Ship full life cycle electronic file construction and traceability system

Abstract

The invention provides a ship full-life-cycle electronic file construction and traceability system, and relates to the technical field of ship engineering, and the system comprises an obtaining module which is used for obtaining the operation state data generated in the operation process of a ship in real time; the extension module is used for serializing the collected running state data, forming a state data set, selecting a reference state vector, and extending two state change axial directions by taking the reference state vector as an original point; the fitting module is used for delimiting a sector according to the direction difference between the two axial directions and setting two types of reference sequences inside and outside the sector respectively; and fitting a continuous elliptical state evolution trajectory of the ship state change characteristics according to time sequence association of the two types of reference sequences. According to the invention, unified management, credible evidence storage and efficient tracing of the full-life-cycle data of the ship are realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of ship engineering, in particular to a ship full life cycle electronic archive construction and tracing system. BACKGROUND

[0002] Although the current ship industry has completed the initial transformation from paper records to digitization, the data of each link in the full life cycle of design, construction, operation and maintenance is scattered in different systems, and there is a lack of unified data standards and integration mechanisms. The data format is not compatible, the ownership is unclear, and the credibility is difficult to verify. At the same time, the electronic log specification has not been completely unified, and the cross-subject data circulation and tracing faces many obstacles.

[0003] When a ship in operation encounters an abnormal equipment failure during navigation, the relevant parties try to trace the root cause of the failure, and find that the technical parameters in the design stage, the installation records in the construction stage, the operation data in the operation stage and the maintenance records in the past are scattered in the independent systems of the design unit, the shipyard, the shipowner and other subjects. The data formats are different and cannot be verified with each other. Some key data also have the risk of being tampered with, which leads to the stagnation of fault tracing work. It exposes that the existing technology lacks a unified data management system covering the full life cycle of the ship, and there is no effective tamper-proof evidence and tracing mechanism. It is difficult to realize the complete integration, credible verification and efficient tracing of data, and cannot meet the core needs of the industry for ship safety supervision, fault diagnosis and compliance management. SUMMARY

[0004] The technical problem to be solved by the present application is to provide a ship full life cycle electronic archive construction and tracing system to realize the unified management, credible evidence and efficient tracing of ship full life cycle data.

[0005] To solve the above technical problems, the technical scheme of the present application is as follows: In a first aspect, the ship full life cycle electronic archive construction and tracing system comprises: The acquisition module is used to acquire the running state data generated by the ship during operation in real time; The acquisition module is used to serialize the collected running state data, form a state data set and select a reference state vector, and extend two state change axes from the reference state vector as the origin; The acquisition module is used to divide sectors according to the direction difference between the two axes, and set two types of reference sequences inside and outside the sectors; according to the time sequence association of the two types of reference sequences, a continuous elliptical state evolution trajectory of the ship state change characteristics is fitted; The acquisition module is used to calculate a data calibration coefficient according to the elliptical state evolution trajectory, and calibrate the original running state data through the calibration coefficient to obtain calibrated data; The acquisition module is configured to drive and update the digital twin ship model synchronized with the physical ship through the calibrated data, and simultaneously input the running state data into a preset artificial intelligence anomaly detection model for real-time analysis and monitoring to obtain an identification result. The acquisition module is configured to encrypt and chain the update information of the digital twin ship model, the identification result and the original running state data through a blockchain technology to obtain a blockchain log that conforms to the electronic log specification of the maritime organization and is tamper-proof. The acquisition module is configured to use the blockchain log as a unique trusted data source to build a ship lifecycle electronic file, and provide a traceability query interface based on the blockchain log for trusted verification and traceability of ship historical state, operation record and abnormal event.

[0006] Further, the running state data generated by the ship during operation is acquired in real time, including: The encrypted data stream periodically transmitted by at least one unmanned ship is received in real time through a satellite communication link; The structured original running state data packet is extracted by decrypting and analyzing the communication protocol of the encrypted data stream, and the integrity of the parsed data packet is checked and the source identity is verified to filter out the trusted data packet; The trusted data packet is collected and cached according to the corresponding ship identifier and data type to form an original data set preliminarily organized by ship and time sequence; the data includes ship position, speed and direction, power parameters, equipment state, cabin monitoring data and emergency signal; The original data set is processed by timestamp alignment, missing value marking and uniform data format standardization to finally obtain uniform time sequence running state data.

[0007] Further, the collected running state data is sequenced to form a state data set and a reference state vector is selected, and two state change axes are extended from the reference state vector as the origin, including: The uniform time sequence running state data is arranged in time sequence and organized into a time series data set containing multi-dimensional state variables; According to the time series data set, a state vector at a representative time point is selected from the data set as a reference state vector for analyzing the evolution of the ship state; The selected reference state vector is taken as the coordinate origin, and two mutually orthogonal state change axes are calculated and defined according to the statistical characteristics and change law of each state variable in the time series data set.

[0008] Further, sectors are demarcated according to the direction difference between the two axial directions, and two types of reference sequences are respectively set inside and outside the sectors; according to the time sequence association of the two types of reference sequences, a continuous elliptical state evolution trajectory of the ship state change characteristics is fitted, including: Each state vector in the time series data set is projected into a two-dimensional analysis coordinate system composed of two mutually orthogonal state change axial directions, forming a series of time series data points; According to the time series data points, the direction angle of each data point relative to the origin of the reference state vector is calculated; according to the pre-set angle threshold, the plane of the entire two-dimensional analysis coordinate system is divided into several sectors; For each sector divided, the time series data point set within the sector is defined as the first type of reference sequence; the adjacent time series data point set associated with each sector outside the sector boundary is defined as the second type of reference sequence; According to the time sequence association relationship between the two types of reference sequences corresponding to each sector, all time series data points projected in the two-dimensional analysis coordinate system are fitted through an elliptical fitting algorithm to obtain a continuous and closed elliptical curve as a state evolution trajectory representing the long-term and periodic change characteristics of the ship state.

[0009] Further, a data calibration coefficient is calculated according to the elliptical state evolution trajectory, and the original operating state data is calibrated through the calibration coefficient to obtain calibrated data, including: According to the obtained elliptical state evolution trajectory, the major axis length, eccentricity and orientation parameter relative to the two-dimensional analysis coordinate system are extracted; According to the predetermined calculation rule, a set of data calibration coefficients for compensating for the deviation of the two state change axial directions in the two-dimensional analysis coordinate system are obtained through the major axis length, eccentricity and orientation parameter; The data calibration coefficient is applied to the uniform time series operating state data to correct the component values of the data in the two state change axial directions defined by the two-dimensional analysis coordinate system, and calibrated operating state data is obtained to remove long-term trend errors.

[0010] Further, the calibrated data is used to drive and update the digital twin ship model synchronized with the physical ship, and at the same time, the operating state data is input into the pre-set artificial intelligence anomaly detection model for real-time analysis and monitoring to obtain the identification result, including: According to the calibrated operating state data, the digital twin ship model corresponding to the physical ship is driven in real time, and the state parameters in the twin model are updated to keep the state parameters synchronized with the operating state of the physical ship; The calibrated running state data is input into a preset artificial intelligence anomaly detection model; the anomaly detection model performs mode recognition and deviation analysis based on the data in real time to obtain an identification result of a device health state, an anomaly type, and an occurrence probability.

