Shipping logistics tracking method

Abstract

The invention relates to the technical field of logistics tracking, in particular to a shipping logistics tracking method. Comprising the following steps: detecting a signal interruption time point and duration to obtain a signal interruption interval; according to the signal interruption interval, the navigation speed, direction and position which are finally recorded before interruption of the ship are extracted from a historical navigation trajectory database, and an initial state vector is generated; predicting a future transportation path of the ship through the corrected position sequence, and generating a predicted path set; according to the prediction path set, constructing a ship motion track in the three-dimensional virtual scene, and generating a visual track model; obtaining a decision support view by combining the confidence coefficient of the reckoning position sequence; and updating a port scheduling plan in real time through a decision support view, and generating an optimized loading and unloading time sequence. According to the method, the problem that the calculation precision of the position of the ship is not high in a complex scene of signal interruption is solved, and the ship position precision and the prediction precision of a future transportation path are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of logistics tracking, in particular to a shipping logistics tracking method. BACKGROUND

[0002] In modern shipping logistics, the real-time position and navigation trajectory of a ship are the core technologies to ensure shipping safety and improve transportation efficiency. However, due to various factors such as adverse weather, equipment failure, signal interference, etc., ships often face signal interruption during navigation. Such signal interruption not only leads to the loss of ship position data, but also affects the execution of port scheduling plans, further increasing the complexity and uncertainty of shipping logistics. Traditional ship tracking technology relies on real-time sensor data to provide ship position and state information, but once the signal is interrupted, the system cannot accurately obtain the dynamic position of the ship, bringing great challenges to port scheduling and transportation path prediction.

[0003] Currently, for the tracking problem after ship signal interruption, traditional methods usually use static compensation or rely on a single data source for calculation. These methods often cannot effectively solve the problem of accurate calculation of ship position in complex dynamic sea conditions and variable weather conditions. In addition, existing technologies mainly rely on historical data for path prediction, without fully considering real-time dynamics of environmental changes, ship state, and state estimation and correction after signal interruption. With the continuous expansion of global shipping scale, the improvement of port scheduling and logistics efficiency puts forward higher requirements for real-time and accurate trajectory calculation and path prediction. Therefore, how to accurately calculate the position of the ship and predict the future transportation path in the complex scenario of signal interruption has become a key problem to be solved in the field of shipping logistics.

[0004] The present application provides a new shipping logistics tracking method, aiming to solve the problem of ship position calculation and path prediction in the case of signal interruption. Through multi-source data fusion and advanced state estimation technology, the present application can accurately calculate the motion trajectory of the ship during signal loss and provide a visual ship trajectory model in a three-dimensional virtual scene, providing scientific decision support for port scheduling and transportation path optimization. This method not only makes up for the shortcomings of existing technologies, but also significantly improves the efficiency and safety of shipping logistics. SUMMARY

[0005] The present application provides a shipping logistics tracking method, mainly including: By collecting ship sensor data and external environment data, detecting the signal interruption time point and duration, and obtaining the signal interruption interval; According to the signal interruption interval, the last recorded navigation speed, direction and position of the ship before interruption are extracted from the historical navigation trajectory database to generate an initial state vector; By the initial state vector, combined with real-time acquisition of sea state data and weather data, the possible position of the ship during signal interruption is calculated through Kalman filtering algorithm to obtain a sequence of calculated positions; If the deviation of the sequence of calculated positions from the historical trajectory exceeds a preset threshold, the sequence of calculated positions is corrected by integrating multi-source data, fusing port scheduling data and AIS data of nearby ships to obtain a sequence of corrected positions; Through the sequence of corrected positions, the future transportation path of the ship is predicted through a particle filtering algorithm to generate a set of predicted paths; According to the set of predicted paths, a ship movement trajectory is constructed in a three-dimensional virtual scene, real position data and calculated position data are distinguished by color coding, and a visual trajectory model is generated; Through the visual trajectory model, combined with the confidence of the sequence of calculated positions, the reliability of the estimated data is presented through transparency mapping to obtain a decision support view; Through the decision support view, the port scheduling plan is updated in real time to generate an optimized loading and unloading time sequence.

