Session snapshot and state-aware based cross-device ai interaction continuity assurance method

Abstract

This invention relates to the field of digital information transmission and discloses a method for ensuring the continuity of cross-device AI interaction based on session snapshots and state awareness. The method includes: a primary terminal monitoring the local session state and extracting the memory address offset and monotonically increasing vector clock parameters to generate a state offset identifier; embedding this identifier into the extended header of the service message transport layer and sending it to the cloud; and the cloud routing node updating the sliding synchronization window in the cache. In response to the handshake signaling of the backup terminal, the cloud routing node intercepts the differential context within the sliding synchronization window and distributes incremental session data to reconstruct the local session state machine of the backup terminal. This invention achieves logical decoupling between network topology switching and application layer state recovery, reduces state transition latency, and utilizes a vector clock mechanism to eliminate out-of-order packet conflicts caused by network jitter, effectively ensuring the timing consistency of cross-node interactions.

Description

Technical Field

[0001] This invention relates to a method for ensuring the continuity of cross-device AI interaction based on session snapshots and state awareness, belonging to the field of digital information transmission technology. Background Technology

[0002] In current distributed multi-terminal collaborative architectures, maintaining the synchronization of the interactive state machine is a fundamental process for ensuring business continuity. Typically, a connection-state-aware synchronization mechanism is adopted, where cloud routing nodes monitor the online status of terminal nodes through probe messages and trigger the retransmission of historical interactive state messages after determining that the link is interrupted. As the capacity of the context sequence and parameter vector carried by the interactive message increases, the instantaneous channel capacity required for the synchronization action also increases. Existing post-triggered synchronization methods rely on the terminal node still having a time window to maintain the uploading of large-capacity messages during channel fading. However, in real radio frequency environments, the step blockage of the physical channel often precedes the timeout perception of the transmission protocol, causing critical messages queued in the transmission buffer to disappear with the physical channel.

[0003] Besides optimizing the underlying transmission architecture and node layout, existing technologies have limitations in terms of session logic control and state alignment. For example, Chinese invention patent application CN121603505A discloses a seamless migration method, system, device, and storage medium for edge sessions. It generates migration tokens and establishes parallel paths in the target relay, using sequence number offset and timestamp mapping to maintain session continuity. Simply increasing the detection frequency or increasing the transmission buffer will lead to network congestion and increased processing latency, which cannot meet the ordering requirements of high-fidelity interaction. Analysis shows that the fundamental contradiction of existing technologies lies in the strong coupling between the state upload action and the physical connection's continued state, causing the state boundary held in the cloud to lag behind the moment of physical blockage.

[0004] Therefore, how to ensure lossless migration of session state messages and reduce cross-node connection latency under unpredictable conditions of underlying physical link blockage has become the technical problem to be solved by this invention. Summary of the Invention

[0005] To address the problems in the background art, the technical solution of the present invention is as follows: A method for ensuring the continuity of cross-device AI interaction based on session snapshots and state awareness, comprising the following steps: Step 101: The primary terminal node monitors the dynamic context in the local session state machine and extracts the technical parameters that represent the interaction state. Based on the technical parameters, a state offset identifier that represents the session state characteristics is generated. Step 102: The primary terminal node embeds the state offset identifier into the transport layer extended header field of the uplink message to be sent, and sends it to the cloud routing node along with the uplink message. Step 103: The cloud routing node receives the uplink message and extracts the state offset identifier from the transport layer extended header field, and maintains timing synchronization information in the cloud to record the latest session state boundary of the primary terminal node. Step 104: In response to the access request from the backup terminal node, the cloud routing node determines the differential context based on the timing synchronization information and distributes the generated incremental session data to the backup terminal node so that the backup terminal node can restore its local session state.

[0006] Preferably, the generation of the state offset identifier in step 101 includes: the primary terminal node obtaining the currently pending interaction token sequence and extracting the corresponding physical address offset as a pointer variable; using a monotonically increasing vector clock as a timing constraint, performing a bitwise logical AND operation on the length of the interaction token sequence, the pointer variable, and the monotonically increasing vector clock to generate a denoised composite feature vector; inputting the denoised composite feature vector into a cryptographic hash algorithm for calculation and extracting a specific high-order data segment to generate a fixed-length hexadecimal string as the state offset identifier.

[0007] Preferably, step 102, embedding the state offset identifier into the uplink message to be sent, includes: step 1021, the primary terminal node identifies the protocol encapsulation type of the uplink service message and allocates a storage space of a preset width in the transport layer extended header field; step 1022, the state offset identifier is serialized and embedded in the storage space, and a preset payload label is set in the identifier bit of the transport layer extended header field.

