Vehicle-mounted physical fingerprint migration method

Abstract

This invention discloses a method for migrating physical fingerprints in vehicles. The server generates drift detection objects and migration consensus results based on the deviation between the physical fingerprint digest and the historical baseline. When a threshold is reached, the vehicle terminal generates a set of baseline objects containing both new baseline objects and objects retained from the old baseline, which then participate in verification within a preset coexistence window. The server then confirms the migration, continues observation, or reverts to the old baseline based on post-migration stability, rollback detection, and dispute review results.

Description

Technical Field

[0001] This invention relates to the fields of physical fingerprint drift detection, secure baseline migration, and protected relearning, and particularly to a method, system, and storage medium for vehicle-mounted physical fingerprint lifecycle migration based on dual baseline coexistence verification and anti-rollback counter. Background Technology

[0002] While existing physical fingerprint-based vehicle authentication solutions can identify and replace circuit boards, simulate injection, or counterfeit devices, they often face the following problems in long-term mass production deployment: 1. Changes in temperature, voltage reference, load condition, and installation environment can cause physical fingerprints to drift gradually; 2. If the threshold is set too strictly, it can easily lead to a higher false rejection rate and affect the business loop. 3. Directly relaxing the threshold could weaken the ability to verify physical authenticity; 4. Without a safety baseline migration mechanism, equipment may need to be returned to the factory for re-registration after aging or legitimate maintenance. 5. If the migration process lacks protected execution and multi-factor consensus, attackers may use the guise of migration to induce the system to accept a forged baseline; 6. Existing relearning schemes often employ manual re-registration or directly overwrite old baseline parameters, making it difficult to distinguish between continuous natural drift and sudden abnormal jumps, and also making it difficult to preserve a complete chain of migration evidence.

[0003] Furthermore, in real-world deployments, so-called "fingerprint drift" is not caused by a single reason, but rather by a combination of factors. For example, for the same vehicle, seasonal changes, battery replacements, minor shifts in mounting positions, changes in vibration sensor bias, changes in power supply texture due to aging, and changes in local wiring harness contact impedance after maintenance can all cause deviations between the historical baseline and the current summary. If the system lacks a lifecycle migration protocol, it can only make a rough choice between "strict rejection" and "lenient approval": the former leads to an accumulation of false rejections, while the latter may weaken the ability to verify authenticity.

[0004] On the other hand, attackers may also exploit the system's tolerance for "drift" by fabricating maintenance, update, or natural change scenarios to induce the system to accept a new, forged baseline. Unlike traditional instantaneous spoofing, this type of attack doesn't necessarily aim to fool the system all at once, but rather to gradually hijack the system's trusted baseline in the direction the attacker desires. Once the new baseline is incorrectly confirmed, all subsequent comparisons may be based on flawed premises.

[0005] Therefore, a method different from traditional re-registration and simple threshold adaptation is needed to enable the controlled evolution of the physical fingerprint baseline under the continuous binding relationship of the same vehicle. This method should at least solve the following problems: how to detect whether the deviation is a continuous natural drift or a sudden abnormal jump; how to determine whether a candidate migration has sufficient evidence to support it; how to enable the old and new baselines to coexist and be verified during the transition period; how to ensure that the migration process is constrained by the authorized object and the rollback counter; and how to replay the complete chain of evidence before, during and after the migration in case of disputes. Summary of the Invention

[0006] Purpose of the invention The purpose of this invention is to provide a method, system and storage medium for vehicle physical fingerprint lifecycle migration based on dual baseline coexistence verification and anti-rollback counter, so as to reduce the risk of false rejection caused by natural drift in long-term mass production deployment, and prevent attackers from bypassing physical authenticity verification by forging the migration process. At the same time, it realizes the protected baseline update protocol within the continuous binding relationship of the same vehicle without introducing cross-vehicle migration governance complexity. Technical solution

[0007] To achieve the above objectives, the present invention adopts the following technical solution: This invention is executed collaboratively by an in-vehicle terminal and a server, and the method is performed for updating the physical fingerprint baseline lifecycle under the continuous binding relationship of the same vehicle. The in-vehicle terminal collects one or more of the following within a preset window: physical fingerprint summary, environmental state summary, device continuity summary, and third-party high-reputation anchor summary. The server generates drift detection objects based on the deviation trend between the current baseline and historical baselines. The drift detection objects include at least one or more of the following: drift direction result, drift speed result, environmental consistency result, and anomaly degree result. When the drift detection objects meet preset migration candidate conditions, the server further generates migration candidate objects and calculates migration consensus results based on one or more of the following: environmental consistency result, multi-window stability result, multi-machine graph witness result, maintenance confirmation result, third-party high-reputation anchor result, and cloud-based group baseline result. When the migration consensus result reaches a threshold, the in-vehicle terminal generates a baseline object set, including at least a new baseline object and an old baseline retention object, as well as optional migration trace objects, within a secure component or trusted execution environment, and allows the new baseline object and the old baseline retention object to participate in verification within a preset coexistence window. The server decides to confirm the migration, continue observation, revert to the old baseline, or transfer to manual review based on one or more of the following: post-migration stability results, rollback detection results, and dispute review results. The migration is based on the condition that a continuous drift trend and multi-window stable offset are both met, rather than directly overwriting the baseline for a single abnormal sample.

[0008] In some embodiments of the present invention, the drift detection object may also include one or more of drift_window_ref, abrupt_jump_flag, local_confidence_ref, maintenance_hint_ref, and device_profile_ref. By explicitly separating "continuous changes" from "abrupt jumps," occasional anomalies, sensor jitter, sampling errors, or attack attempts can be avoided from being directly interpreted as legitimate precursors to migration.

[0009] In some embodiments of the present invention, migration candidates, in addition to being bound to candidate new baseline summaries and old baseline references, may also be bound to one or more of migration_scope_ref, coexist_window_ref, rule_ver, candidate_quality_ref, and witness_bundle_ref. The witness_bundle_ref may indicate auxiliary consistency materials from maintenance confirmation anchors, third-party high-reputation anchors, multi-machine graph witnesses, historical batch equipment statistics, or other trusted sources. The server calculates the migration consensus result based on the above materials, but no single auxiliary material is allowed to independently determine the success of the migration.

[0010] In some embodiments of the present invention, the server issues a signed migration authorization object after the migration consensus result reaches a threshold. The migration authorization object may include at least one or more of migration_ticket_ref, baseline_ver_old, baseline_ver_new, counter_range_ref, expiry_ref, rule_ver, and migration_policy_ref. The vehicle terminal only allows writing to the new baseline object when the object has been verified within a security component or trusted execution environment and the local rollback counter meets the range constraints. This prevents attackers from illegally replacing the baseline using old parameter packets, forged tickets, or rollback counter attacks.