[0006] The technical scheme provided by the embodiment of the application can include the following beneficial effects: The application discloses a technical scheme for track calculation and scheduling optimization in a shipping logistics ship signal interruption scenario, and provides a complete solution for the business problems of track loss caused by ship signal interruption and low port scheduling efficiency. The application collects logistics ship sensor and environmental data, detects a signal interruption interval, extracts a pre-interruption sailing state to generate an initial state vector, combines sea state and weather data, and calculates a position sequence during the interruption through Kalman filtering. If the calculation deviation exceeds a threshold, the application fuses port scheduling and nearby ship AIS data to correct the position sequence, and then predicts a future path through particle filtering to generate a set of predicted paths. The application constructs a movement trajectory in a three-dimensional virtual scene, distinguishes real and calculated data through color coding, and forms a decision support view by mapping the confidence through transparency, and optimizes a port loading and unloading time sequence in real time. The application significantly improves the track calculation accuracy and port scheduling efficiency in a signal interruption scenario, and provides efficient and reliable decision support for maritime transportation. BRIEF DESCRIPTION OF DRAWINGS

[0007] Figure 1 A shipping logistics tracking method flowchart in the embodiment of the application; Figure 2 A shipping logistics tracking system structure diagram in the embodiment of the application. DETAILED DESCRIPTION

[0008] The technical solutions of the present application will be described clearly and completely below in connection with the embodiments. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0009] As Figure 1 The shipping logistics tracking method in the embodiment can specifically include: Step S1: acquiring ship sensor data and external environment data, determining a signal interruption interval through time series analysis, extracting the last sailing state of the ship before the interruption from a historical sailing track database according to the signal interruption interval, and generating an initial state vector; The method for determining the signal interruption interval through time series analysis includes: The sensor data and the environment data are acquired through the ship sensor and the external environment acquisition equipment, the time point of signal interruption in the data stream is determined through a time series analysis method, if the sensor data or the environment data is lower than a preset threshold, the starting time point of signal interruption is obtained by detecting continuous abnormal points through a sliding window algorithm, the end time point of signal interruption is determined by acquiring the interruption duration through a time difference calculation method according to the starting time point of signal interruption, the sensor data and the environment data are integrated through a data fusion technology, a fusion data set is generated, if the environment data fluctuation in the fusion data set exceeds a preset threshold, the correlation between the environment data and the signal interruption is analyzed through a decision tree algorithm, the interruption interval classification is determined, the interruption interval sequence is obtained by separating the interruption interval and the normal interval through a time series segmentation method according to the interruption interval classification result, and the time distribution characteristics of the interruption interval are generated by extracting the features of the interruption interval sequence through time series analysis.

[0010] In particular, the above technical solution is based on the continuous acquisition of ship sensor data such as ship position, speed, heading, and external environmental data such as wind speed, wave height, and air pressure by ship sensors and environmental collection devices. After time stamp alignment, an ordered time series is formed. Through time series analysis, the data stream is continuously monitored, and through the collection time difference between adjacent two data, it is judged whether the data has been interrupted in the collection process. When the above collection time difference is greater than the set time, the interruption occurs between the above two data, otherwise, it is not interrupted. If it is detected that the sensor data or environmental data is lower than the set threshold, the sliding window algorithm is automatically used to perform window statistical analysis on the continuous sampling points, identify continuous abnormal points, and take the first abnormal point time as the signal interruption start time. According to the time difference between the signal interruption start time and the subsequent data recovery time, the interruption duration and signal interruption end time are directly calculated. In order to further improve the detection accuracy, the sensor data and environmental data are fused by weighting to form a fused data set to enhance the sensitivity to signal interruption under complex working conditions. If the environmental data fluctuation of the fused data within the interruption interval exceeds the threshold, the decision tree algorithm is called to analyze the correlation between environmental factors and signal interruption, and the interruption interval is automatically classified to realize accurate attribution and classification of signal interruption. Based on the classification result of the interruption interval, the time series is sliced by the time series segmentation method to accurately separate the interruption interval and the normal interval, and the complete interruption interval sequence is extracted. The time series analysis method is applied to the interruption interval sequence again to extract time characteristic information including start and end time, duration, interval distribution, etc., to generate the time distribution characteristics of the interruption interval, and realize the regular modeling of the signal interruption event. In actual shipping scenarios, for example, a ship loses GPS signal for several seconds due to bad weather. Based on the above algorithm, the interruption is automatically determined and classified, and the interval distribution data is generated to provide a data basis for subsequent trajectory compensation and scheduling optimization. This technical means closely combines sliding window, weighted data fusion, decision tree classification, time series segmentation and feature extraction algorithm with actual collection system to form an automatic detection, attribution, segmentation and modeling closed loop process, which can realize efficient and accurate identification and classification of signal interruption, overcome the inaccuracy and timeliness of existing technology relying on single signal or manual determination, improve the intelligent level of data processing under abnormal state in shipping logistics scene, and ensure the quality of basic data for subsequent trajectory calculation and scheduling decision.

