the proportion of requests whose calibration version identifier is inconsistent with the node's effective version; specifically, In the time window Within the ingress orchestration layer, the percentage of requests whose calibration version identifier is inconsistent with the currently effective version of the target calibration execution node is the total number of requests.

[0063] This refers to the set of unified calibration score differences between the primary and shadow versions on the same request, or the set of unified calibration score differences obtained by recalculating the same object across entry points and paths; specifically, In the time window Within this scope, it is the set of absolute values ​​of the differences between the unified calibration score of the primary version and the unified calibration score of the shadow version in all requests that enable shadow comparison. In an optional implementation, it may also include the score differences recalculated across nodes for the same object.

[0064] The tail risk value of the scoring difference is represented by the set of differences. The difference corresponding to the p-th percentile. During calculation, the differences are sorted in ascending order, and the p-th percentile is taken. There are several differences, among which Let p be the size of the set. Preferably, p is 0.95 or 0.99 to reflect tail risk;

[0065] The strategy flip rate is used to characterize the proportion of execution results that flip, such as interception and release, degradation and no degradation, under the main and shadow paths or across paths. The failure rate is used to reflect the degree to which calibration parameters experience loading anomalies or integrity risks at the node side. to The fusion weights; to The sensitivity settings can be adjusted based on the business's sensitivities to inconsistency risks, score jumps, policy reversals, and parameter integrity. In one exemplary implementation, it is possible to... , , , The weights can be optimized through training on historical risk event data, or set by domain experts based on business scenarios.

[0066] It is preferable to set a risk threshold when When the threshold is exceeded or continues to rise, the trigger priority of the consistency protection path is increased. For example, priority is given to routing to consistent version nodes, the shadow mapping sampling ratio is increased, or a more conservative degradation strategy is implemented for high-risk sources to control the impact of inconsistent outputs on business decisions at the request level.

[0067] Step 4: Consistent monitoring of self-healing;

[0068] The system provides unified online monitoring and comparative analysis of policy inconsistencies under distributed deployment, canary release, and version iteration conditions for unified calibration scoring output and policy execution chain. This enables users to identify and suppress policy inconsistencies across different entry points, nodes, or service paths under distributed deployment, canary release, and frequent version iteration conditions. The system audits each section at the entry orchestration layer and calibration execution node, requiring audit fields to include at least the calibration version number, calibration fingerprint, source identifier, unified calibration score, and end-to-end tracing identifier. This ensures that the scoring results of any object under different entry points, nodes, or service paths can be uniformly compared and reproduced. Audit fields, along with important runtime information such as node identifiers, data center identifiers, canary domain identifiers, and service instance identifiers, are written to online logs or event streams, providing data support for future difference statistics, anomaly localization, and self-healing.

[0069] Multi-granularity monitoring is implemented for consistency of scores for the same object. The first type of monitoring is cross-node recalculation monitoring: the system triggers a recalculation process for some requests according to a preset sampling ratio, i.e., performing two or more consistency calibration calculations on the same target object on different nodes, different instances, or different service paths, and statistically analyzing the differences in the obtained unified calibration scores. The second type of monitoring is master-shadow comparison monitoring: the system statistically analyzes the distribution of the difference between the unified calibration scores of the master version and the shadow version for requests with shadow comparison enabled, as well as the policy flipping caused by the difference. The third type of monitoring is version consistency monitoring: the system periodically summarizes the currently effective calibration version identifiers reported by each node, detecting whether there are cases within the grayscale region where the version number is the same but the calibration fingerprint is different, or where different version numbers participate in critical traffic simultaneously. The fourth type of monitoring is parameter integrity monitoring: the system statistically analyzes events such as calibration parameter package fingerprint verification failure, signature verification failure, abnormal pull length, and missing fields, and aggregates them by node and grayscale region to identify potential cache retention, parameter truncation, or loading anomalies. Through the above multi-dimensional monitoring, the system can distinguish between risk sources such as version mismatch caused by inconsistent request paths at the parameter distribution level and excessive differences in calibration between new and old versions, thus avoiding misjudgment based on a single indicator.

