Campus intelligent terminal equipment management method and system based on multi-source state synchronization

Abstract

The application discloses a campus intelligent terminal equipment management method and system based on multi-source state synchronization, and relates to the technical field of intelligent terminals.The campus intelligent terminal equipment management method and system based on multi-source state synchronization comprises the following steps:S1, terminal cooperative collection of context state data sets is carried out, and normalization processing is carried out;S2, a migration image synchronization state data set is constructed, and stage switching and playback migration handover are carried out;S3, a pulse interference detection model is constructed, and interference window suppression and online correction are carried out;S4, a double-time reference transaction consistency check and migration submission judgment closed loop is constructed, and linkage rollback and management are carried out.The application effectively improves the state consistency and operation stability in the cross-node migration process of the campus intelligent terminal, and solves the problem of the pulse interference phenomenon in the equipment state migration.

Description

Technical Field

[0001] This invention relates to the field of smart terminal technology, specifically to a campus smart terminal device management method and system based on multi-source state synchronization. Background Technology

[0002] As the scale of devices such as payment terminals, water control terminals, and facial recognition terminals expands in campus settings, and the demand for cross-gateway, cross-data center, and cross-platform access and migration increases, the link status between existing technology terminals and the backend is more prone to hidden offline phenomena such as "the external network is unreachable but the internal network is still connected, and the interface shows that it is connected but it is actually offline." Moreover, external network detection is often based on half-hour monitoring or power-on triggering, resulting in sparse state observation and unobservable intervals within the migration window. At the same time, hard constraints such as dynamic password verification every half hour and facial recognition token validity period in offline mode make the migration process more prone to capability jitter and state reversal at critical moments.

[0003] For example, the invention patent with announcement number CN115601046B discloses a scheduling and control method and system for intelligent terminals, including: a vehicle response module, a status acquisition module, an information feedback module, and a decision execution module; its function is to manage car charging piles and charging spaces. By managing the additional charges after the vehicle is fully charged based on the presence status of the vehicle owner, the different fee system after the vehicle is fully charged can reduce the bad behavior of occupying charging spaces as parking spaces for a long time, improve the utilization efficiency of charging piles, and to a certain extent help alleviate the current problem of the shortage of charging piles.

[0004] For example, invention patent CN104933644B discloses a campus card system based on a smartphone, including: a smartphone, a hardware centralized management chip installed in the smartphone, a smart terminal, and a remote management center; the hardware centralized management chip is connected to the SIM card in the smartphone and controls the smartphone's internet access status in real time according to the class schedule, exam schedule, and daily schedule; the hardware centralized management chip has functions such as attendance, identity authentication, GPS positioning, consumption, and library borrowing through the smart terminal; the remote management center is connected to the hardware centralized management chip, and the remote management center can send location information to administrators in real time based on the GPS positioning function in the hardware centralized management chip; this invention avoids students playing with their phones during class, exams, and breaks, and administrators, teachers, and parents can monitor and manage students' location information, improving students' learning quality, and possessing all the functions of a campus card.

[0005] In existing technologies, when existing systems migrate across nodes, there is a phenomenon of "pulse interference," which means that during the migration process, due to the untimely synchronization of cache and the transfer of state, there will be short-term and drastic performance fluctuations. These fluctuations not only affect the stability of device operation, but also have a serious impact on the quality of upper-layer services, especially in real-time systems that require high reliability.

[0006] Therefore, in order to address the above problems, there is an urgent need for a management method and system for campus intelligent terminal devices based on multi-source state synchronization. Summary of the Invention

[0007] Technical problems to be solved

[0008] To address the shortcomings of existing technologies, this invention provides a campus intelligent terminal equipment management method and system based on multi-source state synchronization, which solves the problem of pulse interference during equipment state transition.

[0009] Technical solution

[0010] To achieve the above objectives, the present invention provides the following technical solution: a campus intelligent terminal device management method and system based on multi-source state synchronization, comprising: S1, terminal collaboratively collecting context state datasets, normalizing the context state datasets to construct device migration observation data frames, and generating a state synchronization input set; S2, based on the device migration observation data frames, constructing a migration mirror synchronization state dataset, performing phase switching and playback migration handover, and generating a migration effective record set; S3, based on the device migration observation data frame sequence, constructing a pulse interference detection model, performing interference window suppression and online correction, and generating a migration state trajectory; S4, constructing a closed loop for dual-time-baseline transaction consistency verification and migration submission judgment, generating a submitable evidence package, performing linked rollback and governance, and forming an operation and maintenance closed loop trajectory.

[0011] Furthermore, the specific process of constructing device migration observation data frames by collaboratively collecting context state datasets on the terminal side and normalizing the context state datasets is as follows: Context state datasets are collected collaboratively by the terminal side and the backend side. These datasets include: filing datasets, link status datasets, authorization datasets, configuration parameter datasets, upload datasets, cancellation datasets, and timestamp datasets. Historical observation sequences already stored under the same school and device number are used as historical operational data. The collected context state datasets are managed by collection strategies and classified and labeled. The terminal-side collection control logic and the backend-side readback interface are used to schedule and trigger multiple source fields in a unified manner. Using a dual time reference of the terminal's local unified clock and the backend received timestamps, clock drift calibration and linear interpolation are used to time-align multi-channel data to obtain the processed context state dataset. The processed context state datasets are then normalized by encoding mapping. The context state datasets summarized in each collection cycle are encapsulated in a unified data structure to construct device migration observation data frames. Finally, the device migration observation data frames are subjected to binary encoding standardization, field scaling, and format standardization.

[0012] Furthermore, the specific process of generating the state synchronization input set is as follows: input device migration observation data frames and historical operation data, perform time consistency verification, migration segment division, differential detection and pulse candidate identification on the global acquisition queue, mark and correct out-of-order, frame loss and delayed backhaul, and obtain preprocessed device migration observation data frames; generate the state synchronization input set: when a pulse candidate signal is triggered, open the gated window, suppress control writes that will cause state transitions within the gated window, retain only observation writes and delay the submission of control writes; output the pulse interference candidate window marking sequence, the gated window sequence, and the state synchronization input set.

[0013] Furthermore, the specific process of constructing a migration mirror synchronization status dataset based on device migration observation data frames is as follows: Input the device migration observation data frames and the archived dataset; perform migration preprocessing for each device, including target node warm-up, shadow synchronization, and incremental catch-up, to generate the migration mirror synchronization status dataset; for each device in the migration task list, take the device number and device basic configuration record as the migration primary key, and establish device migration sessions on both the source and target nodes; start the shadow synchronization publisher on the source node side, publish the migration observation data frames and key change events according to the observation frame sequence number, and start shadow synchronization on the target node side. The subscriber replays and constructs shadow states in frame sequence; it performs incremental catch-up on the shadow states, using the difference within the migration window as the catch-up target, and introduces a migration warm-up threshold for the incremental catch-up process, allowing entry into the handover stage only when the target node's shadow state meets the warm-up threshold; it embeds gating control and quality scoring mechanisms into the publishing, subscription replay, and incremental catch-up link: at the source node publishing end, it adds a gating state mark, interference window number, and current quality score summary to each observation data frame to distinguish between observation frames and control frames; it outputs the target node migration mirror synchronization state dataset, incremental catch-up difference list, and warm-up threshold determination results.

[0014] Furthermore, the specific process of performing phased switching and replay migration handover to generate a migration effective record set is as follows: Input the migration mirror synchronization status dataset, the preheating threshold judgment result, and the migration task order. Perform two-phase switching token handover, replay migration handover, transaction consistency replay, and rollback effective registration for device migration, ensuring that the device status is stable and transaction consistency is verifiable after the target node takes over: Perform the first phase pre-commit switching: the target node generates a pre-commit switching token and issues it to the terminal. The terminal only updates and does not immediately switch external services; Perform the second phase commit switching: when the continuous sampling period meets the handover stability condition, the target node... The node issues a commit command and requests confirmation from the terminal. Upon successful confirmation, the terminal switches the connection parameters to the active state, and the source node enters read-only shadow mode. After the handover, transaction consistency replay is performed: transactions and undo transactions before and after the migration boundary are replayed and verified to generate transaction consistency results. A rollbackable migration effective record set is generated: migration effective records are registered for each device, including: migration batch number, source node identifier, target node identifier, pre-commit token, commit token, commit confirmation timestamp, migration boundary timestamp, and pending queue index. The migration effective record set, transaction consistency replay result set, and rollbackable control parameter set are output.

[0015] Furthermore, the specific process of constructing a pulse interference detection model based on the device migration observation data frame sequence is as follows: Input the device migration observation data frame sequence, perform time-delay direction encoding and weighted vector fusion on the network state, offline constraints, transaction upload consistency, and revocation disturbance within the migration window, construct the pulse interference detection model, and output the interference window: Aggregate the migration observation data frame sequence by device number and migration batch to obtain a multi-source observation subsequence, map the multi-source observation subsequence to a phase interference state vector, forming a quantum-state-like computational framework; Construct a direction encoding component for each type of observation source, the direction encoding component consists of a direction value and a weight value, the direction value is generated using discrete mapping rules, and the weight value is calculated by three factors: freshness factor, consistency factor, and source credibility factor; Perform weighted fusion on all direction encoding components to generate a migration stability score sequence: At each frame time, the weight values ​​and direction values ​​of each observation source are weighted and summed, and normalized using the weight sum to obtain the migration stability score; Output the dominant interference source type and evidence field list for each window to generate the interference window; Output the pulse interference detection model parameter set, migration stability score sequence, and interference window set.

