A private network communication data processing method

Abstract

The application discloses a private network communication data processing method, relates to the technical field of communication, and comprises the following steps: constructing a unified time baseline, establishing a cause-and-effect sequence benchmark on the basis of the unified time baseline, extracting data backflow risk characteristics in link operation data, and generating risk prior data; scanning a cache area based on the risk prior data, identifying residual fingerprints and sequence hollow spectrum, calibrating back-feeding segments with backflow characteristics, and constructing an arrival threshold field consistent with the unified time baseline. The application realizes backflow risk identification by constructing a unified time baseline and a cause-and-effect sequence, blocks error segments from penetrating by combining a one-way submission channel and a shadow pipeline, realizes segment misplacement perception and isolation by introducing time watermarking and counting chains, completes data repair by using time inversion and error correction recovery, converges residual energy by combining an optical frequency comb and micro-injection, constructs a closed-loop regulation mechanism, and significantly improves the stability and security of private network communication.

Description

Technical Field

[0001] This invention relates to the field of communication technology, and more specifically to a method for processing private network communication data. Background Technology

[0002] "Private network communication data processing" refers to the entire process of collecting, transmitting, parsing, optimizing, and securing various types of data generated in a specific dedicated communication network environment. Compared to public network communication, private networks typically serve specific industries such as government, energy, power, transportation, and military / police, emphasizing high reliability, high security, and real-time performance. Therefore, its data processing not only needs to achieve accurate identification and efficient transmission of multi-source data such as voice, video, commands, and sensor information, but also needs to combine encryption mechanisms, identity authentication, traffic scheduling, and fault tolerance recovery strategies to ensure that data can be transmitted and utilized stably, quickly, and without errors, even under conditions of limited bandwidth, complex environments, and even interference. In other words, the core significance of private network communication data processing lies in transforming complex and diverse data into secure, controllable, and usable communication information through dedicated algorithms and mechanisms, providing reliable support for the business operations and decision-making of key industries.

[0003] The existing technology has the following shortcomings:

[0004] In existing technologies, when a private network is in dynamic operating scenarios such as node switching or sudden load changes, the link often experiences transient instability. Due to delays in the buffer scheduling mechanism and data flow control logic response, short-term data backflow can easily occur. This phenomenon causes data segments marked as invalid or erroneous in the buffer to be reinjected into subsequent processing channels, thus mixing into the data stream that has already been parsed and verified. Once erroneous data enters the processed data stream, its propagation is no longer limited to local links but spreads rapidly throughout the entire private network through data forwarding and multi-level processing mechanisms, ultimately forming a global error propagation. This low-probability but highly destructive anomaly lacks effective suppression methods in existing technologies. It not only compromises data integrity but may also directly lead to errors in the execution of critical business instructions or distortion of real-time monitoring results, thereby seriously threatening the security and reliability of private network communication.

[0005] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] The purpose of this invention is to provide a private network communication data processing method to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a private network communication data processing method, comprising the following steps:

[0008] A unified time baseline is constructed, and a causal order benchmark is established on the basis of the unified time baseline. Data backflow risk characteristics are extracted from the link operation data, and risk prior data is generated to constrain the detection time sequence of residual fragments.

[0009] Based on risk prior data scanning cache, residual fingerprints and sequence hole spectra are identified, back-injection fragments with backflow characteristics are labeled, and an arrival threshold field consistent with a unified time baseline is constructed as a boundary condition for data submission.

[0010] Establish a one-way submission channel that meets the threshold field requirements, and construct a shadow pipeline commitment chain in the one-way submission channel to restrict the submission of data fragments to the threshold field, forming a submission boundary to block the reverse infiltration of back-injection fragments.

[0011] By embedding a time watermark and attaching a monotonic counting chain to each data segment at the submission boundary, the segment is frozen and sent to an isolation buffer when a time misalignment is detected, thus separating the erroneous segment from the main data stream.

[0012] Within the isolation buffer, perform time inversion rearrangement and erasure mirror restoration on the frozen fragments, verify causal evidence, output the restored data sequence and generate traceability logs to restore data consistency;

[0013] Based on the recovered data sequence and traced logs, heterogeneous dynamic control is initiated. The link clock is driven by the phase traction of the optical frequency comb, and a phase conjugate feedback field is formed by combining anti-phase damping micro-injection. The residual energy trajectory is converged in real time, the data backflow channel is closed, and the data flow closed-loop repair and link stability are completed.

[0014] Preferably, the steps for generating prior risk data are as follows:

[0015] Collect raw data streams output from multiple communication nodes, extract the source device number and local timestamp of each data segment, calculate the average time drift difference and maximum offset range between nodes, generate clock correction values, and map all timestamps to a unified time axis;

[0016] Based on a unified time baseline, a causal order baseline is established, including sorting all data fragments and identifying response relationships between fields, context number continuity and transaction chain composition, constructing a causal graph and ensuring that all events are monotonically increasing on the time axis;

[0017] Based on the causal order benchmark, data segments with characteristics of temporal misalignment, field overlap, sequence backtracking, and response coverage are identified, field features are extracted, and a backflow risk feature set is generated.

[0018] Based on the backflow risk feature set, a time window interval and a set of verification rules are constructed to form risk prior data, which is used for time-series constraint control of subsequent residual detection.

[0019] Preferably, the steps for establishing the threshold field are as follows:

[0020] Extract the unified time baseline position and field structure features of each backflow risk segment, construct a scanning window before and after its time position, collect cached data segments that have not yet been submitted, and construct a field structure vector as a matching benchmark;

[0021] Calculate the field overlap rate between cached data fragments and risk fingerprints to determine structural consistency. Combine time difference, communication path, byte distribution and length characteristics to identify valid residual fragments and form a set of residual fingerprints.