[0011] Further, the update information of the digital twin ship model, the identification result, and the original running state data are encrypted and chained through blockchain technology to obtain a blockchain log that meets the electronic log specification of the maritime organization and is tamper-proof, including: The twin model update information and the anomaly detection identification result contained in the comprehensive state report are combined and packaged with the original running state data in a unified time sequence according to a predefined structure to obtain a complete data record block; According to the packaged data record block, a standardized data structure that meets the requirements of the maritime organization electronic log specification is converted; The standardized data structure is encrypted and time-stamped signed by applying blockchain cryptography technology, and the processed data is broadcast as a transaction to the blockchain network for consensus verification and chain storage, thereby generating a blockchain log with tamper-proof and traceable characteristics.

[0012] Further, the blockchain log is used as the only trusted data source to build a ship full-life-cycle electronic archive, and a traceability query interface is provided based on the blockchain log for trusted verification and traceability of the ship historical state, operation record, and abnormal event, including: All historical data records related to the target ship are extracted from the blockchain log in chronological order and according to key event identifiers; The historical data records are reorganized, classified, and indexed according to the ship full-life-cycle management specification to build a structured ship electronic archive library, and archive summary information is anchored in the blockchain for evidence; According to the electronic archive library and the anchored information in the blockchain, a traceability query service interface with identity authentication and access control is deployed, and the interface supports trusted retrieval according to time range, event type, and data characteristics; Through the deployed query interface, the user's traceability request is responded to, and the relevant data records are obtained from the electronic archive library, and the integrity and authenticity of the data are automatically verified by comparing the corresponding anchoring information in the blockchain, and finally a traceability report of the historical state, operation record, and abnormal event that has been trusted is returned to the user.

[0013] In a second aspect, a computing device includes: one or more processors; a memory device storing one or more programs, when executed by the one or more processors, cause the one or more processors to perform the system.

[0014] In a third aspect, a computer-readable storage medium stores a program for execution by a processor to perform the system.

[0015] The above scheme of the present application at least has the following beneficial effects: Because the present system adopts the technical means of standardized data acquisition and processing, state evolution modeling and calibration, digital twin real-time updating, artificial intelligence anomaly detection, blockchain encryption evidence storage, and full life cycle archive construction and trusted traceability interface deployment, the technical problems of scattered storage, incompatible format, difficult to guarantee credibility, and lack of unified integration and efficient traceability mechanism of existing ship full life cycle data are effectively overcome, and the unified management, tamper-proof evidence storage and precise traceability of ship full life cycle data are realized, which provides reliable support for ship safety supervision, fault root cause investigation, compliance audit and cross-subject data collaboration, and improves the standardization, safety and efficiency of ship full life cycle management. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 is a ship full life cycle electronic archive construction and traceability system schematic diagram provided by an embodiment of the present application.

[0017] Figure 2 is a process schematic diagram of calculating a data calibration coefficient according to an elliptical state evolution trajectory, calibrating original running state data through the calibration coefficient, and obtaining calibrated data of the ship full life cycle electronic archive construction and traceability system provided by an embodiment of the present application. DETAILED DESCRIPTION

[0018] Exemplary embodiments of the present disclosure will be described below in greater detail with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be accurately conveyed to those skilled in the art.

[0019] As Figure 1 shown, an embodiment of the present application proposes a ship full life cycle electronic archive construction and traceability system, which comprises: an acquisition module configured to acquire running state data generated by a ship during operation in real time; An extension module is configured to serialize the collected operation state data, form a state data set, and select a reference state vector, and extend two state change axes from the reference state vector as the origin; A fitting module is configured to divide sectors according to the direction difference between the two axes, and set two types of reference sequences inside and outside the sectors respectively, and fit a continuous elliptical state evolution trajectory of the ship state change characteristics according to the time sequence association of the two types of reference sequences. A calibration module is configured to calculate a data calibration coefficient according to the elliptical state evolution trajectory, calibrate the original operation state data through the calibration coefficient, and obtain calibrated data. An identification module is configured to drive and update a digital twin ship model synchronized with the physical ship through the calibrated data, and simultaneously input the operation state data into a preset artificial intelligence anomaly detection model for real-time analysis and monitoring to obtain an identification result. An encryption module is configured to encrypt the update information of the digital twin ship model, the identification result, and the original operation state data through blockchain technology to obtain a blockchain log that meets the electronic log specification of the maritime organization and is tamper-proof. A processing module is configured to use the blockchain log as the only trusted data source to build a ship full life cycle electronic file, and provide a traceability query interface based on the blockchain log for trusted verification and traceability of the ship historical state, operation record, and abnormal event.

[0020] In the embodiments of the present application, by using real-time acquisition of ship operation state data, setting a reference state vector and double state change axes after data serialization, constructing a state evolution trajectory through sector division, two types of reference sequences, and elliptical fitting algorithm, calculating a calibration coefficient based on the trajectory to optimize the data, driving the digital twin model update with the calibrated data and combining the AI model for anomaly detection, encrypting the related data through the blockchain technology to form a compliant and tamper-proof log, and building a full life cycle electronic file based on the log and deploying a trusted traceability interface, the technical problems of lack of unified integration mechanism, difficulty in ensuring trustworthiness, and low traceability efficiency of the existing ship full life cycle data are effectively overcome, and the precise optimization of ship operation data, safe storage of key information, and trusted traceability of full life cycle data are realized, providing reliable data support for ship operation supervision, fault troubleshooting, and compliance management, and improving the scientificity and efficiency of ship full life cycle management.

[0021] In a preferred embodiment of the present application, the operation state data generated by the ship during operation is acquired in real time, including: The encrypted data stream periodically transmitted by at least one unmanned ship is received in real time through a satellite communication link, specifically including: first, a stable two-way communication link is established with a global satellite communication network to ensure stable data transmission of the unmanned ship in various navigation areas such as the open sea and the near sea; each unmanned ship is equipped with a special satellite communication terminal and a special component with data encryption function, and the ship collects various running state data in real time during operation, and after the collection is completed, the original data is encrypted by a special symmetric encryption algorithm to form a safe encrypted data stream; then, the unmanned ship sends the encrypted data stream to the satellite communication network at a preset fixed time interval through the satellite communication terminal, and the encrypted data stream is forwarded to the shore-based data receiving center through the satellite communication network, ensuring the safety and stability of the data transmission process and avoiding data theft or tampering during transmission.

[0022] The encrypted data stream is decrypted and the communication protocol is analyzed to extract structured original running state data packets; and the integrity of the parsed data packets is checked and the source identity is verified to filter out trusted data packets, specifically including: after the shore-based data receiving center receives the encrypted data stream, a decryption algorithm matched with the encryption component of the unmanned ship and a preset decryption key are called to decrypt the encrypted data stream in segments to restore the original data transmission packets; then, according to the preset standardized communication protocol, the original data transmission packets after decryption are protocol-analyzed to extract structured original running state data packets containing various running state information of the ship, and different types of original data transmission formats are automatically recognized and converted during the analysis process to ensure that the extracted data packet structure is uniform and standardized; next, the integrity of each data packet after analysis is checked by a cyclic redundancy check algorithm to check whether the data packet has problems such as transmission loss and data damage, and at the same time, the unique ship identity information carried in the data packet is accurately compared with the pre-stored legal ship identity information library to complete the identity verification of the data source; finally, the data packets that fail the integrity check or do not pass the identity verification are removed, and only trusted data packets that meet the safety and standard requirements are retained.