[0011] In some embodiments of the present invention, the new baseline object and the old baseline retention object participate in the verification simultaneously within a preset coexistence window. The server can receive both new_match_result and old_match_result simultaneously, and determine whether to confirm the migration, extend the observation period, roll back the old baseline, or transfer the case to manual review based on one or more of dual_baseline_score, rollback_check_result, migration_stability_result, and dispute_check_result. By having two baselines coexist, the stability of the new baseline can be verified without abandoning the old baseline all at once.

[0012] In some embodiments of the present invention, the server retains drift detection objects, migration candidate objects, migration authorization objects, migration trace objects, dual baseline verification results, rule version references, and audit result references. In case of disputes, the server can replay the stability results before migration, the dual baseline results during migration, and the confirmation results after migration to determine whether the migration is a controlled natural evolution, rather than an abnormal relaxation or a spurious migration induced by an attacker. Beneficial effects

[0013] Compared with the prior art, the present invention has at least the following beneficial effects: 1. By identifying drift detection objects and migration candidate objects, natural drift can be distinguished from anomalous forgery, avoiding the crude treatment of all deviations as counterfeiting or allowing all deviations to be treated as natural changes.

[0014] 2. By distinguishing between continuous drift trends and sudden abnormal jumps, we avoid misinterpreting threshold relaxation or single abnormal overwriting as baseline migration.

[0015] 3. Improve the reliability and interpretability of the migration process through multi-factor consensus, such as multi-window stability, graph witnessing, maintenance confirmation, and high-reputation third-party anchors.

[0016] 4. Prevent unauthorized migrations and parameter rollbacks by using coexistence of old and new baselines within a secure component or trusted execution environment, migration authorization controls, and rollback counter constraints.

[0017] 5. Improve long-term operation and maintenance, audit replay, and historical review capabilities by migrating tracking objects, rule version tracking, and dispute review interfaces.

[0018] 6. The main focus of this invention is the lifecycle update of the physical fingerprint baseline under the continuous binding relationship of the same vehicle, rather than the weak relearning path of cross-vehicle authorization migration, business permission quota control or only outputting reconstruction prompts. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the overall structure of the physical fingerprint lifecycle migration and relearning system according to one embodiment of the present invention.

[0020] Figure 2 This is a schematic diagram of the drift detection object generation and diversion process in one embodiment of the present invention.

[0021] Figure 3 This is a schematic diagram of the migration candidate object and multi-factor consensus calculation process in one embodiment of the present invention.

[0022] Figure 4 This is a schematic diagram of the verification and migration confirmation process for the coexistence of old and new baselines in one embodiment of the present invention.

[0023] Figure 5 This is a schematic diagram of the migration trace, rollback detection, and dispute review process in one embodiment of the present invention.

[0024] Terminology and Object Conventions To facilitate understanding of this invention, some terms are explained uniformly below. "Current baseline" in this document refers to the currently officially enabled baseline object, while "historical baseline" refers to a set of one or more baseline versions retained prior to the current baseline. "Drift detection object" in this document indicates the trend of the current physical fingerprint digest relative to the historical baseline, rather than directly indicating that a migration has been established. "Migration candidate object" in this document represents a set of candidates that have met the requirements for entering the migration evaluation stage, but still require multi-factor consensus and migration authorization. "Migration trace object" in this document is used to store key migration-related states, reference relationships, and audit links.

[0025] The phrase "persistent binding relationship of the same vehicle" in this paper is used to emphasize that this invention focuses on the lifecycle update of the physical fingerprint baseline under the same vehicle, the same terminal, or the same trusted combination, rather than cross-vehicle binding, cross-asset authorization migration, or business permission migration. The "multi-factor consensus" in this paper refers to a comprehensive judgment based on factors such as multi-window stability, environmental consistency, witness materials, and high-reputation anchor results regarding migration candidates, and does not imply the necessity of using a specific cryptographic consensus protocol.

[0026] System Structure like Figure 1 As shown, the system corresponding to this invention includes at least: 1. Physical fingerprint acquisition module, used to collect physical fingerprint summaries such as clock drift, vibration, inertia, and impedance; 2. Environmental status acquisition module, used to acquire temperature, voltage reference, load status, and installation location-related summaries; 3. Device continuity module, used to provide time continuity, window coverage, abnormal interruption records, and session stability results; 4. On-board terminal-side security components or trusted execution environment, used to protect and manage baseline objects, verify migration authorization objects, and control the writing of new baselines; 5. Server-side drift detection module, migration consensus module, migration confirmation module, rule management module, and audit module.

[0027] The physical fingerprint acquisition module can simultaneously support multiple different physical root anchor sources, such as clock drift summaries, vibration spectrum summaries, inertial event summaries, power supply impedance summaries, or other protected authenticity summaries. The environmental status acquisition module provides the interpretive context for these summaries, enabling the server to determine whether the current deviation may be caused by changes in the natural environment. The device continuity module determines whether the current data comes from a stable operating phase, preventing data from short-term plugging / unplugging, sampling failures, or abnormal resets from directly entering the migration main path. Detailed Implementation

[0028] Implementation Method 1: Generation of Drift Detection Objects like Figure 2 As shown, the server generates drift detection objects based on the physical fingerprint digest and environmental state digest uploaded by the vehicle terminal, focusing on the deviation trend between the current baseline and historical baselines. The drift detection objects may include at least one or more of the following: drift_dir_ref, drift_speed_ref, env_match_ref, abnormal_degree_ref, drift_window_ref, local_confidence_ref, and arupt_jump_flag.

[0029] The server doesn't just look at the distance measurement in a single window; instead, it comprehensively judges the direction and speed of deviation across multiple consecutive windows. If the deviation is primarily explained by changes in temperature, voltage reference, load status, or installation location, the drift detection target can be prioritized for observation. If the deviation cannot be explained by environmental changes, the anomaly level is increased. If the deviation exhibits sudden jumps and lacks a continuous trend, it is suppressed from entering the migration candidate path. This triage significantly reduces the risk of misjudging sudden anomalies as legitimate precursors to migration.

[0030] Implementation Method 2: Continuous Drift and Sudden Jump Diversion The server can maintain `gradual_drift_score` and `abrupt_jump_score` separately. `gradual_drift_score` describes whether the deviation direction of the same object is consistent across multiple consecutive windows, whether the deviation magnitude evolves gradually, and whether it has an interpretable relationship with changes in environmental state. `abrupt_jump_score` describes whether the deviation appears suddenly, whether it lacks environmental support, and whether it severely breaks away from historical stable patterns. If `gradual_drift_score` consistently meets the threshold and `abrupt_jump_score` does not hit the high-risk threshold, the migration candidate object is allowed to enter the multi-factor consensus stage. If `abrupt_jump_score` increases significantly in a short period, the process switches to observation, resampling, or manual review, and the candidate new baseline is not directly accepted.