[0022] Based on the unified time series graph, the hole segments in the sequence are identified. By using features such as hole length, time interval, field structure and retransmission flag, the injection segments are marked and a set of uncommittable segments is established.

[0023] A threshold field is constructed based on the residual fingerprint frequency, back-injection marker density, and confidence score to identify the submission boundary. Fragments outside the threshold field will be delayed or transferred to an isolation path.

[0024] Preferably, the boundary formation steps are as follows:

[0025] Within the threshold field, extract data segments with complete structural fields, continuous sequence numbers, and stable source nodes. Construct an increasing time series based on a unified time baseline to form a submittable data channel with a single time direction, continuous numbering, and reliable source.

[0026] Submittable fragments are sequentially input into the shadow pipeline, where they undergo field integrity verification, causal chain confirmation, time consistency verification, sequence continuity analysis, structural comparison, and submission eligibility determination. Failure to pass these checks results in rejection.

[0027] Construct a commitment chain for the verified fragments. Each commitment fragment must satisfy the following requirements: incrementing timestamps, unique source communication node, and continuous field structure. Any break in the chain will abort subsequent submissions.

[0028] The commit process is controlled by a commitment chain trigger, which only allows consecutive complete fragments to be written to the main communication path in the order of commit number, and prohibits reverse insertion, skipping commits and duplicate commits, thus forming a closed-loop control of the commit boundary.

[0029] Preferably, the steps for freezing the segment and sending it to the isolation buffer when a time misalignment is detected are as follows:

[0030] After the data fragment passes through the shadow pipeline, a time watermark generated by an absolute time tag based on a unified time baseline is embedded, and a monotonically increasing count chain field corresponding to the commitment number is added.

[0031] At the main communication channel entrance, the time watermark and the counting chain value are continuously compared. If the current time tag is earlier than the previous segment or the number does not meet the increment rule, it is marked as an abnormal candidate segment.

[0032] The abnormal candidate fragment is set to a frozen state, its advancement permission is stripped, the time watermark, count number and source information are encapsulated, and it is moved to an isolated buffer space outside the communication path to achieve physical and logical separation.

[0033] Preferably, the steps for outputting the recovered data sequence and generating a traceability log are as follows:

[0034] Extract the time watermark and advancement interval of the segment, combine the submission time with the theoretical insertion position to construct the time rearrangement table, and complete the structural consistency verification;

[0035] Compare with the field template set, fill in the missing fields, and use field position matching, hash consistency and sequence number verification to confirm the legality of the mapped fields;

[0036] Extract response identifiers and transaction numbers to construct causal paths, reconstruct logical paths for segments that satisfy causal reachability, and form a recovered data sequence.

[0037] The recovered data fragments are embedded with traceability field information, the recovered data sequence is output and a traceability information set is generated, and then injected into the main data channel after passing the consistency check.

[0038] Preferably, the heterogeneous dynamic regulation process includes the following steps:

[0039] A three-dimensional distribution map of energy perturbation is constructed based on the recovered data sequence and traceability information, and the structural repair factor and residual intensity of causal path for each segment are calculated.

[0040] Obtain the clock frequency parameters and fluctuation characteristics of all communication link nodes, construct a frequency stability score, and generate a dynamic control rule set;

[0041] The node with the highest frequency stability score transmits an optical frequency comb signal embedded with a global time tag and phase reference, driving the link nodes to complete clock synchronization;

[0042] The traction source node injects a weak signal that is out of phase with the residual frequency into the high disturbance path, forming a phase cancellation process, thereby achieving the convergence of disturbance energy;

[0043] Perform time-progression direction restriction operations on historical backflow paths, complete link closed-loop repair, and mark stable operation status.

[0044] The technical effects and advantages provided by the present invention in the above technical solution are as follows:

[0045] This invention constructs a risk identification logic based on a unified time baseline and causal sequence. Before data enters the main data stream, it can identify segments with potential backflow characteristics by constructing prior risk data and a threshold field, achieving fine-grained risk labeling and boundary control. Furthermore, through a shadow pipeline and unidirectional channel mechanism, it structurally blocks the reverse infiltration path of erroneous segments, ensuring the main data stream is not contaminated. Building upon this, it introduces a time watermark and monotonic counting chain mechanism, enabling real-time detection and isolation of segment misalignment behavior. Subsequently, through time inversion and correction / recovery techniques, it achieves structural repair and causal consistency restoration of erroneous segments. Finally, by combining optical frequency comb phase traction and anti-phase damping micro-injection, it completes the real-time convergence and elimination of residual energy disturbances in the physical link, forming a closed-loop processing mechanism that coordinates logical control and physical regulation. This method significantly improves the error prevention capability, data stream stability, and service execution reliability of private network communication in the face of dynamic link disturbances. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0047] Figure 1 This is a flowchart of a private network communication data processing method according to the present invention. Detailed Implementation

[0048] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the description of this disclosure will be more complete and fully convey the concept of the exemplary embodiments to those skilled in the art.

[0049] This invention provides, for example Figure 1 The method for processing private network communication data, as shown, includes the following steps:

[0050] A unified time baseline is constructed based on the cross-source clock correction results, and a causal order benchmark is established on the basis of the unified time baseline. Potential data backflow risk characteristics are extracted from the link operation data to generate risk prior data, providing time constraints for subsequent residual fragment detection.

