Non-perpetual industry chain integrated management and traceability method

Abstract

The invention discloses an integrated management and traceability method for a non-perpetual industry chain, and relates to the technical field of information management and data traceability, and the method comprises the steps: S100, building a uniform time mark baseline of a whole link, carrying out the nanosecond alignment of all write-in channels, extracting and solidifying the time sequence features of each piece of business data, and forming a time sequence fingerprint as a structured reference basis; and S200, constructing a ghosting detection chain based on the structured reference basis, collecting delay distribution and a phase difference track in a high-concurrency writing process, and identifying a potential region of a retention copy and positioning an abnormal point in combination with a delay distribution entropy. According to the method, the nanosecond time fingerprint, the ghost detection chain, the anti-fact playback mechanism and the causal residual spectrum are constructed to realize accurate identification and dynamic control of the whole writing process of the non-perpetual data, and an error frequency signal and a time reversal rearrangement mechanism are introduced to effectively prevent repeated curing and ensure the uniqueness and credibility of the data.

Description

Technical Field

[0001] This invention relates to the field of information management and data traceability technology, specifically to a method for integrated management and traceability of the intangible cultural heritage industry chain. Background Technology

[0002] Integrated management and traceability of the intangible cultural heritage (ICH) industry chain refers to the collection, integration, and management of data across the entire process of production, storage, sales, and transmission of ICH products through a unified information platform. Key data is encrypted and stored using trusted technologies such as blockchain, ensuring data immutability and traceability. This process not only covers raw material sources, technological processes, and inventory circulation, but also incorporates the archives of ICH inheritors and their works into the system, forming a closed-loop management system of "people—skills—products—market." Consumers can directly access complete traceability information for products by scanning a code, achieving transparency and credibility, enhancing market credibility, and promoting the digital transmission and dissemination of ICH value.

[0003] The existing technology has the following shortcomings:

[0004] Existing technologies, when handling high-concurrency write scenarios in the intangible cultural heritage industry chain, generally rely on data transmission and synchronization mechanisms between modules. However, under extreme conditions, some data packets are prone to ghosting interference during cross-module transmission, causing the same business data to be misjudged by the system as multiple independent writes and repeatedly solidified, thus forming "multiple versions of the same source record." When consumers or regulators query traceability information, they will simultaneously obtain multiple versions of data records, making it impossible to clearly distinguish between the real and valid version and the abnormal redundant version. This directly undermines the original unique credibility logic, leading to a decline in the overall credibility of the traceability system, and in severe cases, even causing a systemic collapse of the entire trust system.

[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 an integrated management and traceability method for the intangible cultural heritage industry chain, so as to solve the problems in the background art mentioned above.

[0007] To achieve the above objectives, the present invention provides the following technical solution: an integrated management and traceability method for the intangible cultural heritage industry chain, comprising the following steps:

[0008] S100 establishes a unified time stamp baseline across the entire link, performs nanosecond-level alignment on all write channels, extracts and solidifies the time series characteristics of each business data, and forms a time series fingerprint as a structured reference.

[0009] S200 constructs a ghost detection chain based on structured references, collects the delay distribution and phase difference trajectory during high-concurrency writing, and identifies potential areas of residual copies and locates anomalies by combining the delay distribution entropy.

[0010] S300 enables a counterfactual replay mechanism for identified potential anomaly areas, replays the writing trajectory, removes pseudo-repetitive paths, and generates a unique chain of evidence based on the replay results.

[0011] S400 constructs a causal residual graph based on a unique evidence chain, quantifies the temporal fingerprint intensity of repeated writes, and generates a suppression threshold surface to set a dynamic control threshold.

[0012] S500, under the control of the suppression threshold surface, injects a frequency-misaligned traction signal to disturb the writing beat, weakens the resonant structure caused by the repeated path, and blocks the continuous evolution of repeated solidification.

[0013] After the periodic resonance condition is weakened, the S600 initiates a time-reversal conformal closed-loop mechanism to write the corrected vector data into the global consensus link and applies a dual-mirror time stamp traction mechanism to adaptively rearrange the writing order, thereby achieving dynamic control throughout the entire process.

[0014] Preferably, step S100 includes:

[0015] A unified physical time reference architecture is built across the entire link, and a stable time signal is generated by a temperature-compensated crystal oscillator and transmitted to each service processing node via optical fiber.

[0016] Nanosecond-level time alignment is performed in each write channel, and the three-stage time information of trigger time, execution time and completion time of each write action is collected and marked and corrected.

[0017] The time series characteristics of the entire data acquisition process with time alignment are used to form a structured three-dimensional time series matrix and then transformed into a structured time series fingerprint.

[0018] The structured temporal fingerprint is compared with historical fingerprints to calculate temporal behavior similarity. If the similarity exceeds a set threshold, the current write request is terminated; otherwise, the pre-write verification process is completed.

[0019] Preferably, step S200 includes:

[0020] Using structured temporal fingerprints as the time behavior reference standard, the response time of each key node in the write path is collected in real time and a response delay distribution map is generated.

[0021] The phase difference between adjacent time nodes is extracted from the response delay distribution map and a phase difference change trajectory map is constructed to identify the time periods with periodic abnormal responses.

[0022] Delayed distribution entropy analysis was performed on the phase difference trajectory map to construct a time entropy surface and mark high entropy clusters. Potential regions for generating residual copies were identified through similarity comparison.

[0023] High-entropy clusters are mapped to a three-dimensional behavioral coordinate system for density clustering, anomaly sets are identified, and the source of behavior is traced back to form a chain of delayed copy occurrences.

