Intelligent recommendation method for declaration time of customs declaration form considering overseas holidays and clearance time difference

Abstract

This invention provides an intelligent recommendation method for customs declaration timing that takes into account overseas holidays and customs clearance time differences. It collects time data from key nodes of customs clearance operations through multi-source heterogeneous interfaces, and generates standardized operation logs through time zone normalization, anomaly removal, and structured encapsulation. Further, it performs rhythmic phase alignment and density analysis on the logs to extract rhythmic fingerprint feature vectors reflecting the port operation rhythm, and establishes a rhythmic fingerprint database containing different port and holiday types. During real-time declaration, it can dynamically recommend the optimal declaration time window by combining historical high-efficiency periods and confidence analysis. The system can also adaptively calibrate rhythmic feature parameters based on the deviation of actual customs clearance time. This invention improves the accuracy of efficiency prediction for port customs clearance operations and the elasticity of the recommendation strategy, effectively alleviating problems such as holiday congestion and uneven resource allocation.

Description

Technical Field

[0001] This invention relates to the field of intelligent recommendation for cross-border customs clearance and analysis of port operation behavior, and in particular to an intelligent recommendation method for the timing of customs declarations that takes into account overseas holidays and customs clearance time differences. Background Technology

[0002] Currently, in the field of intelligent recommendation for cross-border customs clearance and analysis of port operation behavior, mainstream technical solutions are based on static parameter modeling. They generally use historical customs clearance data to calculate the time difference of each step and preset standard time intervals through empirical rules, moving averages, or regression models. Combined with geographical time zones, statutory working days, and port announcement timetables, a decision model for declaration timing is formed. Some solutions introduce external factor modeling based on machine learning, such as freight flow forecasting, meteorological impact analysis, and extraction of user customs declaration behavior patterns. However, overall, they still mainly focus on coarse-grained fitting of the homogeneous and periodic characteristics of the entire port, making it difficult to make highly personalized responses to the regular and occasional fluctuations during special periods such as holidays. In practical applications, most mainstream intelligent declaration recommendation systems rely on the average time of fixed customs clearance steps as a parameter driver. Delays and fluctuations in the process between nodes are often corrected through manual abnormal intervention or experience-based adjustments. This approach has a certain degree of usability under off-peak and regular cycles, but during peak operation periods near holidays or the post-holiday backlog release period, extreme fluctuations in customs clearance time differences cause great interference to the model. Not only do many recommendation results become invalid due to "declaring too early" or "declaring too late", but it also leads to frequent adjustments to the recommendation system parameters, affecting the stability and reliability of actual operation. For cross-border customs clearance scenarios, several statistical systems have been deployed in integrated declaration and port collaboration systems. The core feature of rule-based hybrid methods lies in controlling the time consumption of each major customs clearance stage based on a unified working hour template at the port and the average clearance cycle over the past three to six months. However, the applicability of such schemes is mostly limited to port types with relatively smooth cargo volume fluctuations and continuous working hours, lacking the ability to respond sensitively to ports significantly affected by holidays. When encountering peak periods before special holidays, during long holidays, and after holidays, the customs clearance process is prone to sudden and prolonged delays caused by policy changes, on-site operational resource allocation, and a surge in temporary customs declarations. Existing models cannot accurately capture such "rhythmic" fluctuations and lack the ability to adaptively identify and process changes in dense clusters of operations and quiet intervals during critical operational windows. Some studies have attempted to refine cargo data, introduce worker schedules, or even on-site camera signals as inputs, but these rely heavily on external variables and advanced sensing equipment, increasing implementation difficulty and system stability risks. They cannot be scaled up in complex large-scale port networks and low-computing-power environments, and cannot meet the demand for highly deterministic and stable declaration timing recommendations under extreme fluctuation conditions such as holidays. Summary of the Invention

[0003] In order to solve the above-mentioned technical problems, this invention provides an intelligent recommendation method for customs declaration timing that takes into account overseas holidays and customs clearance time differences.

[0004] The technical solution of this invention is implemented as follows: an intelligent recommendation method for customs declaration timing that takes into account overseas holidays and customs clearance time differences, including: S1: Obtain the timestamp sequence of key nodes in the past three years from the Customs Single Window, Electronic Port and Port Terminal, including customs declaration submission, preliminary review approval, tax payment, inspection instruction issuance and release confirmation, to form the original customs clearance operation log dataset. S2: The original customs clearance operation log dataset is time-normalized according to three stages: seven days before the holiday, during the holiday, and ten days after the holiday. The influence of absolute time is eliminated and the distribution characteristics of the relative time interval between each node are focused to generate rhythmic fluctuation spectrum data. S3: Based on the rhythmic fluctuation spectrum data, the sliding window statistical method is used to identify the high-frequency stable operation clusters and their start and end offsets, the number of operation density peaks within the cluster and the variation coefficient of the silent period length between adjacent clusters, and to construct the port operation rhythm fingerprint feature vector. S4: Cluster the port operation rhythm fingerprint feature vectors according to port identifiers and holiday types to generate a port operation rhythm fingerprint database containing key operation cluster structure information and silent period variation parameters; S5: Based on the current declared port identifier and target holiday type, retrieve the matching port work rhythm fingerprint from the port work rhythm fingerprint database, identify the current rhythm stage, extract the historically most frequently occurring average time interval from release confirmation to customs clearance completion under this stage, and generate a candidate set of time difference elastic anchor points. S6: Based on the aforementioned time difference elastic anchor point candidate set, assign a set of dynamic time windows with confidence weights to the application timing recommendation strategy for this application, forming application timing recommendation decision parameters with elastic response capability; S7: Real-time monitoring of the deviation between the actual clearance time of three consecutive batches of declarations under the same rhythmic stage and the center value of the candidate set of time difference elastic anchor points, and determining whether the deviation exceeds the preset fluctuation threshold to trigger the calibration condition; S8: If the deviation exceeds the preset fluctuation threshold, only update the two feature values ​​of the silent period length variation coefficient and the number of peaks of the intra-cluster operation density in the port operation rhythm fingerprint to generate a calibrated port operation rhythm fingerprint to complete the strategy locking optimization.

[0005] The intelligent recommendation method for customs declaration timing that takes into account overseas holidays and customs clearance time differences provided by this invention has the following beneficial effects: (1) This invention treats ports as “living units” with behavioral memory and response inertia, breaking through the limitations of mechanical modeling in traditional customs clearance time difference analysis that relies solely on geographical time zones, legal working hours, and static business rules. It effectively overcomes the problem of inaccurate predictions caused by nonlinear fluctuations in the process during holidays. By collecting and normalizing the timestamp sequences of key nodes in the entire customs declaration process at the minute level, absolute time interference is removed, the relative interval distribution characteristics are focused on, and the sliding window statistical method is used to extract high-frequency and stable “operation clusters” to form a “rhythmic fluctuation spectrum.” This achieves deep decoupling and structured representation of the periodic operation mode of ports, significantly improving the recognition accuracy and robustness of customs clearance behavior dynamics in complex festival scenarios.

[0006] (2) Based on the "rhythmic fingerprint" generated by clustering historical rhythmic fluctuation spectrum, this invention constructs a lightweight decision-making mechanism that does not rely on continuous numerical fitting and does not require external variable input, solving the practical bottlenecks such as poor model interpretability, high computational resource consumption, and high real-time response latency under multi-objective conflict. The rhythmic fingerprint solidifies the three core features of the key operation cluster in the form of discrete symbol encoding: start and end offset, number of density peaks, and coefficient of variation during the silent period. This avoids the high cost defects of traditional LSTM, Prophet and other models that rely on long sequence dependence and parameter tuning, and can still achieve fast matching and stage discrimination in low computing power edge environments. During runtime, by dynamically associating the rhythmic stage of the current application with the historical best time interval, a set of candidate time windows with confidence weights is output as "time difference elastic anchor points", making the application timing recommendation both flexible and deterministic. At the same time, a lightweight fingerprint calibration mechanism is introduced, which only locally updates specific features when continuous deviation exceeds the threshold, freezes the rest of the structure to prevent model oscillation, ensures the stability and long-term availability of the strategy, and significantly reduces the frequency of operation and maintenance intervention and system maintenance costs. (3) This invention realizes the pattern accumulation and semantic reuse of port behavior data, establishing a new paradigm for adaptive, closed-loop evolution of customs clearance time difference analysis, which significantly enhances the forward-looking scheduling capability and anti-disturbance resilience of the cross-border customs declaration system. Since it completely avoids the modeling dependence on external variables such as weather, transportation status or user behavior, the system is not affected by data loss or noise interference, and has stronger environmental adaptability and deployment universality, especially suitable for cross-border nodes with uneven development levels and varying degrees of informatization. The proposed rhythm fingerprint mechanism not only supports cross-holiday transfer learning, but can also be extended to horizontal comparison and collaborative optimization between different ports, laying the foundation for building a national and even global port rhythm knowledge graph. Attached Figure Description