[0011] Further, the initial state vector is generated, including: obtaining the last recorded navigation data of the ship before the signal interruption from the historical navigation trajectory database to obtain a record set containing the navigation speed, heading and position; if the record set is not empty, sorting according to the last record time, extracting the record with the latest timestamp to obtain the last navigation state; converting the navigation speed, heading and position in the last navigation state into a standardized initial state vector through a vector conversion method; smoothing the initial state vector to obtain an optimized state vector. If the deviation of the optimized state vector from the mode of the historical navigation trajectory exceeds a preset threshold, the initial state vector is corrected according to the statistical characteristics of the historical navigation trajectory to obtain a corrected initial state vector.

[0012] Specifically, the above technical solution is based on the historical navigation trajectory database. When the signal interruption occurs, the last recorded navigation speed, heading and position data before the interruption are obtained to form a navigation record set, and the latest record is extracted by sorting according to the timestamp. The navigation speed, heading and position data contained in the record are standardized into an initial state vector by a vector conversion method. Then, the vector is smoothed by a Kalman filter algorithm to reduce the noise effect, and an optimized state vector is obtained. When there is a large deviation between the optimized state vector and the mode of the historical navigation trajectory, the state vector is automatically corrected according to the statistical characteristics of the historical trajectory, so as to form a corrected initial state vector that is more consistent with the actual navigation characteristics. The above technical solution can obtain accurate initial state data, which lays a foundation for calculating the ship position sequence.

[0013] Step S2: According to the initial state vector, real-time sea state data and weather data, the ship position during the signal interruption is calculated through state estimation to obtain a calculated position sequence; if the deviation between the calculated position sequence and the historical trajectory exceeds a preset threshold, the port scheduling data and AIS data of nearby ships are fused to correct the calculated position sequence to obtain a corrected position sequence; The state estimation is used to calculate the ship position during the signal interruption, and a calculation position sequence is obtained, comprising: obtaining an initial state vector, real-time sea state data and real-time weather data, and obtaining a synchronized state data set through data time synchronization processing; obtaining a preliminary state estimation value through state estimation by combining the synchronized state data set with a ship motion model and using a Kalman filtering algorithm; adjusting the state estimation through the updating step of the Kalman filtering algorithm to obtain a corrected state estimation value if the deviation of the preliminary state estimation value from the sensor data fusion exceeds a preset threshold; obtaining a single-step position prediction value by predicting the next time position through the prediction step of the Kalman filtering algorithm according to the corrected state estimation value and fusing the environmental dynamic influence; obtaining a continuous position sequence by iteratively executing the prediction and updating steps of the Kalman filtering algorithm through the single-step position prediction value and combining the ship motion model; verifying the sequence consistency through data time synchronization processing if the time span of the continuous position sequence exceeds the signal interruption time length to obtain a final calculation position sequence; and obtaining a smooth calculation position sequence by optimizing the sequence smoothness according to the final calculation position sequence and fusing the real-time sea state data and weather data.