[0024] Preferably, step S300 includes:

[0025] Retrieve historical write behavior records within potentially abnormal regions, rearrange them in both physical and temporal order to generate a write behavior sequence diagram with time-driven characteristics, and perform boundary verification.

[0026] Extract path segments with time overlap or reversed response order from the timeline replay graph. Compare the behavior duration, time difference and feature label segment by segment according to the writing stage to identify and remove pseudo-repeating paths. At the same time, ensure the path logic is closed by inserting time adjustment nodes.

[0027] After the stripping is completed, a unique chain of evidence is constructed with the actual write behavior as the core node and in chronological order. Behavioral consistency bridging segments are inserted between adjacent nodes to finally generate a unique chain of evidence with temporal continuity and structural closure and solidify and store it.

[0028] Preferably, the behavioral consistency bridging segment records the time jump amplitude, processing stage differences, and signal stability comparison analysis results, which are used to verify the logical continuity and behavioral consistency between adjacent core nodes.

[0029] Preferably, step S400 includes:

[0030] After completing the construction of the unique evidence chain, all time fingerprint data are extracted, the time delay distribution of writing behavior is calculated and potential duplicate nodes are identified, the fingerprint strength is normalized and combined with trajectory perturbation and path offset to construct a causal residual map.

[0031] Cluster analysis and intensity classification are performed on the causal residual map to identify high-risk behavior nodes and abnormal paths. A three-dimensional boundary volume surrounding the high-risk area is constructed through residual interpolation to form an inhibition threshold surface.

[0032] Based on the suppression threshold surface, a dynamic control threshold is generated to match the predicted writing behavior, and data writing pre-judgment logic is embedded to realize full-process behavior early warning filtering and interference suppression.

[0033] Preferably, step S500 includes:

[0034] Under the control of the suppression threshold surface, high-risk areas are selected as target injection areas. A beat spectrum is constructed based on the unique evidence chain, the periodic structure of the repeated writing path is extracted, and a frequency misdirection traction signal based on the golden ratio rhythm window is generated and injected into the writing scheduling channel.

[0035] The path behavior after the injection of traction signal is subjected to rhythm perturbation test. By judging the written beat offset amplitude and the distribution of resonance points, it is confirmed whether the repeating path beat structure is disturbed, and a superimposed beat mechanism is applied to enhance the perturbation effect when necessary.

[0036] After the beat resonance structure is weakened, the temporal distribution of write behavior and the trend of repeated node changes are continuously monitored. The resonance deconstruction effect is judged based on the degree of rhythm variation, and the rhythm interference strategy is dynamically adjusted to ensure the decoupling of write path behavior rhythm and the restoration of stability.

[0037] Preferably, step S600 includes:

[0038] After the periodic resonance is weakened, the effective vector data after path reconstruction and data correction are extracted to construct a set of data paths that satisfy logical completeness.

[0039] Perform time inversion processing on the data path set, establish a double mirror time stamp mapping relationship, and remove abnormal data pairs with excessive offset residuals to form a closed time mapping structure;

[0040] Based on the mirror structure, the order is adaptively rearranged, sorted according to the degree of behavioral interference and the degree of path coupling, and combined with reverse time stamping to perform time offset and rhythm stretching to optimize the behavior execution sequence.

[0041] The rearranged vector data is written into the global consensus link according to the time closed-loop structure, and a behavior stability index, time offset index and path compression factor are attached. A multi-directional index mapping relationship is established to complete the closed-loop control of the whole process.

[0042] The technical effects and advantages provided by the present invention in the above technical solution are as follows:

[0043] This invention establishes a unified nanosecond-level time-marking baseline across the entire data chain and solidifies it into a temporal fingerprint, enabling precise time anchoring of all writing actions. Combined with afterimage detection chains and delay entropy trajectory analysis, it proactively identifies potential generation paths for lingering copies. Through counterfactual playback mechanisms and pseudo-path stripping technology, it accurately reconstructs the true writing trajectory and generates a legally valid and unique chain of evidence. Furthermore, by constructing a causal residual graph and a suppression threshold surface, it forms a dynamic access control mechanism based on data behavior intensity. On this basis, it introduces a golden ratio-driven frequency misalignment traction signal, which effectively disrupts the path resonance rhythm and breaks up repetitive behavior structures. Finally, through time inversion and a dual-mirror marking mechanism, it achieves adaptive rearrangement and closed-loop verification of the writing order, making the entire data writing process identifiable, operable, traceable, and entirely reliable. This greatly enhances the stability, credibility, and anti-interference capabilities of the intangible cultural heritage traceability chain in high-concurrency environments, fundamentally guaranteeing the authenticity and immutability of intangible cultural heritage data. Attached Figure Description

[0044] 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.

[0045] Figure 1 This is a flowchart of the method for integrated management and traceability of the intangible cultural heritage industry chain of the present invention. Detailed Implementation

[0046] 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.

[0047] This invention provides, for example Figure 1 The integrated management and traceability method for the intangible cultural heritage industry chain shown includes the following steps:

[0048] S100 establishes a unified time stamp baseline across the entire link, performs nanosecond-level time alignment on all write channels, collects the time series characteristics of each business data, and solidifies these time series characteristics into a time fingerprint, which serves as a structured reference for subsequent comparison processes.