[0051] When node switching or load surges occur in a dedicated communication network, the link may experience short-term instability, leading to backflow of expired data segments in the cache. This paper proposes a backflow risk identification method combining time baselines and causal ordering to address this problem. Specifically, it includes the following steps:

[0052] To address the clock skew issue inherent in the local time stamps carried by multi-source heterogeneous communication devices during data generation, raw data streams output from multiple communication nodes were collected. Each data segment contained the source device number and local timestamp information. These data segments were grouped by node, and the time series of each group was extracted. The average time drift difference and maximum offset range between each pair of communication nodes were calculated to construct a cross-source time alignment matrix. This matrix was used to generate clock correction values, and the local timestamp of each node was converted segment by segment, mapping it to a global absolute time under a unified time axis. To ensure the consistency of the corrected time stamps, a reference clock with a stability better than 0.01ppm was selected as a reference. A weighted sliding window method was used to smooth the short-period jitter of each node, eliminating the asymmetric part of network propagation delay. After time series unification, each data segment possesses a unique network-wide time reference coordinate, forming a unified time baseline and providing a standard reference for subsequent causal analysis.

[0053] Based on a time baseline, the behavioral triggering relationships between data segments are analyzed to construct a causal structure reflecting the true temporal sequence of communication events. First, all data segments are chronologically sorted on a unified timeline, and key fields such as communication content identifier, sequence number, response marker, trigger identifier, retransmission marker, and business context number are extracted. By judging the correlation between these fields—for example, whether it is a response relationship, whether it has context number continuity, and whether it forms a complete transaction chain—data segment pairs with causal triggering relationships are identified. Each pair of causally related data segments is included in a causal chain, and a causal sequence graph is constructed sequentially according to time. Further, causal chains in different communication paths are cross-referenced to construct a cross-path causal fusion set. Each data segment in the graph has a predecessor node and a successor node, indicating its event sequence and response relationship in the entire communication process. The causal graph must not contain time reversal or closed-loop self-referencing structures, and all nodes must be strictly monotonically increasing on the timeline to ensure the logical consistency of the causal sequence benchmark. Thus, the causal structure reflecting the logical sequence of actual communication behaviors is constructed.

[0054] After the causal order baseline is established, the risk analysis phase of the link data segments begins. First, all data segments during cache recovery and link reconstruction windows are analyzed retrospectively, with particular attention paid to segments whose time span is within the mutation window, originate from the secondary buffer, have retransmission flags enabled, and whose sequence numbers exhibit abnormal jumps. These data are compared with the causal order baseline to determine if any of the following conditions exist: 1) the timestamp is earlier than its preceding triggering segment; 2) its content fields overlap with the previous causal segment; 3) its sequence number jumps back; 4) the request segment corresponding to its response identifier has been overwritten by a subsequent transaction. Segments meeting any of these conditions are identified as potential backflow risk segments. Further, the field structure features of these segments are extracted, including the start identifier position, content length, CRC checksum, source node number, and the time interval between the most recent submission, and these are recorded as backflow fingerprint features. Each fingerprint embeds the time offset, content redundancy ratio, field overlap rate, and context scenario number to form a complete backflow risk feature set. This feature set will serve as the basis for the next step of risk modeling.

[0055] Based on the aforementioned feature set, a time window interval is first established for each backflow risk segment. Centered on the segment's unified timestamp, the time difference between its associated predecessor segments is traced back, and the time is extended forward to the latest response time of any subsequent segments it may influence, forming a specific time boundary. The time windows of all segments are then overlapped and clustered to generate multiple high-risk concentrated segments. Each segment is associated with a set of typical backflow feature fingerprints and occurrence background parameters. Subsequently, based on the causal chain density of each high-risk segment, a set of temporal constraint rules is constructed, explicitly stipulating that all data segments appearing within these time windows must pass a complete causal chain verification mechanism. Verification includes: whether the end time of the previous valid transaction is less than the current segment's timestamp; whether the current segment's field content overlaps with the previous transaction's field space; whether the response identifier of the previous segment is referenced by the current segment; and whether its sequence number matches the current timestamp. Before all conditions are met, the segment is prohibited from entering the subsequent processing flow. Finally, the time window, high-risk fingerprint, verification rules, and communication background parameters are packaged into a risk prior data set for triggering high-precision residual identification logic in the subsequent detection phase. This completes the closed-loop preparation process, from identifying backflow risks to implementing predictive prevention and control measures.

[0056] A fine-grained scan of the cache area is performed based on risk prior data to identify residual fingerprints and sequence hole spectra in the cache data, and injection segments with data backflow characteristics are identified. An arrival threshold field is constructed based on a unified time baseline as a boundary condition for data segment submission.

[0057] After constructing the prior risk data, it is necessary to perform precise analysis of the communication data in the cache area based on this data. This will identify potential residual fragments and anomalous injection behaviors, and establish strict timing control boundaries to prevent backflow data from entering subsequent processes. The implementation method includes the following steps:

[0058] The generated risk prior data is imported into the analysis engine to extract the unified time baseline position, field structure features, sequence trend parameters, context communication identifier values, time range, and field repetition rate corresponding to each backflow risk segment. Using this time position as the core, a scanning window is constructed by selecting 10 data segments before and after it. Within the window, all currently uncommitted data segments in the cache are collected and sorted in ascending order by timestamp, removing segments with abrupt timestamp changes to eliminate interference from identified anomalous data. A field structure vector is constructed for each segment to be scanned, recording the field start offset, total number of fields, byte length of each field, arrangement order, field padding value features, tail check field, and overall field density distribution. This field structure vector will serve as the structural fingerprint matching benchmark in subsequent comparisons, ensuring effective discrimination of disguised segments with similar structures but different contexts. Through the above methods, a cache scanning target segment with both time domain constraints and structural reference attributes is established.