[0008] Preferably, the maintenance of timing synchronization information in the cloud in step 103 includes: the cloud routing node maintaining a linearly arranged snapshot index table in the cache based on the timing characteristics in the state offset identifier, storing the extracted state offset identifier at the tail of the sliding synchronization window, and simultaneously removing expired indexes at the head of the sliding synchronization window that exceed the preset lifespan threshold.

[0009] Preferably, step 104, which determines the differential context based on the timing synchronization information, includes: step 1041, where the cloud routing node parses the version identifier reported by the standby terminal node and determines the initial state position of the standby terminal node; step 1042, where the cloud routing node retrieves the historical state offset identifier between the initial state position and the tail pointer record in the sliding synchronization window and retrieves the corresponding differential data packet.

[0010] Preferably, step 104 further includes: the cloud routing node monitoring the real-time available bandwidth of the backup terminal node, and sampling the incremental session data based on the real-time available bandwidth; the cloud routing node reading the priority control bit field in the uplink packet header, and performing a drop-and-truncation operation on the low-priority packet payload in the incremental session data according to the contribution ranking indicated by the priority control bit field, so that the cloud routing node can preferentially strip the parameter vector of non-critical dimensions in the incremental session data according to the preset semantic priority rules and distribute the text token carrying the semantic breakpoint.

[0011] Preferably, the cloud routing node also performs conflict resolution based on a monotonically increasing vector clock, including: when multiple packets with the same service identifier but different state offset identifiers are received, the cloud routing node extracts the monotonically increasing vector clock value from each packet; the cloud routing node determines the state offset identifier corresponding to the monotonically increasing vector clock with the largest value as a valid state and updates the existing records in the sliding synchronization window.

[0012] Preferably, after distributing incremental session data, the cloud routing node sends a status confirmation message to the backup terminal node; in response to the status confirmation message, the backup terminal node locks the local application layer input interface and reconstructs the memory image according to the received incremental session data, and after the reconstruction is completed, it sends a synchronization response back to the cloud routing node, triggering the cloud routing node to start sliding synchronization for the backup terminal node.

[0013] Preferably, step 101 further includes: the primary terminal node performs homomorphic encryption on the data in the dynamic context; the hash calculation of the state offset identifier is performed on the encrypted ciphertext field; and the cloud routing node performs state boundary synchronization in the ciphertext state.

[0014] Compared with the prior art, the beneficial effects of the present invention are: 1. In ensuring the continuity of AI interaction, by encapsulating the state bias watermark in the extended header field of the transport layer message, high-frequency pre-anchoring of the session state machine is achieved. Since the state information is parasitic on regular service messages and heartbeat messages and is continuously uploaded with the existing radio frequency link, the cloud routing node can maintain a real-time updated sliding anchor window, decoupling the strong relationship between the session state encapsulation action and the underlying physical connection status. Even if the primary terminal node encounters a momentary physical power failure or a step fading of the channel, the cloud routing node can still accurately retrieve the corresponding incremental state data in the global session database based on the latest state bias watermark locked and retained in the sliding anchor window. This avoids the risk of data packet loss caused by the inability to complete large-capacity snapshot uploads during the channel blocking edge period of traditional technology.

[0015] 2. By utilizing the asynchronous processing mechanism of state bias watermarking by cloud routing nodes, the logic of network topology switching and application layer state recovery is decoupled. When a backup terminal node accesses the network, the cloud routing node responds to the handshake signaling of the backup terminal node and freezes the sliding anchor window. It extracts the timing boundaries within the window and performs differential context sequence interception and push. This process does not depend on the on-site response of the primary terminal node, nor does it require starting a full memory copy or complex cross-comparison signaling interaction after the backup terminal node comes online. This reduces the physical time consumption of state migration between distributed nodes and is based on the ability of the interactive system to maintain timing consistency in a fragmented network environment.

[0016] 3. By introducing a monotonically increasing vector clock label into the message extension header, and in conjunction with the conflict resolution priority rules of the cloud routing nodes, an asynchronous transmission order preservation mechanism with mathematical determinism is constructed. In heterogeneous topologies where multiple bound terminals frequently go online or offline, the backup terminal node forcibly overwrites the local old version of the state tree based on the vector clock in the received incremental session snapshot packet. This effectively eliminates message out-of-order or dirty data collisions caused by network jitter, ensuring the absolute consistency of digital information and the reliability of state machine reconstruction during cross-node distribution. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating the interaction between state offset identifier embedding and differential snapshot reconstruction in this invention. Figure 2 This is a logic diagram for maintaining the sliding synchronization window of the cloud routing node and resolving vector clock conflicts in this invention.