[0016] Furthermore, the specific process of generating the migration state trajectory by performing interference window suppression and online correction is as follows: Input the interference window set, migration stability score sequence, and migration mirror synchronization state dataset; suppress interference windows during state writing and node switching in the migration process; combine offline constraints and upload gaps for edge prediction and online correction; output a pulse-controlled migration state trajectory: perform differential and gating control within the interference window, construct differential gating control logic and constrain migration writing; perform edge prediction based on offline constraints and probe sparsity, lock the interference window in advance and adjust the gating release rhythm; execute online correction closed loop during gating opening to reduce the gap and... Eliminate the pulse source; construct a pulse suppression quality score and trigger re-gating or rollback: compare the quality score with the quality threshold in real time; when the quality score is lower than the quality threshold, extend the gating window and prohibit migration submission; when the quality score is higher than or equal to the quality threshold, determine that the current pulse suppression effect meets the standard and the migration status is in the submittable range; when the quality score is continuously lower than the quality threshold and the order gap cannot be converged or the implicit offline occurs repeatedly, trigger rollback, retain the shadow state to continue to catch up with the difference until the submittable conditions are met before migration submission, output gating control parameter set, migration status trajectory, pending submission queue index, online correction record and pulse suppression quality score sequence.

[0017] Furthermore, the specific process of constructing a closed loop for dual-time-based transaction consistency verification and migration submission judgment, and generating a submittable evidence package, is as follows: Input the filing dataset, upload dataset, and timestamp dataset; based on the terminal-side transaction flow subsequence, back-end order details, and report statistics, construct a dual-time-based transaction consistency verification view according to the device transaction time and the actual order upload time; perform consistency verification and generate a submittable evidence package; for each control change in the submission queue index, perform a two-stage submission of control change: the first stage is reconciliation confirmation, which is only allowed to enter the second stage when the catch-up progress reaches the submission threshold and there are no new gaps within a continuous stable window; the second stage is migration submission, which is submitted in the order of shadow state followed by formal state when the gating quality score is higher than the quality threshold: this constitutes a migration submission judgment closed loop, outputting a submittable evidence package, migration submission result records, and gap time series.

[0018] Furthermore, the specific process of performing coordinated rollback and governance to form an operational closed-loop trajectory is as follows: Layered governance is performed on the abnormal residues after handling the interference window, triggering coordinated rollback and retaining the shadow state to continue catching up and forming an operational closed-loop trajectory. Governance actions are executed in a three-stage closed loop: first downgrade, then repair, and finally recovery. In the downgrade stage, rollback is triggered when the migration quality score is consistently below the quality threshold and order gaps cannot converge or hidden offline events repeatedly occur. In the repair stage, for gaps where transactions have occurred but uploads are delayed, supplementary acquisition and retransmission are prioritized. In the recovery stage, when the quality score is above the quality threshold, recovery is initiated, and the gating system switches from frozen to batch release. Each batch submits a maximum of N control writes, and the duration of a single batch does not exceed the continuous upper limit, with batch intervals not less than the cooling interval upper limit. Submissions to the queue are prioritized. The operational closed-loop trajectory and the post-governance migration status trajectory are output.

[0019] Furthermore, the second aspect of the present invention provides a campus intelligent terminal device management system based on multi-source state synchronization, applied to a campus intelligent terminal device management method based on multi-source state synchronization, comprising: a multi-source state acquisition module, used for terminal collaborative acquisition of context state datasets, normalization processing of the context state datasets to construct device migration observation data frames, and generating a state synchronization input set; a cross-node state migration module, used for constructing a migration mirror synchronization state dataset based on the device migration observation data frames, performing migration handover for stage switching and playback, and generating a migration effective record set; a pulse interference suppression module, used for constructing a pulse interference detection model based on the device migration observation data frame sequence, performing interference window suppression and online correction, and generating a migration state trajectory; and an operation assurance module, used for constructing a closed loop for dual-time-baseline transaction consistency verification and migration submission judgment, generating a submitable evidence package, performing linked rollback and governance, and forming an operation and maintenance closed loop trajectory.

[0020] Beneficial effects

[0021] The present invention has the following beneficial effects:

[0022] (1) This invention collects multi-source data such as network link status, offline constraint events, configuration changes, transaction occurrence time and order upload time through the collaboration between the terminal side and the back-end side, and encapsulates them into migration observation data frames with unified time base alignment and normalization, so as to realize that the key evidence of the migration process is traceable and reproducible, and avoids misjudgment and omission caused by unclear data sources in migration judgment.

[0023] (2) This invention introduces differential detection and gating control strategies to handle triggering conditions such as sudden increase in upload latency, implicit offline transition, missing half-hour cycle of dynamic password, and failure of critical refresh of face recognition token. It freezes control-type writes within the interference window, retains only observation-type writes, and establishes a queue to be submitted, so as to avoid instantaneous spikes directly driving node switching and external state reversal.

[0024] (3) This invention, by performing online correction closed loop during the gating opening period, triggers external network reachable supplementary collection and reporting path reconnection for implicit offline windows, triggers gap order supplementary transmission for transaction gaps, and verifies the consistency of canceled transactions by transaction group replay, can quickly reduce gaps and eliminate pulse sources without expanding the inconsistency surface.

[0025] (4) This invention constructs a migration quality scoring and threshold triggering mechanism to comprehensively evaluate stability, differential mutation intensity, gap convergence trend and critical event density, and realizes a controllable submission strategy of "extending the gate and prohibiting submission when the threshold is below, triggering rollback when the threshold is continuously below, and releasing submission in batches when the threshold is reached, thereby improving the robustness and controllability of the migration process.

[0026] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0027] Figure 1 This is a flowchart of the campus intelligent terminal device management method based on multi-source state synchronization according to the present invention;

[0028] Figure 2 This is a framework diagram of the campus intelligent terminal device management system based on multi-source state synchronization of the present invention;

[0029] Figure 3 This is a diagram illustrating the effect of shadow state synchronization and catching up in this invention;

[0030] Figure 4 This is a dynamic stability change analysis diagram of the present invention;

[0031] Figure 5 This is a flowchart of the dual-time-baseline transaction consistency verification process of 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] Please see Figures 1-5 This invention provides a technical solution: a campus intelligent terminal device management method and system based on multi-source state synchronization, comprising: S1, terminal collaboratively collecting context state datasets, normalizing the context state datasets to construct device migration observation data frames, and generating a state synchronization input set; S2, based on the device migration observation data frames, constructing a migration mirror synchronization state dataset, performing migration handover for stage switching and playback, and generating a migration effective record set; S3, based on the device migration observation data frame sequence, constructing a pulse interference detection model, performing interference window suppression and online correction, and generating a migration state trajectory; S4, constructing a closed loop for dual-time-baseline transaction consistency verification and migration submission judgment, generating a submitable evidence package, performing linked rollback and governance, and forming an operation and maintenance closed loop trajectory.

[0034] Specifically, the process of terminal collaboratively collecting context state datasets and normalizing these datasets to construct device migration observation data frames is as follows: The context state dataset is collected collaboratively by the terminal and backend sides. This dataset includes: a registration dataset, a link status dataset, an authorization dataset, a configuration parameter dataset, an upload dataset, a revocation dataset, and a timestamp dataset. The registration dataset includes: device number, the school to which the device belongs, device type, device registration time, and device activation flag. This data is generated by the backend side when adding virtual devices and registering device numbers through the management backend, and is used for POS basic configuration and activation. The link status dataset includes: intranet connection status, external network connectivity status, and other parameters. The reachability status, external network probe trigger type, interface network display flag, and implicit offline flag are collected through the terminal-side network detection logic. The internal network connection status comes from the real-time detection of the connection status between the terminal and the router. The external network reachability status comes from the detection results of external network monitoring every half hour or once upon startup. When the external network is disconnected but the internal network is still connected, the terminal may show that it is connected to the network but is actually offline, and data will not be uploaded until the external network is restored. The combined evidence is written into the implicit offline flag and the implicit offline start and end time is recorded. The authorized dataset includes: offline confirmation event, offline start and end time, dynamic password challenge timestamp, dynamic password verification result, face recognition acquisition timestamp, refresh cycle, and remaining valid time. This is obtained by the terminal pressing confirm when the network is disconnected. When switching to offline mode, the confirmation action is recorded. According to offline rules, a dynamic password must be entered every half hour to collect the challenge and verification process. Simultaneously, according to offline face recognition rules, data is collected every hour when connected to the internet, with a usable time of 36 hours and a maximum offline face recognition time of 36 hours. Data refresh and validity period are collected. The configuration parameter dataset includes: device number backfill value, server address, port number, consumption mode, configuration change timestamp, and configuration password verification result. This is collected through the terminal-side device configuration menu. The configuration password is generated according to the rule of "8 digits: first two digits month + middle four digits device number last four digits (default 0000 if not set) + last two digits date". The collected password verification result is used to prove that the configuration record source is genuine and consistent with the device number. The dataset is uploaded. This includes: transaction occurrence time, order upload time, upload confirmation flag, and statistical caliber version. The transaction occurrence time is collected through the transaction log records on the terminal side, and the order upload time is read through the backend logs and report calibers. The caliber constraint of "only counting data that has been successfully consumed on the front end and uploaded to the backend, excluding refund orders" is used to directly measure the time difference between transaction occurrence and upload confirmation as a measure of cache synchronization lag. The cancellation dataset includes: cancellation window, cancellation trigger key sequence, cancellation order number, cancellation confirmation result, and cancellation occurrence time. It is collected through cancellation operations on the terminal side, and cancellation events are bound to the corresponding order numbers to distinguish between "short-term rollback caused by manual cancellation" and "short-term rollback caused by migration synchronization".The timestamp dataset includes: terminal local timestamp, background received timestamp, acquisition frame sequence number, migration batch number, source node identifier, target node identifier, and data source identifier. These are uniformly assigned values ​​by the acquisition control logic after each aggregation of multiple source fields for alignment and traceability. Historical observation sequences already stored under the same school and device number are considered historical operational data. This historical operational data includes: historical hidden offline segments, statistics on failed external network probes, statistics on failed dynamic password challenges, statistics on failed refreshes, distribution of transaction occurrence and upload confirmation delays, distribution of revocation events, and configuration change records. These are used to set thresholds and identify abnormal windows.