[0059] Within a clearly defined scanning target area, each data segment is processed sequentially. First, the overlap rate between its field structure vector and the fingerprint structure in the risk prior is calculated. The overlap rate calculation includes five aspects: field name matching degree, field length consistency, field arrangement order consistency, field occurrence frequency ratio, and field tail checksum similarity. If any three or more of these five indicators exceed a set matching threshold, it is considered a structurally similar segment and included in the residual pending set. This pending segment undergoes further verification, including but not limited to: whether the time difference between the timestamp and the preceding causal segment is below a set lower limit, whether the segment's source communication path is the same as the original backflow segment, whether the field content is consistent with the byte distribution of the original fingerprint, and whether the segment length falls within the same statistical interval as typical residual segments. If three of the four verifications pass, it is confirmed as a valid residual segment and included in the residual fingerprint set. This set will be used in the next stage to assist in locating areas of concentrated suspicious data density, serving as one of the input conditions for constructing the threshold boundary.

[0060] Fragments from the residual fingerprint set are inserted into a unified time series graph. All cached fragments are rearranged according to time labels, and their sequence number fields are extracted. During the scanning process, each location where the sequence number is discontinuous is identified, i.e., there are gaps between the sequence numbers of fragments from different times. These gaps are extracted as "hole segments," and the length of the hole segment, the time interval between the preceding and following fragments, the similarity between the field structure of the starting fragment and the residual fingerprint, and the retransmission marker status of the fragment at the end of the hole are calculated. If the hole length is within the allowable range (e.g., 2 to 5 sequence intervals), and the overlap rate between the structure of the fragment at the end of the hole and the risk fingerprint exceeds a threshold, and the time interval is less than twice the average interval of the prior fragments, then the hole region is judged to have a high probability of backflow behavior. In the hole region, if any fragment's sequence number is found to be less than the sequence number of its corresponding preceding fragment in the causal order, and its time label is earlier than the causal predecessor timestamp of the fragment, then the fragment is marked as a back-injection fragment. A unique back-injection marker is given to the fragment, and its field mapping relationship is recorded, serving as a member of the set of fragments that cannot be submitted in the threshold field construction.

[0061] All data fragments in the current cache that are not marked as residual or re-injected are aggregated and reassembled in a unified time baseline order to construct a continuous time series. For each time point, the frequency of residual fingerprints, re-injection marker density, sequence continuity status, and field overlap variation within the 10 fragments before and after it are statistically analyzed. These indicators are then comprehensively assigned to generate a "commit credibility score" for each fragment. Based on high-risk areas in the prior risk data, a dynamic window interval is set on the time baseline, for example, extending forward and backward by 2 seconds from the time label to form a high-alert boundary segment. Within the boundary segment, a data fragment is allowed to enter the submission channel only if its commit credibility score is higher than a preset threshold, its field structure does not overlap with any residual fingerprint, its timestamp is in the correct position in the causal sequence chain, and its sequence number shows no reversal trend; otherwise, it is directly blocked from entering the process. The entire submission credibility score is plotted as a two-dimensional scene diagram on the timeline, with the horizontal axis representing the time label and the vertical axis representing the credibility score. All areas in the diagram below the security score threshold constitute the "outside the threshold area". All data segments outside the threshold area will be delayed in processing or transferred to an isolation path until they pass causal verification and structural review before being processed.

[0062] Enable a one-way submission channel that meets the arrival threshold field requirements. Build a shadow pipeline commitment chain within this channel, so that the submission process of data fragments is restricted by the previously built arrival threshold field, forming a submission boundary to block the reverse infiltration behavior of back-injected fragments and prevent erroneous data from entering the normal data flow.

[0063] After completing the construction of the threshold field, a strict one-way commit flow path needs to be established. Data commit behavior should be constrained through a shadow pipeline and commit commitment chain mechanism to form an irreversible, orderly, and secure commit boundary, preventing identified back-injection fragments from crossing the temporal defense line and interfering with normal data flow. Specifically, this includes the following steps:

[0064] Extract all reliable time regions from the formed arrival threshold field, and identify data segments located within these regions that have complete structure fields, consecutive sequence numbers, and whose source nodes have not been reconstructed as "committable segments". Traverse the cached data, filter out the set of segments that meet the above conditions, and form an increasing time series based on their timestamps on a unified time baseline, which serves as the input sequence for the one-way submission channel. When constructing the submission channel, assign a unique submission number to each segment, which must be greater than the submission number of the previous segment and cannot be backdated. Any data segment attempting to submit with a timestamp earlier than an already submitted number will be immediately rejected. To prevent false positives, source verification is also performed on each segment: check if it comes from a marked residual cache or back-injected segment region; if it belongs to such a source, it is removed from the submission candidate list. Through the above filtering and identification process, a committable data channel with a single time direction, consecutive numbers, and reliable sources is formed.

[0065] The selected committable data segments are sequentially fed into the shadow pipeline according to their commit numbers. This pipeline consists of multiple serial processing nodes, each representing an operation stage in the data commit process. These stages are, in order: field integrity verification, causal chain confirmation, time tag consistency verification, sequence continuity analysis, field structure comparison, and commit eligibility determination. Each segment, upon entering the pipeline, must complete all node verifications step-by-step, receiving a stage pass flag upon completion. Only after all stages are passed can final commit permission be granted. Within the pipeline, each segment occupies a fixed position, preventing subsequent segments from interrupting or changing positions to prevent order disruption due to traffic fluctuations or cache misordering. If a segment fails verification at any stage, it is immediately marked as uncommittable and removed from the pipeline. Simultaneously, the previous segment is notified to stop generating commitment flags, ensuring the commit status within the pipeline remains fully controllable. This pipeline design ensures that the data commit operation exhibits strictly linear, non-rollbackable, and non-jumping execution characteristics, structurally forming a natural barrier against reverse behavior.