[0049] In the process of tracing and managing the entire process of data writing in the intangible cultural heritage industry chain, it is necessary to establish a unified time stamp baseline to achieve accurate temporal alignment of business data under different writing paths, and based on this, generate a unique temporal fingerprint as a structured reference for subsequent comparisons. Specifically, this includes the following steps:

[0050] By constructing a unified physical time reference architecture, a time-stamping baseline is established across the entire link. This step begins with the installation of a temperature-compensated crystal oscillator, selecting a temperature-controlled crystal oscillator with a daily stability better than 0.01 ppm as the reference time signal source for the entire link. This oscillator transmits signals to local service processing nodes via directional optical fibers, and optical beam splitters and phase synchronization devices are deployed at each node to receive and synchronize the master clock signal. To eliminate physical delays caused by long-distance propagation, an optical fiber loop is deployed on the signal transmission line, and the round-trip time difference is calculated using a delay retrieval device. Before any write operation is triggered, the write path needs to undergo baseline time-stamp verification to ensure that the time-stamp deviation is less than 0.5 nanoseconds. The verification process uses a phase coincidence test method, fine-tuning is achieved by adjusting the local delay correction capacitor until the reference timelines of all nodes are statistically completely coincident, forming a highly consistent time axis covering the entire write process.

[0051] After unifying the time baseline, nanosecond-level time alignment is performed on the write operations of each business data. During this stage, a reference time extractor is installed at each write start point to acquire time pulse signals from the main oscillator in real time and embed these signals into the data write process. Each write operation is time-sampled in three stages: First, at the instant the write command is triggered, the trigger time is recorded by an optically isolated signal; second, when the data has been verified and is ready to be written, the execution time is recorded by a high-frequency capture device; third, when the data actually enters the storage path, the completion time is recorded by a reflective time capture probe. All three samples are transmitted to the time stamp writer via a thermally coupled anti-interference cable, marked with nanosecond precision, and compared with the aforementioned main time axis. An interpolation compensation algorithm corrects for propagation jitter experienced by each write in the physical channel, ultimately ensuring that all write actions are strictly aligned on a unified time stamp axis, eliminating time drift during concurrent writes.

[0052] For data write operations that have completed time alignment, the entire time-series characteristics of the process are collected and generated, and converted into a structured time-series fingerprint. In this stage, a fast sampling time tracker records the trajectory of time signal changes at 100 picosecond intervals, forming a raw time pulse sequence. Then, through time segmentation mapping, the pulse sequence is transformed into a three-dimensional time-series mapping matrix, where one dimension represents the trigger timing node, one dimension represents the execution response node, and one dimension represents the completion confirmation node, with the channel position information corresponding to the currently written data appended. To verify the timeliness and completeness of the collected sequence, after each time series is formed, it is fitted and compared with the main time baseline to check for abrupt changes at each time point. If the change amplitude exceeds 3 nanoseconds, it is determined to be an abnormal write process, and no fingerprint is generated. For time-series sequences that meet the requirements, a SHA-3 type structured hash function is used to fuse and compress the three-stage time segments, generating a 256-bit structured time-series fingerprint. This fingerprint is written to a read-only hard storage medium, possessing immutability and non-replicability, ensuring that each piece of business data has end-to-end uniqueness in the time dimension.

[0053] The generated temporal fingerprints serve as a structured basis for comparison, used for identifying, classifying, and filtering data behavior during subsequent writing processes. In practice, before each new data write action is triggered, all temporal fingerprints generated along the same path within the most recent period are retrieved and compared point-to-point with the expected write time curve of the data to be written. The comparison process is based on a three-stage time series with consistent sampling step size, and the Euclidean distance algorithm is used to calculate similarity. When the temporal behavior similarity between a fingerprint and the data to be written exceeds a 95% threshold, the system enters a deep cross-validation phase, extracting the written content and source location within the same time period from historical fingerprints and performing logical overlap analysis. If a duplicate write trend is confirmed, the write request is terminated and marked as a potential ghosting source; otherwise, the write behavior is allowed to continue through the pre-write verification process. Throughout the process, a temporal behavior prediction model is also constructed to judge the risk of possible duplicate paths based on the current fingerprint trend, and risk areas are marked in real time on the time-marked baseline to prepare for intervention in advance. In this way, temporal fingerprints not only exist as static references, but also play a role in precise identification and behavior screening in dynamic processes, supporting the uniqueness of data writing behavior in the entire intangible cultural heritage industry chain under high concurrency conditions.

[0054] S200 constructs a ghost detection chain based on structured references, collects the response delay distribution of each write channel during high-concurrency processing, extracts the phase difference change trajectory, and identifies potential areas of residual copies by combining the delay distribution entropy evolution trend, and locates suspicious anomalies.

[0055] In the integrated data writing process of the intangible cultural heritage industry chain, to identify repeated writing behavior that may be caused by high-concurrency interference, it is necessary to construct a ghosting detection chain based on the generated structured temporal fingerprint for anomaly detection, thereby identifying the potential generation areas of residual copies and their specific locations. The construction process of this ghosting detection chain includes the following steps:

[0056] After constructing and successfully applying the structured time-series fingerprint to various business data entries, it is necessary to use this as a time behavior reference standard to collect real-time changes in response time along all business write paths to construct a complete response latency distribution map. This data collection relies on pre-deployed time synchronization hardware nodes, specifically located at five key positions: the data acquisition start point, the preliminary processing point, the verification and confirmation point, the write execution point, and the final data encapsulation point. Each node is equipped with a high-precision time stamp recording device, using a fiber optic synchronization signal from a unified oscillation source for real-time synchronization, and is isolated from interference by an independent power supply and a thermal shield to ensure the stability and robustness of the recorded data. During each data write process, all five nodes record the actual response time of the data stream passing through them and convert the recorded values ​​into a high-precision timestamp format to write the time axis according to a unified time-domain alignment standard. Based on this, a complete write path response latency trajectory is formed to reconstruct the dynamic behavior of the data in the write process.