[0018] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0019] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0020] A method for ensuring the continuity of cross-device AI interaction based on session snapshots and state awareness includes the following steps: Step 101: The primary terminal node monitors the dynamic context in the local session state machine and extracts the technical parameters that represent the interaction state. Based on the technical parameters, a state offset identifier that represents the session state characteristics is generated. Step 102: The primary terminal node embeds the state offset identifier into the transport layer extended header field of the uplink message to be sent, and sends it to the cloud routing node along with the uplink message. Step 103: The cloud routing node receives the uplink message and extracts the state offset identifier from the transport layer extended header field, and maintains timing synchronization information in the cloud to record the latest session state boundary of the primary terminal node. Step 104: In response to the access request from the backup terminal node, the cloud routing node determines the differential context based on the timing synchronization information and distributes the generated incremental session data to the backup terminal node so that the backup terminal node can restore its local session state.

[0021] Preferably, the generation of the state offset identifier in step 101 includes: the primary terminal node obtaining the currently pending interaction token sequence and extracting the corresponding physical address offset as a pointer variable; using a monotonically increasing vector clock as a timing constraint, performing a bitwise logical AND operation on the length of the interaction token sequence, the pointer variable, and the monotonically increasing vector clock to generate a denoised composite feature vector; inputting the denoised composite feature vector into a cryptographic hash algorithm for calculation and extracting a specific high-order data segment to generate a fixed-length hexadecimal string as the state offset identifier.

[0022] Preferably, step 102, embedding the state offset identifier into the uplink message to be sent, includes: step 1021, the primary terminal node identifies the protocol encapsulation type of the uplink service message and allocates a storage space of a preset width in the transport layer extended header field; step 1022, the state offset identifier is serialized and embedded in the storage space, and a preset payload label is set in the identifier bit of the transport layer extended header field.

[0023] Preferably, the maintenance of timing synchronization information in the cloud in step 103 includes: the cloud routing node maintaining a linearly arranged snapshot index table in the cache based on the timing characteristics in the state offset identifier, storing the extracted state offset identifier at the tail of the sliding synchronization window, and simultaneously removing expired indexes at the head of the sliding synchronization window that exceed the preset lifespan threshold.

[0024] Preferably, step 104, which determines the differential context based on the timing synchronization information, includes: step 1041, where the cloud routing node parses the version identifier reported by the standby terminal node and determines the initial state position of the standby terminal node; step 1042, where the cloud routing node retrieves the historical state offset identifier between the initial state position and the tail pointer record in the sliding synchronization window and retrieves the corresponding differential data packet.

[0025] Preferably, step 104 further includes: the cloud routing node monitoring the real-time available bandwidth of the backup terminal node, and sampling the incremental session data based on the real-time available bandwidth; the cloud routing node reading the priority control bit field in the uplink packet header, and performing a drop-and-truncation operation on the low-priority packet payload in the incremental session data according to the contribution ranking indicated by the priority control bit field, so that the cloud routing node can preferentially strip the parameter vector of non-critical dimensions in the incremental session data according to the preset semantic priority rules and distribute the text token carrying the semantic breakpoint.

[0026] Preferably, the cloud routing node also performs conflict resolution based on a monotonically increasing vector clock, including: when multiple packets with the same service identifier but different state offset identifiers are received, the cloud routing node extracts the monotonically increasing vector clock value from each packet; the cloud routing node determines the state offset identifier corresponding to the monotonically increasing vector clock with the largest value as a valid state and updates the existing records in the sliding synchronization window.

[0027] Preferably, after distributing incremental session data, the cloud routing node sends a status confirmation message to the backup terminal node; in response to the status confirmation message, the backup terminal node locks the local application layer input interface and reconstructs the memory image according to the received incremental session data, and after the reconstruction is completed, it sends a synchronization response back to the cloud routing node, triggering the cloud routing node to start sliding synchronization for the backup terminal node.

[0028] Preferably, step 101 further includes: the primary terminal node performs homomorphic encryption on the data in the dynamic context; the hash calculation of the state offset identifier is performed on the encrypted ciphertext field; and the cloud routing node performs state boundary synchronization in the ciphertext state.