[0066] Once a fragment completes all stages of verification within the shadow pipeline and is not discarded, it is identified as a "committed fragment." Each committed fragment, in addition to its commit number, receives a hash identifier derived from the previous committed fragment, forming a chain connection with its own timestamp and sequence number. This connection not only reflects the sequential relationship but also carries the inheritance of data integrity between upstream and downstream segments. The generation rules for the commitment chain are as follows: each fragment's commitment identifier must simultaneously meet the following three conditions: its timestamp is strictly later than the previous committed fragment; its source communication node does not appear repeatedly in any residual buffer; and its field structure has a continuous content expansion relationship with the preceding and following fragments, rather than a redundant substitution relationship. If any fragment in the commitment chain violates the above conditions, the entire chain will break at that point. All commit actions after the broken fragment will be postponed until subsequent fragments re-pass through the complete pipeline process and establish a new commitment relationship. This chain structure effectively prevents data fragments with backflow intentions from using structural similarity to disguise themselves and enter the commit process, logically constructing closed-loop verification conditions for the commit boundary.

[0067] The final commit process for all segments that successfully construct a commitment chain through the shadow pipeline is controlled by the commitment chain trigger. That is, only consecutive and complete segments in the commitment chain can enter the main data channel in sequence, and must adhere to a strictly incremental commit number principle. At each commit operation, the commit controller synchronously verifies whether the segment is the latest commit point in the commitment chain, whether there are any incomplete predecessor segments, and whether there are any historical delay remnants with timestamps earlier than the previous commit segment. If any of these conditions are not met, the current commit action is forcibly aborted. After a successful commit, the current segment is written to the main communication path and its status is set to read-only, preventing it from re-entering the commit process. Any subsequent commit action can only be initiated after the previous segment has been committed. This mechanism not only controls the consistency of the commit order but also prevents multiple segments from being interleaved due to concurrent flow, thus ensuring that there are no arbitrary forms of reverse insertion, skipped commits, or duplicate commits in the entire data flow commit path. Compared with traditional submission strategies based on time sliding windows or buffer depth, this closed-loop control mechanism has stronger precision control capabilities and structural consistency guarantees, significantly improving data flow stability and backflow resistance.

[0068] Under the submission boundary conditions, a time watermark is embedded in each data segment and a monotonic counting chain is attached. When a segment is detected to have signs of time misalignment, the segment is immediately frozen and sent to an isolation buffer to achieve complete separation of the erroneous segment from the main data stream.

[0069] To further ensure the consistency of submitted segments in terms of time dimension and sequence number, and to immediately prevent them from entering the main data stream in the event of anomalies, time watermark embedding, monotonic counting chain appending, misalignment identification, and freeze isolation operations are performed under submission boundary conditions to achieve continuity assurance of the main data stream and structural removal of erroneous segments. This operation consists of the following steps:

[0070] When a data segment completes all verification stages in the shadow pipeline and is about to receive submission permission, its structure is immediately expanded by adding two types of embedded fields: a time watermark field and a monotonic counting chain field. The time watermark field is based on the absolute timestamp of the current segment within a unified time baseline, incorporating the segment's committed position sequence number in the pipeline and its time advancement factor calculated through a time window to construct a high-precision timestamp uniquely identifying the segment's time state. This timestamp uses a sub-millisecond precision time encoding structure, consisting of two parts: a base time stamp and a link scheduling offset correction value, ensuring accurate sorting even under slight network fluctuations. Simultaneously, the monotonic counting chain field uses an incrementing integer structure, with its initial value derived from the count value of the previous segment plus one, and skipping or reusing numbers is not allowed. To avoid interference, the count value must be mapped consistently to the pipeline committed sequence number and is forcibly written into a fixed segment within the segment structure, ensuring direct readability by subsequent verification mechanisms. Through this operation, each segment carries both watermark information representing "time position" and count information representing "sequence position" in its physical transmission structure, providing a complete reference for subsequent misalignment detection.

[0071] All submitted segments are sequentially fed into the main communication data channel, where a dedicated time continuity detection process verifies their time watermarks and counting chain values ​​one by one. Specifically, whenever a segment reaches the data channel's entry point, its time watermark is parsed, its absolute timestamp is extracted, and compared to the time watermark value of the previous segment. If the current timestamp is earlier than the previous segment's timestamp value, or if the time interval between the current and previous segments is less than the minimum advance interval set by the unified time baseline, the segment is considered to have a "time misalignment." Simultaneously, the counting chain field value is verified for continuity. If the current segment's count number is not equal to the previous segment's number plus one, or if the count number is greater than the previous number exceeding a preset jump threshold, the segment is considered to have a "number misalignment." If either time misalignment or number misalignment is met, the segment is marked as an abnormal candidate segment, and its further advancement in the main data stream is suspended. This dual verification process ensures that each segment undergoes rigorous time and sequence consistency checks before being formally incorporated into the main communication path, preventing abnormal segments from spreading in the transmission chain.

[0072] Once a fragment is identified as an anomalous candidate fragment, its status is immediately marked as "frozen." This status means the fragment loses the right to proceed and triggers an independent isolation process. The freezing operation invalidates the fragment's commit status flag field by modifying it; simultaneously, it stops any participation in the main channel, including path forwarding, reordering and merging, and response calls. Subsequently, the fragment, along with its time watermark, counter number, source channel identifier, current link delay information, and upstream causal fragment reference information, is packaged and transferred to a pre-defined isolation buffer space. This space is located outside the communication path, has an independent address area and an independent data transmission control process, ensuring that the frozen fragment is truly physically and logically isolated from the main data stream in this area. During the isolation process, to prevent the frozen fragment from being mistakenly invoked into the main channel, its embedding flag field is set to unrecoverable again, and a one-time verification channel identifier is set on its transmission path, allowing only unidirectional access by the subsequent error correction and recovery process; no path in the main data stream can call this fragment. This isolation operation not only ensures the purity of the main data stream, but also provides an independent processing entry point for subsequent time reversal and deletion recovery mechanisms, enabling the entire communication chain to have closed-loop purification capabilities.