[0057] After generating the response delay distribution map, it is necessary to extract the phase change information between adjacent nodes to construct a detailed phase difference change trajectory map. In practice, the time difference is calculated for each pair of adjacent time nodes, and this time difference is mapped to the phase difference amplitude. This calculation uses a three-segment sampling method: the first segment is the shortest delay interval, the second segment is the average delay interval, and the third segment is the longest delay interval, representing the response under different write path conditions. Each segment of data is sampled point-by-point in nanoseconds, and a continuous phase difference change curve is constructed based on the time jump position. To ensure analysis accuracy, phase synchronization scanning technology is introduced, using a triangular wave scanning signal to slightly perturb the sampling window, observing the response shape of the sampling curve under perturbation, and comparing the results with the original static curve. If a segment of the curve exhibits periodic rebound or amplified peak response under perturbation, it indicates a potential risk of duplicate writing in that segment. The phase difference change map obtained through the above method can effectively reveal the time consistency change pattern of the write path under high concurrency environment, providing a structural basis for subsequent identification of delayed replicas.

[0058] After extracting the phase difference change trajectory, a delay distribution entropy analysis is performed on the trajectory to determine whether there is an unstable temporal behavior distribution trend in the write path and to further identify potential regions for generating delayed copies. The delay distribution entropy analysis involves setting a fixed-width sliding time window within the phase difference trajectory, and statistically analyzing the number of time jumps, the magnitude of change, and the direction of change within each window to form a density distribution map of delayed behavior within that window. The density maps of multiple windows are superimposed to construct a complete temporal entropy surface. Regions in this surface where the entropy value increases significantly and the direction of change is not clearly predictable are marked as "high-entropy clusters." These regions typically represent phenomena such as data processing delays, repeated triggering, or path loops. After identifying high-entropy clusters, a similarity analysis is further performed on their adjacent temporal behaviors, comparing the similarity between the temporal behavior of this region and historical fingerprint behavior. If the similarity exceeds 85%, the high-entropy region is identified as a potential region for generating delayed copies, and a structured marker is generated for subsequent point-to-point scheduling and path tracing in the behavior profiling stage.

[0059] After identifying potential regions for generating persistent copies, it is necessary to further perform precise three-dimensional spatial localization of these regions and identify suspicious anomalous behavior points. Specific operations include: first, constructing a three-dimensional behavior coordinate system based on the timeline, write path number, and write behavior sequence; then mapping all delayed behavior data in this region to coordinate points, each containing a timestamp, path number, and behavior stage; next, using density clustering to identify highly clustered write behavior groups within a short period, and further determining whether they exhibit repetitive triggering characteristics based on their temporal behavior features; if at least two behavior curves in this group have a response time difference of less than 0.5 nanoseconds, and the path numbers are consecutive or interleaved, then this group is marked as an anomalous point set; finally, by tracing its historical origin, extracting its original triggering behavior and path execution sequence, forming a persistent copy occurrence chain. This chain contains specific time points, physical paths, behavior triggering methods, and response patterns, providing clear target data input for the next stage of counterfactual playback.

[0060] S300 activates the counterfactual playback mechanism in the identified potential abnormal areas, replays the historical writing trajectory in the area in timeline, analyzes the formation path and peels off suspected pseudo-duplicate paths step by step, and generates a unique chain of evidence based on the peeling analysis results.

[0061] After locating the abnormal regions of the retained copies, to further clarify the relationship between real and redundant paths in the write behavior, a counterfactual replay mechanism needs to be initiated within the identified potential abnormal regions to replay the entire historical write trajectory. Based on the temporal behavior analysis, pseudo-repetitive paths are systematically stripped away, ultimately constructing a unique chain of evidence that is structurally closed, logically continuous, and temporally verifiable. This process includes the following steps:

[0062] Based on the extracted high-entropy clusters and residual source points, all historical write data behavior records are retrieved within the corresponding time period and path interval. These records are then rearranged according to both physical processing order and chronological order to generate a write behavior sequence diagram with time-driven characteristics. This sequence diagram is constructed using a three-in-one behavior node structure: event trigger point, response confirmation point, and execution completion point. Each node uses a nanosecond-level timestamp as its baseline time anchor information and records its corresponding data source number, channel number, and trigger sequence number. During the rearrangement process, boundary checks are performed on the start and end times of each write path to exclude data points that deviate significantly from the unified time-marked baseline. Subsequently, all behavior nodes are connected according to the construction order to form a write path map on the timeline. This map not only reflects the entire process trajectory of the write command from trigger to completion but also clearly shows the distribution of multiple duplicate trigger branches or delayed response branches between paths, providing a dual spatiotemporal basis for subsequent path comparison and stripping processing.

[0063] After the timeline replay graph is constructed, pseudo-duplicate paths are identified and stripped step by step. The specific process is as follows: First, extract all path segments with overlapping times or reversed response orders from the graph, defining them as a set of suspected duplicate paths. For each suspected path, segment comparison is performed according to its writing stage, including the instruction receiving stage, the processing response stage, and the data writing stage. The behavior duration between each segment, the time difference between adjacent nodes, and the behavior feature label are used as judgment indicators. If the behavior duration in a segment is found to be less than 20% of the average, or the behavior feature label is inconsistent with the standard path, then that segment is identified as a pseudo-duplicate path segment. Subsequently, the structural integrity of each identified path segment is verified. If it lacks a forward trigger record or does not completely cover the three stages of the writing process, then that segment is stripped from the main path and recorded as a redundant writing unit. To ensure that the path still maintains behavioral logical closure after stripping, compensation processing is required between the preceding and following nodes. A time adjustment node is inserted between the preceding and following nodes, and delay parameters and execution confirmation flags are manually set to make the path connection coherent and logically closed.