[0029] Example 1: A specific operational instance in a highly heterogeneous network environment such as a large rail transit transfer hub. In this application scenario, the primary terminal is in a continuous state of motion. Its underlying physical channel is affected by multipath effects and high-density metal shielding, resulting in random step fading. AI interaction involves a dynamic context including token sequences, interface call states, and parameter vectors. Existing synchronization protocols based on connection disconnection events cause critical state data packets queued in the transmission buffer to be lost the moment the physical channel disappears due to physical blockage occurring before the timeout perception of the application layer protocol. This leads to a mismatch in the session state machine version and semantic interruption after the backup terminal takes over. The primary terminal continuously monitors the dynamic context in the local session state machine and extracts technical parameters representing the length of the interaction sequence stream, memory address offset, and monotonically increasing vector clock. The memory address offset serves as a pointer variable to the physical address offset. The technical parameters are used to perform hash calculations on the ciphertext field to generate a fixed-length hexadecimal string-form state offset identifier, intercepting the data to be sent in the network protocol stack. The system identifies the protocol encapsulation type of the uplink service message and allocates a storage space of a preset width in the transport layer extended header field. The status offset identifier is serialized and embedded in the storage space. At the same time, a preset payload label is set in the identifier bit of the transport layer extended header field. The encapsulated message is generated and sent to the cloud routing node. Before performing the above-mentioned ciphertext field hash calculation, the primary terminal node constructs a dynamic timing mask using a monotonically increasing vector clock. The extracted interaction sequence stream length and pointer variable are then subjected to a bitwise AND operation with the dynamic timing mask. This operation mechanism does not directly generate a fixed-length checksum, but uses the mask attribute to forcibly filter out random noise flip bits in the underlying memory allocation that are unrelated to the current core business logic. This ensures that the underlying input variables entering the hash channel remain absolutely deterministic within the same clock cycle. The primary terminal node then sends the clean parameters, which have undergone mask denoising, into the standard cryptographic hash algorithm pipeline. After recombination, a specific high-order data segment is extracted and converted into the final fixed-length hexadecimal string output.

[0030] The cloud routing node receives and encapsulates packets, strips the transport layer extended header fields to extract the state offset identifier, and creates a linearly arranged snapshot index table in the cache as a sliding synchronization window for the primary terminal. It updates the tail pointer record of the sliding synchronization window using the state offset identifier, and simultaneously removes expired indexes in the sliding synchronization window header that exceed a preset lifespan threshold. This continuously anchors the latest state timing boundary of the primary terminal in cloud memory. When multiple packets with the same service identifier but corresponding to different state offset identifiers are received, conflict resolution is performed using a monotonically increasing vector clock. The monotonically increasing vector clock value V in each packet is extracted, and it is determined that the conditions are met. The condition's state offset is set to a valid state, and the record in the sliding synchronization window is updated. The i-th message carries a monotonically increasing vector clock value. The cloud routing node verifies the receiving queue based on the timestamp. When the primary terminal's message receiving interval exceeds a preset physical blocking threshold, it locks the sliding synchronization window. Responding to the access handshake signaling from the backup terminal, it parses the version identifier reported by the backup terminal to determine the initial state position of the backup terminal. It then retrieves the historical state offset identifier between the initial state position and the tail pointer record within the sliding synchronization window. It monitors the available bandwidth of the backup terminal, performs sampling processing on the incremental session data based on the available bandwidth, and retrieves the corresponding differential data packets. According to semantic priority rules, it strips the parameter vectors of non-critical dimensions from the incremental session data. It distributes and asynchronously routes the text token carrying the semantic breakpoint to the backup terminal, driving the backup terminal to lock the local application layer input interface and reconstruct the memory image based on the received incremental session data. Specifically, during the stripping operation, since the cloud routing node operates at the transport layer, it does not directly parse the application layer's tensor semantic structure. When encapsulating uplink service messages, the primary terminal node pre-extracts the dimension attribute labels of each parameter vector at the application layer and maps them to priority control bits in the standardized message extension header. After receiving the message, the cloud routing node reads the control bits to identify the contribution ranking of the parameter matrix corresponding to the message payload. Thus, without intruding on the ciphertext semantic kernel, it performs a discard truncation operation on message payloads with low priority indicated by the control bits according to the preset bandwidth-limited rules. This asynchronous state pre-anchoring mechanism based on message header steganography and cloud sliding anchoring solves the technical dilemma of high-frequency state synchronization requirements and instantaneous bandwidth limitations of the underlying physical channel. By transforming the heavy-load session snapshot post-packaging action into lightweight state watermark transmission along the way, it achieves logical decoupling between network topology switching and application layer state recovery. This ensures that the preservation of interactive state is not controlled by sudden blockages of the underlying channel, guaranteeing the temporal consistency and reliability of state machine reconstruction of the distributed AI interaction system in fragmented network topologies.

[0031] Example 2: This example serves as an experimental report of the aforementioned method for ensuring the continuity of cross-device AI interaction based on session snapshots and state awareness in a weak network environment. Addressing the challenge of application-layer interaction interruption caused by channel step fading in mobile scenarios, it utilizes an experimental platform consisting of a mobile terminal equipped with a 5G communication module, a cloud routing node deployed with a sliding synchronization window algorithm, and a programmable network impairment instrument. The cloud routing node employs a computing server with 256GB / s memory bandwidth. The network impairment instrument simulates a dynamic channel environment containing 20dB Gaussian white noise and a random packet loss rate between 5% and 15%. The sampling period is set based on the logical constraint relationship between the average throughput R of the interaction sequence stream and the terminal cache depth L. It is determined that under typical operating conditions, R is 512kbps and L is 1024 bytes. Based on this, the sampling period is set to 16ms to ensure that the update frequency of the state offset identifier covers the sampling boundary of the underlying channel fluctuation.