[0070] Furthermore, the above monitoring results are summarized to form a quantitative basis for consistency risk, and the consistency risk index described in step three is preferred. This serves as the core criterion for triggering self-healing. The system can set tiered risk thresholds based on the business's tolerable volatility level, when... When the first threshold is reached, the stop-loss priority mode is entered. When a higher threshold is reached or the risk level remains high for multiple consecutive time windows, the system enters a rollback priority mode, thereby matching the self-healing action with the risk level.

[0071] In the loss-prevention priority mode, the system preferably performs at least one or a combination of the following actions: freeze the further spread of the new calibration version and shrink the grayscale range; suspend unified activation of unconverged nodes or unverified domains; perform forced re-pulling, re-verification and restart loading on nodes detected as abnormal, and remove the node from the critical traffic pool before verification passes, so as to avoid it continuing to output unified calibration scores inconsistent with the main cluster; increase the trigger priority of the consistency protection path, such as prioritizing routing to consistent version nodes, expanding the recalculation sampling ratio or shadow comparison ratio, and temporarily downgrading or switching to a conservative mapping table for scoring sources with abnormal differences, so as to reduce the amplification effect of inconsistent scores on the strategy results; when the differences are mainly concentrated in a specific grayscale domain or a specific data center, temporarily switch the traffic of that domain back to the previous stable calibration version or temporarily exit the grayscale domain until the risk indicators recover.

[0072] In the rollback-first mode, controlled rollback and consistent recovery are performed. If a serious consistency violation is detected, such as the same version number but inconsistent calibration fingerprints, policy inversion rate reaching a predetermined limit, or a high consistency risk index, the release controller issues a rollback command to the target grayscale domain, allowing each node to switch its currently effective version to the rollback backup version. The node side preferentially performs the rollback using an atomic switch operation consistent with step two. After the rollback is complete, a lightweight health check is triggered to verify the identifier of the effective version in the grayscale domain. To avoid version mismatch during the rollback process, the system preferentially implements rigid constraints on request-level version binding within the rollback window. That is, requests that do not meet version consistency requirements are forcibly routed or conservatively downgraded, but abnormal nodes remain isolated until the consistency monitoring results are available.

[0073] At the same time, the system preferentially uses the release convergence credibility described in step two. As a criterion for release promotion and grayscale expansion control, it is used to constrain whether unified activation is allowed, whether grayscale expansion is allowed, and whether freezing or shrinking is required. The system can set a trust threshold: when If the threshold is not reached, freeze unified activation or pause grayscale expansion, and prioritize completing the cleanup of abnormal nodes, catching up of tail nodes, and reducing consistency risks; when Only when the threshold is reached and the consistency risk index falls back to a safe range and remains stable, will the system allow the next stage of gray-scale diffusion or full-domain activation to proceed. By linking the release status with consistency risk feedback, a system is formed... Driven stop loss and rollback, The control relationship between constraint activation and gray expansion, and their coupled closed loop, is used to continuously suppress the strategy inconsistency problem caused by the inconsistency of the output of the same object across nodes in engineering.

[0074] Example 2: The design of a consistency calibration system for multi-source model scoring according to the present invention is based on the method in Example 1, specifically as follows... Figure 2 The following modules are shown:

[0075] The access package module is used to access multi-source model scores corresponding to the same target object in a unified scoring link. It establishes a source file for each scoring source and manages its registration, and encapsulates the original scores into a unified scoring event record. This scoring event record includes at least the target object identifier, source identifier, model version identifier, original score value, score value domain declaration, and quality marker fields, providing an alignment basis for subsequent consistency calibration and traceable reproducibility. This module is also used to form a sample set based on the scoring event records and generate calibration mapping parameters for each scoring source. The mapping parameters and effective constraints are then encapsulated into a calibration parameter package, and a calibration fingerprint is calculated for the calibration parameter package. The calibration version number is bound to the calibration fingerprint to form a calibration version identifier. The calibration parameter package also reserves status fields and audit fields such as release batch number, grayscale domain identifier, minimum ready coverage threshold, and calibration version identifier field name specifications for link transmission.

[0076] The distribution and activation module is used to publish calibration parameter packages to the release channel and implement a two-stage release mechanism of preloading and consistent activation at each scoring node, using the calibration version identifier as the unique anchor point for cross-node consistency. The release channel is implemented by a combination of a configuration center, object storage, and message publishing components. In the preloading stage, the scoring node pulls the calibration parameter package based on the calibration version identifier carried in the notification, performs matching verification between the calibration version number and the calibration fingerprint, and performs structure / length verification. After the verification is successful, it is marked as a ready version and reported to the release controller. In the consistent activation stage, the release controller issues a unified activation command based on the grayscale configuration and the ready status. The node switches the ready version to the currently effective version in an atomic switching manner, ensuring that a request hits only one specific calibration version at any given time. Furthermore, this module is also used to build release convergence credibility during grayscale release. Based on this, unified activation and gray expansion are constrained and promoted.