[0007] Figure 1 This is a flowchart of the intelligent recommendation method for customs declaration timing that takes into account overseas holidays and customs clearance time differences, as presented in this invention. Figure 2This is a sub-flowchart of the intelligent recommendation method for customs declaration timing that takes into account overseas holidays and customs clearance time differences in the present invention. Figure 3 This is another sub-flowchart of the intelligent recommendation method for customs declaration timing that takes into account overseas holidays and customs clearance time differences in the present invention. Detailed Implementation

[0008] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0009] The following disclosure provides many different embodiments or examples for implementing different structures of the invention. To simplify the disclosure, specific examples of components and arrangements are described below. Of course, these are merely examples and are not intended to limit the invention. Furthermore, reference numerals and / or letters may be repeated in different examples; such repetition is for simplification and clarity and does not in itself indicate a relationship between the various embodiments and / or arrangements discussed. like Figure 1 As shown, this invention provides an intelligent recommendation method for customs declaration timing that takes into account overseas holidays and customs clearance time differences, specifically including: S1: Obtain the timestamp sequence of key nodes in the past three years from the Customs Single Window, Electronic Port and Port Terminal, including customs declaration submission, preliminary review approval, tax payment, inspection instruction issuance and release confirmation, to form the original customs clearance operation log dataset. S2: The original customs clearance operation log dataset is time-normalized according to three stages: seven days before the holiday, during the holiday, and ten days after the holiday. The influence of absolute time is eliminated and the distribution characteristics of the relative time interval between each node are focused to generate rhythmic fluctuation spectrum data. S3: Based on the rhythmic fluctuation spectrum data, the sliding window statistical method is used to identify the high-frequency stable operation clusters and their start and end offsets, the number of operation density peaks within the cluster and the variation coefficient of the silent period length between adjacent clusters, and to construct the port operation rhythm fingerprint feature vector. S4: Cluster the port operation rhythm fingerprint feature vectors according to port identifiers and holiday types to generate a port operation rhythm fingerprint database containing key operation cluster structure information and silent period variation parameters; S5: Based on the current declared port identifier and target holiday type, retrieve the matching port work rhythm fingerprint from the port work rhythm fingerprint database, identify the current rhythm stage, extract the historically most frequently occurring average time interval from release confirmation to customs clearance completion under this stage, and generate a candidate set of time difference elastic anchor points. S6: Based on the aforementioned time difference elastic anchor point candidate set, assign a set of dynamic time windows with confidence weights to the application timing recommendation strategy for this application, forming application timing recommendation decision parameters with elastic response capability; S7: Real-time monitoring of the deviation between the actual clearance time of three consecutive batches of declarations under the same rhythmic stage and the center value of the candidate set of time difference elastic anchor points, and determining whether the deviation exceeds the preset fluctuation threshold to trigger the calibration condition; S8: If the deviation exceeds the preset fluctuation threshold, only update the two feature values ​​of the silent period length variation coefficient and the number of peaks of the intra-cluster operation density in the port operation rhythm fingerprint to generate a calibrated port operation rhythm fingerprint to complete the strategy locking optimization.

[0010] Step S1: Obtain the timestamp sequence of key nodes in the past three years from the Customs Single Window, Electronic Port, and port on-site terminals, including customs declaration submission, preliminary review approval, tax payment, inspection instruction issuance, and release confirmation, to form the original customs clearance operation log dataset. Specifically, this includes: S1.1: Perform protocol parsing and connection establishment on the multi-source heterogeneous interfaces of the Customs Single Window, Electronic Port System and Port Field Terminal to obtain raw message flow data containing customs declaration number, declaration port identifier and business status code; The execution objects for protocol parsing and connection establishment of multi-source heterogeneous interfaces of the Customs Single Window, Electronic Port System and Port Field Terminal include RESTful interfaces of HTTP / HTTPS protocol, TCP-based message exchange channels and dedicated encrypted VPN tunnel transmission channels. The input conditions are access control credentials, data format definition files and network connection parameters of each interface. Based on the protocol type of each interface, the message header structure is analyzed and processed. The data type, encoding format and delimiter rules in the message are parsed using a preset protocol field mapping table to eliminate the semantic differences of the transport layer between different systems. Perform packet boundary identification operations on the parsed protocol fields, use fixed-length pattern matching or delimiter search algorithms to determine the start and end positions of a single service message, and split the multi-packet mixed stream into independent raw message units; Locate the specific fields containing the customs declaration number, port of entry identifier, and business status code in the extracted original message units, perform data extraction operations based on field length and encoding rules, and perform character set conversion processing on non-UTF-8 encoded field content; The message units that have undergone field extraction and encoding conversion are used to perform connection establishment verification. A handshake response detection mechanism is used to confirm the real-time access status of each source system. After the verification is passed, the message units are cached in the original message stream queue in chronological order. Through the above protocol parsing and connection establishment processing method, the original data access request from the multi-source system in the previous step is transformed into original message stream data with a unified structure, unified encoding and containing key identification fields, so as to provide a highly consistent and complete input source for subsequent key field filtering; For example, in a scenario involving the connection to the Customs Single Window RESTful interface, the access control credential is a 128-bit API key, and the network connection parameters include the TLS 1.3 protocol and port 443. When parsing the message header, a mapping table is used to convert the Content-Type from application / xml to application / json format. In packet boundary identification, this is achieved through scanning... The delimiter locates message units with a length of 1024 bytes. When locating fields, the customs declaration number is 18 digits, the port of entry identifier is 4 letters, and the business status code is 3 digits. Character set conversion converts GBK encoding to UTF-8 to ensure cross-system compatibility. The handshake verification process uses three consecutive successful ACK responses as the verification standard. After successful access, messages are cached in the queue according to their timestamps. Verification results show that the cache queue can stably write 50 original messages per second. Subsequent filtering steps can directly achieve millisecond-level matching on this dataset, improving the real-time performance and stability of the data processing link. S1.2: Based on the original message stream data, perform key field filtering operations to filter out five types of core business events: customs declaration submission, preliminary review approval, tax payment, inspection instruction issuance, and release confirmation, in order to generate a key business event sequence; Field-level precise matching processing is performed on the raw message stream data obtained through multi-source heterogeneous interface protocol parsing. Core event identification rules based on business status codes are set, and the types of business events that need to be retained are limited to five core nodes: customs declaration submission, preliminary review approval, tax payment, inspection instruction issuance and release confirmation, so as to ensure that the input data for subsequent rhythm feature analysis has a unified business meaning. The matched business event fields are parsed, and the position and length of the event identifier in the message structure are determined by regular expression patterns. Miscellaneous fields that do not conform to the preset format standard are removed, forming a primary event record set composed of event identifiers and associated time information. Duplicate entries in the primary event record set are deduplicated. A uniqueness check is performed using a composite key consisting of the customs declaration number and the event type. Multiple redundant records under the same event type for the same customs declaration are merged into a single valid record to ensure the integrity and uniqueness of the event sequence. The deduplicated event record set is grouped according to the customs declaration number and sorted according to the business process sequence of the event type, forming a chronological arrangement of five core events within the same customs declaration lifecycle, which facilitates subsequent timestamp normalization processing. Perform data structure encapsulation operations on the sorted event record set, mapping each group of sorted event records into a structured object containing event type, customs declaration number, port of entry identifier and original time field, generating a key business event sequence that can be directly read by the downstream time zone normalization processing module; Through the above field filtering and structured processing methods, the raw message stream results obtained from the interface are transformed into a sequence of key business events containing unified business meaning, complete time information and no redundancy, so as to realize the basic data preparation required for high-precision rhythm analysis. For example, the system imports raw message streams containing fields such as customs declaration number, port of entry identifier, business status code, and timestamp into a cross-border trade system. Status codes are set as follows: 1001 corresponds to customs declaration submission, 2001 to preliminary approval, 3001 to tax payment, 4001 to inspection instruction issuance, and 5001 to release confirmation. When performing field-level matching, the system only retains records with status codes within the aforementioned set. Multiple records with the same customs declaration number and status code in the raw data are merged, and the record with the earliest timestamp is retained as the valid record for that event type. In the generated sequence of key business events, the events in a customs declaration's lifecycle are arranged as follows: status code 1001 corresponds to timestamp 2023-01-05T09:12:00, status code 2001 corresponds to timestamp 2023-01-05T14:45:30, status code 3001 corresponds to timestamp 2023-01-06T10:03:15, status code 4001 corresponds to timestamp 2023-01-07T08:20:50, and status code 5001 corresponds to timestamp 2023-01-07T16:55:40. This sequence is directly used as input for subsequent S1.3 time zone normalization processing, which can significantly improve the accuracy of node positioning and data consistency in the rhythm analysis of holiday scenarios. S1.3: Perform time zone normalization and precision alignment on the non-standard time format fields in the key business event sequence to eliminate time base differences between multiple source systems and generate a standardized timestamp sequence; S1.4: The standardized timestamp sequence is subjected to continuity verification and outlier removal using a sliding time window algorithm to repair the data interruption caused by network jitter and generate cleaned and effective work trajectory data. S1.5: Based on the cleaned and effective operation trajectory data, perform a structured encapsulation operation to aggregate discrete timestamp records into complete full lifecycle log entries according to the customs declaration dimension, so as to form the original customs clearance operation log dataset.