[0023] The trusted data packets are collected and cached according to corresponding ship identifiers and data types to form a preliminary organized raw data set according to ships and time sequences; the data includes ship positions, speeds, headings, power parameters, equipment states, cabin monitoring data and emergency signals, specifically including: performing classification and collection processing on the screened trusted data packets, first, distinguishing the data packets of different unmanned ships according to the unique ship identity carried in each data packet, ensuring that the data of each ship is independently classified and stored, and avoiding confusion of data of different ships; then, further subdividing the trusted data packets of each ship according to data types, and respectively classifying different types of data such as ship position data, speed and heading data, power parameter data, equipment state data, cabin monitoring data and emergency signal data into corresponding exclusive data set directories; at the same time, a cache server is enabled to temporarily store the classified data packets, and a time mark corresponding to the receiving time is added to each data packet during the caching process, and finally a preliminary organized raw data set is formed, which is distinguished according to ship identity, classified according to data type, and sorted according to receiving time.

[0024] By performing timestamp alignment, missing value marking and uniform data format standardization processing on the raw data set, uniform time sequence running state data is finally obtained, specifically including: first, taking the standard time of the shore-based data receiving center as the reference, calibrating the time marks of all data packets in the preliminary organized raw data set, correcting the time stamps of the data packets with time deviation, ensuring that the time stamps of all data packets are consistent, and realizing time synchronization of different types and different receiving time period data; then, checking each data set corresponding to each data type piece by piece, if it is found that there is no corresponding data received in a certain time period, a missing value mark is added to the corresponding position of the data, and the specific time node of the missing data is noted, to avoid misjudgment due to data missing during data processing; finally, according to the preset standardized data format requirement, all data after timestamp alignment and missing value marking are converted into a unified format, and the originally different raw data formats are all converted into the recognized standard data format, and finally the running state data is obtained, which is distinguished according to ships, arranged in time sequence, unified in data format and consistent in time sequence.

[0025] In the embodiment of the application, because the satellite communication link is used to receive encrypted data streams of unmanned ships, integrity check and source identity authentication are performed after decryption and analysis, and the data is collected and cached according to ship identifiers and data types, and then timestamp alignment, missing value marking and uniform format standardization processing are performed, so that the technical problems of insufficient safety of unmanned ship running state data transmission, difficult verification of source credibility, disordered and non-uniform data format are effectively overcome, and safe and credible, orderly and uniform time sequence running state data is obtained, which provides a high-quality data basis for ship state evolution modeling, data calibration and full life cycle file construction.

[0026] In a preferred embodiment of the present application, the collected operating state data is serialized to form a state data set and a reference state vector is selected, and two state change axes are extended from the reference state vector as the origin, including: The uniformly timed operating state data is arranged in time sequence and organized into a time series data set containing multi-dimensional state variables, specifically including: first, extracting the timestamp information of all data entries in the uniformly timed operating state data, taking the standard time of the shore-based data receiving center as the reference, sorting all data entries in ascending order of timestamp to ensure that the data is continuous and ordered in time dimension; then, combing all state variable types contained in the batch of data, covering ship position longitude, ship position latitude, speed, heading, power output, propeller speed, equipment operating temperature, equipment operating voltage, cabin air pressure, cabin humidity, emergency alarm signals and all other collected variable types; then, associating and binding each timestamp data after sorting with all corresponding state variable values to form a data record containing complete multi-dimensional state variable information at each time point; finally, integrating all associated time series data records in time sequence to construct a structured time series data set, each data set entry corresponds to a unique time point and the full-dimensional operating state data of the ship at that time point.

[0027] According to the time series data set, a state vector of a representative time point is selected from the data set as a reference state vector for analyzing the evolution of the ship state, specifically including: selecting the reference state vector by combining the preset rule with the representative time point; first, segmenting the time series data set, dividing the complete data set into multiple continuous data segments according to a fixed time interval, each data segment containing the same number of time point data; for each data segment, calculate the coefficient of variation of all state variables, the coefficient of variation being the ratio of the standard deviation to the mean, if the coefficient of variation of all state variables in a certain data segment is less than 0.3 and there is no emergency signal record in the segment, it is determined that the data segment is a stable running period of the ship; select the data segment with the longest duration from all stable running periods, if there are multiple longest stable segments with the same duration, select the segment with the middle timestamp as the target segment; take the middle time point of the target segment as the representative time point, extract all state variable numbers corresponding to the time point, combine the state variable numbers in the preset variable order to form the reference state vector for analyzing the evolution of the ship state.

[0028] The two mutually orthogonal state change axes are calculated and defined according to the statistical characteristics and variation laws of each state variable in the time series data set, specifically including: taking the selected reference state vector as the coordinate origin, first, statistical characteristic analysis is performed on all state variables in the time series data set; the variance, maximum value, minimum value, average change rate and other statistical indicators of each state variable in the entire data set range are calculated, wherein the change rate is calculated by the ratio of the difference between the variable values of two adjacent time points and the time interval; according to the statistical results, the state variable with the most significant change characteristic is selected, and the screening standard is that the state variable with the top five variance; then, the correlation coefficient between the five state variables is calculated, and the Pearson correlation coefficient calculation method is adopted, if the absolute value of the correlation coefficient of two variables is less than 0.3, it is determined that the linear correlation between the two is extremely low; from the five variables, a pair of variables with the smallest absolute value of the correlation coefficient is selected, and the first variable is defined as the first state change axis, which reflects the main change trend of the core operating parameters of the ship; based on the vector orthogonality principle, the second state change axis perpendicular to the first state change axis is constructed with the reference state vector as the origin, and the second state variable needs to be completely orthogonal to the first state variable and can reflect the change characteristics of another key dimension of the ship, for example, when the first axis is the power-related change dimension, the second axis can be defined as the sailing posture-related change dimension; finally, two mutually perpendicular orthogonal axes that can comprehensively cover the main state change of the ship are formed.

[0029] In the embodiment of the application, because the technical means of organizing the unified time sequence operating state data into a multi-dimensional time series data set in time sequence, selecting a representative state vector as a reference state vector, and then defining two mutually orthogonal state change axes with the reference vector as the origin and according to the statistical characteristics and variation laws of each state variable are adopted, the technical problems of lack of unified analysis reference for ship multi-dimensional operating state data and difficulty in systematically quantifying the state change trend are effectively overcome, thereby providing a structured analysis framework for ship state evolution trajectory fitting and data calibration, and making the quantitative analysis of ship state change more targeted and accurate.

[0030] In a preferred embodiment of the application, sectors are divided according to the direction difference between the two axes, and two types of reference sequences are set inside and outside the sectors; according to the time sequence association of the two types of reference sequences, a continuous elliptical state evolution trajectory of the ship state change characteristics is fitted, including: Projecting each state vector in the time series data set into a two-dimensional analysis coordinate system defined by two mutually orthogonal state change axis directions to form a series of time series data points, specifically including: first extracting each complete state vector in the time series data set, each state vector containing multi-dimensional state variable information during the ship operation process; for each state vector, analyzing the correlation of each state variable with the two mutually orthogonal state change axis directions defined previously, and converting the comprehensive value of the multi-dimensional state variable into a single projection value corresponding to each axis direction according to the influence weight of each state variable on the two axis directions; in this way, each state vector can find a unique corresponding coordinate point in the two-dimensional analysis coordinate system composed of two mutually orthogonal state change axis directions, and the time stamp information corresponding to the original state vector is associated with the coordinate point; after all state vectors are projected and converted, a series of time series data points arranged in time stamp order are formed, each data point containing both spatial position information in the two-dimensional coordinate system and corresponding time dimension information.