[0073] In the isolation buffer, the frozen fragments are subjected to time reversal and rearrangement operations. Combined with the erasure mirror restoration mechanism, causal evidence is verified, the cleaned recovery data sequence is output, and a corresponding traceability log is generated to rebuild a consistent data state.

[0074] After the fragments are frozen and transferred to the isolation buffer, to prevent erroneous data from contaminating the main communication stream and to restore its availability as much as possible, this implementation performs time reversal rearrangement, structural deletion restoration, causal chain verification, and data output purification on these fragments to achieve data consistency reconstruction. The specific steps are as follows:

[0075] Before entering the isolation buffer, each frozen data segment carries a time watermark, a monotonic counting chain number, and an identifier indicating its sequential relationship with its predecessor segment. Within the isolation buffer, all segments are first scanned in reverse order of their freezing time. The time watermark value of each segment and its expected advancement interval recorded in the pipeline are extracted. Then, the theoretical insertion position is deduced by combining this with the actual submission time of the previous segment. This theoretical position is used to construct a time reordering table, which uses the time axis as the main line to reposition all segments. During segment reordering, the consistency of their field structure is checked, including the total number of fields, the field order, the starting offset of each field, and the hash digest of the field values. If the structure of a segment deviates from other segments within the same time period by more than the allowed threshold (e.g., a reduction of more than two fields or a change in the field order of more than three digits), it is considered structurally abnormal and will not participate in the reordering. Finally, a sequence of data segments reorganized in chronological order with controlled field structure consistency is obtained, serving as the entry data for the cleanup process.

[0076] Because some segments had experienced data truncation, loss of redundant fields, or content overflow before being frozen, structural recovery was required using erasure mirroring technology. First, a complete field template set was established. This set was constructed by analyzing complete field segments in the rearranged sequence and included detailed features such as field name, field type, preset length, context content identifier, and field dependencies. Then, each incomplete segment was compared item by item with the field template set to find missing fields and their corresponding values. If a segment was missing the field "context number," the value of that field was searched for in complete segments within the same time window, and a completion operation was performed based on field position matching and hash consistency verification. To prevent erroneous completion, field mirroring was only allowed from adjacent segments within a time window of no more than three segments. Simultaneously, triple consistency checks were performed on the field hash digest value, source sequence number, and field density distribution to ensure that the completed field was a true mapping. Through this process, structurally abnormal segments were restored to cleaned candidate segments with complete fields and logically continuous content.

[0077] Based on a complete structure, to prevent logical chain breaks, the effectiveness of each segment in the upstream and downstream communication behavior chain must be further verified. First, the response identifier, transaction number, causal trigger field, and time progression step of each segment are extracted to construct causal candidate paths. Each segment must simultaneously meet the following three conditions: First, its response identifier must be traceable to a trigger segment in the committed main data stream, and the trigger segment's time must be earlier than the current segment by at least a set transmission delay threshold; second, its transaction number must form a continuous increasing relationship with the predecessor segment; missing numbers or jumps exceeding two units are considered causal breaks; third, the trigger field value in the segment's field structure must appear in the field set of the upstream segment, and both its position and content must match. Only segments that meet all the above conditions can be marked as "causal chain reachable segments"; otherwise, even if the structure is complete, they cannot be included in the recovery process. All reachable segments are re-labeled with sequence numbers, forming a set of data sub-chains with complete upstream and downstream logical paths, providing complete semantic support for subsequent merging.

[0078] All segments that pass causal verification are reordered according to a unified time baseline to form a logically continuous, structurally consistent, and causally complete data recovery sequence. This recovery sequence is not directly injected into the main communication stream but enters a pre-defined relay buffer area, waiting to be incorporated when a structural hole appears in the main channel or when the recovery window is opened by the scheduling control process. In each cleaned segment, a traceability field is written, including: a freeze time stamp, isolation trigger condition, erasure field source number, causal path hash chain value, and a summary of structural repair steps. All structural data is embedded in the segment's end area and locked as read-only, making it tamper-proof. Before the recovered data stream is injected into the main channel, the scheduling control scans all traceability information to ensure clear sources, valid fields, stable time progression, and a closed-loop causal logic before allowing the segment to participate in the communication process. This structured traceability information embedding mechanism not only ensures that the data state is verifiable and traceable before regression but also enables the recovered data to be re-identified, constructing a true "cleaned data return loop."

[0079] Based on the recovered data sequence and traced logs, a heterogeneous dynamic control process is initiated. The link clock is driven by the phase traction method of optical frequency comb, and a phase conjugate feedback field is formed by anti-phase damping micro-injection to achieve real-time convergence of the residual energy trajectory. Finally, the data backflow channel is closed, and the closed-loop repair of the data flow and the stability of the entire link operation status are completed.