[0031] This offloading design has significant engineering implications. In real-world deployments, loose sensors, power supply fluctuations, one-time plugging and unplugging, short-term firmware anomalies, or attacker probing can all create "jump-type anomalies." If the system directly incorporates these anomalies into the baseline update process, it can easily lead to baseline contamination. Natural aging, long-term environmental changes, and gradual changes after legitimate maintenance are usually more continuous and interpretable, and therefore can be distinguished using the aforementioned object chain.

[0032] Implementation Method 3: Generation of Migration Candidates like Figure 3 As shown, when a drift detection object meets the migration candidate conditions, the server generates a migration candidate object. The migration candidate object can be bound to at least one or more of the following: candidate new baseline summary, old baseline reference, environmental consistency result, multi-window stability result, multi-machine graph witness result, maintenance confirmation result, third-party high-reputation anchor result, cloud-based group baseline result, and candidate_quality_ref. Based on this, the server calculates the migration consensus result and determines whether to allow entry into the protected migration phase.

[0033] Migration candidates do not directly replace the old baseline. Instead, a set of migration execution constraints is first generated to limit the write scope, validity period, and concurrent observation window of the new baseline object. In other words, migration candidates only indicate that "there is sufficient evidence to warrant a serious evaluation of the migration," not that "the system will immediately accept the new baseline."

[0034] Implementation Method 4: Multi-Factor Consensus Computation The server computes a migration consensus result around migration candidates. The multi-factor consensus may integrate at least one or more of the following factors: 1. Multi-window stability results, used to measure whether a candidate new baseline is stable across multiple consecutive windows; 2. Environmental consistency results, used to measure whether current deviations can be explained by changes in temperature, voltage, load, or installation location; 3. Multi-machine graph witness results are used to measure the compatibility of the current object in close-range mutual witnessing, group comparison, or other credible witness chains; 4. Maintenance confirmation results, used to prove whether the current changes are related to legitimate repairs, replacements, or installation adjustments; 5. Third-party high-reputation anchor results, used to provide supplementary consistency material from controlled high-reputation nodes; 6. Cloud-based group baseline results, used to compare the current object with long-term statistical results of the same hardware batch, same installation location, or same vehicle model domain; 7. Risk pool stability results are used to measure whether the long-term stable performance of the current object in the historical risk pool, suspected drift pool, or high volatility pool supports protected migration.

[0035] The server can assign different weights to the above factors, but no single factor is allowed to be the sole determinant of migration. For example, maintenance confirmation alone should not automatically lead to successful migration, nor should long-term deviations automatically replace the old baseline. Multi-factor consensus reduces the risk of attackers forging single supporting evidence to induce migration.

[0036] Implementation Method 5: Distributing Migration Authorization Objects like Figure 4 As shown, when the migration consensus result reaches a threshold, the server issues a signed migration authorization object to the vehicle terminal. The migration authorization object may include at least one or more of migration_ticket_ref, rule_ver, baseline_ver_new, baseline_ver_old, counter_range_ref, expiry_ref, migration_scope_ref, and migration_policy_ref.

[0037] The vehicle-mounted terminal allows writing to a new baseline object only if the migration authorization object is verified successfully within a secure component or trusted execution environment and the local rollback counter meets the range constraints. If the ticket is invalid, the signature is incorrect, the counter_range_ref does not match, the ticket range exceeds the limit, or the local terminal is in a prohibited update state, writing to the new baseline is refused, and a local migration failure record is generated. In this way, attackers are prevented from illegally updating the baseline using old tickets, fake tickets, or tickets reused across terminals.

[0038] Implementation Method Six: Coexistence of Old and New Baselines and Transition Period Verification In some embodiments of the present invention, after writing the new baseline object, the old baseline retention object is not immediately discarded. Instead, both are allowed to participate in the verification within a preset coexistence window indicated by `coexist_window_ref`. The terminal or server generates `new_match_result` and `old_match_result` respectively, and the server calculates `dual_baseline_score` accordingly. If the new baseline is stably valid in multiple windows, and `old_match_result` significantly degrades or the environment interpretation supports the migration, the server confirms the migration. If the new baseline passes occasionally but cannot remain stable, or if there are significant contradictions in the `dual_baseline_score`, the observation period is extended or the old baseline is reverted.

[0039] The dual-baseline coexistence mechanism is equivalent to adding a "grayscale period" during baseline migration. Compared to directly covering the old baseline, this method is more conducive to discovering short-term coincidences, attacker-induced samples, or candidates that are not yet sufficiently stable, thereby significantly reducing the risk of erroneous updates.

[0040] Implementation Method Seven: Migration Confirmation, Rollback, and Manual Review The server determines whether to confirm the migration, continue observation, revert to the old baseline, or transfer to manual review based on one or more of the following: post-migration stability results, rollback detection results, and dispute review results. If new_match_result consistently reaches the threshold and no rollback anomalies are found, the migration is confirmed; if inconsistencies, forged consensus, counter anomalies, suspected ticket reuse, or abrupt_jump_score increases again, the old baseline is reverted or manual review is initiated.

[0041] The server can be configured with different migration confirmation strategies for different risk levels. For low-risk scenarios, a shorter concurrency window can be used; for high-value objects, the concurrency window can be extended and the weighting requirements of maintenance anchors or high-reputation third-party anchors can be increased. The manual review channel is only used as a fallback path and is not part of the main system process.

[0042] Implementation Method 8: Migration Tracking Objects and Historical Recalculation like Figure 5 As shown, the server retains drift detection objects, migration candidate objects, migration trace objects, migration authorization objects, and rule version references to support dispute review and historical recalculation. In dispute scenarios, the server can output one or more of the following: pre-migration stability results, during-migration dual-baseline results, migration authorization object references, post-migration confirmation results, and rollback_check_result, to indicate whether the migration belongs to controlled natural evolution.

[0043] Migration traces can also include one or more of the following: triggered_by_ref, maintenance_context_ref, witness_bundle_ref, confirm_reason_ref, and reject_reason_ref. This allows for clarification during subsequent audits, regulatory reviews, or internal debriefings as to whether a particular migration was triggered by a long-term stable drift, a legitimate maintenance event, a device version adjustment, or an unusual dispute.

[0044] Implementation Method Nine: Physical Fingerprint Types and Joint Migration Deployment A physical fingerprint digest can include at least one or more of the following: clock drift digest, vibration spectrum digest, inertial event digest, power supply network impedance digest, or other protected physical authenticity digest. Different physical fingerprints can maintain corresponding current and historical baselines separately, or they can be used to construct joint migration candidates.