[0032] The present invention's sample group uses an in-path embedded state offset identifier method, while the control group removes the transport layer extended header feature and uses a full snapshot synchronization method triggered after a disconnection. When the network impairment instrument injects a 50ms step jitter and induces packet out-of-order delivery, the reconstructed memory image of the control group has an average semantic position deviation of 4.2 from the original context, and its state transition latency is 1245.8ms. The present invention's sample group extracts the state offset identifier containing the memory address offset from the primary terminal and asynchronously sends the state watermark to the cloud using the transport layer extended header field of the uplink service packet. The cloud routing node updates the tail pointer record of the sliding synchronization window in real time in the cache. When a conflict packet containing different monotonically increasing vector clock values ​​V is received, according to... The criteria for determining the legal state position and updating the record were used. Ultimately, the migration delay of the sample group of this invention during backup terminal connection was reduced to 138.4ms, and its timing consistency verification pass rate reached 99.2%, an improvement compared to the control group's 82.5%. Here, V is the value of the determined legal monotonically increasing vector clock. Let be the monotonically increasing vector clock value carried by the i-th message. To determine the optimal working window, a gradient test system is introduced to conduct boundary tests on the sampling period range. When the sampling period is within the range of 5ms to 50ms, the incremental data distribution accuracy of the system exhibits a non-linear logarithmic growth trend as the period shortens. The measured value increases from 96.8% at 50ms to 99.5% at 10ms. When the sampling period is compressed to below 5ms, the processing overhead of the system's transport layer protocol stack experiences a step increase, causing the terminal CPU utilization rate to jump from 12.4% to over 45.8%. Furthermore, due to the high-frequency update of the message header causing a decrease in the effective payload ratio, the available transmission throughput decreases by 18.2%, exhibiting diminishing marginal returns in performance. Conversely, when the sampling period exceeds 5ms... After 0ms, a physical break occurs in the historical state sequence within the sliding synchronization window, and the failure rate of the backup terminal connection increases from 1.2% to 24.6%. The occurrence of the above performance inflection point confirms that the parameter range of 5ms to 50ms is the optimal working range to ensure the computational load of the interactive reconstruction and control system. Experimental data shows that by transforming the heavy-load session snapshot post-packaging action into lightweight state watermark transmission along the way, and combining it with cloud sliding anchor window for timing alignment, the preservation of interactive state is no longer controlled by the instantaneous bandwidth limitation of the physical channel. This solves the contradiction between high-frequency state synchronization requirements and the uncertainty of the underlying channel. In an environment containing noise and packet loss, this method maintains the determinism of state machine reconstruction and realizes the logical decoupling of network topology switching and application layer state recovery.

[0033] Example 3: In a large-scale heterogeneous node access scenario in a smart park, due to the frequent roaming handovers between the mobile node and the 5G cellular base station, the transport layer link experiences high-frequency transient interruptions and topology reconstruction requirements. The primary terminal node obtains the dynamic context in the local session state machine by intercepting the application layer memory allocation handle and extracts the interaction sequence stream length identifier. Memory address offset identifier And a monotonically increasing vector clock value V. The primary terminal node initiates the application-layer state machine object serialization procedure. Based on the cross-platform object serialization principle, a unified linear continuous address space carries heterogeneous memory structures. The primary terminal node traverses the active variables and stack frames in the local dynamic context, converting them into a cross-platform standard byte stream sequence according to a preset breadth-first search hierarchy. This byte stream sequence conversion process is specifically executed by the virtual machine image capture module embedded in the terminal interaction engine. This module uses a dynamic link library pre-injected into the process space to be mounted to the front end of the operating system's application-layer memory allocator. When a snapshot generation instruction is received, the module automatically suspends the application-layer worker thread, sequentially reads the heap and stack metadata in the process descriptor table, and identifies and filters out the underlying system metadata of the operating system. After the level dependency handle is established, only the tensor parameter array and logical variables strongly associated with the current session semantics are copied sequentially to a pre-defined data structure in a reserved independent contiguous buffer area, thereby autonomously completing standardized extraction and encapsulation. The memory address offset is set to the relative logical first address offset in the serialized byte stream, replacing the absolute physical memory pointer. When the backup terminal node connects, it reverses the byte stream according to the relative logical first address offset to avoid the risk of pointer out-of-bounds errors caused by kernel addressing differences. Before hash calculation, a lossless alignment mechanism is started. For each identifier variable, a composite feature vector is constructed by cyclically shifting left and misaligning according to a preset displacement, avoiding the loss of feature information caused by multi-variable bitwise logical operations. The primary terminal node places V in the high 16 bits of a 64-bit unsigned integer as the timing reference. Mapping to the middle 24 bits represents the data load characteristics, and... The lower 24 bits are embedded in the remaining bit space to lock the physical memory location. The 64-bit composite vector is hashed using a preset cyclic redundancy check function to obtain a 16-character hexadecimal string as the status offset identifier. The primary terminal node writes the status offset identifier into the optional header field of the TCP packet and sets the extended option identifier to a preset business private label, so that the status watermark is sent to the cloud routing node synchronously along with the uplink heartbeat signaling or instruction reporting message.