[0035] The collected context state dataset is managed using a collection strategy and categorized and labeled with context. Based on the field update rate and its impact on migration stability, intranet connection status, transaction occurrence time, and revocation events are set as high-priority event-triggered collection channels; external network reachability status and external network probe trigger types are set as medium-priority periodic collection channels; dynamic password challenges, refreshes, and configuration changes are set as strong event-triggered collection channels. Context state labels are added to samples in the collection cache that are implicitly offline, have dynamic passwords nearing expiration, have remaining validity periods approaching their lower limit, have experienced a sudden increase in transaction occurrence-upload confirmation latency, or have been frequently revoked within the revocation window. Terminal-side collection control is utilized. The control logic and backend readback interface perform time-series scheduling and unified triggering for multi-source fields. Using a dual time base of the terminal's local unified clock and the backend received timestamps, clock drift calibration and linear interpolation are employed to align multi-channel data, resulting in a processed context state dataset. This ensures that network status, offline constraints, configuration parameters, transaction timing, and upload confirmations obtain comparable timestamp pairs within the same acquisition cycle. Out-of-order, lost, and delayed transmissions are detected and marked through frame sequence number consistency checks. When migration batch switching or node parameter changes occur, migration boundary records are automatically inserted, and source and target nodes are marked on the timeline. The processed context state is then processed accordingly. The dataset undergoes field normalization processing through encoding mapping; network status fields output by different terminal models and firmware versions are enumerated and mapped, uniformly encoding the status into standard status codes while retaining the original status words as traceable original fields; the external network probe trigger type field is uniformly mapped from "half-hour monitoring or power-on monitoring or manual probe" to trigger type codes to avoid aggregation failures caused by different texts used by different terminals; the dynamic password challenge interval, remaining valid duration, and continuous amounts of transaction occurrence and upload confirmation delay are standardized in units and subjected to upper and lower bound pruning, eliminating abnormal extreme values ​​(such as negative values, valid durations exceeding the 36-hour limit, and upload delays exceeding a reasonable daily span). Mark as dimensional anomalies and write to the quality mark field; perform whitelist verification and format standardization on configuration parameters such as server address, port, and device number, write records that fail verification to the configuration anomaly field and retain the original input; uniformly use millisecond precision continuous time axis to represent the timestamp field, and calculate the time deviation between the terminal time and the backend receiving time as the time alignment reliability field for subsequent differential detection; uniformly use three types of source codes for the collection source field: "terminal side collection, backend side readback, and report side caliber", and attach an interface name or log channel identifier to each type of source to ensure that each normalized field can be traced back to the specific collection location and collection method.

[0036] The context state datasets collected in each collection cycle are encapsulated using a unified data structure to construct device migration observation data frames. These frames include: school identifier, device number, device type, migration batch number, source node identifier, target node identifier, collection frame sequence number, terminal local timestamp, background reception timestamp, intranet connection status, external network reachability status, external network probe trigger type, interface network display flag, implicit offline flag and start / end time, offline confirmation event and switchover time, dynamic password challenge timestamp and verification result, acquisition timestamp, remaining valid duration, configuration change timestamp, server address and port, transaction occurrence time, order upload time, upload confirmation flag, statistical caliber version, cancellation window flag, and cancellation order number and cancellation result. A quality flag field is reserved to record sample credibility and evidence chain integrity scores. On the terminal side, a global collection queue ordered by time and a local collection queue divided by device number are maintained. Queues approaching the cache limit are prioritized for retention, and data frames are bucketed and archived according to migration batches. The cache limit is determined by a combination of available terminal storage, average byte length per frame, and target cache time window.

[0037] Furthermore, the data frames for device migration observation are subjected to binary encoding standardization, including field scaling and format standardization. External network reachability, implicit offline status, and upload confirmation boolean fields are uniformly encoded; timestamp fields are standardized to millisecond precision, and a deviation field is established between terminal time and backend receiving time; units for remaining valid duration, dynamic password interval, and continuous transaction and upload latency are standardized and have upper and lower limits pruned; an abstract data source identifier encoding is used to distinguish between terminal-side collection and backend-side readback, ensuring that each field can be traced back to its specific source and collection method.

[0038] This implementation plan can locate short-term spikes caused by cache synchronization lag and untimely state transmission earlier and more accurately within the migration window, suppress the spread of spikes into migration jitter, reduce the risk of order gap rebound and erroneous switching, and improve the integrity and reproducibility of the evidence chain in the migration process, providing a stable and reliable data foundation for gated submission, online rollback and closed-loop governance of operation and maintenance.

[0039] Specifically, the process of generating the state synchronization input set is as follows: inputting the device migration observation data frames and historical operation data, performing time consistency verification, migration segment division, differential detection, and pulse candidate identification on the global acquisition queue, marking and correcting out-of-order, frame loss, and delayed backhaul, and obtaining the preprocessed device migration observation data frames; time consistency verification includes: sorting the observation data frames using the acquisition frame sequence number and the terminal's local timestamp as keys, marking records with backward, jump, or duplicate frame sequence numbers as time sequence anomalies; using stable sorting to rearrange samples with out-of-order but timestamps still within the error range, so that the observation sequence monotonically increases on a unified time axis; marking the time interval between adjacent samples exceeding the interval threshold as a time gap event and recording the missing duration; migration segment division includes: using the migration batch number, source node, and target node boundary record as the dividing point, dividing the global sequence into continuous sub-segments and assigning segment numbers to each segment.

[0040] Differential detection and pulse candidate identification include: calculating the difference between adjacent sampling points and generating pulse candidate signals based on key quantities such as transaction and upload delay, implicit offline marker, dynamic password interval, and remaining valid duration; combining the revocation event density and revocation result, marking short-term flips within the revocation window as human-induced disturbances and distinguishing them from synchronization pulses. A state synchronization input set is generated: the state synchronization input set includes segment number, key quantity differential results, pulse type label, and evidence chain field; when a pulse candidate signal is triggered, a gating window is opened, suppressing control writes that would trigger state transitions within the gating window, retaining only observation writes, and delaying the submission of control writes; the triggering condition for the pulse candidate signal includes at least one of the following: the implicit offline marker jumps from 0 to 1 or repeatedly jumps within the sliding window; the external network reachability status changes from reachable to inaccessible within the observation window, and simultaneously, combined evidence of "internal network connection normal, interface displays network connection" appears; when a transaction occurs and upload is confirmed... The following triggering conditions are determined by the corresponding fields in the device migration observation data frame: a sudden increase in latency exceeding the latency surge threshold occurs in adjacent sampling points or within a short sliding window; the number of missing orders accumulates rapidly within the window and exceeds the gap growth threshold; dynamic password challenges are missing within a half-hour period, the number of verification failures exceeds the failure threshold, or the challenge interval exceeds the interval limit; the refresh record shows that the remaining valid duration of the token is close to the critical lower limit and the number of consecutive refresh failures exceeds the failure threshold; the revocation transaction group shows a short-term inconsistency of "only revocation events seen or only original order upload confirmations seen" within a fifteen-minute window and exceeds the inconsistency threshold. Observation writes include: network status, prompts and confirmation events, dynamic password challenge records, refresh records, transaction and upload time comparisons, and revocation transaction group records; control writes include: node switchover confirmation, target node connection parameters officially taking effect, migration completion markers, and publicly released device online or offline status; when k consecutive sampling points meet the following conditions, the gating window is closed, and the pending submission queue is released in the order of "observation alignment first, control activation later," to avoid instantaneous spikes caused by untimely cache synchronization and status transmission spreading into migration jitter. The output includes a sequence of impulse interference candidate window markers, a gating window sequence, and a status synchronization input set.

[0041] In this implementation plan, instantaneous fluctuations that may spread into migration jitter are confined within a gating window, preventing erroneous node switching from taking effect and external state reversal. This improves the stability of cross-node migration, gap convergence efficiency, and commit controllability, and provides a structured and verifiable state synchronization input set and gating trajectory for shadow chasing, online correction, and operation and maintenance closed loop.