[0045] For example, some vehicle models are more sensitive to clock drift, while others are more stable to vibration frequencies. The server can choose to migrate one type of physical fingerprint individually, or increase the migration consensus weight when multiple physical fingerprints point to the same trend. This flexible design improves adaptability to different vehicle models and installation scenarios.

[0046] Implementation Method 10: Environmental Consistency and Maintenance Anchor Deployment The environmental status summary may include at least one or more of the following: temperature bin, voltage reference bin, load status bin, installation location version reference, and hardware profile reference. Maintenance verification results may be derived from maintenance verification anchors, incident investigation and filing anchors, or high-reputation third-party anchors to determine whether a particular installation, repair, or replacement was a legitimate change.

[0047] Maintenance confirmation results serve only as a supplementary factor for migration consensus, not as the sole determinant of migration. This is because, in reality, there may still be instances of falsified maintenance reports, abnormal parts replacements, or abuse of authorized links. Maintenance confirmation results are only suitable as migration support material when they are validated in conjunction with multi-window stability and environmental consistency.

[0048] Implementation Method Eleven: Deploying Cloud-Based Group Baseline and Multi-Machine Witness The cloud-based group baseline results can be formed from long-term statistical results of devices in the same hardware batch, installation location, or vehicle model domain; multi-machine graph witness results can be output from near-field mutual verification graphs, third-party in-store strong anchors, or other trusted witness systems. The server can use these results as auxiliary factors for migration consensus calculation, rather than determining the migration result alone.

[0049] The stability results of the risk pool can be derived from the statistics of the risk pool built by the server around high-risk historical objects, long-term observed objects, and previously migrated objects. If an object deviates from the current baseline but exhibits long-term stability, no rollback, no sudden jumps, and compatibility with similar legitimate migration trajectories in the risk pool, it can provide positive support for migration consensus. Conversely, if the object frequently triggers abnormal jumps, forged maintenance, or document conflicts in the risk pool, it can be prevented from entering the migration authorization phase.

[0050] For example, when a batch of hardware generally exhibits slow drift in a specific direction during the same season, the population baseline results can help interpret the current object's `gradual_drift_score`; conversely, if the current object shows a mutation that is significantly incompatible with devices in the same batch, it can improve the anomaly labeling. Such population information can enhance interpretability, but it should still be judged in conjunction with individual continuous window results.

[0051] Implementation Method Twelve: Controlled Output Boundary Deployment The server only outputs one or more of the following to external parties: migration status code, observation status code, rollback status code, dispute review reference, or baseline version reference. It does not output the complete physical fingerprint plaintext, the complete baseline object, or the complete migration consensus details to low-privilege subjects. This controlled output boundary reduces the risk of exposing internal system policy details to attackers.

[0052] Implementation Method Thirteen: Failure Paths and Protective Non-Migration Strategy When a drift detection object shows a long-term offset but the migration consensus result is insufficient, the evidence chain is incomplete, the ticket is not obtained, the rollback counter is abnormal, or the witness_bundle_ref cannot be established, the server outputs the results of continued observation, resampling, or manual review, instead of directly accepting the candidate new baseline. This "protective non-migration" strategy is particularly important for high-value objects because the damage from erroneous migration is usually greater than the short-term false rejections caused by delaying migration.

[0053] Implementation Method Fourteen: Deployment Strategy and Phased Launch In some implementations, the system can be deployed in phases. The first phase only enables drift detection and baseline reconstruction prompts, without automatically issuing migration authorizations. The second phase enables migration candidates and dual baselines to coexist, but manual review remains the primary method. The third phase, after specific vehicle models, specific hardware configurations, and sufficient stable data accumulation, gradually enables automatic migration confirmation. Phased deployment reduces the operational risks associated with enabling automatic migration all at once.

[0054] Implementation Method Fifteen: Examples of Typical Scenarios For example, in a certain vehicle model batch, the terminal was deployed near the engine compartment for a long time. With seasonal changes and accumulated vibration, the clock drift summary, vibration spectrum summary, and power supply impedance summary all showed slow changes. The server observed a steady increase in `gradual_drift_score` across multiple consecutive windows, while `abrupt_jump_score` remained consistently low; simultaneously, the maintenance confirmation anchor indicated that a legitimate sensor bracket adjustment had been performed recently. Based on this, the system generated migration candidate objects and issued migration authorization objects. After the terminal wrote the new baseline, within the concurrent window, `new_match_result` remained stable while `old_match_result` gradually degraded, and finally, the server confirmed the migration. This example demonstrates how the present invention can accept natural changes in a controlled manner under real-world operational conditions, rather than simply re-registering or permanently rejecting them.

[0055] Implementation Method Sixteen: Baseline Object Structure Expansion In some implementations, the baseline object may include not only feature center values, but also one or more of the following: dispersion description, applicable environment bucket, number of historical confidence windows, most recent stable interval, disabled conditional references, and last migration references. The purpose of this design is to make the baseline object not "a static value," but a composite object with contextual interpretation capabilities. When comparing the current summary with the baseline object, the server can dynamically select a more suitable comparison path based on the environment bucket and the most recent stable interval.

[0056] Implementation Method Seventeen: Expanding the Minimum Permissions of the Authorized Object In some implementations, the principle of least privilege is applied to migration authorization objects. For example, `migration_scope_ref` only allows updating a subset of physical fingerprints and does not allow coverage of the entire joint baseline; `expiry_ref` is only valid within a limited window; and `counter_range_ref` only covers a specified counter range. Through this least privilege design, even if individual authorization objects are abused, their impact will be limited.

[0057] Implementation Method 18: Multi-Source Determination of Rollback Detection In some implementations, `rollback_check_result` not only relies on the local rollback counter but can also combine baseline version continuity, migration trace object chains, recent ticket references, and the consistency of dual baseline results for joint judgment. If the terminal reports that `baseline_ver_new` is enabled, but the corresponding migration authorization object does not exist in the server records, or the necessary transition window is missing in the dual baseline history, it can be determined that there is suspicion of rollback or unauthorized write.

[0058] Implementation Method Nineteen: Dividing Legal Changes After Maintenance into Fake Maintenance Procedures In some implementations, the system maintains a "reliable but not solely decision-making" principle regarding maintenance confirmation results. Legitimate maintenance typically provides not only documentation or anchor points but also an interpretable, stable transition window; while falsified maintenance often fails to simultaneously achieve stability across multiple windows, environmental consistency, and group baseline comparison. Therefore, the server can observe or manually review objects that "only have maintenance materials but no stable transition" instead of directly issuing migration authorization.