[0034] The cloud-based routing node receives TCP packets and extracts the state offset identifier. It maintains a sliding synchronization window in its cache using a linear hash table. The cloud-based routing node initiates an online network baseline calibration procedure. Based on a queuing theory Markov arrival process model, the channel congestion and physical blocking delay distribution exhibit a non-linear, long-tailed state. During the initialization phase, a baseline probe window is opened, and a preset number of probe packets are sent to the primary terminal node at a preset probe frequency. The round-trip delay of each packet is recorded, and outliers in the first and last 5% of the round-trip delay are filtered out. A statistical baseline is established based on this sample set. The packet loss rate recorded according to the probe window is consulted in a preset channel quality mapping table to separate environmental noise interference from inherent network delay. The mapping table is consulted to confirm the link disturbance status, and a fixed value is output as an independent coefficient compensation amount to participate in the decision boundary calculation. The cloud-based routing node then completes the preset physical blocking threshold calculation. Dynamic calibration, i.e., real-time statistical analysis of the average network round-trip time (RTT) of the primary terminal node over the most recent 100 sampling periods. and latency jitter standard deviation The decision boundary is determined according to the formula. The calculation formula is as follows: ,in, To preset the physical blocking threshold, This represents the average network round-trip latency. The standard deviation of latency jitter, This is the packet loss tolerance factor, with a value of 3.0; The environmental noise compensation factor is set to 2.5. In actual continuous operation, in order to bridge the Markov arrival process model with the initial static boundary derived from the above-mentioned mean and standard deviation, the cloud routing node maintains a monitoring matrix based on Markov chain state transitions in parallel in the system background. This matrix continuously absorbs the actual packet loss and delay events of the current physical channel to update the transition probability of each congestion level in real time. Whenever the monitoring matrix deduces that the joint probability of deep link fading occurring within a future preset clock cycle exceeds the system setting limit, the control system triggers an adaptive correction action, increases the packet loss tolerance coefficient and environmental noise compensation factor variables in the aforementioned calculation process, and dynamically compensates the nonlinear long-tail fading distribution into the product term of the linear parameters, maintaining the consistency and coherence of the calculation logic.

[0035] The cloud-based routing node determines that the message reception interval exceeds Furthermore, upon receiving the access handshake signaling from the backup terminal node, the parameter vector of the corresponding primary terminal node is retrieved. Given that the available bandwidth of the backup terminal node is lower than the bandwidth required for full snapshot reconstruction, the cloud routing node initiates weight-sensitive filtering. Parameter matrices with neuron activation weights less than 0.05 and contributing to the bottom 20% of the output are zeroed out, retaining only the interaction token carrying the contextual logic and the key weight vector. An asynchronous distribution mechanism drives the backup terminal node to complete state machine reconstruction within 150ms. The aforementioned lower limit of the numerical threshold for neuron activation weights and the post-screening benchmark for the proportion of parameter output contribution are not arbitrarily generated but are based on preliminary offline controlled variable experiments. Comprehensive statistical data from applying gradient masking operations in batches to the standard session analysis model during the platform pre-testing phase shows that when the contribution parameter segments at the bottom ranking are specifically removed and extremely low-activation neurons are directly filtered, the corresponding test set exhibits the following performance after reconstruction: The attenuation of terminal intent recognition accuracy is limited to a very small and controllable range. Simultaneously, under the same Gaussian interference and congestion conditions, an increased effective transmission bandwidth margin is achieved. This set of dual-parameter joint settings is thus established by the system as the optimal technical balance point for balancing semantic fidelity after reconstruction and the improvement in network transmission efficiency. Bit-level state coding and latency-sensitive threshold calibration mechanisms maintain the continuity of AI interaction semantics under bandwidth fluctuations by performing nonlinear dimensionality reduction on the parameter vector based on weighted contributions. In environments where the underlying physical channel experiences second-level switching or deep fading, this method maintains the alignment of session states between primary and backup terminals, ensuring the operational stability of the distributed system in heterogeneous network environments. In digital information transmission scenarios containing privacy-sensitive interaction data, the system executes a homomorphic encryption process for the session state machine. The primary terminal node uses a preset asymmetric encryption algorithm to generate a public and private key pair, identifying the monotonically increasing vector clock value V and the memory address offset. As the original vector input, the original vector is transformed into ciphertext parameters by performing a modulo-exponential operation. The ciphertext parameters are then routed to the cloud routing node using the transport layer extended header field of the uplink service message. While maintaining the ciphertext state, the cloud routing node performs a multiplicative homomorphic operator within the ciphertext space to offset and superimpose the sliding synchronization window pointer. When the result of the modulo inverse operation of the ciphertext parameters matches the preset verification benchmark, the cloud synchronization record is overwritten. This establishes the temporal boundary of the session state without decrypting the original memory index. To achieve linear comparison and boundary confirmation in the ciphertext state, the cloud routing node operates in a high-speed cache... An encrypted skip list matching the homomorphic structure of the ciphertext field is maintained in memory. The ciphertext field parameters uploaded by the primary terminal node not only include the address offset value after modulo operation, but also carry a ciphertext distance indicator constructed using homomorphic properties. When the cloud routing node needs to verify the boundary alignment, it performs a ciphertext subtraction homomorphic operation on the current sliding window pointer and the received ciphertext distance indicator, and initiates a verification challenge to the primary terminal node using a zero-knowledge proof interaction protocol based on the blinding factor. If the blinding credential corresponding to the result of the subtraction homomorphic operation is equal to the preset verification element, it is confirmed that the window pointer has been accurately synchronized to the latest state boundary, and the system directly overwrites the record accordingly.