[0031] According to the time series data points, the direction angle of each data point relative to the origin of the reference state vector is calculated; according to the pre-set angle threshold, the plane of the entire two-dimensional analysis coordinate system is divided into several sectors, specifically including: taking the coordinate origin corresponding to the reference state vector as the core, calculating the direction angle of each time series data point in the two-dimensional analysis coordinate system. When calculating, the first state change axis direction is taken as the starting edge, and the angle between the data point and the origin is measured relative to the starting edge in the counterclockwise direction, obtaining the direction angle of each data point, the value range of the direction angle covering 0 degrees to 360 degrees; then set the angle threshold to 30 degrees, and divide the plane of the entire two-dimensional analysis coordinate system into sectors according to the fixed threshold, starting from the 0-degree position corresponding to the first state change axis direction, every 30 degrees is divided into a sector boundary, and 12 equal-sized sector regions are divided in turn, each sector corresponds to a fixed angle range, for example, from 0 degrees to 30 degrees is the first sector, from 30 degrees to 60 degrees is the second sector, and so on, until the sector division of the full plane of 360 degrees is completed, ensuring that each time series data point can be accurately classified into the corresponding sector according to the direction angle.

[0032] For each sector divided, the set of time series data points within the sector is defined as the first type of reference sequence; the set of adjacent time series data points associated with each sector outside the sector boundary is defined as the second type of reference sequence, specifically including: for each sector that has been divided, first traverse all time series data points, and determine whether each data point is within the angle range of the current sector according to the direction angle of each data point; filter out all time series data points whose direction angles fall within the angle range of the current sector, and sort them in order of corresponding timestamps to form the first type of reference sequence, which reflects the running characteristic data of the ship in the state change interval corresponding to the current sector; then, find the two sectors adjacent to the current sector, filter out the time series data points in these two adjacent sectors closest to the boundary of the current sector, and also include the adjacent data points outside the boundary of the current sector and continuous with the data points of the current sector in terms of timestamp, and arrange these data points in order of timestamp to form the second type of reference sequence; the second type of reference sequence supplements the front and back connection information of the data points of the current sector.

[0033] According to the time series correlation between the two types of reference sequences corresponding to each sector constructed, an elliptical curve is fitted through an elliptical fitting algorithm, and a continuous and closed elliptical curve is obtained as a state evolution trajectory representing the long-term and periodic variation characteristics of the ship state, specifically including: first, time series correlation analysis is performed on the first type of reference sequence and the second type of reference sequence corresponding to each sector, the time sequence of the data points in different sequences, and the numerical value change connection logic between the data points are sorted out to ensure that all sequences form a complete and coherent data stream along the time axis; then, an elliptical fitting algorithm is started, and all time series data points in the two-dimensional analysis coordinate system are used as the basis sample for fitting analysis, and the algorithm automatically analyzes the overall distribution characteristics, numerical fluctuation rules and time series correlation patterns of these data points; in the fitting process, the boundary connection data and internal characteristic data provided by the two types of reference sequences are fully utilized to continuously adjust the fitting parameters and correct the curve shape, so as to ensure that the fitted curve accurately fits the distribution trend of the data points, while maintaining the smoothness, continuity and closure characteristics of the curve; after multiple rounds of parameter optimization, a continuous and closed elliptical curve is finally obtained, which comprehensively and accurately represents the periodic characteristics of the state change of the ship in the long-term running process.

[0034] In the embodiment of the present application, because the embodiment adopts the technical means of projecting the state vector in the time series data set into a two-dimensional analysis coordinate system, dividing sectors by the direction angle and the angle threshold, setting the observation reference sequence inside and outside the sector, and combining the time sequence correlation of the two types of reference sequences to fit the state evolution trajectory by the ellipse fitting algorithm, the technical problems of the chaotic and disordered ship multi-dimensional running state data, and the difficulty in systematically capturing and intuitively representing the long-term periodic change characteristics are effectively overcome, and a continuous trajectory reflecting the long-term evolution law of the ship state is accurately constructed, providing a concrete and quantifiable core basis for data calibration and ship state anomaly recognition.

[0035] As shown in Figure 2 In another preferred embodiment of the present application, a data calibration coefficient is calculated according to the elliptical state evolution trajectory, and the original running state data is calibrated by the calibration coefficient to obtain calibrated data, including: According to the obtained elliptical state evolution trajectory, the major axis length, eccentricity and orientation parameter relative to the two-dimensional analysis coordinate system are extracted, specifically including: first, the elliptical state evolution trajectory obtained by fitting is fully traversed, and the two-dimensional coordinate information of all points on the trajectory is recorded; the straight line distance between any two points on the trajectory is calculated, and the two points with the maximum distance are selected, and the connecting line between the two points is the major axis of the ellipse, and the distance value between the two points is the major axis length; then, all point pairs perpendicular to the direction of the major axis on the elliptical trajectory are found, and the straight line distance between the point pairs is calculated, wherein the connecting line corresponding to the longest distance is the minor axis of the ellipse, and the minor axis length is recorded; according to the relationship between the major axis length and the minor axis length, the eccentricity of the ellipse is calculated, and the specific process is that the square of the major axis length is subtracted from the square of the minor axis length, and the result is squared and then divided by the major axis length, and finally the eccentricity value reflecting the flatness of the ellipse is obtained; finally, the specific direction of the major axis is determined, and the included angle between the major axis center line and the first state change axis of the two-dimensional analysis coordinate system is measured in the counterclockwise direction, and the included angle is the orientation parameter of the ellipse relative to the two-dimensional analysis coordinate system, and the value range is between 0 degrees and 180 degrees, and the parameter value is accurately recorded.

[0036] Using the principal axis length, eccentricity, and orientation parameters, and following predetermined calculation rules, a set of data calibration coefficients is obtained to compensate for axial deviations between two states in the two-dimensional analysis coordinate system. Specifically, this includes: pre-setting standard parameters for the elliptical trajectory under normal ship operating conditions, where the standard principal axis length is 100, the standard eccentricity is 0.5, and the standard orientation parameter is 0 degrees; according to the predetermined calculation rules, firstly, the principal axis length calibration coefficient is calculated, which is equal to the ratio of the standard principal axis length to the actual extracted principal axis length; then, the eccentricity calibration coefficient is calculated, which is equal to the ratio of the standard eccentricity to the actual extracted eccentricity; finally, the orientation parameter calibration coefficient is calculated, if the actual orientation parameter is 0 degrees... The coefficient is set to 1. For every 0-30 degree deviation of the actual orientation parameter, the coefficient is adjusted by 0.1. The adjustment range increases proportionally as the deviation angle increases. The three individual coefficients are weighted and summed with equal weights to obtain the comprehensive data calibration coefficient for the first state change axis. Using the same calculation logic, and combining the correlation characteristics between the second state change axis and the elliptical trajectory, the weight ratio of each parameter is adjusted. The principal axis length calibration coefficient accounts for 40%, the eccentricity calibration coefficient accounts for 30%, and the orientation parameter calibration coefficient accounts for 30%. The comprehensive data calibration coefficient for the second state change axis is calculated, and finally a complete set of data calibration coefficients is formed to compensate for the deviation of the two state change axes.