[0011] Step S2: The original customs clearance log dataset is time-normalized according to three stages: seven days before the holiday, during the holiday, and ten days after the holiday. This process removes the influence of absolute time and focuses on the distribution characteristics of relative time intervals between nodes, generating rhythmic fluctuation spectrum data. Specifically, this includes: S2.1: Perform holiday phase alignment processing on the absolute timestamp sequence in the original customs clearance operation log dataset. Based on the preset time window division rules of seven days before the holiday, during the holiday, and ten days after the holiday, map the discrete time points to relative time offsets relative to the holiday reference point to generate a phase-aligned time sequence with a unified time reference system. The standardized timestamp sequence generated by the encapsulation of the original customs clearance operation log dataset through the S1.5 sub-step is used to call the holiday benchmark point configuration module to read the benchmark time identifier of the corresponding holiday and the time window division parameters of seven days before the holiday, during the holiday, and ten days after the holiday. The absolute timestamp sequence is input into a time phase mapping algorithm, and the relative time offset is calculated using the difference between the holiday reference time identifier and the absolute timestamp. The calculation formula is as follows: in Let be the relative time offset of the i-th event. Let be the absolute time of the i-th event. The base time for the holiday; Window segment classification processing is performed on the relative time offset, by comparison. The boundary between the numerical value and the three time intervals of seven days before the holiday, during the holiday, and ten days after the holiday is used to determine the stage label to which the event belongs, and a stage field is added to the output data structure. The time zone normalization module is invoked to numerically correct the relative time offsets of different source systems based on a unified reference time zone. The calculation formula is as follows: in To correct for relative time offset, This is a cross-system time zone difference correction factor; The corrected phase offset sequence is sorted and indexed to reconstruct the time series to ensure that the time series for subsequent interval vector calculations is continuous and intact. By using phase alignment processing, the absolute timestamp data from the previous step is transformed into a relative time series with a unified time reference system and holiday phase labels, thereby achieving phase consistency preprocessing of holiday logs from multiple sources and providing accurate and unbiased input conditions for the interval vector calculation in S2.2. For example, if the base time for the Spring Festival is set to 00:00 on February 12th of a certain Gregorian calendar year, the window for the seven days before the festival is -168 hours to -1 hour, the window during the festival is 0 to +168 hours, and the window for the ten days after the festival is +169 hours to +408 hours. For a certain absolute time event... =2021-02-10 08:15:00, Base Time =2021-02-12 00:00:00, offset calculation is as follows This is categorized as the seven-day period before the holiday. If the original timestamp comes from the +8 time zone port system, and the reference time zone is set to UTC0, then the correction factor... =-8 hours, corrected offset =-47 hours, the final output record is "Event 1, offset -47 hours, stage: seven days before the holiday". In this scenario, all events are sorted according to a unified phase reference system after this processing, which ensures the stability and accuracy of subsequent calculation of the interval between adjacent nodes. The verification results show that this method can significantly improve the correctness of holiday stage classification and greatly improve the consistency of rhythm analysis when merging cross-system data. S2.2: Based on the time difference between adjacent key nodes in the phase-aligned time series, perform interval vector calculation operation, and use the difference algorithm to extract the relative time interval values ​​between continuous links such as customs declaration submission to preliminary review approval and preliminary review approval to tax payment, forming a set of relative time interval vectors that characterize the efficiency of the workflow. S2.3: Apply a sliding window statistical algorithm to the set of relative time interval vectors to perform local density scanning processing. Set a fixed-width time sliding window to traverse and move on the relative time axis and count the frequency distribution of operation events within the window to generate a local density distribution curve that reflects the trend of work intensity changing with time. Perform sliding window parameter initialization processing on the input end of the relative time interval vector set, set a fixed width time sliding window according to the holiday period span, and bind the sliding window width parameter and the window movement step parameter as a time axis traversal control vector; The fixed-width time sliding window is gradually translated on the relative time axis from the starting offset position. The event extraction module in the window is called to read all the relative time interval samples covered by the current sliding window, and the number of samples is recorded as the local event frequency value. For each sliding window position, perform frequency accumulation calculation on the local event frequency value, combine it with the start and end offset of the sliding window coverage area to generate density metric pairs, and store them in the local density cache sequence to form a continuous density change trajectory. The normalized density is obtained by comparing the frequency values ​​in the local density cache sequence with the sliding window width parameter, and the normalized density curve is calculated using the following formula: in, For normalized density values, Frequency of operations within the window Width of the sliding window; The normalized density curves are organized into multiple continuous curve data segments according to the sliding window traversal order, which serve as local density distribution curves reflecting the trend of work intensity changing with relative time. Through the above sliding window statistical algorithm, the set of relative time interval vectors output in step S2.2 is transformed into a local density distribution curve for rhythmic feature peak identification, thereby realizing the visualization and quantitative analysis of the work flow time pattern. For example, during the seven days leading up to the Spring Festival at a certain cross-border port, the sliding window width parameter is set to... Minutes, window movement step size is Minutes are used to iterate through the set of relative time interval vectors. At a certain window location, coverage is achieved. Calculate the local event frequency value based on the interval between the submission of a customs declaration and its initial approval. The normalized density value is That is, every minute This is an operation event. The window is moved to the morning of the third day before the holiday. During the specified time period, the local frequency value increased to , normalized density value The curve forms a significant density peak during this period. After performing a sliding window scan throughout the seven days before the holiday, the local density distribution curve exhibits a stable rhythmic characteristic of a midday trough and a morning peak. The output data can be directly used for subsequent peak identification and noise suppression processing, significantly improving the accuracy and robustness of rhythmic feature extraction. S2.4: Based on the local density distribution curve, peak identification and noise suppression processing are performed. An extreme value detection algorithm is used to locate the center position of the high-frequency stable operation cluster and filter out occasional fluctuation interference, and extract the set of rhythmic feature peak points with significant statistical significance. S2.5: The rhythmic feature peak point set and its corresponding relative time interval distribution pattern are structurally encapsulated and integrated into rhythmic fluctuation spectrum data containing time phase information and density feature information according to the preset data format standard, which serves as the direct input source for constructing the port work rhythmic fingerprint feature vector.