[0059] Implementation Method 20: Layered Deployment of Business Interfaces and Alarms In some implementations, the business system only receives limited states such as KEEP_BASELINE, OBSERVE_DRIFT, MIGRATION_PENDING, MIGRATION_CONFIRMED, ROLLBACK_SUSPECT, and MANUAL_REVIEW, instead of directly receiving the complete migration consensus details. Only high-privilege auditing systems can further read the detailed relationships between drift detection objects, migration candidates, and migration trace objects. This layered interface reduces the risk of policy leakage caused by external systems over-relying on internal details.

[0060] Implementation Method 21: Deployment of Candidate Isolation Pools and Cooling Periods In some implementations, the server can place migration candidates into the isolation pool corresponding to `candidate_quarantine_ref` after they are formed, instead of immediately entering the ticket issuance stage. The isolation pool can record the first trigger time, the cumulative number of stable windows, the number of recent abnormal windows, the maintenance anchor validity period, and the environmental consistency confidence level. Further calculation of `migration_ready_flag` is only allowed when a candidate continuously meets several minimum stability conditions during the cooling-off period.

[0061] The significance of the cooling-off period lies in preventing "short-term forged drift" from directly entering the migration chain. If an attacker creates seemingly plausible new baseline features only within a few windows, the results are usually difficult to withstand the test of consecutive windows in the isolation pool. Conversely, changes from legitimate natural evolution or legitimate maintenance are more likely to exhibit stable trends during the cooling-off period. This design significantly reduces the risk of erroneous migration and baseline contamination.

[0062] Implementation Method 22: Dual-Baseline Weighted Decision Making and Window Decay Deployment In some implementations, the server, in calculating the dual_baseline_score, not only compares new_match_result with old_match_result, but also assigns different weights to different time points within the concurrent window. Earlier windows can focus on checking for any residual abrupt jumps, while later windows can focus on checking whether the new baseline remains stable. If new_match_result is occasionally high in the first few windows but then rapidly degrades, it indicates that the candidate new baseline lacks persistence and should not be confirmed for migration.

[0063] In some implementations, the server can also use `window_decay_ref` to gradually decay the weights of the old baseline, instead of invalidating it all at once. This avoids the old baseline becoming stagnant and hindering natural evolution, and also prevents the new baseline from immediately monopolizing the decision chain after being written. This weighting and decay mechanism makes the coexistence of the old and new baselines more consistent with the gradual transition pattern in a real production environment.

[0064] Implementation Method 23: Issuance, Revocation, and Invalidation of Migration Authorization Tickets In some implementations, the migration authorization object, in addition to containing migration_ticket_ref, expiry_ref, and migration_scope_ref, can also be bound to revoke_epoch_ref, issuer_profile_ref, and usage_limit_ref. If the server detects a new abnormal transition, a ticket being referenced across terminals, a counter_range_ref no longer being trusted, or a third-party anchor being revoked within the ticket's effective window, it can proactively issue a revocation object or add the corresponding migration_ticket_ref to the revocation list.

[0065] In some implementations, the terminal can verify whether `revoke_epoch_ref` matches the locally cached revocation digest before each use of the migration authorization object. Even if the terminal is temporarily offline, the number of times a ticket can be used or the usage window can be limited via `usage_limit_ref`. Through the ticket revocation and invalidation mechanism, this invention further discloses the subsequent governance logic for issued authorization objects, rather than assuming that tickets are irreversible once issued.

[0066] Implementation Method 24: Local Write Atomicity and Exception Recovery In some implementations, when writing `baseline_ver_new`, the terminal can use a temporary area write, atomic switch after successful verification, and generate `commit_marker_ref` after the switch is complete. This avoids a state where "neither the old nor the new baseline is complete" in the event of power failure, abnormal restart, or write interruption. If an exception occurs during the write process, the terminal maintains the old baseline as the primary one, records `write_fail_ref`, and returns a retried partial failure message to the server.

[0067] In some implementations, the security component may also maintain `last_good_baseline_ref` for rapid recovery to the most recent trusted baseline after a migration execution exception. This atomic write and exception recovery path is crucial for production-ready implementation because it demonstrates that the invention does not merely describe "allowing updates" under ideal conditions, but explicitly discloses protective actions in the event of update interruptions, power jitter, and local anomalies.

[0068] Implementation Method 25: Multi-Controller Collaborative Migration Deployment In some implementations, the physical fingerprint digest of the same vehicle may originate from multiple controllers, multiple sensor domains, or multiple installation locations. The server can construct `controller_group_ref` and `source_consistency_ref` to describe whether different sources point to a consistent trend within the same migration cycle. For scenarios where only individual controllers undergo stable changes while other sources remain unchanged, the system may allow only local sub-baseline migrations without updating the overall joint baseline.

[0069] Through this multi-controller collaborative mechanism, the present invention can adapt to complex vehicle architectures. If only a single sensor is replaced or a single controller firmware is upgraded, the system can be partially migrated; if multiple sources show consistent natural changes simultaneously, the overall migration consensus can be improved; if multiple sources contradict each other, manual review or continued observation is required. This improves accuracy and prevents local anomalies from being mistakenly amplified into global migrations.

[0070] Implementation Method 26: Separation of Legitimate and Abnormal Part Replacement Procedures In some implementations, legitimate component replacements during maintenance can be described using background information provided by fields such as `maintenance_ticket_ref`, `part_replace_ref`, and `service_window_ref`. However, the system still needs to check whether an interpretable and stable transition occurs after the replacement. For example, after replacing a certain type of inertial sensor, the vibration spectrum summary may experience a stable shift, but the clock drift summary and power supply impedance summary may not be reconstructed synchronously. If all physical fingerprint types experience drastic changes simultaneously, it may indicate abnormal modification or overall forgery, rather than a single component replacement.

[0071] In some implementations, the server can maintain `expected_change_profile_ref` for different `part_replace_ref`s. If the actual change direction is largely consistent with `expected_change_profile_ref`, the system can shorten the observation period; if there is a significant discrepancy, the priority of manual review is increased. This design further enhances the sufficiency of disclosure because it refines "the existence of maintenance anchors" into implementable logic of "whether the maintenance anchors match the change pattern".

[0072] Implementation Method 27: Explanation of Abnormal Attack Modes and Suppression Logic In some implementations, attackers may influence the migration chain by replaying old baseline summaries, forging maintenance records, inducing the server to issue migration tickets, or tampering with rollback counters on the endpoint. The server can establish anomalous pattern recognition around `replay_similarity_ref`, `ticket_reuse_flag`, `counter_discontinuity_ref`, and `witness_conflict_ref`. If the same object repeatedly shows highly similar new baseline summaries across different epochs, or if a ticket is referenced on an inappropriate endpoint, the system can raise the anomaly level.