[0042] Specifically, the process of constructing a migration mirror synchronization state dataset based on device migration observation data frames is as follows: Input the device migration observation data frames and the archived dataset, perform migration preprocessing such as target node warm-up, shadow synchronization and incremental catch-up for each device, and generate a migration mirror synchronization state dataset: For each device in the migration task list, take the device number and the device basic configuration record as the migration primary key, and establish device migration sessions on the source node and the target node respectively; The device number comes from the archive and is used for terminal basic configuration and activation, serving as a unique association key during cross-node migration to avoid state jumps caused by device number mismatch.

[0043] On the source node side, a shadow synchronization publisher is started, and migration observation data frames and key change events are published externally according to the observation frame sequence number. On the target node side, a shadow synchronization subscriber is started, and the shadow state is replayed and constructed in sequence according to the frame number. The shadow synchronization publisher publishes at least the network mode field, offline constraint field, configuration change field, transaction occurrence time, upload confirmation field, and reversal event field. When the external network probe trigger type is detected as "half-hour monitoring / power-on monitoring" which causes sparse updates of the external network reachability status, the "order upload time" on the backend side is used as the anchor point for alignment to ensure that the transaction occurrence and upload confirmation can correspond one-to-one in the target node's shadow state, avoiding inconsistencies such as "transactions occur but upload confirmations are missing and the source cannot be located". Incremental catching-up is performed on the shadow state, using the differential components within the migration window as the catching-up targets. These differential components include: transaction occurrence and upload confirmation latency gap, transaction cancellation gap, configuration change gap, and offline constraint gap. The transaction occurrence and upload confirmation latency gap is calculated using the dual time fields of "transaction time as the time the transaction occurred on the device and order upload time as the actual upload time in the background," used to identify cache synchronization lag. The transaction cancellation gap is determined by differential judgment of "cancellation event has occurred within the cancellation window but background upload confirmation has not been completed," used to identify non-synchronization of state rollback caused by cancellation. The configuration change gap is determined by differential judgment of server address or port change timestamp, used to identify that the terminal still points to the old node. The offline constraint gap is determined by changes in dynamic password challenge interval and remaining valid duration, used to identify critical risks in offline capability.

[0044] A migration warm-up threshold is introduced for the incremental catch-up process. The handover phase is only allowed when the target node's shadow state meets the warm-up threshold. The warm-up threshold includes at least the following: the implicit offline status is marked as 0 or has ended; the dynamic password challenge appears within a half-hour period and the most recent verification is successful; the remaining valid duration of face recognition is higher than the security threshold and the most recent refresh is successful; the transaction and upload latency gap is lower than the gap limit; the transaction cancellation gap is 0; and the configured address and port pointing to the target node have taken effect or have been pre-committed. Implicit offline status refers to the combined evidence of "showing network connection but actually offline, and no data upload" caused by the external network being disconnected but the internal network being connected. This belongs to the high-risk window of the migration pulse and the handover is prohibited before it is eliminated. Gating control and quality scoring mechanisms are embedded into the publish, subscribe, replay, and incremental catch-up links: At the source node's publish end, each observation data frame is appended with a gating status flag, interference window number, and current quality score summary to distinguish between observation frames and control frames; at the target node's subscribe, replay end, gating constraints are applied to control frames. Control changes are only allowed to be promoted from the shadow state to the committable state when the quality score consistently exceeds the quality threshold and the gap convergence trend is stable; otherwise, the control frame is frozen and placed in the commit queue, and the replay of observation frames continues; when the quality score is low... When the quality threshold or interference window is open, incremental catch-up only allows convergence actions such as "supplementary data collection, supplementary transmission, reconciliation, and transaction group replay verification," prohibiting node switching and external status publication transitions. Once the quality score recovers, the queue to be submitted is released in batches, with a replay verification and score recalculation triggered immediately after each batch release. If the score drops or the gap rebounds, gating is automatically reopened and the system reverts to catch-up mode only, thus limiting the common shadow synchronization paradigm to a controllable migration closed loop linked to gating and scoring. Outputs include the target node migration mirror synchronization status dataset, the incremental catch-up differential list, and the preheating threshold determination results.

[0045] Table 1 shows the incremental catch-up data record table for the target node shadow status, which records the entire incremental catch-up process data of the device (device number: DEV-POS-0001, migration batch: MIG-20250601-001) during the cross-node migration: When the catch-up time is 0 minutes, the initial unsynchronized transaction gap is 8 transactions, the current unsynchronized transaction gap is 8 transactions, the cumulative number of synchronized transactions is 0 transactions, the synchronization rate is 0 transactions / minute, the network status is intranet online + external network reachable, the offline constraint status is dynamic password valid (25 minutes remaining), no retransmission is triggered, and the number of retransmissions is 0. At this time, incremental catch-up is started, and the target node shadow... State initialization is complete, the queue of transactions to be synchronized has been loaded, meeting the start conditions for migration preprocessing; when the catch-up time is 5 minutes, the initial unsynchronized transaction gap remains at 8, the current unsynchronized transaction gap has decreased to 7, the cumulative number of synchronized transactions is 1, the synchronization rate is 0.2 transactions / minute, the network status remains online on the intranet and reachable on the external network, the offline constraint status is that the dynamic password is valid (20 minutes remaining), no retransmission has been triggered, the number of retransmissions is still 0, 1 transaction was synchronized normally during this period, there were no network fluctuations, the offline constraint meets the migration warm-up threshold, and the shadow status is consistent with the data time sequence of the source node; when the catch-up time is 10 minutes, the initial unsynchronized transaction gap is... There were 8 transactions initially, with 6 currently unsynchronized transactions. The cumulative number of synchronized transactions increased to 2, and the synchronization rate remained at 0.2 transactions / minute. The network status was intranet online and external network reachable. The offline constraint status was that the dynamic password was valid (15 minutes remaining). No retransmission was triggered, and the number of retransmission attempts was 0. Transactions continued to synchronize stably, and the shadow node's transaction sequence was consistent with the source node's, with no data inconsistencies. When the catch-up time was 15 minutes, the initial unsynchronized transaction gap was 8 transactions, and the current unsynchronized transaction gap rebounded to 7 transactions. The cumulative number of synchronized transactions dropped back to 1, and the synchronization rate was -0.2 transactions / minute. The network status changed to intranet online and external network fluctuating, and the offline constraint status remained unchanged. The status is "Dynamic password valid (10 minutes remaining)," triggering a retransmission with one attempt. Due to external network fluctuations, two transactions were delayed, causing a gap rebound. The system automatically triggered the first retransmission request. When the catch-up time was 20 minutes, the initial unsynchronized transaction gap was 8, which decreased to 6. The cumulative number of synchronized transactions recovered to 2, with a synchronization rate of 0.2 transactions / minute. The network status recovered to "internal network online + external network recovered." The offline constraint status was "Dynamic password valid (5 minutes remaining)," triggering a retransmission with two attempts. After the external network recovered, one backlogged transaction was successfully retransmitted, the gap narrowed, and the synchronization rate returned to normal.

[0046] Table 1. Data Recording Table of Target Node Shadow State Incremental Catching-up

[0047] Chasing Time Initial unsynchronized trading gap Current unsynchronized trading gap Cumulative number of synchronized transactions Synchronization rate Network status Offline constraint state Trigger retransmission? 0 8 8 0 0 Intranet online + Internet accessible Dynamic password valid (25 minutes remaining) no 5 8 7 1 0.2 Intranet online + Internet accessible Dynamic password valid (20 minutes remaining) no 10 8 6 2 0.2 Intranet online + Internet accessible Dynamic password valid (15 minutes remaining) no 15 8 7 1 -0.2 Intranet online + External network fluctuation Dynamic password valid (10 minutes remaining) yes 20 8 6 2 0.2 Intranet online + extranet recovery Dynamic password valid (5 minutes remaining) yes

[0048] like Figure 3The shadow state synchronization catch-up effect diagram shown depicts the dynamic change of the gap between terminal-side transactions and back-end upload confirmations as the catch-up process is gradually converged, with the catch-up time as the horizontal axis and the number of asynchronous transaction gaps as the vertical axis. Among them, the red broken line represents the number of gaps "transactions have occurred but have not yet formed back-end upload confirmations" counted at each catch-up time, the green dashed line represents the gap convergence target (gap is zero), and the yellow shaded area represents the gap rebound period (corresponding to short-term unobservable or unuploadable windows caused by sparse external network detection, implicit offline, offline constraint criticality, etc.). Looking at the curve changes, the number of gaps in the early stage of catching up is at a relatively high level and fluctuates slightly, indicating that there is a certain scale of delayed uploading and misalignment of states near the migration boundary. When entering the gap rebound period in the yellow shaded area, the number of gaps shows a phased rise or slowed down, reflecting that new transactions are still being generated during the period, but the progress of uploading confirmation is hindered, or the "shadow state alignment" is delayed due to the unobservable network state. When the rebound period ends, the number of gaps enters a stable downward channel and eventually approaches and reaches the target of the green dotted line, indicating that the shadow state has completed the alignment of gap orders one by one, supplementary collection and retransmission, and confirmation readback, and the key consistency gaps in the migration process have been converged and cleared to zero.