[0012] like Figure 2 As shown, step S3 involves using a sliding window statistical method based on the rhythmic fluctuation spectrum data to identify high-frequency, stably occurring operational clusters and their start and end offsets, the number of peaks in the operational density within a cluster, and the coefficient of variation of the silent period length between adjacent clusters, thereby constructing a port operational rhythm fingerprint feature vector. Specifically, this includes: S3.1: Perform fixed-length sliding window scanning processing on the local density distribution curve in the rhythmic fluctuation spectrum data, use the frequency accumulation statistics algorithm to traverse the relative time axis and calculate the aggregation degree of operation events in each window to generate a sliding window frequency sequence that characterizes the spatiotemporal distribution of work intensity; S3.2: Based on the frequency sequence of the sliding window, extreme value detection and neighborhood merging are performed. An adaptive threshold filtering algorithm is used to locate the peak intervals that are continuously higher than the preset density baseline and merge adjacent overlapping intervals to extract the original set of operation clusters representing high-frequency stable operation behavior. Based on the sliding window frequency sequence, a set of density baseline parameters is defined on the relative time axis. The mean and standard deviation in the historical rhythm fluctuation spectrum data are used as the basis for adaptive threshold calculation to form a dynamic density baseline for different festival stages. Extreme value detection is performed on the data points in each frequency sequence. A peak capture algorithm combining first-order difference sign analysis and second-order difference amplitude determination is used to identify intervals that are continuously higher than the density baseline. Isolated abnormal peaks are removed during the detection process to ensure that the output peak intervals have statistical stability. The detected peak intervals are processed by neighborhood fusion, which combines interval intersection and adjacent interval length determination. If the adjacent interval interval length is less than the preset merging threshold, a fusion block is constructed by interval union to merge adjacent or overlapping peak intervals. Record the start and end time offsets of the intervals in the merged peak interval set, and associate them with the corresponding operation event density features within the window to form the original set data of the structured operation cluster; The following adaptive threshold calculation formula is used to determine the density baseline values ​​for different holiday periods: in, This represents the average frequency within the window. The standard deviation of the frequency within the window. The stage sensitivity coefficient is calculated using this formula. As a dynamic baseline for filtering peak ranges, the density threshold is adaptively changed with the holiday period; Through the above processing method, the sliding window frequency sequence of the previous step is transformed into the original set of operation clusters with unique time span identifiers and density feature identifiers, so as to realize the accurate extraction of high-frequency stable operation behavior and provide reliable input for subsequent morphological feature quantification and fingerprint construction. For example, during the seven days leading up to the Spring Festival, the input sliding window frequency sequence parameters were configured with a window width of 30 minutes and a window step size of 5 minutes. Based on this sequence, the mean was calculated to be 48.3, the standard deviation was 7.5, and the sensitivity coefficient was... Set it to 1.2, and substitute it into the formula to obtain the dynamic baseline value. All intervals with a frequency sequence value continuously higher than 57.3 were marked, and three isolated peaks were removed by second-order difference amplitude determination. The merging threshold for neighborhood fusion processing was set to 10 minutes, resulting in five merged peak intervals. The start and end offsets of each interval and the corresponding density peak data were encapsulated into original set entries for the operation clusters. In the verification results, this set can completely cover the high-frequency operation period in the seven days before the Spring Festival. The density stability of all clusters in subsequent boundary localization and peak counting is significantly improved, supporting the accuracy of rhythm fingerprint generation. S3.3: Perform precise boundary positioning and internal structure analysis on each independent operation cluster in the original set of operation clusters, use the start and end point search algorithm to determine the time span of the cluster and apply the peak counting technique to count the number of significant density protrusions in the cluster, so as to generate a basic morphological feature set containing the start and end offset of the operation cluster and the number of operation density peaks in the cluster. The input for each independent operation cluster in the original set of operation clusters is the set of peak intervals obtained by step S3.2 after adaptive threshold filtering and neighborhood merging, wherein each operation cluster contains start and end boundaries and local density distribution data on the relative time axis; Based on this input, the boundary precise location processing is performed. The start and end point search algorithm is used to iteratively scan from the peak center to both sides in the local density curve until the density value is lower than the preset density baseline threshold for the first time. The relative time coordinate of this point is recorded as the boundary endpoint of the cluster. The relative time coordinates of the start and end points are normalized and mapped to the unified time reference system of the festival reference point to obtain the start and end offset sequence of the operation cluster. The density curves within clusters with defined boundaries are processed using peak counting techniques. A second derivative sign change detection method is used to identify significant protrusions in the curves. Minimum peak height thresholds and minimum peak spacing thresholds are set to filter out low-amplitude and excessively dense noise peaks. The number of peaks meeting each condition is used as an index of the number of peaks in the cluster's operational density, and combined with start and end offset data to form basic morphological feature units. For the relative time span... The calculation uses the following formula: in and These are the relative time coordinates of the end point and the start point of the cluster boundary, respectively; Through the above processing method, the original set of operation clusters in S3.2 is transformed into a basic morphological feature set containing the start and end offsets of operation clusters and the number of peaks in the operation density within the cluster, so as to realize the input preparation for subsequent calculation of the coefficient of variation of the silent period length and fingerprint feature vector encapsulation. For example, in the circadian rhythm data of Port A on the third day before the Spring Festival, the peak interval of the sliding window frequency sequence is between 12.5 and 13.8 hours with a relative time offset, and the local density mean is higher than the baseline threshold of 0.75 events / minute. The start-end point search algorithm extends to both sides of the peak center (13.1 hours), with the density value decreasing to 0.74 on the left at 12.5 hours and to 0.73 on the right at 13.8 hours, recording the offset start point at 12.5 hours and the end point at 13.8 hours. During the peak counting process, the change in the sign of the second derivative of the density curve in this interval detected three valid peak positions, all with peak heights exceeding 0.05 events / minute and peak spacing not less than 0.10 hours. According to the formula... The time span was 1.3 hours. The basic morphological feature units output were the start and end offsets (12.5h, 13.8h) and the number of peaks in the intra-cluster operation density (3), which served as direct inputs for generating the subsequent silent period length variation coefficient sequence. In this scenario test, the extracted offsets and peak features showed a significant improvement in matching accuracy with historical batches on the third day before the Spring Festival, and the accuracy of the calibrated fingerprint generation was significantly enhanced, ensuring the stability of rhythm stage determination. S3.4: Based on the time interval data of adjacent operation clusters in the basic morphological feature set, perform discrete metric processing, use the standard deviation normalization algorithm to calculate the fluctuation amplitude of the silent period length between each adjacent cluster and eliminate the influence of dimensions, so as to generate a silent period length variation coefficient sequence that characterizes the difference in the stability of the operation rhythm. S3.5: Perform multi-dimensional vector encapsulation processing on the operation cluster start and end offsets, the number of peaks in the operation density within the cluster, and the variation coefficient sequence of the silent period length in the basic morphological feature set. Use structured coding rules to map the three types of discrete features into a fixed-dimensional numerical array to generate a port operation rhythm fingerprint feature vector with unique identification capabilities.