[0073] In some implementations, the results of anomaly pattern recognition do not directly replace dual-baseline judgment, but rather serve as a protective barrier before and after migration authorization. That is, even if the candidate new baseline appears to have a certain degree of stability, as long as a high-risk anomaly pattern is detected, the system can still freeze the migration, require supplementary certification, or transfer it to manual review. Through this combined design, the present invention can simultaneously resist "content-level forgery" and "process-level abuse".

[0074] Implementation Method 28: Gray-Scale Deployment and Model Governance In some implementations, migration policies for different vehicle models and different physical fingerprint types do not need to be enabled simultaneously. The server can first enable the new version of migration_policy_ref for a small batch of vehicle models and observe the performance of this version in different temperature ranges, maintenance densities, and sensor batches. If the new policy causes an abnormal increase in migration_pending statuses or rollback_suspects, it can automatically roll back.

[0075] In some implementations, model governance should not simply involve adjusting thresholds, but should manage rule_ver, candidate_quality_ref calculation methods, group baseline participation weights, and third-party anchor participation weights separately. Version splitting allows subsequent audits to clearly identify which type of policy update influenced a particular migration conclusion, thereby reducing the uninterpretability of complex systems during continuous iteration.

[0076] Implementation Method 29: Historical Review and Audit Evidence Package Deployment In some implementations, the server can generate an audit_bundle_ref around a migration event, which includes at least a summary of the drift detection object, a summary of the candidate object, a document summary, a dual baseline window result, a rollback detection result, and a final confirmation reason. Upon receiving a compliance request or dispute request, the audit system can output a structured evidence package for review by high-privilege entities without exposing the complete original feature plaintext.

[0077] In some implementations, the audit_bundle_ref can be encapsulated in a layered manner: the first layer contains only status codes and version references; the second layer contains partial window statistics; and the third layer, under legitimate authorization, contains more granular witness_bundle_ref and maintenance_context_ref. This layered audit package allows the invention to meet regulatory interpretability requirements while still adhering to the principle of minimum disclosure.

[0078] Implementation Method 30: Factory Initialization, Discontinuation and Cross-Lifecycle Management In some implementations, an initial baseline can be established during initial device assembly via `init_baseline_ref`, it can undergo natural evolution during long-term operation via `migration_ticket_ref`, and its subsequent migration capabilities can be frozen via `decommission_ref` when the device is scrapped or permanently removed. Upon receiving a `decommission_ref`, the server can stop issuing new migration authorizations for the object and put the existing baseline into a read-only audit state. By placing initialization, runtime migration, and decommissioning within the same lifecycle governance framework, this invention can cover the complete lifecycle of a device from assembly, aging, maintenance to decommissioning. This clarifies the multi-stage global collaborative characteristics of "lifecycle migration," avoids fragmented single-point functional design, and facilitates maintaining long-term governance consistency during subsequent mass production deployments.

[0079] Implementation Method Thirty-One: Deployment of Candidate Quality Scoring Structure In some implementations, `candidate_quality_ref` can be further subdivided into `stability_component_ref`, `environment_component_ref`, `witness_component_ref`, `maintenance_component_ref`, and `anomaly_penalty_ref`. When calculating the migration consensus result, the server can first calculate each sub-component separately, and then combine them according to `risk_tier_ref` and `migration_policy_ref`. If a component is missing for a long period of time, such as when the high-reputation anchor is temporarily unavailable, the system can still maintain the observation path while other components provide strong support, without simply interrupting the entire migration process.

[0080] This component-based structure helps to reasonably regulate the technical reasons why a migration candidate, despite possessing strong multi-window stability, is not accepted, or the control logic that allows a candidate to enter the coexistence phase even without some auxiliary anchors. This enables migration consensus computation to have specific and implementable subdivided component logic, improving the determinism and traceability of the consensus determination process.

[0081] Implementation Method 32: Cross-Version Compatibility and Deployment of Legacy Baseline Hosting In some implementations, the terminal may briefly coexist with the old and new protocols before and after the migration_policy_ref upgrade. The server can allow older versions of the terminal to continue reporting old-format drift detection objects via compatibility_window_ref, but requires that they also include protocol_ver_ref. For older versions of the terminal, the server can choose to enable only the observation and candidate phases, while restricting ticket issuance to the window after the upgrade is completed.

[0082] In some implementations, hosting old baselines does not mean retaining them indefinitely. The server can determine how long and to what granularity the old baseline version is retained after migration is confirmed, based on `retention_policy_ref` and `dispute_retention_ref`. This maintains rollback and auditing capabilities while avoiding the governance burden of unlimited accumulation of historical baselines.

[0083] Implementation Method Thirty-Three: Deployment of End-Side Caching and Breakpoint Resumption In some implementations, after receiving a migration authorization object, the terminal may not be able to complete the migration immediately due to a momentary power outage, signal interruption, or a busy state of the security components. The terminal can generate a pending_migration_ref and cache the minimum necessary part of migration_ticket_ref in a protected area. Once conditions are restored, the terminal can continue to complete the write and report the migration trace object within the limits allowed by expiry_ref.

[0084] If pending_migration_ref fails to complete for an extended period, the server can proactively revoke the migration opportunity and re-enter the candidate evaluation. Through breakpoint resume and timeout revocation mechanisms, this invention provides a more complete disclosure of the behavior of real-world devices under non-ideal power and network environments.

[0085] Implementation Method Thirty-Four: Multi-Anchor Conflict Arbitration Conflicts may arise between maintenance confirmation anchors, third-party high-reputation anchors, and group baseline results. For example, the maintenance anchor may indicate that the replacement has been completed, but neither the group baseline nor the multi-window stability supports the establishment of a new baseline; or the third-party anchor may support the existence of changes, while the local rollback counter clearly conflicts with the authorized ticket chain. The server can construct `anchor_conflict_ref` to indicate which type of support material is conflicting and the level of conflict intensity.

[0086] Once the `anchor_conflict_ref` reaches a high level, the system enters a manual review or extends the coexistence window, instead of continuing automatic migration confirmation. Through conflict arbitration objects, this invention discloses anomalous traffic splitting under multi-anchor coexistence conditions, rather than assuming all evidence is inherently consistent.

[0087] Implementation Method Thirty-Five: Long-Term Audit Sampling and Regulatory Review The platform can perform post-migration reviews on confirmed migration targets based on a sampling ratio. The sampling logic can revolve around vehicle model domain, maintenance frequency, historical dispute rate, and risk_tier_ref. Selected targets can have their dual_baseline_score history, invoice issuance chain, and subsequent stable window performance re-examined to verify the reliability of the automatic migration strategy in long-term operation.