[0037] The data calibration coefficient is applied to the unified time-series operational status data to correct the component values ​​of the data along the two state change axes defined in the two-dimensional analysis coordinate system, resulting in calibrated operational status data free from long-term trend errors. Specifically, this involves: extracting the component values ​​of each data point along the two state change axes in the unified time-series operational status data; for the first state change axis, multiplying the component value corresponding to each data point by the comprehensive data calibration coefficient for that axis to obtain the corrected first-axis component value; for the second state change axis, using the same method, multiplying the second-axis component value of each data point by the corresponding comprehensive data calibration coefficient to obtain the corrected second-axis component value; during the correction process, each data point is checked individually to ensure that each component value is accurately calculated. For data points whose corrected values ​​exceed a reasonable range, the calibration coefficient and the original component values ​​are rechecked, and the correction results are retained after confirming that the calculations are correct. Through this correction process, the original operational status data, which originally contained long-term trend errors and deviations, is adjusted to a reasonable range that conforms to the elliptical state evolution trajectory, ultimately resulting in calibrated operational status data with improved accuracy and free from long-term trend errors.

[0038] In this embodiment of the invention, because the embodiment uses the technique of extracting the principal axis length, eccentricity and orientation parameters from the elliptical state evolution trajectory, calculating the data calibration coefficient to compensate for the axial deviation according to a predetermined rule, and applying the coefficient to the uniform time-series operating state data to correct the axial component value, it effectively overcomes the technical problem of insufficient data accuracy caused by long-term trend errors and deviations in the original ship operating state data. As a result, it obtains calibrated operating state data with higher accuracy and smaller errors, providing high-quality data support for the accurate updating of digital twin ship models and the reliable analysis of artificial intelligence anomaly detection models.

[0039] In a preferred embodiment of the present invention, the calibrated data drives and updates the digital twin ship model synchronized with the physical ship, while simultaneously inputting the operational status data into a preset artificial intelligence anomaly detection model for real-time analysis and monitoring to obtain identification results, including: Based on the calibrated operational status data, the digital twin ship model corresponding to the physical ship is driven in real time, updating the status parameters in the twin model to keep them synchronized with the operational status of the physical ship. Specifically, the construction of the digital twin ship model is based on fundamental data from the entire ship lifecycle, including detailed drawing dimensions from the design phase, material specifications and installation location data from the construction phase, rated performance parameters of equipment at the time of delivery, and baseline operational data from the initial operational phase. Through 3D modeling and multi-source data integration technologies, a full-dimensional virtual model is established that corresponds one-to-one with the physical ship in terms of structure, equipment, and function. The full-dimensional virtual model has a pre-defined unified parameter system, with each parameter corresponding to a specific operational status indicator of the physical ship, covering all key dimensions such as ship navigation status, power status, operational status of various equipment, and cabin environment status. A real-time mapping channel is established between the calibrated operational status data and the parameters of the digital twin ship model. The calibrated data is continuously transmitted to the model's parameter update module at fixed 1-second intervals. This module processes the received data... The system performs classification and analysis, accurately matching navigation data such as ship position (longitude, latitude, speed, and heading), power parameters such as power output and propeller speed, equipment status data such as operating temperature and voltage, and environmental data such as cabin pressure and humidity to the corresponding parameter items in the model. The model adjusts the corresponding virtual parameter values ​​in real time based on the analyzed calibration data. For example, when calibration data shows the actual ship's propeller speed is 1500 revolutions per minute, the virtual propeller speed parameter in the model is updated to 1500 revolutions per minute; when the equipment operating temperature is 75 degrees Celsius, the corresponding equipment temperature parameter in the model is adjusted to 75 degrees Celsius. Simultaneously, a parameter synchronization verification mechanism is set up, comparing the model parameters with the real-time status sampling data fed back by the actual ship via satellite communication every 5 seconds. If a parameter deviation exceeds 0.5, a secondary calibration update is immediately initiated, ensuring that all status parameters of the digital twin ship model remain highly synchronized with the actual operating status of the actual ship, providing accurate virtual mapping data for ship status monitoring and full lifecycle management.

[0040] The calibrated operational status data is simultaneously input into a pre-set artificial intelligence anomaly detection model. The anomaly detection model performs real-time pattern recognition and deviation analysis based on the data to obtain identification results regarding equipment health status, anomaly types, and their probabilities. Specifically, this includes: first, a detailed explanation of the construction process of the artificial intelligence anomaly detection model, i.e., the model's origin; the first step is to collect training data, gathering historical calibrated operational status data from multiple similar vessels under normal operating conditions, covering various navigation environments (near and far seas), load conditions (light and heavy loads), and various data under different operating durations (short and long voyages). Simultaneously, data on past equipment anomaly failures of these vessels is collected, including operational data before the failure, and data at the time of the failure. The first step involves detailed information such as abnormal data, specific fault types, causes of faults, and fault handling results, with a total data volume exceeding 100,000 records. The second step is data preprocessing, which cleans the collected historical data, removes data entries with missing values ​​exceeding 10%, and corrects extreme values ​​with excessively large fluctuations using the mean replacement method. Subsequently, the data is standardized according to the same format and standards as the real-time calibrated operational status data to ensure complete structural consistency between the training data and the real-time input data. The third step is feature extraction, which extracts key features closely related to the health status of ship equipment from the preprocessed historical data, including statistical features such as the mean, variance, rate of change, peak value, and duration of various status parameters, and screens them out. The first step involves selecting 50 core features to form the feature set for model training. The second step divides the processed feature set into training, validation, and test sets in a 7:2:1 ratio. The training set is used for core model training, the validation set is used to adjust model parameters during training, and the test set is used to ultimately verify the model's generalization ability and recognition accuracy. The third step involves model training, using the gradient boosting tree algorithm as the base algorithm. The training set is input into the algorithm with a learning rate of 0.1, 100 decision trees, and a tree depth of 5. Through multiple rounds of iterative training, the model gradually learns the feature patterns of normal operation and different fault types. The sixth step involves model validation and optimization, using the validation set to refine the trained model. The model undergoes performance testing, and the anomaly detection accuracy is calculated. If the accuracy is below 95%, parameters such as the learning rate and the number of decision trees are adjusted and the model is retrained. This iterative optimization continues until the model's anomaly detection accuracy on the validation set reaches above 95%. At the same time, the test set is used for final performance verification to ensure that the model can maintain stable recognition performance in unseen data scenarios. The seventh step is to determine the judgment threshold. Based on the fault case data in the validation and test sets, the distribution range of feature deviations under normal and various fault states is statistically analyzed. The threshold for a single feature deviation is set to 5, the cumulative threshold for multiple feature deviations is set to 20, and the anomaly probability threshold is set to 80, which serve as the judgment standard for real-time detection. Finally, a preset artificial intelligence anomaly detection model is formed. After the model is built, the specific process of real-time analysis and monitoring is as follows: The continuously received calibrated operating status data is processed according to the feature extraction rules used during model training to extract 50 core features in real time. These features are then input into the artificial intelligence anomaly detection model. The model first performs pattern recognition on the input real-time features, comparing the current feature pattern with the normal operating status feature patterns learned during training, and calculating the deviation value between each core feature and the corresponding feature of the normal pattern. Subsequently, deviation analysis is performed, checking whether the deviation value of a single feature exceeds a set threshold of 5, and whether the cumulative deviation values ​​of multiple features exceed a set threshold of 20. If the deviation value of a single feature exceeds 5 or the cumulative deviation value of multiple features exceeds 20, the model further... The model first matches the current abnormal feature pattern with various fault feature patterns in the fault case feature library, calculates the similarity between the current abnormal pattern and each fault type, and then infers the probability of occurrence of each abnormal type. When the probability of occurrence of a certain abnormal type exceeds 80%, the model determines that the abnormal type is the current possible fault type. If the probability of occurrence of all abnormal types is less than 80%, the health status of the ship's equipment is determined to be normal. If the probability of occurrence of multiple abnormal types exceeds 80%, they are arranged in descending order of probability, and the main abnormal types and their corresponding probabilities are listed. Finally, the model outputs a complete identification result that includes whether the health status of the ship's equipment is normal or abnormal, the specific name of the abnormal type, and the corresponding probability of occurrence.