[0064] After stripping away all pseudo-repeating path segments and structurally repairing the behavior path graph, the unique evidence chain construction phase begins. This evidence chain uses each confirmed genuine write action as a core node, arranged in chronological and execution order. Each evidence node records three timestamps: trigger time, execution time, and completion time; three location parameters: write channel number, physical behavior location number, and write data index number; and three behavior quality indicators: response latency, behavior stability score, and structural consistency score with the standard process. A behavior consistency bridging segment is inserted between every two adjacent evidence nodes, recording the time jump magnitude, processing stage differences, and signal stability comparison analysis results between them. The evidence chain constructed in this way possesses temporal continuity, structural closure, and behavioral verifiability. It not only serves as the unique proof of the data write path but also allows for reverse behavior tracing or behavior consistency verification at any subsequent point in time. Finally, the evidence chain is encapsulated in a highly secure encrypted data format, solidified into the chain-structured data storage area using a read-only writing method, and its number is incorporated into a trusted index system to achieve unique evidence support throughout the entire process and the entire chain.

[0065] S400 constructs a causal residual graph based on this unique evidence chain, performs multi-level quantitative analysis on the temporal fingerprint intensity of each repeated write, forms a suppression threshold surface based on the quantification results, and uses this suppression threshold surface to set the dynamic control threshold for repeated write triggering.

[0066] To ensure the effective suppression of repetitive triggering behaviors within the intangible cultural heritage industry chain, after constructing a unique evidence chain, it is necessary to further identify and quantify interference at the temporal behavior level. This involves extracting abnormal path features by constructing a causal residual graph, generating a suppression threshold surface with behavioral constraints, and setting a dynamic control threshold with real-time judgment capabilities. This process includes the following steps:

[0067] After completing the structured encapsulation of the unique evidence chain, all temporal fingerprint data is extracted. Based on three dimensions—timestamp, behavior intensity, and path sequence number—the write behavior is analyzed hierarchically to construct a causal residual map. The generation process of this map is as follows: For each piece of written data, three key time points in the write process are read: write trigger time, processing execution time, and final completion time. The interval difference between each time point is calculated to form the time delay distribution of a single behavior. Based on this, it is identified whether there is temporal fingerprint overlap between different channels for this write behavior. If there is timestamp overlap, path overlap, or stage sequence reversal, the behavior is marked as a potential duplicate write node. All behavior nodes with potential overlap risk are extracted as causal nodes, and a time series chain is established according to their occurrence order. Subsequently, the fingerprint intensity of this time series chain is normalized, and the residual value of each node is calculated by combining the perturbation amplitude of the forward behavior trajectory and the offset direction of the backward behavior path. Finally, a causal residual map is drawn in the form of a three-dimensional spatial graph. Each point in the graph represents a specific writing behavior, and its spatial coordinates correspond to the time delay intensity, path intersection weight, and behavior similarity, respectively. The entire graph constitutes a structured interference distribution map with behavioral visualization features.

[0068] After constructing the causal residual map, cluster analysis and intensity classification are performed on all residual nodes within the map to establish behavioral risk partitions for writing behaviors. A suppression threshold surface is then constructed based on these partition characteristics. This process unfolds in three aspects: First, based on fingerprint intensity, all behavioral nodes are divided into three regions: low-risk, medium-risk, and high-risk. High-risk nodes are overlapping nodes with high fingerprint intensity, high path similarity, and small temporal differences. Second, frequency band clustering analysis is performed based on the frequency of behavior occurrence. Nodes that appear multiple times within a dense area of ​​the map per unit time are identified as anomaly accumulation points, and the residual mean and variance of these points are calculated to generate a risk heatmap. Third, the connectivity of each node in the map is analyzed based on behavioral persistence. If a behavioral path maintains a high residual state across multiple consecutive time slices, it is determined to be an anomalously persistent path. The results of the above analyses are integrated, and a set of behavioral response envelopes is generated through residual interpolation, forming a three-dimensional boundary volume surrounding the high-risk area in space, which is the suppression threshold surface. The suppression surface has nonlinear dynamic characteristics, and the surface undulations represent the range of time fluctuations that can be tolerated under different behavioral triggering intensities, which can serve as the core reference structure for subsequent dynamic control mechanisms.

[0069] Based on the constructed suppression threshold surface, a dynamic control threshold matching the real-time write behavior is generated and embedded into the pre-judgment logic of the data write behavior. The dynamic control threshold is generated as follows: Before each new write request is triggered, the system uses a time prediction mechanism to estimate when, along what path, and at what stage the write behavior will be completed. Using the theoretical landing point of the predicted behavior point in the causal residual graph as a reference, the spatial location of that point on the suppression threshold surface is found. If the residual tolerance range at that location includes the expected behavior value of the predicted write behavior, then the write behavior meets the time stability requirements, and its dynamic control threshold is set to the allow state. If the predicted behavior point is outside the threshold surface or located in a high residual danger area, the control threshold corresponding to that behavior is set to the block state, and the behavior is marked as a write attempt that may have a recurring trend. To further enhance the adaptability of the threshold judgment, real-time concurrency pressure and current path usage density also need to be considered. Dynamic contraction or expansion operations are performed on the suppression threshold surface, so that its boundaries are adjusted in real-time according to the fluctuating business load. The dynamic control threshold constructed in the above manner can not only provide early warning and filtering of behavior before writing, but also continuously intervene throughout the writing process, providing the ability to judge the stability of behavior and suppress interference throughout the process.