[0036] Example 4: In the scenario where a distributed interactive terminal establishes a physical connection with a cloud routing node, the primary terminal node reads a pre-set semantic dictionary file stored in its local memory, calculates the hash check value of the initial interaction token sequence, assigns the initial state value of the monotonically increasing vector clock value V to 0, and concatenates the hash check value to generate a version descriptor in its local register. Simultaneously, the cloud routing node receives the access handshake message sent by the primary terminal node, allocates a linear storage space with a preset physical bit width in the cache address space as a sliding synchronization window based on the state offset identifier in the message payload, and resets the tail pointer of the window index table using the version descriptor to establish the timing reference for the primary terminal node in the subsequent state transition process. When the real-time available bandwidth of the network channel is lower than the preset transmission rate threshold, the cloud routing node queries the weight quantization lookup table stored in its local memory. The weight quantization lookup table is obtained by statistically analyzing the influence factors of the parameters of each processing level of the AI ​​model on the interaction result through controlled experiments.

[0037] The cloud-based routing node obtains the available bandwidth parameter B and the differential data size D from the backup terminal node, calculates the expected reconstruction latency under the current bandwidth conditions, and determines whether the expected reconstruction latency is greater than the preset reconstruction latency threshold. When the trigger condition is met, the cloud routing node traverses the weight quantization lookup table in ascending order of weight quantization index, masks the non-critical dimension parameter components ranked in the bottom 30%, and encapsulates the generated compressed parameter vector into the uplink packet payload, driving the backup terminal node to complete memory image reconstruction. When the distributed system is in a state where the amount of interactive data exceeds the current communication link capacity of the backup terminal node, the cloud routing node starts a sensitivity ranking algorithm based on the first derivative of the weight, calculates the gradient magnitude G of the parameter vector in each level of the AI ​​model, constructs a parameter priority sequence in descending order of G value, and the cloud routing node reads the static weights in the weight quantization lookup table. By combining the dynamic interaction frequency factor F to calculate the comprehensive contribution index, when the differential data size D exceeds the link carrying limit, the system prioritizes retaining the top 40% of the key kernel components in the priority sequence and performs fixed-length bit-width truncation on the remaining low-sensitivity neuron bias terms, thereby achieving rapid reconstruction of the session state machine under the constraint of minimizing semantic fidelity loss.

[0038] Example 5: When the system is in a state where the dynamic switching frequency between primary and backup terminals is higher than a preset threshold, the cloud routing node completes the standardized engineering calibration procedure for the sliding synchronization window depth W. The primary terminal node obtains the session state sampling frequency f by reading the local processor clock. Simultaneously, the cloud routing node obtains the maximum latency jitter period J in the network environment by monitoring the physical link layer feedback. The cloud routing node then calculates the maximum latency jitter period J according to the formula. The physical bit width of the linear storage space within the cache is determined to maintain coverage of state information gaps caused by random fading of the physical channel in the cloud memory. Here, W is the sliding synchronization window depth, f is the state sampling frequency, J is the maximum latency jitter period, and K is a preset safety redundancy coefficient, ranging from 1.2 to 1.5. When the backup terminal node receives incremental session data distributed by the cloud routing node and initiates local session state machine reconstruction, the backup terminal node completes the memory image consistency verification procedure, i.e., by calculating the checksum of the reconstructed interaction token sequence. and the status offset identifier carried in the uplink message Perform a bitwise XOR operation and determine if the condition is met. The condition for determining whether the reconstruction is complete is confirmed and the lock state of the application layer input interface is released. If the calculation result is not 0, the standby terminal node determines that the status bit mismatch has been detected and starts a recursive backtracking request based on the monotonically increasing vector clock V. This drives the cloud routing node to retrieve the differential data packet corresponding to the initial status bit within the sliding synchronization window until the status watermark fed back by the local register and the tail pointer record of the sliding synchronization window are aligned bit by bit.