[0013] like Figure 3 As shown, step S4 involves clustering the port operation rhythm fingerprint feature vectors according to port identifiers and holiday types to generate a port operation rhythm fingerprint database containing key operation cluster structure information and silent period variation parameters. Specifically, this includes: S4.1: Perform composite key-value encapsulation processing on the port work rhythm fingerprint feature vector, and use the port identification field and holiday type field as a joint primary key to reorganize the discrete fingerprint feature data into port rhythm index units with unique index identifiers, so as to generate a port rhythm index dataset with structured addressing capability. Perform field integrity verification on the port identifier field and holiday type field in the port work rhythm fingerprint feature vector, and call the preset field verification rule base to determine whether its value range conforms to the unified coding standard to ensure the uniqueness constraint of subsequent composite key value encapsulation; The verified fingerprint feature vector is input into the composite key generation module. The composite primary key string is formed by concatenating field values ​​and adding delimiters. The composite primary key structure is sorted by priority and ensures that there are no duplicate or null values, thereby achieving unique indexing capability. A hash transformation operation is performed on the generated composite primary key string, and a fixed-length hash value is calculated using a secure hash algorithm as a structured addressing index. This hash value corresponds one-to-one with the original composite primary key, which is used to reduce index storage and improve retrieval efficiency. The original port operation rhythm fingerprint feature vector is bound to the hash value index. The three core discrete features of the feature vector are associated with the hash index through the mapping table data structure, and a port rhythm index unit containing index field, feature data field and metadata field is generated to ensure that the unit can be uniquely addressed in multi-dimensional space. All generated port rhythm index units are written into the port rhythm index dataset in order of index value, and a unified structured index storage file is established to standardize the input required for subsequent multidimensional spatial mapping and clustering operations. Through the above composite key-value encapsulation process, the feature vector results of the previous step are transformed into a port rhythm index dataset with unique index identification capabilities, thereby achieving addressability and efficient retrieval performance in the subsequent database construction process. For example, in the scenario of overseas holidays, the input port operation rhythm fingerprint feature vector includes the port identification field value "HKKT01" and the holiday type field value "CN_NY". Field integrity checks ensure that the values ​​are not empty and conform to encoding standards. A composite primary key string "HKKT01|CN_NY" is generated, concatenated using the preset separator "|" while maintaining the port identification in the order of priority. This composite primary key is then subjected to a SHA-256 hash operation to obtain a 256-bit hash value index. For example, the input to the hash function... After the operation module, the output is a fixed-length hash string "87a8...e6b2". The hash index is structured and bound to the operation cluster start and end offset array [15,27], the number of peaks in the cluster operation density [3], and the coefficient of variation of the silent period length [0.42] in the feature vector to form an index unit record {index: "87a8...e6b2", start and end offset: [15,27], number of peaks: [3], coefficient of variation: [0.42], metadata: {collection time: "2023-01-15"}}. Multiple port rhythm index units are sorted by index and stored in the file "IndexDataset.db". It is verified that the corresponding index unit can be quickly located by inputting the joint primary key during retrieval, which significantly shortens the retrieval time and ensures the efficiency and accuracy of subsequent clustering mapping; S4.2: Perform multidimensional spatial mapping operation based on the port rhythm index dataset, and use hierarchical clustering algorithm to aggregate port rhythm index units with the same port identifier and similar holiday types into the same feature cluster to generate a set of port rhythm feature clusters that reflect the commonality of port operation behavior; Each index unit in the port rhythm index dataset is subjected to feature dimension expansion processing to extract three types of core feature fields, including operation cluster start and end offset, number of peaks in operation density within the cluster, and coefficient of variation of silent period length, and to construct a multidimensional data vector that can be used for spatial computation. The above multidimensional data vectors are combined with the port identifier and holiday type fields in the joint primary key to form composite feature labels, which serve as the initial classification basis for the hierarchical clustering algorithm, to ensure that geographical and holiday behavioral characteristics are considered simultaneously during the clustering process. Based on the established distance measurement criteria, the similarity matrix between each data vector is calculated using Euclidean distance or Manhattan distance. The three types of features, namely start and end offset, number of peaks and coefficient of variation, are normalized and then synthesized into a comprehensive distance value according to the weight ratio, forming a comparable multidimensional spatial distance set. The similarity matrix is ​​used to perform hierarchical clustering tree construction operations, starting from the most similar index unit and gradually aggregating, recursively generating cluster merging paths and updating the cluster center vector after each merging to maintain the stability of the clustering structure; When the clustering tree reaches the preset number of clusters or similarity threshold, merging stops. The index units of the members within each cluster are extracted and reorganized into a set of port rhythm feature clusters. This set is used to achieve a common description of the same port identification and similar holiday type operation behavior. By using multidimensional spatial mapping and cluster merging processing, the port rhythm index data generated in step S4.1 is transformed into a structured set of port rhythm feature clusters, which enables high-confidence induction of operational behavior patterns and the basic data required for subsequent centroid extraction. For example, the port rhythm index cell, which includes the start and end offsets of the operation cluster (in minutes), the number of peaks in the operation density within the cluster (in units), and the coefficient of variation of the quiet period length (dimensionless), is transformed into a three-dimensional vector. The weights for the number of peaks are set to 0.3, the start and end offsets to 0.4, and the coefficient of variation to 0.3. After standardizing all index cells, the combined distance between each pair of cells is calculated using Euclidean distance. in, , , These are the weighting factors for start and end offsets, number of peaks, and coefficient of variation, respectively. , , These are the feature values ​​of the current index cell. , , These are the feature values ​​of the comparison index unit. A similarity threshold of 0.65 is set in the hierarchical clustering using a distance matrix. Clusters with the same port identification and a distance below this threshold are gradually merged, ultimately forming five port rhythm feature cluster sets. In this embodiment, the rhythmic pattern within each cluster set remains significantly stable despite fluctuations in customs clearance efficiency during holidays. The output cluster sets, when used for center point extraction in subsequent S4.3, can significantly improve the accuracy of pattern recognition and shorten the computation time. S4.3: Perform center point extraction and boundary delineation processing on the port rhythm feature cluster set, use the centroid calculation algorithm to locate the representative fingerprint vector in each feature cluster and determine the distribution radius of the members in the cluster, so as to generate port rhythm prototype vectors representing typical operation modes and corresponding cluster range parameters. For each cluster in the port rhythm feature cluster set, extract the numerical array of member fingerprint vectors as the centroid calculation input; Based on the three types of discrete features of structured coding, vector weighted summation is performed on each feature. The values ​​of each feature dimension are accumulated and normalized according to the number of members to generate a set of candidate values ​​for the centroid of the cluster. The numerical values ​​of each dimension in the centroid candidate value set are combined into a single feature vector, and the Euclidean distance formula is used. in For member characteristic values, Let n be the centroid feature value, n be the total feature dimension of the rhythm fingerprint, and i be the feature dimension index. The distance from each member of the cluster to the centroid is calculated. ; The highest quality centroid vector is determined as the representative fingerprint vector of the cluster based on the principle of minimizing the sum of squared distances. The maximum value of the distance sequence from all members to the centroid is extracted and used as the initial value of the cluster radius. The radius is then adjusted by combining the standard deviation amplification factor to generate the cluster range parameter. By defining the combination of centroid and cluster radius, a port rhythm prototype vector and corresponding cluster range parameters representing the typical operation mode of the cluster are constructed, thereby achieving a precise mapping from clustering results to structured pattern index. For example, in the clustering results of Spring Festival rhythm data for a certain port, the number of cluster members is set to 20. The three features are the start and end offset of the operation cluster, the number of peaks in the operation density within the cluster, and the coefficient of variation of the silent period length, with average values ​​of 3.2, 4, and 0.15, respectively. The summation of each feature value and the division by the number of members yields a centroid candidate value set [3.2, 4, 0.15], which is used to construct the centroid feature vector. The Euclidean distance is used to calculate the distance between each member and the centroid, where the distance between a certain member vector [3.0, 5, 0.20] and the centroid is... The calculated value is 0.37. The maximum distance between all members and the centroid is set to 0.45, and a cluster range radius of 0.495 is generated by combining the magnification factor of 1.1. The output centroid vector [3.2,4,0.15] and the corresponding radius of 0.495 are stored in the rhythm prototype record for subsequent database storage and fast retrieval, realizing accurate representation and stable locking of typical operation modes in the Spring Festival scenario; S4.4: Perform structured storage mapping operation based on the port rhythm prototype vector and cluster range parameters, and write the core features such as the start and end offset of key operation clusters, the number of peaks in the operation density within the cluster, and the coefficient of variation of the silent period length into the preset database table structure to generate port work rhythm fingerprint database record entries containing complete rhythm semantic information. S4.5: Utilize the entries recorded in the port work rhythm fingerprint database to perform global index building processing, establish a fast retrieval mapping relationship from the combination of port identifier and holiday type to the specific fingerprint storage address, so as to generate a port work rhythm fingerprint database that supports millisecond-level query response; The execution object for global indexing and constructing the records in the port work rhythm fingerprint database is the complete port rhythm fingerprint data set that has completed structured storage mapping, which includes core feature records with port identifier and holiday type as composite keys; For this set, a combined key-value mapping table is first established in the database storage node. The port identification field and holiday type field of each record are converted into a fixed-length index code through hash encoding, and the index code is bound to the corresponding physical storage address. In this process, a multi-branch B-tree is used to construct a multi-level index path, with the hash index as the first-level addressing node, the port identifier bucket index as the second-level node, and the holiday type bucket index as the third-level node, thereby achieving multi-dimensional accurate positioning. To improve retrieval response speed, leaf node caching optimization is performed on the above B-tree index. A ring cache structure based on the most recently used strategy is adopted to map frequently accessed leaf node indexes to memory addresses to reduce disk I / O latency. After the index is built, redundant backup links are added, and the index mapping relationship is maintained in both primary storage and mirror storage through a dual-write strategy to reduce the risk of index data loss. For the index query process, a parallel retrieval mechanism of skip list structure and B-tree structure is introduced. Skip list is used for fast scanning of short key range, and B-tree is used for global exact matching. The two are automatically switched in the query control module through a priority hit strategy to achieve millisecond-level response performance. Through the above processing method, the rhythm fingerprint database record entries of the previous step are transformed into index mapping data with global fast retrieval capability, so as to achieve the expected technical effect of locating the specific fingerprint storage address within milliseconds when the port identifier and holiday type combination are used as input. For example, in a cross-border port rhythm fingerprint database, 5000 complete feature vector records containing port identifiers and holiday types are stored. The port identifier uses a 3-digit numeric code, and the holiday type uses a 2-letter code. During global index construction, the port identifier and holiday type are hashed separately to generate a 16-bit composite index code, which is then mapped to the physical storage address of the corresponding record. When constructing a multi-way B-tree, the branch factor of the first-level nodes is set to 256, and the high 8 bits of the composite index code are bucketed; the branch factor of the second-level nodes is 64, and the port identifier code is bucketed; the branch factor of the third-level nodes is 32, and the holiday type code is bucketed. The leaf node cache has a capacity of 1024 index records, and a recently used eviction policy is used to maintain the memory residency of frequently accessed nodes. To verify retrieval performance, a skip list structure was simultaneously enabled in the query control module, configured with a step size of 4, to quickly scan index items within a range of ports with the same identifier and similar holiday types; meanwhile, a B-tree was retained for long-distance exact matching. In performance testing, 1000 searches were performed for randomly distributed combinations of port identifiers and holiday types, and the average response time remained within a certain range. Within milliseconds, and without any index hit errors, the output results are all the storage addresses of the corresponding record entries, achieving a significant improvement in retrieval speed and a remarkable enhancement in system responsiveness.