[0070] S500, under the control of the suppression threshold surface, injects a frequency-shifting traction signal with golden ratio beat characteristics to disturb the writing beat of the entire link, weaken the periodic resonance structure formed by the repeated path, and block the continuous evolution of the repeated solidification conditions.

[0071] After constructing the causal residual map and setting the suppression threshold, to further suppress the synchronization trend of the repetitive path at the temporal behavior level, it is necessary to introduce an off-frequency traction signal with golden ratio rhythm characteristics to inject interference beats into the physical write path. By disrupting the periodic rhythm of the write behavior, the resonance basis of the repetitive path is weakened, thereby blocking the continuous evolution of repetitive write solidification. This process includes the following steps:

[0072] Within the constructed suppression threshold surface, high-risk regions with residual values ​​exceeding the average threshold are selected as target injection areas. Write rhythm restoration processing is then performed on historically repetitive behavior nodes within these regions. This process uses the previously identified unique evidence chain as a temporal reference, extracting the start time, execution response time, and completion time of all write actions in the target path, and arranging them chronologically to form a beat spectrum. This spectrum reveals the periodic structure and rhythmic patterns of write events within the path, analyzing whether there are phenomena such as periodic repetition, beat overlap, or behavioral rhythm solidification. Assuming a stable repetitive write beat exists within the path, a non-integer rhythm window is constructed based on the golden ratio value of 0.6180339. Using the current average write cycle of the path as a reference, this cycle is multiplied by the golden ratio and its complement to generate two rhythm segments of unequal duration, representing the write delay interval and the trigger interval, respectively. Subsequently, these two rhythm segments are combined to form a frequency-shifting traction sequence, set as an alternating high and low traction signal waveform, used to perturb the original trigger beat within the target path. The traction signal is implanted into the scheduling port of the write action through the time control unit of the physical trigger point, causing the actual trigger time of the write event to deviate slightly from the original planned cycle, thus creating a clock disruption effect.

[0073] After injecting a frequency-shifting traction signal into the target path, the rhythm perturbation stage begins. Interference response tests are performed on the entire write behavior of this path to confirm whether rhythm decoupling has been achieved. Specifically, within a specified time window after the traction signal is applied, the trigger time difference and completion time difference of each write operation on the path are monitored in real time and compared with the rhythm baseline before traction. If the trigger point offset exceeds 15% of the original rhythm average within at least three consecutive write cycles, and the write interval exhibits non-linear fluctuations, it is determined that the traction signal has effectively perturbed the original beat. Simultaneously, the response time of periodically repeating nodes in the write behavior is recalculated. If these nodes no longer cluster near the original rhythm peak, it indicates that the original beat resonance point has been broken up. To enhance the perturbation effect, a superimposed beat mechanism is introduced during the test. A second set of golden ratio variation rhythm sequences (e.g., swapping 0.382 and 0.618) is added to the first set of rhythms, forming multi-level frequency-shifting interference, further breaking the rhythm resonance structure formed by the periodic coordination between the processor waiting window, data confirmation window, or processing trigger window in the path.

[0074] After completing the beat perturbation and confirming that the path rhythm has been disrupted, the process enters the resonance weakening and behavioral stability assessment stage. This stage uses the degree of variation in the periodic behavioral characteristic indicators within the write path as the core evaluation criterion to determine whether the repetitive path structure has been deconstructed. Key indicators include: whether the standard deviation of the beat sequence interval has significantly increased, whether the number of periodically repeating behavioral nodes has significantly decreased, and whether the temporal distribution of write behavior tends towards asymmetric diffusion. If all three indicators simultaneously reach a preset interference threshold, the repetitive beat foundation of the path is considered to have been disrupted. Based on this, further monitoring is conducted to see if subsequent write behavior exhibits a regular recovery tendency. If periodic reconstruction is detected, the interference signal injection process is restarted, and the rhythm window length is fine-tuned. A dynamic rhythm change sequence based on Fibonacci sequence ratios is introduced, allowing the interference rhythm to maintain its non-periodicity while possessing natural decay characteristics, avoiding long-term interference that could damage the stability of the normal path. Through these methods, physical rhythm frequency misalignment is used to induce beat misalignment of write nodes at the behavioral level, causing the resonance structure upon which the repetitive path depends to lose synchronous support, achieving the technical effect of deeply blocking the continuous evolution of repetitive write behavior.

[0075] After the periodic resonance condition is weakened, the S600 initiates the time-reversal conformal closed-loop mechanism, writes the vector data processed by path reconstruction and data correction into the global consensus link, and applies the dual-mirror time stamp traction mechanism to perform adaptive rearrangement of all writing order, so as to realize online dynamic control of data writing throughout the entire process of the intangible cultural heritage industry chain.

[0076] After disrupting the periodic resonance structure of repeating paths through the golden ratio rhythm perturbation mechanism, to achieve temporal order stability and behavioral consistency control under high-concurrency writing environments, a time-inversion conformal closed-loop mechanism needs to be further activated. This mechanism processes the reconstructed and corrected write vector data throughout the entire process, sequentially completing behavioral time-inversion mapping, path closed-loop structure construction, adaptive order rearrangement, and global consensus link writing. Ultimately, this achieves dynamic closed-loop control of the intangible cultural heritage industry chain data writing process. This process includes the following steps:

[0077] After the periodic perturbation is completed, all valid write vector data, after redundancy stripping, duplication suppression, and cycle perturbation optimization, are extracted from the high-risk paths. This data is then organized and categorized according to three elements: path number, processing stage, and behavior time. Each vector data record records its complete processing lifecycle, including write request initiation time, response confirmation time, execution landing time, physical path location, and behavior segment identifier. After extraction, all vector data is imported into the time-series reconstruction engine. Write time difference analysis and alignment with the behavior response sequence are used to determine if there are time jumps, out-of-order responses, or logical gaps. For write behaviors that do not meet logical closure requirements or have broken behavior segments, time interpolation points are manually set to complete the path. A response-preserving mechanism is used to insert minimum processing delay segments to fill structural gaps, ensuring that all write vector paths meet the behavioral logic completeness requirements. After the vector set is organized and passes structural verification, a set of data paths suitable for closed-loop reconstruction is obtained.