[0077] The Consistent Calibration module is used to perform version binding for consistent calibration of business requests at the ingress orchestration layer. Based on the already consistently activated calibration parameter package, it determines the calibration version identifier to be used in this request and writes this identifier into the request context as a link pass-through field, while also writing a full-link tracing identifier to support reproducibility. This module is also used at the calibration execution node to verify the consistency between the calibration version identifier carried in the request and the currently effective version of the node. If the verification passes, it reads the matching calibration mapping parameters, performs mapping conversion on the multi-source original scores according to the source identifier, and generates a unified calibration score. It maintains the request-level version binding unchanged throughout the request lifecycle to avoid unreproducible cross-version calibration within a single link. Simultaneously, this module supports shadow comparison: shadow calibration is performed in parallel for requests that meet the sampling conditions, and the main shadow difference, hit segment information, and possible policy reversal results are written into the shadow comparison record, providing a basis for risk analysis and rollback decisions.

[0078] The self-healing monitoring module is used to perform online monitoring and comparative analysis of consistency differences between calibration score output and policy execution links under distributed deployment, canary release, and version iteration conditions. It performs unified auditing at the entry orchestration layer and calibration execution nodes, requiring audit fields to include no fewer than calibration version number, calibration fingerprint, source identifier, unified calibration score, and end-to-end tracing identifier, thereby achieving consistent comparison and reproduction across entry points, nodes, and paths. This module further performs multi-granularity monitoring, including cross-node recalculation monitoring, master-shadow comparison monitoring, version consistency monitoring, and parameter integrity monitoring, and summarizes the monitoring results to form a quantitative basis for consistency risk. When a serious consistency violation is detected, the release controller enters rollback priority mode and issues a rollback command. The node uses the same atomic switching method as consistency activation to roll back to the rollback backup version and triggers a lightweight health check to verify the canary domain effective version identifier. Simultaneously, it combines the release convergence credibility... As a criterion for release promotion and gray-scale control, a coupled closed-loop control of stop loss / rollback and activation / gray-scale is formed.

[0079] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0080] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0081] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0082] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0083] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for consistency calibration of multi-source model scores, characterized in that, Includes the following steps: In the unified scoring chain, multi-source model scores corresponding to the same target object are accessed, and the original scores are encapsulated in a structured manner to form scoring event records; Based on the scoring event records, calibration mapping parameters for each scoring source are generated. The calibration mapping parameters and effective constraints are encapsulated into a calibration parameter package. The calibration fingerprint of the calibration parameter package is calculated, and the calibration version number is bound to the calibration fingerprint to form a calibration version identifier. The calibration parameter package is released to the release channel. Through a two-stage release mechanism of preloading and consistent activation, the calibration parameters are switched consistently among distributed scoring nodes using the calibration version identifier as the anchor point. During the canary release, the release convergence credibility is built and monitored. The release convergence credibility is generated based on the statistics of ready coverage, synchronization lag, number of abnormal nodes and consistency risk index within the time window. The progress of unified activation and canary release is controlled according to the release convergence credibility. At the entry point of the business request, the calibration version identifier to be used in this request is determined based on the activated calibration parameter package and written into the request context and passed through the call chain; the calibration execution node in the scoring node checks the consistency between the calibration version identifier and the version loaded or currently effective in this node. If they are consistent, the matching calibration mapping parameters are used to calibrate the multi-source original scores to generate a unified calibration score, and if they are inconsistent, the consistency protection path is entered. Multi-granularity monitoring is performed on the unified calibration score output in a distributed environment. The consistency risk index is generated based on the monitoring results, which is based on the calibration version inconsistency rate, policy inversion rate, verification failure rate, and tail risk value of score difference. The release convergence credibility and consistency risk index are coupled to form a closed-loop control logic to perform anomaly detection, automatic stop loss or controlled rollback operations according to the risk level.