[0088] If a review reveals a systematic deviation in a certain type of migration_policy_ref under a specific scenario, the platform can trace the affected objects and readjust the policy. Through post-event sampling review, this invention expands migration governance from a "single-time determination" to a "continuous audit of verifiable lifecycle processes," further enhancing the adequacy of disclosure.

[0089] Implementation Method Thirty-Six: Migration Freeze and Protective Preservation of Old Baseline Deployment If the system detects high-level anchor_conflict_ref, counter_discontinuity_ref, or ticket_reuse_flag before or after the migration process, it can immediately enter the migration_freeze_ref state. Even if an object in this state already has a certain degree of matching with the new baseline, automatic confirmation should not proceed. Instead, the old baseline should be forcibly maintained as the primary baseline, new ticket issuance should be suspended, and freeze_reason_ref should be recorded.

[0090] Through the migration freeze mechanism, this invention adopts a conservative control strategy when facing high-risk anomaly scenarios. When anomalies are detected in the system, the reliability of the old baseline is prioritized, thus constructing a dynamic anomaly defense and security fault-tolerance control path, effectively avoiding potential data trust risks introduced by blindly pushing automatic migration.

[0091] Implementation Method Thirty-Seven: Distributed Evidence Synchronization and Time-Series Reconciliation Candidates, multi-machine witness results, group baseline results, and maintenance anchors may originate from different systems. The server can perform time-series reconciliation of these heterogeneous pieces of evidence around `evidence_sync_ref` and `evidence_ts_ref`, accepting only mutually interpretable combinations of evidence within the allowable deviation window. If an anchor point's time is significantly later than the drift occurrence time, or if the group baseline reference is significantly lagging behind the current policy version, its supporting weight can be reduced.

[0092] By synchronizing distributed evidence, this invention provides explicit processing logic for the temporal consistency of evidence from multiple sources. This disclosure helps avoid questions during review regarding "how multi-anchor consensus can be aligned in a real-world system."

[0093] Implementation Method Thirty-Eight: Post-Migration Follow-up Window and Stability Verification Even after migration is confirmed, the server can still set `post_migration_review_ref` to continue monitoring `new_match_result`, environment consistency, and abnormal replay results in subsequent windows. If, shortly after confirmation, `abrupt_jump_score` increases, `dual_baseline_score` shows significant discrepancies, or `rollback_check_result` becomes abnormal, a second review or even a rollback can be triggered. This follow-up window serves as continuous verification after successful migration.

[0094] By designing a follow-up window, this invention expands migration confirmation from an "endpoint" to a "stage conclusion," more closely reflecting the reality of continuously changing equipment states in mass production environments. In other words, the system allows for continued defense after confirmation, rather than assuming perpetual reliability once the migration is complete.

[0095] Implementation Method Thirty-Nine: Deployment of Intra-Domain and Extra-Domain Feature Isolation Physical fingerprint types can be categorized into strongly correlated and weakly correlated domains. Features within strongly correlated domains, such as clock drift summaries and inertial event summaries from the same sensor chain, can collectively influence candidate quality. Features outside weakly correlated domains, such as power supply impedance summaries and certain external anchoring results, are more suitable for auxiliary participation in consensus. Servers can manage this isolation relationship through `feature_domain_ref` to prevent weakly correlated features from being incorrectly assigned excessive weight.

[0096] This feature isolation design improves the robustness of transfer consensus and enables differentiated and refined management of input features of different categories in multi-factor consensus. By constructing a structured grouping and weight governance mechanism, it avoids indiscriminate processing of heterogeneous input data and, from an architectural perspective, ensures the system's sensitivity to key fingerprint features and its resistance to interference from stray features.

[0097] Implementation Method 40: The Departure and Reconstruction of Long-Term Failure Objects If an object consistently fails candidate isolation, dual baseline coexistence, or rollback verification over a prolonged period, it can enter the `rebaseline_required_ref` state. In this state, the system may require a more stringent initialization sampling, re-establish a locally reliable baseline, or guide the system into offline maintenance and troubleshooting. Only after the re-initialization results stabilize can the system be allowed to re-enter the normal migration chain.

[0098] By employing a mechanism for the exit and reconstruction of failed objects, this invention avoids permanently locking long-term abnormal objects in a semi-migration state and prevents the system from repeatedly consuming resources on invalid candidates. This path completes the final link in the lifecycle closure loop.

[0099] Implementation Method 41: Deployment of Cross-Vehicle System Rule Inheritance and Disabling Strategies Some vehicle model domains can inherit certain migration_policy_refs, such as shared environment consistency thresholds or some group baseline logic; however, for vehicle series with significant differences, the server can explicitly set inheritance_block_ref to prevent simple inheritance of policies from older vehicle series. This improves rule reuse efficiency and prevents erroneous migration across vehicle series.

[0100] By combining inheritance and disabling in parallel management, this invention further discloses a strategy governance approach for large-scale vehicle series expansion, enhancing its engineering integrity during long-term operation across multiple platforms.

[0101] Implementation Method 42: Multi-Period Regression Validation After migration confirmation, the server can execute `regression_review_ref` on the same object at weekly, monthly, and quarterly scales. The weekly scale focuses on whether `recent_window_stability_ref` remains valid; the monthly scale focuses on whether the difference between the old and new baselines still meets expectations; and the quarterly scale focuses on whether the overall trend of the environment bucket after migration remains compatible with the population baseline. If a systematic deviation occurs at any scale, the migration confidence level can be reduced or a reassessment can be triggered.

[0102] This multi-period regression verification prevents certain objects from faking legitimate migration effects only in the short term, while exposing incompatible characteristics over a longer period. Through this mechanism, the system overcomes the limitation of instantaneous confirmation at a single point in time, constructing a closed-loop verification logic from a global time dimension, effectively ensuring the long-term stability and data reliability after device baseline migration.

[0103] Implementation Method 43: Deployment of Migration Event Knowledge Base and Strategy Freeze The platform can store confirmed legitimate migrations, confirmed abnormal migrations, disproven forged maintenance records, and revoked invoices as migration_casebook_refs. Each event can be bound to a triggering factor, conflict factor, final handling result, and subsequent regression performance. Before updating migration_policy_ref, the policy engine can first search migration_casebook_ref to determine whether the new policy will conflict with existing high-risk cases.

[0104] If `migration_casebook_ref` indicates a high risk of erroneous migration for a certain type of policy change, the platform can generate `policy_freeze_ref` to temporarily freeze the relevant policy updates until more thorough gray-scale verification is completed. By linking the knowledge base with policy freezing, this invention explicitly incorporates long-term operational experience into the migration governance chain, making the documentation closer to a real, sustainable operating system.