[0041] In this embodiment of the invention, because it uses high-precision calibrated operational status data as a basis to drive the digital twin ship model to update in real time to maintain synchronization with the physical ship's status, and simultaneously inputs the calibration data into a preset artificial intelligence anomaly detection model for pattern recognition and deviation analysis, it effectively overcomes the technical problems of the lack of accurate data support for the digital twin ship model's status update and the unreliability of the identification results of artificial intelligence anomaly detection due to errors in the original data. Thus, it achieves accurate synchronization between the digital twin ship model and the physical ship's operational status, as well as accurate identification of the ship's equipment health status, anomaly types, and probability of occurrence, providing timely and reliable decision-making basis for ship operation monitoring and anomaly handling.

[0042] In a preferred embodiment of the present invention, the updated information, identification results, and original operational status data of the digital twin ship model are encrypted and uploaded to the blockchain using blockchain technology to obtain an immutable blockchain log that conforms to the Maritime Organization's electronic log specifications, including: The twin model update information and anomaly detection and identification results contained in the comprehensive status report are combined and encapsulated with the unified time-series raw operational status data according to a predefined structure to obtain a complete data record block. Specifically, this includes: firstly, a comprehensive collection and classification of three types of core data; secondly, twin model update information covering the update details of all status parameters in the model, including the original values ​​of each parameter, the updated new values, the equipment name corresponding to the parameter, and the specific time point of the update; thirdly, anomaly detection and identification results including equipment health status judgment results, the name of the identified anomaly type, the probability value of the anomaly occurrence, the time point of anomaly detection, and the preliminary risk level given by the model; and fourthly, the unified time-series raw operational status data encompassing the ship's position... All uncorrected raw data, including latitude, speed, heading, power parameters, equipment operation data, cabin monitoring data, and emergency signals, are collected, with each data point accompanied by a complete timestamp and a unique ship identifier. The three types of data are then combined according to a predefined fixed structure. First, a general data identifier field is set, along with the ship number and data batch corresponding to the record block. Then, modules for twin model update information, anomaly detection and identification results, and raw operational status data are sequentially divided. Within each module, data is sorted by importance, with key core data placed at the top. Finally, the combined data is encapsulated using a unified encapsulation format to integrate all data content, forming a single data record block that is structurally complete, logically clear, and without any omissions.

[0043] Based on the encapsulated data record blocks, the data is converted into a standardized data structure conforming to the requirements of the Maritime Organization's electronic log specifications. This includes: first, obtaining the data fields, field order, data format, unit standards, and mandatory field requirements as specified in the Maritime Organization's electronic log specifications, which will serve as the unified basis for data structure conversion; then, parsing the encapsulated data record blocks, extracting the twin model update information, anomaly detection and identification results, and the content of each field corresponding to the original operational status data; and finally, adjusting the field names and their order according to the requirements of the Maritime Organization's electronic log specifications, uniformly modifying the internally defined field names to the standard field names specified in the specifications to ensure the accuracy of the field names. The meaning is unambiguous when circulating across entities; at the same time, the data format is uniformly standardized, for example, time data is uniformly converted to Coordinated Universal Time format, accurate to the millisecond level; all numerical data is uniformly retained to two decimal places, and the units are converted to the international standard units required by the standard; text data is converted according to the character encoding format required by the standard; in addition, the required fields required by the standard are supplemented, such as the ship's International Maritime Organization number, data acquisition equipment number, data processing node identifier, etc. If the original record block lacks relevant information, it will be automatically extracted and supplemented from the pre-stored ship basic information database; finally, a standardized data structure that fully complies with the requirements of the Maritime Organization electronic log is formed.

[0044] By applying blockchain cryptography to encrypt and timestaminate standardized data structures, and broadcasting the processed data as a transaction to the blockchain network for consensus verification and on-chain storage, a blockchain log with immutable and traceable characteristics is generated. Specifically, this involves: first, encrypting the standardized data structure using an asymmetric encryption algorithm; using a dedicated private key to perform encryption operations on the entire standardized data to generate encrypted ciphertext data; and simultaneously generating corresponding encrypted verification information using a public key for decryption and authenticity verification by the data recipient; then, adding a precise timestamp to the encrypted ciphertext data. The timestamp is based on a globally unified time base and is accurate to the millisecond level. The system records the exact moment the data encryption process is completed, and this timestamp is bound to the encrypted data and cannot be modified independently. The encrypted data, corresponding public key verification information, and precise timestamp are then combined into a complete blockchain transaction. This transaction data is broadcast to the entire blockchain network through blockchain network nodes. Upon receiving the transaction data, each node in the blockchain network initiates a consensus verification process, employing a practical Byzantine fault-tolerant consensus mechanism. Nodes mutually verify the validity of the encrypted signature, the compliance of the standardized data structure, the rationality of the timestamp, and the completeness of the data content. Only when more than two-thirds of the nodes have verified the transaction can it pass consensus verification. After verification, the transaction data is permanently stored in a new block of the blockchain. The new block records all information about the transaction and generates a unique block hash value. This block hash value is also associated with the hash value of the previous block, forming a chained storage structure. Through this series of operations, a blockchain log with immutable and traceable characteristics is generated.

[0045] In this embodiment of the invention, because the embodiment combines and encapsulates the twin model update information, anomaly detection and identification results, and unified time-series original operating status data, converts them into a standardized data structure that conforms to the Maritime Organization's electronic log specifications, and then encrypts, timestamps, and verifies the data through blockchain cryptography before storing it on the blockchain, it effectively overcomes the technical problems of inconsistent ship-related core data formats, lack of compliance support, susceptibility to tampering, and insufficient credibility of evidence storage. This generates a blockchain log that conforms to industry standards and has the characteristics of immutability and traceability, providing a solid guarantee for the credible evidence storage, compliance review, and traceability of ship data.

[0046] In a preferred embodiment of the present invention, a blockchain log is used as the sole trusted data source to construct an electronic archive of the ship's entire lifecycle. A traceability query interface is provided based on the blockchain log for trusted verification and tracing of the ship's historical status, operation records, and abnormal events, including: By extracting all historical data records related to the target vessel from the blockchain log in chronological order and based on key event identifiers, the process involves: first, identifying the target vessel's unique identifier, such as its International Maritime Organization (IMO) number or registration number, to ensure accurate vessel location; then, initiating a blockchain log traversal process to read transaction data from each block of the blockchain, comparing the vessel identifier information within the data to filter out all transaction records related to the target vessel; and simultaneously extracting the timestamp for each record and processing the selected records in ascending order of timestamp. The data is sorted to ensure continuity and integrity over time. Simultaneously, key event identifiers are identified and marked in each record. These identifiers include ship design parameter confirmation, construction milestone completion, equipment malfunction, maintenance operation execution, major digital twin model update, and emergency response activation. Records with these identifiers are categorized separately to ensure no relevant data from any key stage of the ship's entire lifecycle is omitted. Finally, the sorted time-series data and the marked key event data are aggregated to form a complete historical data set covering the entire lifecycle of the target ship, from design and construction to operation, maintenance, and scrapping.