[0049] This implementation plan reduces the probability of erroneous migration switching and state transitions from the source; improves the efficiency and stability of gap convergence in shadow state catching up; reduces the impact of gap rebound caused by external network fluctuations on migration effectiveness; and provides structured basis for migration submission judgment, linkage rollback, and operation and maintenance closed-loop record keeping.

[0050] Specifically, the process of performing phased switching and replay migration handover and generating a migration effective record set is as follows: Input the migration image synchronization status dataset, the preheating threshold judgment result and the migration task order, and perform two-phase switching token handover, replay migration handover, transaction consistency replay and rollback effective registration for device migration, so that the device status is stable and the transaction consistency can be verified after the target node takes over: Perform the first phase of pre-commit switching: The target node generates a pre-commit switching token and issues it to the terminal. The terminal only updates and does not immediately switch external services; The pre-commit phase focuses on verifying the consistency between the terminal configuration source and the device number: The configuration password is generated according to the rule of "8 digits: the first two digits are the month + the middle four digits are the device number and the last four digits (0000 if not set) + the last two digits are the date". The terminal side records the password verification result and the configuration writing timestamp as evidence that "the configuration was indeed executed by the on-site device and matches the device number"; At the same time, the pre-commit value of the server address and port is recorded to avoid the target node still connecting to the old node after taking over.

[0051] The second phase of the handover is executed: when the continuous sampling period meets the handover stability conditions, the target node issues a submission command and requests the terminal to confirm. After successful confirmation, the terminal switches the connection parameters to the effective state, and the source node enters read-only shadow mode. The handover stability conditions include at least: no hidden offline, external network reachability restored, dynamic password challenge not expired, face recognition not approaching the hourly critical threshold and refresh successfully, and transaction and upload latency falling back to within the threshold. If the external network probe is still "triggered by power-on" and the reachability status is not dense enough, "continuous advancement of order upload time" is used as a substitute for stability evidence to avoid misjudging instability due to sparse probes.

[0052] After the handover, perform transaction consistency replay: replay and verify transactions and rollback transactions before and after the migration boundary, and generate transaction consistency results; verify whether all orders have been uploaded and confirmed by comparing "transaction occurrence time and order upload time" as the main line; bind the original order and rollback event to the rollback transaction by window, treat the rollback transaction as the same transaction group, and require the target node to have both the original order and the rollback result in the replay verification to avoid inconsistency between the account and the actual situation caused by only replaying one; write the transaction group that fails the replay verification into the pending queue and mark the failure reason code as "upload gap or rollback gap or configuration not effective or offline constraint critical". Generate a rollbackable migration effective record set: Register migration effective records for each device. The rollbackable migration effective record set includes: migration batch number, source node identifier, target node identifier, pre-commit token, commit token, commit confirmation timestamp, migration boundary timestamp, and pending queue index. When replay verification fails within the set time window, or when implicit offline occurs again, resulting in a "displays online but actually offline, data not uploaded" window, a rollback is triggered: the commit token is revoked, the source node is restored as the primary node, and the target node retains its shadow subscription and continues to catch up with the differential, ensuring that transactions and reversals that occurred during the migration process can still be traced after the rollback. Output the migration effective record set, the transaction consistency replay result set, and the rollbackable control parameter set.

[0053] In this implementation plan, by registering a rollback migration effective record set that includes a pre-commit token, a commit token, a confirmation timestamp, a migration boundary timestamp, and a pending queue index, the commit token can be quickly revoked and the process rolled back to the source node master when the replay verification fails to pass within the limited time window or when a hidden offline window reappears. The target node retains the shadow subscription to continue catching up with the differential, thereby achieving controllable, verifiable, rollback, and fully traceable migration.

[0054] Specifically, the process of constructing a pulse interference detection model based on device migration observation data frame sequences is as follows: Input the device migration observation data frame sequence, perform time-delay direction encoding and weighted vector fusion on network status, offline constraints, transaction upload consistency, and cancellation disturbances within the migration window, construct the pulse interference detection model, and output the interference window: Aggregate the migration observation data frame sequence by device number and migration batch to obtain multi-source observation sub-sequences. These multi-source observation sub-sequences include at least: implicit offline evidence sub-sequences, sparse external network detection evidence sub-sequences, dynamic password challenge sub-sequences, facial recognition validity period sub-sequences, transaction time and upload time gap sub-sequences, and cancellation transaction disturbance sub-sequences. Implicit offline evidence sub-sequences are generated from a combination of evidence such as "external network disconnected but internal network still connected, device showing network connection but actually offline, data not uploaded until external network restored," and are one of the most common sources of short-term fluctuations during migration. Sparse external network detection evidence... The subsequence records the external network detection trigger method as "monitoring once every half hour or monitoring once upon system startup," used to explain the instantaneous spikes caused by the lag in state updates; the dynamic password challenge subsequence is based on the challenge record of "requiring a dynamic password every half hour," used to identify critical state mutations caused by offline constraints; the face recognition validity period subsequence is based on the validity period and refresh results of "obtaining a token every hour online, the token being usable for 36 hours, and offline face recognition for a maximum of 36 hours," used to identify jitter caused by the critical failure of face recognition capabilities; the transaction time and upload time gap subsequence is calculated using the dual time fields of "transaction time is the time when the transaction occurs on the device side, and order upload time is the actual time the order is uploaded to the backend," used to quantify untimely cache synchronization; the transaction cancellation disturbance subsequence is based on the cancellation event of "orders can be cancelled within 15 minutes," used to distinguish between manual rollback and synchronization jitter.

[0055] Multi-source observation subsequences are mapped to phase interference state vectors, forming a quantum-state-like computation framework. A direction encoding component is constructed for each type of observation source, consisting of a direction value and a weight value. The direction value is generated using discrete mapping rules. "Transaction occurs but upload is not progressing, upload delay increases, implicit offline occurs, dynamic password challenge is missing, token is critical and refresh fails" are identified as lagging directions, while "upload confirmation progresses continuously, gap converges, implicit offline is resolved, dynamic password challenge appears periodically and succeeds, token refresh is successful and validity period increases" are identified as progressing directions. When implicit offline combined evidence appears, the direction value of the observation source is forcibly set to the opposite direction to the online evidence direction to form conflict cancellation during the fusion process, avoiding misjudgment of stability when "interface shows online but actually offline" and "online heartbeat" coexist. The amplitude is used to characterize the reliability of the observation source's evidence at the current moment, and the phase is used to characterize the direction of the observation source's delay deviation relative to the migration boundary. The weight value is calculated jointly by three factors: freshness factor, consistency factor, and source credibility factor.

[0056] The freshness factor, consistency factor, and source reliability factor are all limited to a range of 0 to 1, and all three factors are dimensionless values. The freshness factor is calculated based on the time interval between the current frame and the most recent update time of the observation source, and is set to 1 when the time interval is 0 and 0 when the time interval exceeds the expiration limit. The intermediate interval is mapped according to a monotonically decreasing rule. When the external network detection triggering method is half-hour monitoring or power-on triggering, a stricter expiration limit or a faster decay rate is adopted, resulting in a lower freshness factor for the same time interval, to reflect the increased uncertainty caused by observation sparsity. The consistency factor is calculated based on the degree of conflict between the observation source and the observation of the key anchor point. Key anchor point observations include at least the progress of order upload confirmation on the backend side, the progress of transaction occurrence on the terminal side, implicit offline combined evidence, and the consistency of the reversal transaction group. The greater the conflict, the lower the consistency factor. When completely consistent, the factor is 1, and when the conflict reaches the conflict limit, the factor is 0. The source credibility factor is assigned according to the source classification table. The terminal side confirmation key event, dynamic password challenge record, configuration write event, and backend side order upload confirmation event are set to high credibility. Weak evidence such as only interface prompts and lack of back-read corroboration on one side are set to medium or low credibility and assigned a lower credibility value. After the three factors are calculated, the original weight value is obtained by multiplication and then the original weight value is trimmed and normalized.

[0057] A migration stability score sequence is generated by weighted fusion of all directional coding components: at each frame, the weight values ​​and directional values ​​of each observation source are weighted and summed, and then normalized using the weighted sum to obtain the migration stability score; the higher the migration stability score, the more consistent and reliable the multi-source evidence is; a downward trend in the migration stability score or a continuous decrease in a short period of time indicates increased conflict between multi-source evidence or dominance of lagging evidence; based on the threshold overrun, duration of decrease, and slope of the migration stability score, the impulse interference window is jointly determined, and a list of dominant interference source types and evidence fields is output for each window to generate an interference window; when implicit offline is triggered, because the directional value is opposite to the online evidence, the fusion result will decrease rapidly and remain low until the implicit offline is lifted; when transaction upload gaps accumulate, lagging directional components gradually become dominant, and the fusion result shows a continuous decay; when dynamic password challenges are missing or token critical refresh fails, the critical event causes a step drop near the boundary point; when revoked transactions are inconsistent for a short period of time within a fifteen-minute window, it is marked as an artificial disturbance window and output separately from the synchronization impulse window. The output includes a parameter set for the impulse interference detection model, a migration stability score sequence, and a set of interference windows. The interference window set should at least include the window start and end times, the type of the dominant interference source, a list of corresponding evidence fields, and the migration batch number and device number associated with the window, facilitating direct reference and traceability in the gating suppression and online rollback processes.