[0105] Implementation Method 44: Field Minimization and Security Level Layering of Migrated Objects Although migration trace objects, migration candidate objects, and migration authorization objects all serve the same migration event, their external exposure scope does not need to be the same. The platform can classify these three types of objects into security levels based on `disclosure_tier_ref`: ordinary business operations only read the migration status code and `baseline_ver` reference; the audit domain can read `candidate_quality_ref` and `dual_baseline_score` summaries; and only the high-privilege review domain can read fine-grained fields such as `witness_bundle_ref`, `triggered_by_ref`, and `counter_range_ref`. By minimizing fields and classifying security levels, external systems can be prevented from inferring internal policies from the migration interface.

[0106] The technical significance of this design lies in the fact that migration systems often connect to multiple upstream and downstream systems during long-term operation. Without a layered security classification, any of these business systems could become an entry point for policy leaks. The specification here clarifies how to achieve minimal disclosure without compromising auditing capabilities.

[0107] Implementation Method 45: Deployment of Conservative Migration Protocols for High-Value Objects For high-value vehicles, high-risk accounts, or core equipment, the platform can enable `conservative_migration_ref`. This strategy requires a longer candidate quarantine period, a higher `dual_baseline_score` stability window, stricter third-party anchor weights, and a longer `post_migration_review_ref` follow-up period. Even if an object meets the normal migration criteria, it may not be automatically confirmed under the high-value strategy, but will instead undergo enhanced manual review first.

[0108] By employing a conservative migration protocol, this invention can adapt to different security level requirements without having to apply the same migration strength to all objects. This disclosure demonstrates that the migration system possesses tiered deployment capabilities, making it more suitable for real-world risk management scenarios.

[0109] Implementation Method 46: Restrictions on the Secondary Use of Exit Baselines A `baseline_ver_old` that has been retired from the old baseline should not be used again as a direct source for initializing new objects. The platform can prevent its reuse in other terminals, other mount points, or other vehicle domains via `retired_baseline_lock_ref`. Even on the same terminal, it is only allowed as a read-only audit reference and cannot be reactivated as the primary baseline. This prevents historical baselines from being brought back into the main decision chain during cross-object migrations, incorrect imports, or tool misoperations.

[0110] By limiting the reuse of the exit baseline, this invention further supplements the details of post-lifecycle governance, making the migration loop more complete.

[0111] in conclusion This invention establishes a secure baseline migration protocol for long-term mass production environments by employing drift detection objects, migration candidate objects, multi-factor consensus results, coexistence verification of old and new baselines, protected migration authorization, rollback counter constraints, and migration trace objects. This protocol can inherit the natural evolution of real physical fingerprints within the same vehicle's continuous binding relationship, reducing the risk of false rejections without requiring factory rework, and significantly suppressing the risk of attackers inducing the system to accept incorrect baselines by forging drifts, forging tickets, or rollback parameters.

Claims

1. A method for vehicle-mounted physical fingerprint lifecycle migration based on dual-baseline coexistence verification and anti-rollback counter, characterized in that, The method, executed collaboratively by an in-vehicle terminal and a server, wherein the in-vehicle terminal includes a security component or a trusted execution environment, is performed for updating the physical fingerprint baseline lifecycle under a continuous binding relationship within the same vehicle. The method includes: the in-vehicle terminal collecting one or more of the following within a preset window: physical fingerprint summary, environmental state summary, device continuity summary, and third-party high-reputation anchor summary; the server generating drift detection objects based on the deviation trend between the current baseline and historical baselines, wherein the drift detection objects include at least one or more of the following: drift direction result, drift speed result, environmental consistency result, and anomaly degree result; when the drift detection objects meet preset migration candidate conditions, the server generating migration candidate objects based on environmental consistency results and multi-window stability results. The migration consensus result is calculated from one or more of the following: multi-machine graph witnessing results, maintenance confirmation results, third-party high-reputation anchor results, and cloud-based group baseline results. When the migration consensus result reaches a threshold, the vehicle terminal generates a set of baseline objects, including at least the new baseline object and the old baseline retention object, as well as optional migration trace objects, within a security component or trusted execution environment. The new baseline object and the old baseline retention object participate in the verification together within a preset coexistence window, wherein the writing of the new baseline object is constrained at least by the migration authorization object and the local rollback counter range. The server decides to confirm the migration, continue observation, roll back the old baseline, or transfer to manual review based on one or more of the following: post-migration stability results, rollback detection results, and dispute review results.

2. A vehicle-mounted physical fingerprint lifecycle migration system based on dual-baseline coexistence verification and anti-rollback counter, characterized in that, The system includes an in-vehicle terminal and a server; wherein the in-vehicle terminal includes a physical fingerprint acquisition module, a security component or a trusted execution environment, and a baseline object management module, and the server includes a drift detection module, a migration consensus module, and a migration confirmation module; the system is configured to perform the method of claim 1.

3. A computer-readable storage medium having a computer program stored thereon, the computer program, when executed by a processor, implementing the method of claim 1.

4. The method of claim 1, wherein the physical fingerprint digest includes at least one or more of the following: clock drift digest, vibration spectrum digest, inertial event digest, power supply network impedance digest, or other protected physical authenticity digest.

5. The method according to claim 1, wherein the environmental status summary includes at least one or more of temperature bins, voltage reference bins, load status bins, installation location version references, or hardware profile references.

6. The method according to claim 1, wherein the migration consensus result is calculated based on at least one or more of the following: multi-window stability result, graph witness consistency result, maintenance confirmation anchor result, third-party high-reputation anchor result, cloud group baseline deviation result, or risk pool stability result.

7. The method according to claim 1, wherein the new baseline object and the old baseline retention object output the new baseline matching result and the old baseline matching result respectively within a preset coexistence window, and the server confirms the migration is complete only when the new baseline matching result continuously reaches the threshold and there is no rollback exception.

8. The method of claim 1, wherein the security component or trusted execution environment allows writing to the new baseline object only when a signed migration authorization object is received and the local rollback counter satisfies the range constraint, and the migration authorization object further includes one or more of a migration range reference, an expiration reference, or a counter interval reference to suppress attackers from triggering illegal migrations through old parameter packets, unauthorized scopes, or forged authorizations.

9. The method according to claim 1, wherein when the migration consensus result does not reach the threshold but the drift detection object shows a long-term stable offset, the server combines the risk pool stability result to output one or more of the following: continued observation result, resampling result, or manual review result, instead of directly accepting the new baseline object.

10. The method of claim 1, wherein the server retains drift detection objects, migration candidate objects, migration trace objects, migration authorization objects, and rule version references to support subsequent audit replay, historical recalculation, and dispute review.

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