[0047] Historical data is recorded, reorganized, classified, and indexed according to the ship lifecycle management standards to construct a structured electronic ship archive. The archive summary information is then stored and anchored on the blockchain. Specifically, based on the ship lifecycle management standards, the extracted historical data is divided into five core categories according to stages: design stage data, construction stage data, operation stage data, maintenance stage data, and scrapping stage data. Design stage data includes parameters related to ship design drawings, material selection standards, performance design indicators, and design review records. Construction stage data includes material procurement contracts and quality inspection reports, construction and installation logs, acceptance data for each construction process, ship launching test records, and delivery and acceptance documents. Operation stage data includes daily navigation status records, real-time equipment operating parameters, fuel consumption statistics, anomaly detection results records, and digital twin model update logs. Maintenance stage data includes periodic maintenance plan documents, detailed repair operation records, replacement component models and installation information, and post-maintenance performance test reports. Scrapping stage data includes ship scrapping assessment reports, dismantling process planning and execution records, and environmental compliance documents. Within each stage category, further subdivisions are made based on data type, such as equipment parameters, event logs, and document reports. A multi-level indexing system is then established: the first-level index is categorized by stage, the second-level index is arranged chronologically, and the third-level index is classified by event type and data characteristics. Each index item is associated with the storage path of the corresponding data in the electronic archive, ensuring rapid location of the required data. Subsequently, a structured ship electronic archive is constructed, storing all categorized data in an orderly manner according to the indexing system. Simultaneously, archive summary information is generated, containing key information such as the hash value of core data for each stage, the archive's creation time, update time, the data time period covered, and a list of key events. Finally, the archive summary information is broadcast to the blockchain network via blockchain transactions. After consensus verification by more than two-thirds of the nodes in the network, the summary information is permanently stored in a newly generated block on the blockchain, completing the notarization and anchoring of the archive summary on the blockchain.

[0048] Based on the electronic archives and their anchoring information in the blockchain, a traceability query service interface with identity authentication and access control is deployed. The interface supports trusted retrieval by time range, event type, and data characteristics. Specifically, this includes: first, integrating the storage path information of the electronic archives with the evidence anchoring information of the archive summaries in the blockchain to build the underlying data association architecture of the query interface, ensuring that the interface can quickly link the electronic archives and blockchain evidence data; embedding identity authentication during interface deployment, requiring users to submit identity information before initiating a query request, including their organization name, user name, user role, and authorization certificate number. The submitted identity information is compared item by item with a pre-stored authorized user information database. Only users with an identity information matching rate of 60% or higher and whose authorization certificates are valid can pass identity authentication and enter the query stage; simultaneously, a hierarchical access control mechanism is set up, dividing user permissions into three levels: Level 1 permissions are for maritime regulatory agencies, allowing querying all categories of data throughout the ship's entire lifecycle; Level 2 permissions are for ship owners and operating units, allowing querying all data except for classified design data; Level 3 permissions are for maintenance units, allowing querying only maintenance-related stage data and corresponding equipment operation data. The interface supports multi-condition combined search functionality. Users can set time range search conditions, precisely inputting the start and end times of the query, with time precision down to the year, month, day, hour, minute, and second. They can also select event type search conditions, including design changes, construction milestone completion, equipment failure, routine maintenance, emergency response, and model updates. Furthermore, users can set data feature search conditions, inputting features such as equipment name, parameter value range, and data file type, which will then be used for precise data matching based on the user-defined combination of conditions. The interface also includes auxiliary functions such as search result preview, data export, and report generation. Additionally, a query request logging module automatically records information such as user identity, query conditions, query time, and query results for each query, ensuring traceability of query operations.

[0049] The system responds to user tracing requests via a deployed query interface, retrieves relevant data records from the electronic archive, and automatically verifies the integrity and authenticity of the data by comparing it with the corresponding evidence anchoring information in the blockchain. Finally, it returns verified historical status, operation records, and abnormal event tracing reports to the user. Specifically, this includes: submitting a tracing request through the deployed tracing query interface, requiring key parameters such as the target vessel identifier, the time range of the query, the event type, and data characteristics; and uploading relevant supplementary authorization documents if necessary. Upon receiving the user's tracing request, the system first re-verifies whether the user's identity and permissions match the category of the queried vessel data. If the user's permissions are insufficient or the identity information is inconsistent with the previous authentication results, an insufficient permissions message is returned, and the request is recorded in the abnormal request log. If the permissions verification is successful, based on the user's submitted query parameters, the system accurately locates and extracts the corresponding historical data records from the structured electronic archive using a multi-level indexing system. This includes a time series table of the vessel's operating status parameters within the query range, detailed process records of key operations, and relevant data such as the time, location, cause, and handling results of abnormal events. Subsequently, the system extracts... The hash values ​​of the archive summaries corresponding to some data are compared one by one with the corresponding stage summary information anchored in the blockchain. At the same time, the timestamps, key event identifiers, data lengths and other information of the data are checked to see if they are completely consistent with the blockchain records. If the hash values ​​are consistent and there are no differences in the key information, the integrity and authenticity of the data are verified. If the hash values ​​are inconsistent or there are deviations in the key information, the data is determined to have been tampered with or damaged. The data is automatically marked as a verification anomaly and the verification process log is recorded. After verification, all valid data that has passed verification is organized and classified according to time sequence and event logic to generate a structured traceability report. The report includes a description of the query conditions, a description of the data source and verification status, a time-series statistical chart of the ship's historical status, a detailed list of key operation records, and an in-depth analysis of the time, cause and handling results of the abnormal event. The report is presented in a combination of text and graphics to ensure that it is intuitive and easy for users to understand. Finally, the traceability report is returned to the user through the interface, and a PDF report download function is also provided for users to archive or submit to relevant review departments. This ensures that users can obtain reliable, complete and detailed traceability results, effectively supporting fault tracing and compliance review work.

[0050] In this embodiment of the invention, because it uses blockchain logs as the sole trusted data source, extracts historical data of the target vessel in chronological order and key event identifiers, constructs a structured electronic archive by reorganizing and classifying the index, and anchors the summary information to the blockchain for evidence storage, and deploys a traceability query interface with identity authentication and access control that supports multi-condition retrieval, and automatically verifies the integrity and authenticity of the data and returns a trusted traceability report when responding to user requests, it effectively overcomes the technical problems of lacking a unified trusted archive carrier for the entire life cycle of vessel data, lacking accurate retrieval capabilities for traceability queries, and difficulty in verifying the authenticity of data. Thus, it achieves efficient and trusted traceability of the vessel's historical status, operation records, and abnormal events, providing an authoritative and reliable basis for the entire life cycle management of vessels, fault tracing, liability determination, and compliance review.

[0051] Embodiments of the present invention also provide a computing device, including: a processor and a memory storing a computer program, wherein the computer program, when executed by the processor, performs the system as described above. All implementations in the above system embodiments are applicable to this embodiment and can achieve the same technical effects.

[0052] Embodiments of the present invention also provide a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the system as described above. All implementations in the above system embodiments are applicable to this embodiment and can achieve the same technical effects.