[0080] After obtaining the recovered data sequence and its complete traceability information, in order to completely eliminate the residual energy disturbance caused by error propagation in the communication link and prevent the backflow path from being reactivated, it is necessary to initiate a heterogeneous dynamic control process. Combining optical frequency comb driving and anti-phase micro-injection technology, the link closed-loop repair and stable operation are gradually achieved. The specific operations are as follows:

[0081] For each recovered data segment, its freeze time, watermark sequence number, field completion count, causal path span, and its mapping position in the communication link are extracted and represented as a two-dimensional tensor using the time axis and transmission path. After all segments are aligned to the time baseline, a perturbation factor matrix is ​​constructed based on the number of field repairs. This matrix characterizes the structural reconstruction energy experienced by the segments during the repair process. Further calculations are made to determine the sequence progression continuity and the integrity of logical upstream and downstream relationships between segments, identifying logical breakpoints and jump points as concentrated areas of potential energy residue. The causal chain length and field recombination frequency are superimposed on each residue point to obtain a residual energy weight score. Finally, a three-dimensional energy distribution map is constructed, clearly showing the energy perturbation trajectory left by the propagation of erroneous data through the link, using time, link location, and perturbation intensity as three dimensions, providing a basic reference for subsequent precise control.

[0082] The system acquires the local clock frequency, frequency drift direction, short-cycle jitter amplitude, instantaneous frequency hopping rate, and historical synchronization success rate of all link nodes under their current operating status. These parameters are standardized and plotted as a frequency stability profile. Each node is assigned a frequency stability score based on its frequency fluctuation level. Nodes with higher scores have clock frequencies closer to the reference standard and are more suitable as traction center nodes; those with lower scores are marked as control target nodes. The physical connections between nodes are mapped to a set of adjacent topology pairs, which will be used for subsequent traction signal propagation path modeling. Based on this, a dynamic control rule set is generated. Each rule includes the node frequency adjustment direction, allowable adjustment range, time window size, and frequency control precision. These operations ensure that each node has a clear target frequency direction and specific control parameters when receiving traction signals, avoiding frequency transition out-of-control problems during the control process.

[0083] The optical frequency comb signal transmission process is initiated by the node with the highest frequency stability score. This signal consists of a set of highly coherent, uniformly spaced optical pulses, each pulse embedding a corresponding global timestamp and phase reference value. The optical pulses reach each node in the link via the fiber optic transmission path, are converted into electrical signals, and are compared in real-time with the local clock output. The comparison result generates a set of frequency offset error signals, which adjust the output of the local voltage-controlled oscillator through a frequency control loop, thereby approximating the standard frequency of the optical frequency comb signal. The entire process employs a step-by-step phase advancement method, with each adjustment not exceeding a set microhertz frequency step size to ensure continuous control and response stability. During the propagation period of the traction signal, each node must report its own frequency convergence status. If synchronization fails to be completed three times consecutively, the traction source node will suspend the traction signal of the target node to prevent disturbance backflow. This optical frequency traction mechanism essentially constructs a self-diffused synchronization drive chain, unifying originally asynchronous nodes to the same phase control reference.

[0084] Based on the generated residual energy distribution map, each region where the disturbance intensity exceeds a set threshold is selected as the injection target. A set of low-power, weak electrical signals with the same frequency but opposite phase as the residual energy trajectory are generated by the traction source node and injected into the target link segment through the transmission path. These micro-injected signals have the same wavelength, pulse width, and frequency modulation mode, but their initial phase is completely opposite to the original disturbance energy, causing phase cancellation with the residual trajectory during superposition, forming a wave mutual suppression effect. The micro-injection process continues for multiple propagation cycles, and the link signal strength, bit error rate, and time jitter indicators are evaluated after each cycle. If all indicators drop below the low disturbance state within three consecutive cycles, it is determined that the current link residual energy has been completely absorbed. This step significantly differs from existing technologies that rely solely on data cleanup logic or cache cleaning methods to handle the error propagation path problem; instead, it achieves true link stable closure through physical energy path control.

[0085] After confirming that all link nodes have completed optical frequency synchronization and eliminated disturbance energy, path direction restriction processing is immediately applied to all historically identified backflow channels. A unidirectional data propagation structure is deployed at each suspected backflow entry point. This structure uses timestamp order as the criterion, prohibiting any data with a timestamp earlier than the current propagation baseline from re-entering the cache or processing flow. For nodes that still experience occasional misalignment during convergence, their cache control logic will automatically enter a frozen state until their synchronization state stabilizes. Furthermore, a "closed-loop repair completed" status is marked on the entire communication link path diagram. This status serves as a prerequisite for any subsequent data injection operations, ensuring that all new segments meet the three criteria of frequency consistency, phase synchronization, and interference-free propagation before entering the link. Thus, the entire communication link's closed-loop repair process—from initial error propagation, data freezing, structural repair, logical cleanup to physical control and backflow removal—is completed.

[0086] This invention constructs a risk identification logic based on a unified time baseline and causal sequence. Before data enters the main data stream, it can identify segments with potential backflow characteristics by constructing prior risk data and a threshold field, achieving fine-grained risk labeling and boundary control. Furthermore, through a shadow pipeline and unidirectional channel mechanism, it structurally blocks the reverse infiltration path of erroneous segments, ensuring the main data stream is not contaminated. Building upon this, it introduces a time watermark and monotonic counting chain mechanism, enabling real-time detection and isolation of segment misalignment behavior. Subsequently, through time inversion and correction / recovery techniques, it achieves structural repair and causal consistency restoration of erroneous segments. Finally, by combining optical frequency comb phase traction and anti-phase damping micro-injection, it completes the real-time convergence and elimination of residual energy disturbances in the physical link, forming a closed-loop processing mechanism that coordinates logical control and physical regulation. This method significantly improves the error prevention capability, data stream stability, and service execution reliability of private network communication in the face of dynamic link disturbances.