[0058] Directional values ​​are represented using discrete sets and mapped to a unified number axis: advancing directions are mapped to positive values, lagging directions to negative values, and neutral or undeterminable directions to zero. The weighted fusion result is defined as the weighted sum of the directional values ​​of each observation source and the normalized weights, with the fusion result ranging from negative one to positive one. The migration stability score is generated from the fusion result through a monotonic mapping function, which converts the absolute value and sign information of the fusion result into a score within the range of 0 to 1. The closer the fusion result is to positive one, the closer the score is to 1; the closer the fusion result is to zero, the closer the score is to the median value; and the closer the fusion result is to negative one, the closer the score is to 0, ensuring a monotonic correspondence between the score and "consistent evidence direction and high reliability." Simultaneously, the uncertainty interval width is introduced as a confidence adjustment term. The uncertainty interval width is characterized by the dispersion of the observation source weight distribution. When the weights are concentrated in a few strong evidence sources, the uncertainty interval is narrower; when the weights are dispersed and there is multi-source conflict, the uncertainty interval is wider. The score output also carries a confidence field for gating and submission judgment.

[0059] like Figure 4 The stability dynamics analysis chart shown uses migration time as the horizontal axis and stability score as the vertical axis to illustrate the dynamic changes in stability over time during migration and their correspondence with thresholds and interference windows. Solid lines represent actual stability scores, dashed lines represent ideal stability scores, shaded areas represent score uncertainty intervals, red dashed lines represent interference detection thresholds, green dashed lines represent safety thresholds, and different colored background areas correspond to different types of interference windows. During periods without significant interference, the actual stability score generally remains above the safety threshold, and the uncertainty interval is relatively narrow, indicating consistent multi-source observation evidence and good state synchronization convergence. When entering an interference window, the actual stability score rapidly declines, accompanied by a significant widening of the uncertainty interval, indicating short-term inconsistencies in cross-node state transmission or evidence conflicts caused by sparse observations. In this case, the time period is identified as a "pulse interference candidate window," triggering a gating strategy to restrict control-type writes, retaining only observation-type writes, and performing online corrections. The ideal stability score curve changes slowly over time, representing the drift of the "expected stability level" under background conditions such as reduced observation freshness and sparse external network detection. When the actual stability score deviates significantly from the ideal curve and falls below the interference detection threshold, the deviation is used as a trigger and graded handling is carried out in combination with the interference window type. When the actual stability score is continuously and stably higher than the safety threshold, the migration and submission stage of "batch release control writing" is entered to avoid secondary spikes caused by releasing the gating all at once.

[0060] This implementation plan enables interference windows to be directly referenced by gating suppression, online correction, and rollback processes, avoiding instantaneous spikes from spreading into migration jitter and reducing the risk of erroneous switching. Combined with dynamic changes in stability and output of the width of the uncertainty interval, it can simultaneously indicate the degree of evidence conflict and the uncertainty caused by observation sparsity when the score declines, thereby improving the interpretability, traceability, and controllability of the migration process.

[0061] Specifically, the process of generating the migration state trajectory by suppressing interference windows and correcting them online is as follows: Input the interference window set, the migration stability score sequence, and the migration mirror synchronization state dataset; suppress interference windows for state writing and node switching during the migration process; combine offline constraints and upload gaps for edge prediction and online correction; output a pulse-controlled migration state trajectory; perform differential and gating control within the interference window, construct differential gating control logic and constrain migration writing; perform differential detection on the latency changes between transactions and upload confirmations, identifying changes such as "transaction occurrence time advances but background upload confirmation does not advance, or upload confirmation is significantly delayed" as a sudden increase in upload latency; perform differential detection on implicit offline markers, identifying transitions where "implicit offline changes from non-existent to present" as implicit offline triggers; and perform dynamic... Differential detection is performed on the password challenge interval to identify "no dynamic password challenge or a sudden increase in the number of failed challenges within a half-hour period" as a missing password period; differential detection is also performed on the remaining validity period of the face recognition token to identify "the token is approaching the expiration date and refresh fails" as a critical risk to the face recognition capability; when any trigger condition is met, gating is activated, freezing "control writes" into the pending queue, and only allowing "observation writes" to continuously enter the shadow state; control writes include: node switch activation, external publication of online or offline status, official confirmation of connection parameters, and migration completion markers; observation writes include: network prompts and confirmation key events, dynamic password challenge records, token refresh records, transaction time and upload time comparison records, and reversal transaction group records, thereby avoiding short-term spikes directly driving state transitions.

[0062] Edge prediction is performed based on offline constraints and detection sparsity to lock interference windows in advance and adjust the gating release rhythm. When the external network detection trigger method is "half-hour monitoring or power-on monitoring", there is an unobservable interval between the two detection points. Within the interval, the gating sensitivity is increased and external network reachable evidence is collected first. Using the half-hour cycle of dynamic passwords as a hard boundary, the period before and after the next password challenge is judged as a high-risk switching point to avoid performing node switching submission at the password critical point. Using the validity period of face recognition tokens as a hard boundary, the interval where token refresh fails is judged as the face recognition capability jitter zone, and it is prohibited to force the terminal to switch to face recognition priority or use face recognition as the only payment channel within the interval. Using the accumulation speed of transaction and upload gaps as a soft boundary, the interval where the gap continues to expand is judged as the cache synchronization lag zone, and priority is given to triggering supplementary collection, supplementary transmission and queue catching up, rather than immediately submitting the migration to take effect.

[0063] During the gating period, an online correction loop is executed to narrow the gap and eliminate the pulse source. For the hidden offline window, a reconnection to the external network reachable supplementary data collection and reporting path is triggered first, and the transaction data within the window is kept in the local queue. After the external network is restored, it is uploaded in sequence, and the recovery is continuously advanced based on the backend upload confirmation time. For the transaction and upload gap window, the transaction occurrence record and the backend upload confirmation record are aligned by order number. If a gap is found, a gap order supplementary transmission request is triggered and the number of supplementary transmissions and the supplementary transmission result are recorded. For the cancellation disturbance window, the cancellation event is bound to the original order as a transaction group. The shadow state is required to have both the original order and the cancellation result before control write is allowed to be submitted, so as to avoid inconsistency between the account and the actual situation caused by only synchronizing one. For the dynamic password and face recognition token critical window, password verification and token refresh are triggered first. After successful refresh, the gating is released in a smooth manner and submitted to the submission queue in batches to avoid instantaneous release causing secondary spikes.

[0064] Construct a pulse suppression quality score and trigger re-gating or rollback: Combine migration stability, upload latency surge intensity, gap accumulation level, number of implicit offline occurrences, number of password cycle missing occurrences, and number of token critical refresh failures into a quality score, and compare the quality score with the quality threshold in real time; when the quality score is lower than the quality threshold, extend the gating window and prohibit migration submission; when the quality score is higher than or equal to the quality threshold, determine that the current pulse suppression effect meets the standard and the migration status is in the commit range, close or gradually converge the gating window, and adjust the gating status from "strict freeze" to "gradual release" to avoid secondary spikes caused by a one-time release; release the queue to be submitted in batches according to the order of "observation first, control later, weak first, strong later": submit the control that does not change the externally visible state of the business but is used to complete the consistency loop first. The control write process, once completed, will affect the external state control write. After release, each batch of control writes will undergo a post-commit readback verification, with verification items including at least: the implicit offline flag remains zero; external network reachability evidence has not shown a reverse jump within the recent window; dynamic password challenges and token validity periods are within a safe margin range; the gap between transaction occurrence and upload confirmation is below the gap upper limit and the gap convergence trend is stable. When a batch of submissions experiences a quality score drop but still does not fall below the quality threshold, the release rate is reduced and the batch size is decreased. When the quality score is consistently above the threshold for multiple consecutive sampling periods, the release rate is allowed to increase until the submission queue is cleared. The quality score, gating release trajectory, submission batch number, and key evidence fields of this migration batch are written into the migration effectiveness record as a basis for auditing and issue tracing. When the quality score remains below the quality threshold and the order gap cannot converge or implicit offline occurs repeatedly, a rollback is triggered, and the shadow state is retained to continue catching up with the difference until the submission conditions are met before migration submission. The output includes the gating control parameter set, migration status trajectory, submission queue index, online correction record, and impulse suppression quality score sequence.

[0065] This implementation plan improves the convergence efficiency and consistency reliability of shadow state catching up; it implements a controllable submission strategy of "extending the gating to prohibit submission below the threshold, gradually releasing batch submissions after meeting the standard, and reviewing and recalculating scores after the batch", which ensures the security of migration effectiveness and avoids new pulses caused by releasing the gating at once; it can be directly used for operation and maintenance auditing and problem tracing.