[0078] For this set of data paths, time inversion processing is performed to establish a complete time mirror mapping relationship. Specifically, using the global write time reference value as the central axis, the three key time points of each valid write vector are symmetrically projected to generate a forward time stamp group and a reverse time stamp group, forming a complementary time double mirror structure. While generating the double mirror structure, residual evaluation is performed on the time difference between each pair of mirror points. The evaluation criteria include: mirror consistency, behavioral offset magnitude, and response overlap. If a pair of mirror data has an offset residual greater than 30% of the original write cycle, it is considered unsuitable for participating in closed-loop path construction and is removed or subjected to further path correction. For all data pairs that meet the mirror consistency criteria, a bidirectional mapping relationship is established in the 3D behavioral map. Each behavioral node has corresponding points on both the forward and reverse time axes, achieving a closed mapping of the entire behavioral lifecycle on the time axis, laying the foundation for subsequent adaptive path rearrangement.

[0079] Based on the constructed dual-mirror temporal behavior structure, the closed-loop path sequential adaptive reordering is performed. The core principles of reordering are: maintaining the logical continuity of write behavior, avoiding behavior conflicts, and reducing write cycle overlap. First, the trigger time of all vector paths is extracted as the sorting anchor point, and a priority sorting table is established based on behavior interference and path coupling. Write behaviors with low intensity, small residual values, and non-intersecting processing paths are prioritized. For paths with path intersections or dense behavior rhythms, a time offset strategy based on reverse time stamps is introduced to shift the execution time of such paths to the low-density segment of the overall behavior, avoiding rhythm overlap with other paths. During the reordering process, if the time difference between any two behavior nodes is lower than the set write interference threshold, a time buffer segment is inserted to lengthen the write cycle, ensuring that the physical execution of the write process does not result in data collisions or path blocking. The entire sequential reordering operation forms a set of behavior sequences optimized by mirror interference. This sequence has high temporal controllability, behavior stability, and path separation in physical execution, meeting the controllable writing requirements of intangible cultural heritage data in complex concurrent scenarios.

[0080] After the behavior sequence is rearranged, the data writing phase of the global consensus chain officially begins. The goal of this phase is to irreversibly write the rearranged vector data, which possesses a time-loop structure, into the distributed consensus chain, ensuring that it is unmodifiable, traceable, and uniquely identified. Before writing, each behavior is appended with three structured pieces of information derived from double-mirror tags: a behavior stability index, a time offset index, and a path compression factor. These are used for behavior verification and chained archiving during the consensus confirmation phase. Subsequently, the data is written sequentially into the chained storage path. Each write event occupies a unique logical position in the chain, and the writing order is consistent with the time-loop path, possessing self-describing behavior capabilities and event reversal tracing capabilities. After the chain writing is completed, a global index mapping table is constructed to realize multi-directional reference relationships between each data write and the original processing behavior, path source, and behavior mirror. Through the above-mentioned whole-process processing, the online closed-loop control of the entire process of data writing in the intangible cultural heritage industry chain is finally completed. A dynamic control structure with full process, full process, and multiple dimensions is established, which is from writing behavior recognition, rhythm disturbance, sequence rearrangement to consensus solidification. This significantly improves the data reliability processing capability in high-concurrency environments and breaks through the limitations of existing technologies in time control and behavior conflict avoidance.

[0081] This invention establishes a unified nanosecond-level time-marking baseline across the entire data chain and solidifies it into a temporal fingerprint, enabling precise time anchoring of all writing actions. Combined with afterimage detection chains and delay entropy trajectory analysis, it proactively identifies potential generation paths for lingering copies. Through counterfactual playback mechanisms and pseudo-path stripping technology, it accurately reconstructs the true writing trajectory and generates a legally valid and unique chain of evidence. Furthermore, by constructing a causal residual graph and a suppression threshold surface, it forms a dynamic access control mechanism based on data behavior intensity. On this basis, it introduces a golden ratio-driven frequency misalignment traction signal, which effectively disrupts the path resonance rhythm and breaks up repetitive behavior structures. Finally, through time inversion and a dual-mirror marking mechanism, it achieves adaptive rearrangement and closed-loop verification of the writing order, making the entire data writing process identifiable, operable, traceable, and entirely reliable. This greatly enhances the stability, credibility, and anti-interference capabilities of the intangible cultural heritage traceability chain in high-concurrency environments, fundamentally guaranteeing the authenticity and immutability of intangible cultural heritage data.

[0082] 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 non-heritage industry chain integration management and traceability method, characterized in that, The method comprises the following steps: S100, establishing a unified time marking baseline for the whole link, performing nanosecond-level alignment for all write channels, extracting and solidifying the time sequence features of each service data, and forming a time sequence fingerprint as a structured reference basis; S200, constructing a residual image detection chain based on the structured reference basis, collecting the delay distribution and phase difference trajectory in the high-concurrency write process, combining the delay distribution entropy to identify the potential area of the retained copy and locate the abnormal points; S300, enabling the counterfactual playback mechanism in the identified potential abnormal area, replaying the write trajectory, stripping the pseudo-repeated path, and generating a unique evidence chain based on the playback result; S400, constructing a causal residual graph based on the unique evidence chain, quantifying the time sequence fingerprint intensity of repeated writing, and generating a suppression threshold surface to set a dynamic control threshold; S500, under the control of the suppression threshold surface, injecting a frequency error traction signal to disturb the write rhythm, weakening the resonance structure caused by the repeated path, and blocking the continuous evolution of repeated solidification; S600, after the periodic resonance condition is weakened, starting the time reversal conformal closed loop mechanism, writing the corrected vector data to the global consensus link, and applying the double-mirror time marking traction mechanism to adaptively rearrange the write order.