[0053] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A system for constructing and tracing electronic records throughout the entire lifecycle of a ship, characterized in that: include: The acquisition module is used to acquire real-time operational status data generated by the ship during operation. The extension module is used to serialize the collected running status data to form a status dataset and select a reference status vector, extending two status change axes with the reference status vector as the origin. The fitting module is used to delineate sectors based on the directional differences between two axes, and to set two types of reference sequences inside and outside the sectors respectively; based on the temporal correlation of the two types of reference sequences, a continuous elliptical state evolution trajectory of the ship's state change characteristics is fitted. The calibration module is used to calculate the data calibration coefficient based on the elliptical state evolution trajectory, and to calibrate the original operating state data using the calibration coefficient to obtain calibrated data. The identification module is used to drive and update the digital twin ship model synchronized with the physical ship through calibrated data, and at the same time input the operating status data into the preset artificial intelligence anomaly detection model for real-time analysis and monitoring to obtain the identification results. The encryption module is used to encrypt and upload the updated information, identification results and original operating status data of the digital twin ship model to the blockchain using blockchain technology, so as to obtain an immutable blockchain log that conforms to the Maritime Organization's electronic log specifications. The processing module is used to build an electronic archive of the entire life cycle of a ship using blockchain logs as the sole trusted data source, and to provide a traceability query interface based on blockchain logs for trusted verification and tracing of the ship's historical status, operation records and abnormal events.

2. The ship lifecycle electronic record construction and traceability system according to claim 1, characterized in that, Real-time acquisition of operational status data generated by the ship during operation, including: It receives encrypted data streams periodically sent by at least one unmanned vessel in real time via a satellite communication link. By decrypting the encrypted data stream and parsing the communication protocol, the structured original runtime status data packets are extracted; and the integrity of the parsed data packets is verified and the source is authenticated to filter out trustworthy data packets. Trusted data packets are collected and cached according to their corresponding ship identifiers and data types to form a raw dataset initially organized by ship and time sequence; the data includes ship position, speed and heading, power parameters, equipment status, compartment monitoring data and emergency signals; By performing timestamp alignment, missing value marking, and standardization of the data format on the original dataset, a unified time-series runtime status data is finally obtained.

3. The ship lifecycle electronic record construction and traceability system according to claim 2, characterized in that, The collected operational status data is serialized to form a status dataset, and a baseline state vector is selected. Two state change axes are extended from the baseline state vector as the origin, including: The unified time-series operational status data is arranged in chronological order and organized into a time-series dataset containing multidimensional state variables; Based on the time series dataset, a state vector at a representative time point is selected from the dataset as the benchmark state vector for analyzing the evolution of ship state. By using the selected baseline state vector as the origin of the coordinate system, and based on the statistical characteristics and variation patterns of each state variable in the time series dataset, two mutually orthogonal state change axes are calculated and defined.

4. The ship lifecycle electronic record construction and traceability system according to claim 3, characterized in that, Sectors are defined based on the directional differences between two axes, and two types of reference sequences are established inside and outside each sector. Based on the temporal correlation of the two types of reference sequences, a continuous elliptical state evolution trajectory of the ship's state change characteristics is fitted, including: Each state vector in the time series dataset is projected onto a two-dimensional analysis coordinate system formed by two mutually orthogonal state change axes to form a series of time series data points. Based on the time series data points, calculate the orientation angle of each data point relative to the origin of the reference state vector; based on the preset angle threshold, divide the plane of the entire two-dimensional analysis coordinate system into several sectors; For each sector, the set of time-series data points within the sector is defined as the first type of reference sequence; the set of adjacent time-series data points associated with each sector but located outside the sector boundary is defined as the second type of reference sequence. Based on the temporal correlation between the two types of reference sequences corresponding to each sector, an ellipse fitting algorithm is used to fit all time series data points projected onto the two-dimensional analysis coordinate system, resulting in a continuous and closed elliptical curve as the state evolution trajectory characterizing the long-term and periodic changes in the ship's state.

5. The ship lifecycle electronic record construction and traceability system according to claim 4, characterized in that, The data calibration coefficients are calculated based on the elliptical state evolution trajectory. The original operating state data is then calibrated using these coefficients to obtain the calibrated data, including: Based on the obtained elliptical state evolution trajectory, the principal axis length, eccentricity, and orientation parameters relative to the two-dimensional analysis coordinate system are extracted. Using the principal axis length, eccentricity, and orientation parameters, and in accordance with predetermined calculation rules, a set of data calibration coefficients is obtained to compensate for the axial deviation between two states in the two-dimensional analysis coordinate system. The data calibration coefficients are applied to the uniform time-series operating status data to correct the component values ​​of the data in the two state change axes defined in the two-dimensional analysis coordinate system, thus obtaining calibrated operating status data that removes long-term trend errors.

6. The ship lifecycle electronic record construction and traceability system according to claim 5, characterized in that, The calibrated data drives and updates the digital twin ship model synchronized with the physical ship, while simultaneously inputting operational status data into a pre-set artificial intelligence anomaly detection model for real-time analysis and monitoring to obtain identification results, including: Based on the calibrated operational status data, the digital twin ship model corresponding to the physical ship is driven in real time to update the status parameters in the twin model, so that the status parameters are synchronized with the operational status of the physical ship. The calibrated operating status data is simultaneously input into a preset artificial intelligence anomaly detection model; the anomaly detection model performs pattern recognition and deviation analysis in real time based on the data to obtain the identification results of the equipment health status, anomaly type and probability of occurrence.

7. The ship lifecycle electronic record construction and traceability system according to claim 6, characterized in that, The updated information, identification results, and original operational status data of the digital twin ship model are encrypted and uploaded to the blockchain using blockchain technology to obtain an immutable blockchain log that conforms to the Maritime Organization's electronic log specifications, including: The twin model update information and anomaly detection and identification results contained in the comprehensive status report are combined and encapsulated with the original operating status data in a unified time series according to a predefined structure to obtain a complete data record block. Based on the encapsulated data record blocks, they are converted into a standardized data structure that conforms to the requirements of the Maritime Organization's electronic log specifications; By encrypting and timestamping standardized data structures using blockchain cryptography, and then broadcasting the processed data as a transaction to the blockchain network for consensus verification and on-chain storage, a blockchain log with immutable and traceable characteristics is generated.

8. The ship lifecycle electronic record construction and traceability system according to claim 7, characterized in that, Using blockchain logs as the sole trusted data source, an electronic archive of the entire ship lifecycle is constructed. Based on these blockchain logs, a traceability query interface is provided for the trusted verification and tracing of the ship's historical status, operational records, and abnormal events, including: By extracting all historical data records related to the target vessel from the blockchain log, in chronological order and by key event identifiers; Historical data is recorded, reorganized, classified, and indexed according to the ship lifecycle management standards, a structured electronic archive of ships is constructed, and the archive summary information is stored and anchored in the blockchain; Based on the electronic archive and the anchoring information in the blockchain, deploy a traceability query service interface with identity authentication and access control. The interface supports trusted retrieval by time range, event type and data characteristics. The system responds to user traceability requests through a deployed query interface, retrieves relevant data records from the electronic archive, and automatically verifies the integrity and authenticity of the data by comparing it with the corresponding evidence anchoring information in the blockchain. Finally, it returns trusted and verified historical status, operation records, and abnormal event traceability reports to the user.

9. A computing device, characterized in that, include: One or more processors; A storage device for storing one or more programs, which, when executed by the one or more processors, cause the one or more processors to perform the system as described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a program that, when executed by a processor, performs the system as described in any one of claims 1 to 8.

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