2. The method of claim 1, wherein: During the preloading phase, the distributed scoring nodes pull the calibration parameter package and perform calibration fingerprint consistency verification, then mark it as a ready version. During the consistency activation phase, the distributed scoring nodes are triggered to switch the ready version to the effective version.

3. The method of claim 1, wherein: When encapsulating the raw scores in a structured manner, discrete grade scores, capped scores, or score sources with insufficient resolution are marked separately as bucketed observation sources; and when generating calibration mapping parameters, a bucket-level mapping table is generated for the bucketed observation sources based on the bucket-level statistical results.

4. The consistency calibration method for multi-source model scoring according to claim 1, characterized in that: Synchronization lag is used to characterize the delay from receiving the release notification to entering the ready or active state of the scoring node, and is weighted according to the proportion of node request processing volume; the number of abnormal nodes is used to count nodes that fail to verify, fail to load, are not ready for a long time, or frequently roll back; the consistency risk index is obtained by averaging the statistics within the time window using a sliding window method; the release convergence credibility is used to control the release progress of unified activation, freezing grayscale expansion, or shrinking grayscale domain.

5. The method of claim 1, wherein: The tail risk value of the scoring difference is generated based on the unified calibration scoring difference set of the main version and the shadow version on the same request, or the unified calibration scoring difference set obtained by recalculating the same object across entry points and paths, and the high quantile statistic of the difference set is taken; the consistency risk index is generated by weighted summation of calibration version inconsistency rate, policy reversal rate, verification failure rate and the tail risk value of the scoring difference.

6. The method of claim 1, wherein: It also includes a shadow comparison step, which, upon request that the sampling conditions are met, uses the shadow version mapping parameters in parallel to calibrate the same multi-source original scores, obtains a unified shadow calibration score, and records the difference information.

7. The method of claim 1, wherein: Multi-granularity monitoring includes cross-node recalculation monitoring, master shadow comparison monitoring, version consistency monitoring, and parameter integrity monitoring.

8. The method of claim 1, wherein: When the consistency risk index exceeds the threshold, the consistency protection path is triggered, which includes routing the request to the version consistent node, temporarily reducing the weight of the inconsistency source, or outputting a conservative result.

9. The method of claim 1, wherein: Abnormal nodes include those that fail to validate, fail to load, are not ready for a long time, or frequently roll back, and these nodes are isolated or rate-limited during the release process.

10. A multi-source model score consistency calibration system, characterized in that, The calibration system is used to implement the method according to any one of claims 1-9, and includes the following modules: The access package module is used to access multi-source model scores corresponding to the same target object in the unified scoring link, and to encapsulate the original scores in a structured manner to form a scoring event record; based on the scoring event record, calibration mapping parameters for each scoring source are generated, and the calibration mapping parameters and effective constraints are encapsulated into a calibration parameter package, the calibration fingerprint of the calibration parameter package is calculated, and the calibration version number is bound to the calibration fingerprint to form a calibration version identifier; The distribution activation module is used to publish calibration parameter packages to the release channel. Through a two-stage release mechanism of preloading and consistent activation, it achieves consistent switching of calibration parameters among distributed scoring nodes with calibration version identifier as the anchor point. During the canary release, the release convergence credibility is built and monitored. The release convergence credibility is generated based on the statistics of ready coverage, synchronization lag, number of abnormal nodes, and consistency risk index within the time window. The progress of unified activation and canary release is controlled according to the release convergence credibility. The consistency calibration module determines the calibration version identifier to be used in this request based on the activated calibration parameter package at the entry point of the business request, and writes it into the request context and passes it through the call chain. The calibration execution node in the scoring node verifies the consistency between the calibration version identifier and the version that has been loaded or is currently effective in this node. If they are consistent, the matching calibration mapping parameters are used to calibrate the original scores from multiple sources to generate a unified calibration score. If they are inconsistent, the consistency protection path is entered. The monitoring self-healing module is used to perform multi-granular monitoring of the unified calibration score output in a distributed environment. Based on the monitoring results, it quantifies and generates a consistency risk index. The consistency risk index is generated based on the calibration version inconsistency rate, policy inversion rate, verification failure rate, and tail risk value of score difference. The release convergence credibility and consistency risk index are coupled to form a closed-loop control logic to perform anomaly detection, automatic stop loss or controlled rollback operations according to the risk level.

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