[0066] Specifically, the process of constructing a closed loop for dual-time-baseline transaction consistency verification and migration submission determination, and generating a submitable evidence package, is as follows: Figure 5 The flowchart shown illustrates the dual-time-based transaction consistency verification process. Inputting the filing dataset, upload dataset, and timestamp dataset, it constructs a dual-time-based transaction consistency verification view based on the terminal-side transaction flow subsequence, backend-side order details, and report statistical standards, according to the device transaction occurrence time and the actual order upload time. Consistency verification is then performed, generating a submitable evidence package. The terminal-side transaction flow subsequence originates from the transaction record queue generated locally by the consumer terminal, with the transaction time based on the time the transaction occurred on the device. The backend-side order details are derived from backend order queries and transaction details, including an order upload time field. This field indicates the actual time the transaction order was uploaded to the backend, while retaining the transaction time as the time the transaction occurred on the device, used to identify gaps where transactions have occurred but uploads are delayed. Regarding report statistical standards, consumer-related statistical reports use the device transaction occurrence time as the consumption time, and order statistical standards include data successfully consumed at the front end and uploaded to the backend, excluding refunded orders. This is used to exclude uploaded confirmations and refunds as reconciliation boundary conditions.

[0067] For each control change in the commit queue index (such as node switchover taking effect, external release of online / offline status, migration completion marker), a two-phase commit of control change is performed: The first phase is reconciliation and confirmation, aggregating the number of transactions on the terminal side and the number of successfully uploaded transactions on the backend side at the device level, and calculating the transaction and upload gap; comparing the existence of the same order number on the terminal side, the backend side, and whether there are refund or cancellation markers on the backend side at the order detail level, to ensure that orders outside the statistical scope are not mistakenly included in the consistency at the time of commit; for orders with upload time lag, a catch-up progress bar is generated according to the progress of upload time, and only when the catch-up progress reaches the commit threshold and there are no new gaps within a continuous stable window is it allowed to enter the second phase.

[0068] In the second phase of migration submission, when the gating quality score is higher than the quality threshold, submission is performed in the order of shadow state followed by official state: first, the connection parameters, node identifiers, and policy configurations that have passed reconciliation in the shadow state are written to the official state, then the external state is published and a migration completion marker is added; after submission, the corresponding segment index of the pending submission queue is cleared, and the reconciliation summary on which this submission is based is written to the migration evidence package; when the quality score continues to be higher than the quality threshold, a batch release strategy is adopted: each batch only submits a limited number of control writes, and a reconciliation confirmation is inserted between batches to avoid a secondary spike caused by a one-time release; this constitutes a closed loop for migration submission judgment, outputting a submittable evidence package, migration submission result records, and gap time series.

[0069] This implementation plan can provide a clear and verifiable gap size and gap convergence progress before migration submission, avoiding the omission of upload delays and revocation / refund boundaries due to looking only at a single time field or only at summary reports; it prevents the expansion of discrepancies between accounts and reality due to premature switching before the gap has converged; it provides an interpretable, auditable, and traceable evidence chain for migration submission, and provides direct evidence for rollback governance and operation and maintenance closure.

[0070] Specifically, the process of performing coordinated rollback and governance to form a closed-loop operation and maintenance trajectory is as follows: Layered governance is performed on the abnormal residues after handling the interference window, triggering coordinated rollback and retaining the shadow state to continue catching up and forming a closed-loop operation and maintenance trajectory. Backend device management operation records come from the device management page of the backend consumption system, including operations and maintenance actions such as filtering, adding devices, and batch adding devices. This is used to include "device activation or disabling, device addition, and device group adjustment" in the traceable context after migration. Terminal on-site status and offline constraint events come from the terminal's prompts and mode switching actions in network outage scenarios. In offline mode, there is a hard constraint: "Press the confirmation key to switch to offline mode, and a dynamic password must be entered every half hour to continue offline use." This is used to locate risk switching points caused by "external network inaccessibility and password cycle." Mini-program side user operation events come from account unbinding and limit setting operations on the mini-program side. Account unbinding needs to be coordinated with backend freezing or deletion actions; otherwise, unbinding only on the mini-program side may result in the user still appearing in the user list upon the next login. This is used to avoid introducing new inconsistencies due to "frontend unbinding and backend status not being synchronized."

[0071] The governance actions are implemented in a three-stage closed loop: first downgrade, then repair, and finally recovery. In the downgrade stage, when the migration quality score is consistently below the quality threshold and the order gap cannot be converged or hidden offline events occur repeatedly, a rollback is triggered. The migration commit is frozen, the configuration is rolled back to the previous stable node, and the shadow state is retained to continue catching up with the differential. At the same time, the "downgraded status" is announced to the public, allowing only observational writes to prevent new control writes from amplifying inconsistencies. For the risk of data not being uploaded due to power outages, the device is marked as "power outage or unavailability risk" at the device level, and the device is included in the priority inspection and re-collection queue. For gaps in transactions where uploads are delayed, the repair phase prioritizes triggering supplementary data collection and retransmission. It aligns terminal-side and backend-side details by order number; if missing, it issues a "gap order retransmission request" and records the number of retransmissions and receipts. For critical segments caused by "offline password half-hour cycles," it prohibits node switching-type control writes before and after the next password challenge, prioritizing password verification and network recovery confirmation. For user-side actions such as "frontend unbinding, limit change, or designated card change," it requires the backend to generate a corresponding confirmation readback event before allowing migration submission, avoiding further interference caused by only synchronizing user-side actions while the backend state remains unchanged. When the quality score exceeds the quality threshold, recovery begins. Gating switches from frozen to batch release, with each batch submitting a maximum of N control writes. The duration of a single batch does not exceed the maximum duration limit, and the batch interval is no less than the maximum cooling interval limit. Writes to be submitted are prioritized; writes strongly related to consistency (such as node switch confirmation, external online or offline status announcements) are submitted first, followed by policy-related writes (such as payment preferences, device grouping policies). After recovery, the entire rollback, repair, and recovery process is recorded in the operational closed-loop trajectory, including the triggering reason, interference window number, sequence of operational actions taken, and the final reconciliation success evidence package index. The migration quality score and quality threshold use a comprehensive scoring caliber of "dual-time-axis reconciliation gap convergence index, stability score index, and critical event density index," and the gap convergence criterion and continuous stability window criterion serve as unified triggering conditions for rollback, repair, and recovery, avoiding closed-loop disruption caused by different criteria used in different stages.

[0072] When there are writes in the queue that are strongly related to reconciliation consistency, they are prioritized for inclusion in the first few batches. After each batch is completed, a reconciliation review and quality score recalculation are performed immediately. Only when the quality score is still higher than the quality threshold and the order gap has not rebounded is the next batch allowed. When the quality score drops but is still higher than the quality threshold, the batch size of the next batch is automatically reduced and the batch interval is extended. When the quality score falls below the quality threshold or the gap rebounds, the subsequent batches are immediately stopped and the gating is reopened to enter the repair phase.

[0073] N takes a value of at least 1 and does not exceed the maximum batch size to ensure the minimum controllable release granularity; the duration limit is used to limit the instantaneous impact of a single batch submission on the system; the cooling interval limit is used to provide a buffer time window for uploading to catch up, reading back to confirm, and stabilizing the status, avoiding secondary pulses induced by "continuous batch superposition". Output the operation and maintenance closed-loop trajectory and the migration status trajectory after governance.

[0074] This implementation plan achieves gradual effectiveness and controllable risks in migration submission; avoids inconsistencies in the reporting of different stages that could lead to a break in the closed loop; and provides an operational closed-loop trajectory that enables migration governance to have an interpretable, auditable, and reproducible closed-loop capability, significantly improving the long-term stability and operational efficiency of cross-node migration of campus smart terminals.

[0075] Specifically, the second aspect of this invention provides a campus intelligent terminal device management system based on multi-source state synchronization, applied to a campus intelligent terminal device management method based on multi-source state synchronization, comprising: a multi-source state acquisition module, used for terminal collaborative acquisition of context state datasets, performing time base alignment, caliber normalization, and source labeling on each field of the context state dataset to construct device migration observation data frames, and generating a state synchronization input set; a cross-node state migration module, used for establishing device migration sessions between source and target nodes based on device migration observation data frames and constructing a migration mirror synchronization state dataset, performing migration handover for stage switching and playback, and generating a migration effective record set; a pulse interference suppression module, used for performing directional encoding and weighted fusion on implicit offline, upload lag, password cycle missing, token critical failure, and revocation disturbance anomalies within the migration window based on the device migration observation data frame sequence to construct a pulse interference detection model, performing interference window suppression and online correction, and generating a migration state trajectory; and an operation assurance module, used for constructing a closed loop for dual-time base transaction consistency verification and migration submission judgment, generating a submitable evidence package, and triggering batch release, linkage rollback, and hierarchical governance according to quality thresholds to form an operation and maintenance closed loop trajectory.

[0076] This implementation plan confines short-term spikes within a controllable window and prevents them from spreading into migration jitter; it forms a closed-loop operation and maintenance trajectory that includes the triggering cause, interference window number, governance action sequence, and evidence package index, thereby improving the stability, consistency verifiability, and operation and maintenance auditability of cross-node migration of campus smart terminals.