[0039] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0040] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for session snapshot and state-aware based cross-device AI interaction continuity assurance, characterized in that, Includes the following steps: Step 101: The primary terminal node monitors the dynamic context in the local session state machine and extracts the technical parameters that represent the interaction state. Based on the technical parameters, a state offset identifier that represents the session state characteristics is generated. Step 102: The primary terminal node embeds the state offset identifier into the transport layer extended header field of the uplink message to be sent, and sends it to the cloud routing node along with the uplink message. Step 103: The cloud routing node receives the uplink message and extracts the state offset identifier from the transport layer extended header field, and maintains timing synchronization information in the cloud to record the latest session state boundary of the primary terminal node. Step 104: In response to the access request from the backup terminal node, the cloud routing node determines the differential context based on the timing synchronization information and distributes the generated incremental session data to the backup terminal node so that the backup terminal node can restore its local session state.

2. The method of claim 1, wherein, Step 101, generating the state offset identifier, includes: the primary terminal node acquiring the currently pending interaction token sequence and extracting the corresponding physical address offset as a pointer variable; using a monotonically increasing vector clock as a timing constraint, performing a bitwise AND operation on the length of the interaction token sequence, the pointer variable, and the monotonically increasing vector clock to generate a denoised composite feature vector; inputting the denoised composite feature vector into a cryptographic hash algorithm for calculation and extracting a specific high-order data segment to generate a fixed-length hexadecimal string as the state offset identifier.

3. The method of claim 1, wherein, Step 102, which embeds the state offset identifier into the uplink message to be sent, includes: Step 1021, the primary terminal node identifies the protocol encapsulation type of the uplink service message and allocates a storage space of a preset width in the transport layer extended header field; Step 1022, the state offset identifier is serialized and embedded in the storage space, and a preset payload label is set in the identifier bit of the transport layer extended header field.

4. The method of claim 1, wherein, Step 103, which involves maintaining time synchronization information in the cloud, includes: the cloud routing node maintaining a linearly arranged snapshot index table in the cache based on the time sequence characteristics in the state offset identifier, storing the extracted state offset identifier at the tail of the sliding synchronization window, and simultaneously removing expired indexes at the head of the sliding synchronization window that exceed the preset lifespan threshold.

5. The method of claim 1, wherein, Step 104, which determines the differential context based on the timing synchronization information, includes: Step 1041, the cloud routing node parses the version identifier reported by the standby terminal node and determines the initial state position of the standby terminal node; Step 1042, the cloud routing node retrieves the historical state offset identifier between the initial state position and the tail pointer record in the sliding synchronization window and retrieves the corresponding differential data packet.

6. The method for ensuring the continuity of cross-device AI interaction based on session snapshots and state awareness as described in claim 1, characterized in that, Step 104 also includes: the cloud routing node monitors the real-time available bandwidth of the backup terminal node and samples the incremental session data based on the real-time available bandwidth; the cloud routing node reads the priority control bit field in the uplink packet header and performs a drop-and-truncation operation on the low-priority packet payload in the incremental session data according to the contribution ranking indicated by the priority control bit field, so that the cloud routing node can preferentially strip the parameter vector of non-critical dimensions in the incremental session data according to the preset semantic priority rules and distribute the text token carrying the semantic breakpoint.

7. The method for ensuring the continuity of cross-device AI interaction based on session snapshots and state awareness as described in claim 1, characterized in that, The cloud routing node also performs conflict resolution based on monotonically increasing vector clocks, including: when multiple packets with the same service identifier but different state offset identifiers are received, the cloud routing node extracts the monotonically increasing vector clock value from each packet; the cloud routing node determines the state offset identifier corresponding to the monotonically increasing vector clock with the largest value as a valid state and updates the existing records in the sliding synchronization window.

8. The method for ensuring the continuity of cross-device AI interaction based on session snapshots and state awareness as described in claim 1, characterized in that, After distributing incremental session data, the cloud routing node sends a status confirmation message to the backup terminal node. In response to the status confirmation message, the backup terminal node locks the local application layer input interface and reconstructs the memory image based on the received incremental session data. After the reconstruction is completed, it sends a synchronization response back to the cloud routing node, triggering the cloud routing node to start sliding synchronization for the backup terminal node.

9. A method for ensuring the continuity of cross-device AI interaction based on session snapshots and state awareness as described in claim 1, characterized in that, Step 101 also includes: the primary terminal node performs homomorphic encryption on the data in the dynamic context; the hash calculation of the state offset identifier is performed on the encrypted ciphertext field, and the cloud routing node performs state boundary synchronization in the ciphertext state.

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