[0014] Step S5: Based on the current declared port identifier and target holiday type, retrieve the matching port work rhythm fingerprint from the port work rhythm fingerprint database, identify the current rhythm stage, and extract the historically most frequently occurring average time interval from release confirmation to customs clearance completion within that stage, generating a candidate set of time difference elastic anchor points. Specifically, this includes: S5.1: Perform composite key-value encapsulation on the port identifier field and the target holiday type field in the current declaration request, and use a hash mapping algorithm to convert the discrete business context information into a joint retrieval primary key with unique addressing capability, so as to generate a port rhythm query index for fast database location. S5.2: Based on the port rhythm query index, perform multi-dimensional spatial matching operation in the port work rhythm fingerprint database, and use the nearest neighbor search algorithm to locate the port rhythm prototype vector and the corresponding cluster range parameter that are completely consistent with the joint retrieval primary key, so as to extract the matching port work rhythm fingerprint that represents the commonality of the operation behavior of a specific port under a specific holiday. The port rhythm query index is used to perform a multidimensional spatial matching operation in the port work rhythm fingerprint database. The query index is used as the input condition for precise retrieval, and the joint primary key field is compared one-to-one with the key value in the database index table. During the comparison and matching process, the nearest neighbor search algorithm is called to construct a multi-dimensional feature space, mapping the three core features of each port rhythm prototype vector in the database to a set of coordinate points of the same dimension as the query index; For each candidate coordinate point set, the Euclidean distance value of the corresponding coordinate point in the index is calculated and queried. A threshold filtering logic is used to exclude irrelevant samples whose distance is greater than the cluster radius. For the remaining samples, a cluster range consistency check is performed, using the cluster center coordinates and cluster radius parameters to determine whether the sample completely falls within the corresponding cluster range. Extract the prototype vector data block of the sample that meets the cluster domain range and read the associated cluster domain parameters, and use it as a matching port work rhythm fingerprint to characterize the commonality of the operation behavior of a specific port under a specific holiday. When performing distance calculations, the Euclidean distance is obtained using the following formula: in To query the feature value of the index in the i-th dimension, Let i be the feature value of the database sample in the i-th dimension. The summation symbol represents the cumulative sum of squared differences across all feature dimensions. The square root operation yields the overall distance quantization value. Through the above matching process, the joint retrieval primary key is quickly mapped to the corresponding rhythm prototype vector data, achieving the technical effect of accurate extraction of specific port holiday operation modes and subsequent rhythm stage identification. For example, during the matching process, the input port rhythm query index contains three feature values: the operation cluster start-end offset is encoded as 15, the number of peaks in the intra-cluster operation density is encoded as 8, and the coefficient of variation of the quiescent period length is encoded as 4. The corresponding three-dimensional feature values ​​of a prototype vector in the database are 16, 8, and 5, respectively, and the cluster radius is set to 2. Euclidean distance calculation is then performed. The distance value is obtained The sample is determined to be a matching vector because its size is less than the cluster radius of 2. After extracting the prototype vector and the cluster range parameters, it is passed to the subsequent rhythm stage recognition module. Verification shows a significant improvement in the correspondence between the output and the rhythm stage labels, ensuring the stability of the recommendation strategy during holidays. S5.3: Perform real-time phase alignment processing on the start and end offset sequence of key operation clusters in the matched port work rhythm fingerprint, calculate the relative time offset value of the key operation clusters relative to the festival reference point based on the current system timestamp, and identify the specific rhythm stage label of the current moment through the interval inclusion judgment logic to generate the current rhythm stage identifier that represents the current work rhythm state. S5.4: Based on the current rhythm stage identifier, retrieve the data of the most frequently occurring average time interval from release confirmation to clearance completion in the historical period under the matching port work rhythm fingerprint. Use the statistical mode extraction algorithm to filter out the set of time span values ​​with the highest frequency to generate a set of historical optimal time intervals that reflect the historical optimal clearance efficiency. The input consists of the identified current rhythm stage identifier and the feature storage area that matches the port work rhythm fingerprint, which contains a complete historical record of the time taken from release confirmation to customs clearance completion for that stage. The frequency distribution table of time span is constructed by statistically analyzing the time consumption records for the current rhythm stage in the feature storage area according to the time span value. The mode extraction algorithm is applied to the frequency distribution table of the time span, and the candidate set of the time span with the highest frequency is determined by frequency sorting. Duplicate time span values ​​in the candidate set are merged to ensure that each time span is unique in the set; The merged time span candidate set is associated with its corresponding frequency information to generate a set of historical best time intervals containing frequency weights. By extracting the mode and removing redundancy, the results of the previous step are transformed into time span data that reflects the historical best customs clearance efficiency, thus realizing the construction of basic information for time difference elastic anchor points based on historical data. For example, in the current rhythm phase of the third day before the Spring Festival, a customs declaration system extracted 120 records of the time taken from release confirmation to customs clearance completion during this phase over the past three years by matching the port's work rhythm fingerprint feature storage area. The time spans were concentrated at 28 minutes, 30 minutes, and 31 minutes, with 30 minutes appearing 52 times, 28 minutes 46 times, and 31 minutes 22 times. A frequency distribution table was created for each time span value and its frequency. The mode extraction algorithm was used to identify the 30-minute interval with the highest frequency as the first candidate, and the 28-minute interval with the second highest frequency as the second candidate, forming a set of historically optimal time intervals {28 minutes, 30 minutes}. During processing, the mode extraction algorithm followed the formula... in For time span samples, To determine the frequency of occurrence of the sample, the elements in the output set are sorted by frequency and redundancy is removed. The results show that this set significantly improves the stability and reliability of recommendations for application timing before the Spring Festival. S5.5: The set of historical best time intervals is structurally associated and encapsulated with the current rhythm stage identifier, confidence weight labels are added and converted into a standardized time window object format to generate a candidate set of time difference elastic anchor points that has elastic response capabilities and can be directly used for recommendation strategy calculation.

[0015] Step S6: Based on the candidate set of time difference elastic anchor points, assign a set of dynamic time windows with confidence weights to the application timing recommendation strategy, forming application timing recommendation decision parameters with elastic response capabilities. Specifically, this includes: S6.1: Perform historical matching degree statistical processing on each candidate time window in the time difference elastic anchor candidate set to extract the historical hit frequency and average deviation value of each candidate time window under the corresponding rhythm stage, and generate an original dataset of anchor quality containing frequency features and deviation features. S6.2: Based on the frequency and deviation features in the original dataset of anchor point quality, perform normalized weighted calculation, use the inverse weight allocation algorithm to convert the historical hit frequency into a positive confidence factor and the average deviation value into a negative risk factor, and generate a single-dimensional confidence score sequence that characterizes the reliability of each candidate time window. For the frequency and deviation features in the original dataset of anchor point quality, input condition standardization processing is performed to map the frequency feature vector and deviation feature vector to a unified numerical range to eliminate dimensional differences and ensure the comparability of subsequent weighted calculations. A positive confidence factor is constructed on the normalized frequency feature vector. An inverse weight allocation algorithm is used to set a larger confidence weight for high-frequency hit values ​​and to perform quantization processing to generate a positive confidence factor sequence to represent the historical hit intensity. The normalized deviation feature vector is used to construct negative risk factors. The same inverse weight allocation algorithm is used to set the lower average deviation value to correspond to the smaller risk weight and to quantify it to generate a negative risk factor sequence to represent the stability of the time window. The positive confidence factor sequence and the negative risk factor sequence are linearly combined according to their element indices, and the one-dimensional confidence score is calculated using the following formula: in This is a one-dimensional confidence score. Frequency weighting As a positive confidence factor, Risk weighting It is a negative risk factor; The formula is applied to all candidate time windows to generate a complete unidimensional confidence score sequence while maintaining a structured association with the candidate time window index. This, through normalization and inverse weighting, transforms features of different properties in the original anchor quality dataset into a unified reliability index, enabling a comprehensive evaluation of the historical stability and hit rate of candidate time windows. For example, in the raw dataset of anchor point quality for a specific port of entry on the third day before the Spring Festival, the normalized value of historical hit frequency is set to 0.85, and the normalized value of average deviation is set to 0.12. Frequency weights are then set. The risk weight is 0.7. for 0.3 indicates a positive confidence factor. =0.85, negative risk factor =0.12, calculate the one-dimensional confidence score using the formula: The calculation result is The final confidence score was 0.559. This score is significantly higher than that of other low-frequency, high-bias candidate time windows, indicating that this time window has high recommendation stability and success rate in this rhythmic phase. It has been verified that its hit rate has been greatly improved in the past five cycles, which can effectively reduce strategy fluctuations. S6.3: Perform probability distribution fitting processing on the single-dimensional confidence score sequence, and use the Gaussian kernel density estimation method to map the discrete score values ​​into a continuous probability density function curve to generate a confidence probability distribution model describing the success rate of the application timing. Using the single-dimensional confidence score sequence as the input data object, a computational benchmark for distribution fitting processing is established to ensure the accuracy of probability features in the subsequent dynamic time window boundary cutting process; All discrete values ​​in the scoring sequence are sorted and deduplicated to form a normalized set of scoring values, which serves as the sampling point set for probability density estimation. Kernel density estimation is applied to the sampling point set, using a Gaussian kernel function and calculating the kernel function value according to the preset bandwidth parameter. By constructing a Gaussian distribution curve at each sampling point, the overall score distribution is continuously superimposed. A bandwidth adaptive strategy is adopted to locally optimize the bandwidth parameters and adjust the kernel function width according to the degree of aggregation of the score values ​​in different intervals, so as to improve the resolution in high-density regions and avoid excessive smoothing in low-density regions. All Gaussian kernel curves are summed and superimposed in the order of the scoring sequence, and the cumulative value is normalized so that the confidence probability density is integrated into a single unit value over the entire scoring domain. The Gaussian kernel density estimation model is constructed using the following mathematical expression: in, This represents the confidence probability density value. The number of samples for the rating values. For bandwidth parameters, For kernel function, Position of the target score. This represents the value of the i-th rating sample. By using the Gaussian kernel density estimation method described above, discrete confidence scores are mapped to continuous probability density function curves, generating a confidence probability distribution model that describes the trend of the success rate of the application timing as the score changes, thus realizing the probability basis required for dynamic time window boundary judgment. For example, in a port declaration scenario three days before the Spring Festival, the input one-dimensional confidence score sequence is [0.62, 0.68, 0.71, 0.75, 0.80], the number of score samples n is set to 5, and the bandwidth parameter h is set to 0.05. After sorting and deduplication, a set of sampling points is generated. A Gaussian kernel function is then executed at each point to calculate the kernel value, and normalization is performed according to the bandwidth parameter. In high-density regions, such as between 0.75 and 0.80, the bandwidth is reduced to 0.03 to improve the peak resolution of the curve; in low-density regions, such as between 0.62 and 0.68, the bandwidth is expanded to 0.06 to smooth the probability curve. After superimposing the kernel curves, a continuous probability density function with an integral value of 1 is obtained. The curve reaches its peak at the score value of 0.75, corresponding to the time window with the highest declaration success rate. The output confidence probability distribution model significantly improves the ability to identify rhythm peaks in the dynamic time window determination process in actual recommendation calculations, and maintains the stability of the recommendation strategy under the conditions of fluctuating traffic pressure during holidays. S6.4: The confidence probability distribution model is used to perform dynamic boundary cutting processing on the candidate set of time difference elastic anchor points. Based on the preset confidence accumulation threshold, the time interval range in which the probability density integral reaches the required range is truncated, and a dynamic time window set with upper and lower bound constraints is generated. S6.5: The dynamic time window set and the single-dimensional confidence score sequence are structurally associated and encapsulated, and each dynamic time window is bound with a corresponding confidence score value label to generate the final application timing recommendation decision parameters with flexible response capability.