[0077] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0078] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for managing intelligent terminal equipment in a campus based on multi-source state synchronization, characterized in that, The method comprises the following steps: S1, the terminal cooperates to collect a context state data set, normalizes the context state data set, constructs a device migration observation data frame, and generates a state synchronization input set; S2, based on the device migration observation data frame, a migration image synchronization state data set is constructed, a phase switching and playback migration handover is performed, and a migration effective record set is generated; S3, a pulse interference detection model is constructed based on the device migration observation data frame sequence, interference window suppression and online correction are performed, and a migration state trajectory is generated; S4, a double-time reference transaction consistency verification and migration submission judgment closed loop is constructed, a submittable evidence package is generated, linkage rollback and governance are performed, and an operation and maintenance closed loop trajectory is formed; The specific process of constructing the pulse interference detection model based on the device migration observation data frame sequence is: Input the device migration observation data frame sequence, encode and weight the vector fusion of the network state, offline constraints, transaction upload consistency and disturbance in the migration window in the time delay direction, construct a pulse interference detection model and output an interference window: the migration observation data frame sequence is aggregated according to the device number and the migration batch to obtain a multi-source observation subsequence, the multi-source observation subsequence is mapped to a phase interference state vector, and a quantum-like state calculation framework is formed; a direction coding component is constructed for each observation source, the direction coding component is composed of a direction value and a weight value, the direction value is generated by a discrete mapping rule, and the weight value is calculated by a freshness factor, a consistency factor and a source credibility factor; a migration stability score sequence is generated by weighting and fusing all direction coding components: at each time, the weight value and the direction value of each observation source are weighted and summarized, and the weight sum is normalized to obtain the migration stability score; output the dominant interference source type and the evidence field list for each window, and generate the interference window; Output the pulse interference detection model parameter set, the migration stability score sequence, and the interference window set; The specific process of performing interference window suppression and online correction to generate a migration state trajectory is: Input the interference window set, the migration stability score sequence and the migration image synchronization state data set, suppress the interference window for state writing and node switching in the migration process, and combine offline constraints and upload gaps for edge prediction and online correction to output a pulse-controlled migration state trajectory: perform difference and gate control in the interference window, construct a differential gate control logic and constrain migration writing; based on offline constraints and detection sparsity, edge prediction is performed to lock the interference window in advance and adjust the gate release rhythm; online correction loop is performed during the gate opening period to narrow the gap and eliminate the pulse source; ​ ​ Build pulse suppression quality score and trigger re-gating or rollback: compare quality score with quality threshold in real time; when quality score is lower than quality threshold, extend gating window and prohibit migration commit; when quality score is higher than or equal to quality threshold, determine that current pulse suppression effect meets the standard and the migration state is in the commit interval; when quality score continues to be lower than quality threshold and order gap cannot converge or implicit offline repeatedly occurs, trigger rollback and keep shadow state to continue to chase the difference until the commit condition is met and then perform migration commit; output gating control parameter set, migration state trajectory, to-be-committed queue index, online correction record and pulse suppression quality score sequence; The specific process of the built double-time-benchmark transaction consistency check and migration commit determination closed loop to generate a commitable evidence package is as follows: Input the filing data set, uploaded data set and timestamp data set, build a double-time-benchmark transaction consistency check view according to the terminal-side transaction stream subsequence, background-side order details and report statistical range, perform consistency check and generate a commitable evidence package; perform two-phase commit of control-type change commit for each control-type change in the to-be-committed queue index: the first phase is account confirmation, and only when the catch-up progress reaches the commit threshold and there is no new gap in the continuous stable window, the second phase is allowed to enter; The second phase is migration commit, when the gating quality score is higher than the quality threshold, submit according to the order of shadow state and formal state: form a migration commit determination closed loop, output a commitable evidence package, migration commit result record and gap time sequence; The specific process of the linkage rollback and governance to form the operation and maintenance closed loop trajectory is as follows: Perform hierarchical governance on abnormal residues after interference window disposal, trigger linkage rollback and keep shadow state to continue to chase to form the operation and maintenance closed loop trajectory: perform three-stage closed loop of degradation, repair and recovery for governance actions: when the quality score of migration continues to be lower than the quality threshold and the order gap cannot converge or implicit offline repeatedly occurs, trigger rollback in the degradation stage; in the repair stage, preferentially trigger resampling and retransmission for the gap segment that has occurred but is uploaded late; in the recovery stage, when the quality score is higher than the quality threshold, enter recovery, and the gate is switched from freezing to batch release, each batch submits no more than N control-type writes, and the single batch duration does not exceed the upper limit of the continuous duration, the batch interval is not less than the upper limit of the cooling interval, and the to-be-committed queue is submitted according to priority; output operation and maintenance closed loop trajectory and governance post-migration state trajectory. 2.The campus intelligent terminal device management method based on multi-source state synchronization according to claim 1, characterized in that: The specific process of the terminal cooperative collection of context state data set and the normalization of the context state data set to build the device migration observation data frame is as follows: The context state data set is collected by terminal side and background side in cooperation, and includes: a filing data set, a link state data set, an authorization data set, a configuration parameter data set, an uploading data set, a revocation data set and a timestamp data set; a historical observation sequence which has been completed storage under the same school and the same device number is taken as historical operation data; the collected context state data set is subjected to collection strategy management and context classification marking; a terminal local unified clock and a background receiving timestamp are taken as double time bases to perform time alignment on multi-channel data by clock drift calibration and linear interpolation completion to obtain processed context state data set; and the processed context state data set is subjected to field normalization processing of encoding mapping and normalization; the context state data set collected in each collection period is subjected to unified data structure packaging to construct a device migration observation data frame, and the device migration observation data frame is subjected to field scaling and format standardization processing of binary encoding standardization. 3.The campus intelligent terminal device management method based on multi-source state synchronization according to claim 1, characterized in that: The specific process of generating the state synchronization input set is as follows: The device migration observation data frame and the historical operation data are inputted, time consistency verification, migration segment division, differential detection and pulse candidate identification are performed on the global collection queue, out-of-order, frame loss and delayed return are marked and corrected to obtain the preprocessed device migration observation data frame; The state synchronization input set is generated: when the pulse candidate signal is triggered, the gating window is opened, the control write which can cause state transition is suppressed in the gating window, only the observation write is reserved and the control write is delayed for submission; the pulse interference candidate window marker sequence, the gating window sequence and the state synchronization input set are outputted.

4. The campus intelligent terminal device management method based on multi-source state synchronization according to claim 1, characterized in that: The specific process of constructing the migration image synchronization state data set based on the device migration observation data frame is as follows: The device migration observation data frame and the filing data set are inputted, migration preprocessing of target node preheating, shadow synchronization and incremental chasing is performed on each device to generate the migration image synchronization state data set: for each device in the migration task list, the device number and the device basic configuration record are taken as the migration primary key, and the device migration session is established on the source node and the target node respectively; the shadow synchronization publisher is started on the source node side, the migration observation data frame and the key change event are published according to the observation frame sequence number, the shadow synchronization subscriber is started on the target node side, the shadow state is replayed and constructed according to the frame sequence number in order; Incremental chasing is performed on the shadow state, the differential quantity in the migration window is taken as the chasing target, the migration preheating threshold is introduced into the incremental chasing process, only when the target node shadow state meets the preheating threshold, the handover stage is allowed to enter; the gating control and quality score mechanism is embedded into the publishing, subscribing replay and incremental chasing link: the gating state marker, the interference window number and the current quality score summary are attached to each frame of observation data frame on the source node publishing end to distinguish the observation frame and the control frame; The target node migration image synchronization state data set, the incremental chasing differential list and the preheating threshold determination result are outputted.

5. The campus intelligent terminal device management method based on multi-source state synchronization according to claim 1, characterized in that: The specific process of performing phase switching and migration handover of playback to generate a migration effective record set is: Input the migration image synchronization state data set, the preheating threshold determination result, and the migration task sheet, perform two-stage switching token handover, migration handover of playback, transaction consistency playback, and rollback-eligible effective registration of the device migration, so that the target node takes over the stable state of the device and the transaction consistency can be verified: perform the first stage of pre-commit switching: the target node generates a pre-commit switching token and issues it to the terminal, which only updates and does not immediately switch external services; Perform the second stage of commit switching: when the continuous sampling period meets the stable conditions for handover, the target node issues a commit instruction and requires the terminal to confirm, and after successful confirmation, the terminal switches the connection parameters to the effective state, and the source node enters the read-only shadow mode; Perform transaction consistency playback after handover switching: playback and verification of transactions and revoked transactions before and after the migration boundary to generate transaction consistency results; Generate a rollback-eligible migration effective record set: register migration effective records for each device, including: migration batch number, source node identifier, target node identifier, pre-commit token, commit token, commit confirmation timestamp, migration boundary timestamp, and pending disposal queue index; Output the migration effective record set, transaction consistency playback result set, and rollback control parameter set.

6. The campus intelligent terminal device management system based on multi-source state synchronization, applying the campus intelligent terminal device management method based on multi-source state synchronization according to any one of claims 1-5, characterized in that, It includes: A multi-source state acquisition module for terminal cooperative acquisition of context state data sets, normalization processing of the context state data sets to construct device migration observation data frames, and generation of state synchronization input sets; A cross-node state migration module for constructing a migration image synchronization state data set based on the device migration observation data frames, performing phase switching and migration handover of playback, and generating a migration effective record set; A pulse interference suppression module for constructing a pulse interference detection model based on the sequence of device migration observation data frames, performing interference window suppression and online correction, and generating a migration state trajectory; An operation guarantee module for constructing a dual-time reference transaction consistency verification and migration commit determination closed loop, generating a submittable evidence package, performing linkage rollback and governance, and forming an operation and maintenance closed loop trajectory.

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