[0087] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A method for processing private network communication data, characterized in that, Includes the following steps: Construct a unified time baseline, and establish a causal order benchmark based on the unified time baseline. Extract data backflow risk characteristics from the link operation data and generate risk prior data. Based on risk prior data, the buffer area is scanned to identify residual fingerprints and sequence hole spectra, the backflow fragments with backflow characteristics are identified, and an arrival threshold field consistent with a unified time baseline is constructed. Establish a one-way submission channel that meets the threshold field requirements, and construct a shadow pipeline commitment chain in the one-way submission channel to restrict the submission of data fragments to the threshold field, forming a submission boundary to block the reverse infiltration of back-injection fragments. Embed a time watermark and attach a monotonic counting chain to each data segment at the submission boundary. Freeze the segment and send it to the isolation buffer when a time misalignment is detected. Within the isolation buffer, perform time inversion rearrangement and erasure mirror restoration on the frozen fragments, verify causal evidence, output the restored data sequence and generate traceability logs; Based on the recovered data sequence and traced logs, heterogeneous dynamic control is initiated. The link clock is driven by the phase traction of the optical frequency comb, and a phase conjugate feedback field is formed by combining anti-phase damping micro-injection. The residual energy trajectory is converged in real time, and the data backflow channel is closed.

2. The private network communication data processing method according to claim 1, characterized in that, The steps for generating prior risk data are as follows: Collect raw data streams output from multiple communication nodes, extract the source device number and local timestamp of each data segment, calculate the average time drift difference and maximum offset range between nodes, generate clock correction values, and map all timestamps to a unified time axis; Based on a unified time baseline, establish a causal order benchmark, construct a causal graph, and ensure that all events monotonically increase on the timeline; Based on the causal order benchmark, data fragments are identified, field features are extracted, and a backflow risk feature set is generated. Based on the backflow risk feature set, a time window interval and a set of verification rules are constructed to form risk prior data.

3. The private network communication data processing method according to claim 2, characterized in that, The steps for establishing the threshold field are as follows: Extract the unified time baseline position and field structure features of each backflow risk segment, construct a scanning window before and after its time position, collect cached data segments that have not yet been submitted, and construct a field structure vector as a matching benchmark; Calculate the field overlap rate between cached data fragments and risk fingerprints to determine structural consistency. Combine time difference, communication path, byte distribution and length characteristics to identify valid residual fragments and form a set of residual fingerprints. Based on the unified time series graph, the hole segments in the sequence are identified. By using hole length, time interval, field structure and retransmission marker features, the injection segments are marked and a set of uncommittable segments is established. A threshold field is constructed based on the residual fingerprint frequency, back-injection marker density, and confidence score to identify the submission boundary. Fragments outside the threshold field will be delayed or transferred to an isolation path.

4. The private network communication data processing method according to claim 3, characterized in that, The steps for submitting the boundary formation are as follows: Within the threshold field, extract data segments with complete structural fields, continuous sequence numbers, and stable source nodes, construct an increasing time series based on a unified time baseline, and form a data channel that can be submitted. Submittable fragments are sequentially input into the shadow pipeline, where they undergo field integrity verification, causal chain confirmation, time consistency verification, sequence continuity analysis, structural comparison, and submission eligibility determination. Failure to pass these checks results in rejection. Construct a commitment chain for the verified fragments. Each commitment fragment must satisfy the following requirements: incrementing timestamp, unique source communication node, and continuous field structure. Any break in the chain will abort subsequent submissions. The commit process is controlled by a commitment chain trigger, which only allows consecutive complete fragments to be written to the main communication path in the order of commit number, and prohibits reverse insertion, skipping commits and duplicate commits, thus forming a closed-loop control of the commit boundary.

5. The private network communication data processing method according to claim 4, characterized in that, The steps to freeze the segment and move it to the isolation buffer when a time misalignment is detected are as follows: After the data fragment passes through the shadow pipeline, a time watermark generated by an absolute time tag based on a unified time baseline is embedded, and a monotonically increasing count chain field corresponding to the commitment number is added. At the main communication channel entrance, the time watermark and the counting chain value are continuously compared. If the current time tag is earlier than the previous segment or the number does not meet the increment rule, it is marked as an abnormal candidate segment. The abnormal candidate fragment is set to a frozen state, its advancement permission is stripped, the time watermark, count number and source information are encapsulated, and it is moved to an isolated buffer space outside the communication path to achieve physical and logical separation.

6. The private network communication data processing method according to claim 5, characterized in that, The steps to output the recovered data sequence and generate a traceability log are as follows: Extract the time watermark and advancement interval of the segment, combine the submission time with the theoretical insertion position to construct the time rearrangement table, and complete the structural consistency verification; Compare with the field template set, fill in the missing fields, and use field position matching, hash consistency and sequence number verification to confirm the legality of the mapped fields; Extract response identifiers and transaction numbers to construct causal paths, reconstruct logical paths for segments that satisfy causal reachability, and form a recovered data sequence. The recovered data fragments are embedded with traceability field information, the recovered data sequence is output and a traceability information set is generated, and then injected into the main data channel after passing the consistency check.

7. A private network communication data processing method according to claim 6, characterized in that, The heterogeneous dynamic regulation process includes the following steps: A three-dimensional distribution map of energy perturbation is constructed based on the recovered data sequence and traceability information, and the structural repair factor and residual intensity of causal path for each segment are calculated. Obtain the clock frequency parameters and fluctuation characteristics of all communication link nodes, construct a frequency stability score, and generate a dynamic control rule set; The node with the highest frequency stability score transmits an optical frequency comb signal embedded with a global time tag and phase reference, driving the link nodes to complete clock synchronization; The traction source node injects a weak signal that is out of phase with the residual frequency into the high disturbance path, forming a phase cancellation process, thereby achieving the convergence of disturbance energy; Perform time-progression direction restriction operations on historical backflow paths, complete link closed-loop repair, and mark stable operation status.

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