[0016] Step S7: Real-time monitoring of the deviation between the actual clearance time of three consecutive declaration batches under the same rhythmic stage and the center value of the candidate set of time difference elastic anchor points, and determining whether the deviation exceeds a preset fluctuation threshold to trigger calibration conditions. Specifically, this includes: S7.1: Obtain the release confirmation timestamp and clearance completion timestamp of three consecutive declaration batches in the real-time customs clearance workflow under the same rhythm stage, calculate the actual customs clearance time sequence of each batch based on the time difference algorithm, and generate an actual customs clearance time dataset containing three consecutive samples. S7.2: Read the historical most frequently occurring average time interval corresponding to the current rhythm stage in the candidate set of time difference elastic anchor points, extract the arithmetic center value of the interval as the benchmark reference anchor point, and generate a single scalar benchmark reference anchor point value for deviation calculation. S7.3: Using the actual customs clearance time dataset and the single scalar benchmark reference anchor value, perform a sample-by-sample absolute difference calculation and calculate the arithmetic mean to generate a comprehensive deviation index that characterizes the degree of deviation of the current operation rhythm; S7.4: Call the preset strategy stability control parameter library, retrieve the preset fluctuation threshold set for the current port identification and holiday type combination, and generate a dynamic fluctuation judgment threshold for judging the abnormal state of rhythm. The strategy stability parameter call operation is performed on the calculated comprehensive deviation index. The input condition is the composite key value of the port identification field and the holiday type field to which the current declaration batch belongs. Based on the composite key value, an exact matching retrieval is performed in the strategy stability control parameter library, and a fast addressing module based on the joint index of port level and holiday category is called to map the key value to a unique database query address; Read the original value of the fluctuation threshold preset for the combination of port identifier and holiday type from the mapped database entries, and extract the adjustment coefficient related to the rhythm stage at the same time to ensure that the judgment threshold is rhythm stage dependent. The original value of the fluctuation threshold is subjected to dynamic coefficient weighting operation, using a two-factor adjustment model that combines multiplication and addition. The calculation formula is as follows: in The threshold for determining dynamic fluctuations. The preset fluctuation threshold benchmark is in the parameter library. This is the adjustment coefficient for the corresponding rhythmic stage; The dynamic fluctuation judgment threshold obtained by calculation is processed by precision formatting, converting the value into a unified floating-point standard precision form and binding it with the current rhythm stage label, so as to output a dynamic fluctuation judgment threshold that can be used for subsequent rhythm abnormality judgment. By using strategy stability control parameter library retrieval and dynamic coefficient weighting processing, the comprehensive deviation index judgment condition of the previous step is transformed into a dynamic fluctuation judgment threshold with dual adaptability to port and rhythm stages, thereby achieving high accuracy and scalability in rhythm anomaly judgment. For example, in a customs declaration scenario for a cross-border e-commerce company, the current declaration batch's port identifier is configured as "CN-SZ-001," the holiday type is set to "Spring Festival," and the comprehensive deviation index obtained in the previous step is 0.85. The strategy stability control parameter library has a preset fluctuation threshold benchmark in the joint key-value entry for "CN-SZ-001" and "Spring Festival." The value is 0.80, corresponding to the rhythm stage adjustment coefficient. The value is 0.05. Perform dynamic coefficient weighting calculation: The dynamic fluctuation judgment threshold is obtained. After precision formatting, this threshold is bound to the rhythm phase "pre-holiday intensive pre-screening period". The system will use this threshold to compare with the comprehensive deviation index in subsequent rhythm state judgment. The verification results show that under the situation of rhythm fluctuation interference in port operations before the Spring Festival, this dynamic fluctuation judgment threshold can significantly improve the sensitivity and robustness of abnormal state judgment. S7.5: Perform a logical comparison operation between the comprehensive deviation index and the dynamic fluctuation judgment threshold. If the comprehensive deviation index is greater than the dynamic fluctuation judgment threshold, output a calibration trigger signal; otherwise, output a maintenance lock signal to generate the final rhythm state discrimination result.

[0017] Step S8: If the deviation exceeds a preset fluctuation threshold, only the coefficient of variation of the silent period length and the number of peaks in the intra-cluster operation density of the corresponding rhythmic stage in the port operation rhythm fingerprint are updated to generate a calibrated port operation rhythm fingerprint to complete the strategy locking optimization. Specifically, this includes: S8.1: Based on the preset fluctuation threshold trigger signal, extract the historical clearance time sequence and real-time clearance time sequence under the current rhythm stage, and use the sliding difference statistical algorithm to calculate the time sequence deviation vector between the two to generate a clearance time deviation feature set containing positive and negative deviation direction and amplitude information, which serves as the input basis for subsequent parameter correction. S8.2: Locate the corresponding port work rhythm fingerprint storage unit based on the customs clearance time deviation feature set, and use local weighted regression smoothing technology to resample the adjacent operation cluster interval data in the original operation log to remove instantaneous noise interference and reconstruct the quiet period length distribution curve that reflects the real operation rhythm, forming a high-confidence quiet period length benchmark dataset. S8.3: Based on the benchmark dataset of the silent period length, perform standard deviation normalization operation, and combine the deviation amplitude weight factor in the feature set of the clearance time deviation, dynamically adjust the numerical range of the original silent period length variation coefficient to generate an updated silent period length variation coefficient that can adaptively match the current clearance pressure fluctuation, and complete the elastic anchoring correction of the first key feature. S8.4: Utilize the deviation direction information in the set of customs clearance time deviation features to retrieve the operation cluster density histogram within the corresponding time period, use the peak search algorithm to identify false or missing peaks caused by fluctuations in customs clearance efficiency, and perform noise reduction and enhancement processing on the operation density distribution within the cluster through morphological filtering to generate an updated number of operation density peaks within the cluster that accurately reflects the current work intensity, thus completing the structural correction of the second key feature; Extract deviation direction labels from the set of customs clearance time deviation features, establish a time period index mapping corresponding to the rhythm stage, and locate the operation cluster density data storage area within the time period. Based on the time period index, read the cluster density histogram, input the density value sequence into the peak search algorithm module, and perform point-by-point comparison and judgment according to the preset density baseline to extract the potential peak candidate set. The candidate peak set is subjected to deviation direction consistency screening processing. The deviation direction label is used to determine the upward or downward trend of the peak and noise peaks that are inconsistent with the deviation direction of the rhythm stage are removed. The selected peak set is used to determine false peaks. The local density comparison algorithm is used to compare the differences with the historical density distribution curve to identify statistically insignificant peaks caused by abnormal slowdown or sudden increase in customs clearance efficiency, and these peaks are marked as objects to be removed. For the missing peak positions in the judgment results, compensation identification is performed. The deviation direction label is used to search for density protrusions in the low frequency band of the density histogram that have not reached the peak threshold but show a continuous increasing or decreasing trend, and they are marked as new peak positions. Morphological filtering is performed on the peak set after false peak removal and missing peak compensation. Opening operation is used to eliminate short-term density spikes, and closing operation is used to enhance persistent density spikes in order to optimize peak boundaries and peak amplitude. The number of peak positions after morphological filtering is counted, and the number of peak positions of the updated intra-cluster operation density is output as the second key feature value. This value is synchronized with the first feature correction result to form structured feature update data that can proceed to the next sub-step. By using peak position search, deviation direction screening, false peak position removal, missing peak position compensation and morphological filtering, the deviation feature set of the previous step is transformed into the number of intra-cluster operation density peaks that accurately reflect the current operation intensity, thereby achieving structural calibration of the second key feature. For example, during the rhythmic phase of the third day before the Spring Festival at a certain cross-border port, the extracted customs clearance time deviation feature set showed a positive deviation direction, indicating a significant decrease in current customs clearance efficiency. A time period index was constructed to locate the corresponding operation cluster density histogram. The density threshold baseline was set to 1.2 times the historical average density. The histogram was input into the peak search module, retrieving 15 potential peak candidates. Five peaks inconsistent with the positive trend were removed using deviation direction filtering. Three statistically insignificant peaks caused by localized abnormal customs declaration concentrations were removed as false peaks. Missing peak compensation added two persistent density spikes in the low-frequency range. Morphological filtering used an opening operation with a structuring element length of 3 to eliminate instantaneous spikes and a closing operation to enhance continuous high-density segments, ultimately resulting in a set of nine filtered and optimized peaks. The statistical results were used as the number of peaks in the updated cluster operation density, and were written into the feature vector along with the coefficient of variation of the updated quiet period length generated in the previous step. The verification results showed that the adjusted number of peaks could more accurately characterize the work intensity in the pre-holiday stage, and the stability of the subsequent recommendation strategy was significantly improved in this scenario. S8.5: Write the updated quiescent period length variation coefficient and the updated number of peaks in the cluster operation density into the feature vector storage space of the original port operation rhythm fingerprint, keep the other structural features frozen, and generate a calibrated port operation rhythm fingerprint with the latest rhythm response capability through the version overwrite mechanism, so as to achieve stable locking and continuous optimization of the declaration timing recommendation strategy under complex fluctuation scenarios.