2. The non-heritage industry chain integrated management and traceability method according to claim 1, characterized in that, Step S100 comprises: Constructing a unified physical time reference architecture within the whole link, generating a stable time signal through a temperature-compensated crystal oscillator, and transmitting it to each service processing node through an optical fiber; Performing nanosecond-level time alignment processing in each write channel, collecting the trigger time, execution time, and completion time of each write behavior, and performing label correction; Collecting the time sequence features of the whole process of data acquisition after time alignment, forming a structured three-dimensional time sequence matrix, and converting it into a structured time sequence fingerprint; Comparing the structured time sequence fingerprint with the historical fingerprint, calculating the time behavior similarity, and if it exceeds the set threshold, aborting the current write request, otherwise completing the pre-write verification process.

3. The non-heritage industry chain integration management and traceability method according to claim 1, characterized in that, Step S200 comprises: Using the structured time sequence fingerprint as the time behavior reference standard, real-time collecting the response time of each key node in the write path and generating a response delay distribution graph; Extracting the phase difference of adjacent time nodes in the response delay distribution graph and constructing a phase difference trajectory graph to identify the time period with periodic abnormal response; Performing delay distribution entropy analysis on the phase difference trajectory graph, constructing a time entropy surface and marking a high-entropy aggregation area, and identifying the potential generation area of the retained copy through similarity comparison; Mapping the high-entropy aggregation area to the three-dimensional behavior coordinate system for density clustering, identifying the abnormal point set and tracing the behavior source, and forming a retained copy occurrence chain.

4. The non-heritage industry chain integration management and traceability method according to claim 1, characterized in that, Step S300 comprises: Retrieving historical write behavior records in the potential abnormal area, double-rearranging them according to physical order and time order, generating a write behavior sequence graph with time-driven characteristics, and performing boundary verification; Extracting the path segment with time overlap or reversed response order in the time line replay graph, comparing the behavior duration, time difference, and feature label in each write stage, identifying the pseudo-repeated path and stripping it, and ensuring path logic closure through the insertion of time adjustment nodes; After the peeling is completed, the real write behavior is taken as the core node, a unique evidence chain is constructed in time sequence, a behavior consistency bridging section is inserted between adjacent nodes, and finally a unique evidence chain with time continuity and structural closedness is generated and stored.

5. The non-heritage industry chain integration management and traceability method according to claim 4, characterized in that, The time jump amplitude, processing stage difference and signal stability comparison analysis results in the behavior consistency bridging section are recorded and used to verify the logical continuity and behavior consistency between adjacent core nodes.

6. The non-heritage industry chain integration management and traceability method according to claim 1, characterized in that, Step S400 includes: After the construction of the unique evidence chain is completed, all time fingerprint data is extracted, the time delay distribution of the write behavior is calculated, and potential repeated nodes are identified. The fingerprint intensity is normalized, combined with trajectory disturbance and path deviation, and a causal residual atlas is constructed; In the causal residual atlas, clustering analysis and intensity grading are performed, high-risk behavior nodes and abnormal paths are identified, a three-dimensional boundary body around the high-risk area is constructed through residual interpolation, and a suppression threshold surface is formed; According to the suppression threshold surface, a dynamic control threshold matching the write behavior is generated, and a data write pre-judgment logic is embedded to realize whole-process behavior early warning filtering and interference suppression.

7. The non-heritage industry chain integration management and traceability method according to claim 1, characterized in that, Step S500 includes: Under the control of the suppression threshold surface, the high-risk area is selected as the target injection area, the beat spectrum graph is constructed according to the unique evidence chain, the periodic structure of the repeated write path is extracted, the frequency error traction signal based on the golden ratio rhythm window is generated and injected into the write scheduling channel; The rhythm disturbance test is performed on the path behavior after the injection of the traction signal, the write beat deviation amplitude and the resonance point dispersion are judged to confirm whether the repeated path beat structure is disturbed, and the superimposed beat mechanism is applied to enhance the disturbance effect when needed; After the beat resonance structure is weakened, the time distribution and repeated node change trend of the write behavior are continuously detected, the rhythm variation degree is judged to determine the resonance deconstruction effect, and the rhythm interference strategy is dynamically adjusted to ensure the rhythm decoupling and stability recovery of the write path behavior.

8. The non-heritage industry chain integration management and traceability method according to claim 1, characterized in that, Step S600 includes: After the periodic resonance is weakened, the effective vector data after path reconstruction and data correction processing is extracted, and a data path set satisfying logical completeness is constructed; Time reversal processing is performed on the data path set, a double-mirror time tag mapping relationship is established, and abnormal data pairs with offset residual exceeding the limit are removed to form a time mapping closed structure; On the basis of the mirror structure, sequential adaptive rearrangement is performed, the behavior interference degree and path coupling degree are sorted, the time offset and rhythm stretching are performed combined with the reverse time tag, and the behavior execution sequence is optimized; The rearranged vector data is written into the global consensus link according to the time closed loop structure, the behavior stability index, time offset index and path compression factor are attached, and a multi-direction index mapping relationship is established to complete the whole-process closed-loop regulation and control.

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