[0018] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0019] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and rules of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for intelligently recommending the timing of customs declarations, taking into account overseas holidays and customs clearance time differences, characterized in that... Includes the following steps: S1: Obtain the timestamp sequence of core business events from the Customs Single Window, Electronic Port and port on-site terminals to form the original customs clearance operation log dataset; S2: Perform time normalization processing on the original customs clearance operation log dataset according to the preset time window division rules to generate rhythmic fluctuation spectrum data; S3: Based on the rhythmic fluctuation spectrum data, identify the high-frequency stable operation clusters and their start and end offsets, the number of operation density peaks within the cluster and the variation coefficient of the silent period length between adjacent clusters, and construct the port operation rhythm fingerprint feature vector. S4: Cluster the port work rhythm fingerprint feature vectors according to port identifiers and holiday types to generate a port work rhythm fingerprint database; S5: Based on the current declared port identifier and target holiday type, retrieve the matching port work rhythm fingerprint from the port work rhythm fingerprint database, identify the current rhythm stage, extract the historically most frequently occurring average time interval from release confirmation to customs clearance completion under this stage, and generate a candidate set of time difference elastic anchor points. S6: Based on the aforementioned time difference elastic anchor point candidate set, assign a set of dynamic time windows with confidence weights to the application timing recommendation strategy to form application timing recommendation decision parameters.

2. The intelligent recommendation method for customs declaration timing that considers overseas holidays and customs clearance time differences according to claim 1, characterized in that, Following S6, the following is also included: S7: Real-time monitoring of the deviation between the actual clearance time of three consecutive batches of declarations under the same rhythmic stage and the center value of the candidate set of time difference elastic anchor points, and determining whether the deviation exceeds the preset fluctuation threshold to trigger the calibration condition; S8: If the deviation exceeds the preset fluctuation threshold, update the two feature values ​​of the silent period length variation coefficient and the number of peaks of the intra-cluster operation density in the port operation rhythm fingerprint, and generate a calibrated port operation rhythm fingerprint to complete the strategy locking optimization.

3. The intelligent recommendation method for customs declaration timing that considers overseas holidays and customs clearance time differences according to claim 1, characterized in that, The core business events include customs declaration submission, preliminary review approval, tax payment, issuance of inspection instructions, and release confirmation.

4. The intelligent recommendation method for customs declaration timing that considers overseas holidays and customs clearance time differences according to claim 1, characterized in that, The preset time window division rule is specifically divided into three stages: seven days before the holiday, during the holiday, and ten days after the holiday.

5. The intelligent recommendation method for customs declaration timing considering overseas holidays and customs clearance time differences as described in claim 1, characterized in that, S3 specifically includes: A fixed-length sliding window scanning process is performed on the local density distribution curves in the rhythmic wave spectrum data. The relative time axis is traversed and the aggregation degree of operation events in each window is calculated to generate a sliding window frequency sequence. Based on the frequency sequence of the sliding window, extreme value detection and neighborhood merging are performed to locate peak intervals that are continuously higher than the preset density baseline and merge adjacent overlapping intervals to extract the original set of operation clusters. For each independent operation cluster in the original set of operation clusters, perform precise boundary localization and internal structure analysis to determine the time span of the cluster and apply peak counting technology to count the number of significant density protrusions within the cluster, generating a basic morphological feature set that includes the start and end offsets of the operation cluster and the number of operation density peaks within the cluster. Based on the time interval data of adjacent operation clusters in the basic morphological feature set, perform discrete metric processing to calculate the fluctuation amplitude of the silent period length between each adjacent cluster and generate a silent period length variation coefficient sequence. The operation cluster start and end offsets, the number of peaks in the operation density within the cluster, and the variation coefficient sequence of the silent period length in the basic morphological feature set are subjected to multi-dimensional vector encapsulation processing. The three types of discrete features are mapped into fixed-dimensional numerical arrays using structured coding rules to generate port operation rhythm fingerprint feature vectors.

6. The intelligent recommendation method for customs declaration timing considering overseas holidays and customs clearance time differences as described in claim 5, characterized in that, The extreme value detection and neighborhood merging process specifically involves: Extreme value detection is performed on the data points in each frequency sequence of the sliding window frequency sequence. A peak capture algorithm combining first-order difference sign analysis and second-order difference amplitude determination is used to identify intervals that are continuously higher than the density baseline. Isolated abnormal peaks are removed during the detection process. Neighborhood fusion processing is performed on the detected peak intervals. A combination of interval intersection and adjacent interval length determination is used. If the adjacent interval interval length is less than the preset merging threshold, a fusion block is constructed by interval union to merge adjacent or overlapping peak intervals.

7. The intelligent recommendation method for customs declaration timing considering overseas holidays and customs clearance time differences as described in claim 1, characterized in that, S4 specifically includes: Perform composite key-value encapsulation processing on the port operation rhythm fingerprint feature vector to generate a port rhythm index dataset; Based on the port rhythm index dataset, perform multidimensional spatial mapping operation to aggregate port rhythm index units with the same port identifier and similar holiday types into the same feature cluster, generating a set of port rhythm feature clusters. The center point extraction and boundary delineation process is performed on the port rhythm feature cluster set to locate the representative fingerprint vector in each feature cluster and determine the distribution radius of the members in the cluster, thereby generating the port rhythm prototype vector and the corresponding cluster domain range parameters. Based on the port rhythm prototype vector and the corresponding cluster range parameters, a structured storage mapping operation is performed to write the key operation cluster start and end offsets, the number of peak operation densities within the cluster, and the coefficient of variation of the silent period length into a preset database table structure, thereby generating port work rhythm fingerprint database record entries. The global index is constructed using the entries recorded in the port work rhythm fingerprint database. A fast retrieval mapping relationship is established from the combination of port identifier and holiday type to the specific fingerprint storage address, thereby generating the port work rhythm fingerprint database.

8. The intelligent recommendation method for customs declaration timing considering overseas holidays and customs clearance time differences according to claim 7, characterized in that, The composite key-value encapsulation process specifically involves using the port identifier field and the holiday type field as a combined primary key to reorganize discrete fingerprint feature data into port rhythm index units with unique index identifiers, thereby generating a port rhythm index dataset with structured addressing capabilities.

9. The intelligent recommendation method for customs declaration timing considering overseas holidays and customs clearance time differences according to claim 1, characterized in that, S5 specifically includes: Perform composite key-value encapsulation on the port identifier field and the target holiday type field in the current declaration request, convert the discrete business context information into a joint retrieval primary key with unique addressing capability, and generate a port rhythm query index; Based on the port rhythm query index, a multi-dimensional spatial matching operation is performed in the port work rhythm fingerprint database to locate the port rhythm prototype vector and the corresponding cluster range parameter that are completely consistent with the joint retrieval primary key, and to extract the matching port work rhythm fingerprint. Real-time phase alignment processing is performed on the start and end offset sequence of key operation clusters in the matched port work rhythm fingerprint. The relative time offset value of the key operation clusters is calculated based on the current system timestamp relative to the festival reference point. The specific rhythm stage label of the current time is identified and the current rhythm stage identifier is generated. Based on the current rhythm stage identifier, retrieve the data of the most frequently occurring average time interval from release confirmation to customs clearance completion under the current rhythm stage from the feature storage area of ​​the matching port work rhythm fingerprint, filter out the set of time span values ​​with the highest frequency, and generate the set of historical best time intervals. The set of historical best time intervals is structured and associated with the current rhythm stage identifier, confidence weight labels are added and converted into a standardized time window object format to generate a candidate set of time difference elastic anchor points.

10. The intelligent recommendation method for customs declaration timing considering overseas holidays and customs clearance time differences according to claim 9, characterized in that, The matched port work rhythm fingerprint characterizes the commonalities in the operational behavior of a specific port during a specific holiday.

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