[0014] Specifically, the initial state vector of the ship, i.e. the current position, speed and heading of the ship, is obtained, and real-time sea state data and weather data are obtained at the same time. After time synchronization processing of these data, a unified synchronized state data set is formed, and the above data set is input into the ship motion model to obtain the state estimation value of the ship at the next time under the influence of the above data set through the above Kalman filtering algorithm, i.e. the state transition equation. The ship motion model is trained based on historical synchronized state data sets and corresponding ship positions, speeds and headings. The state estimation value includes the predicted position, speed and heading of the ship. If the deviation of the preliminary estimation value from the fusion result of real-time sensor data exceeds a preset threshold, the state estimation value is corrected based on the sensor data, and the sensor data includes the position, speed and heading of the ship. Through this step, the calculation of the current position of the ship during the signal interruption can be more accurate. In the next step, the corrected state estimation value is used to calculate the specific position of the ship at the next time through the prediction step of the Kalman filtering algorithm, and a single-step position prediction value is generated. After obtaining the single-step position prediction value, the algorithm iteratively executes the prediction and updating steps of the Kalman filtering in combination with the ship motion model, thereby obtaining a series of continuous position sequences. These position sequences can reflect the motion trajectory of the ship during the signal interruption. If the time span of the continuous position sequence exceeds the signal interruption time length, further data synchronization processing is performed to verify the consistency of the calculation result, and the final calculation position sequence generated is ensured to be consistent with the actual situation in time.

[0015] The technical scheme realizes dynamic tracking of the ship during signal interruption by multi-source data fusion, combination of the ship motion model and accurate estimation of the Kalman filtering algorithm, can effectively solve the problem that the position of the ship cannot be updated in real time, and improves the stability and accuracy of the ship navigation system. In addition, the calculation result of the system provides reliable data support for port scheduling and shipping route optimization, further optimizes the efficiency of shipping logistics, especially in complex environments, significantly improves the accuracy and availability of ship position prediction.

[0016] For example, in actual application scenarios, when the ship encounters signal loss or interference, traditional methods are difficult to accurately determine the position of the ship, which affects the port scheduling plan. By adopting the technical scheme, the system can calculate the accurate position of the ship in real time, update the port scheduling plan in time, avoid the delay of loading and unloading caused by inaccurate information in the port, and improve the efficiency of the entire shipping logistics system. The combination of multi-source data fusion and Kalman filtering algorithm overcomes the uncertainty caused by signal interruption and provides more reliable technical support for the shipping industry.

[0017] Further, the corrected position sequence is obtained by correcting the calculated position sequence, comprising: if the deviation of the calculated position sequence and the historical trajectory exceeds the preset threshold, the deviation value is calculated by comparing the calculated position sequence and the historical trajectory to obtain the deviation distribution characteristics; the calculated position sequence is optimized by Kalman filtering algorithm by fusing the port scheduling data and the AIS data of the nearby ship to obtain the preliminary corrected position sequence; the position sequence deviation is adjusted by particle filtering algorithm according to the preliminary corrected position sequence combined with the ship motion state to obtain the optimized corrected position sequence; if the consistency of the optimized corrected position sequence and the historical trajectory is lower than the preset threshold, the time stamp in the port scheduling data and the speed information in the AIS data are extracted by multi-source data integration to update the position sequence to obtain the high-precision position sequence; the real-time requirement of the position sequence is judged according to the high-precision position sequence to obtain the real-time corrected position sequence; the final corrected position sequence is obtained by smoothing the position sequence by weighted average according to the real-time corrected position sequence combined with the trajectory consistency; and whether the position sequence accuracy meets the preset accuracy threshold is judged according to the final corrected position sequence.

[0018] Specifically, the above technical solution corrects the position sequence dynamically to obtain a high-precision and continuous ship movement trajectory. For the case where the deviation between the position sequence and the historical trajectory exceeds the preset threshold, the system automatically compares the two and calculates the deviation, extracts the deviation distribution characteristics, introduces the Kalman filtering algorithm to optimize the calculated trajectory in combination with the port scheduling data and nearby ship AIS data, fuses multiple sources of dynamic data in real time, generates a preliminary corrected position sequence, and on this basis, introduces the particle filtering algorithm according to the actual movement state of the ship to further adjust the position deviation, so that the sequence fits the complex and nonlinear movement changes. When the consistency between the optimized and corrected position sequence and the historical trajectory is still lower than the standard, the system retrieves the time stamp and AIS speed information of the port scheduling data, integrates multiple sources, dynamically completes and corrects the trajectory points, and obtains a high-precision position sequence. Further, the high-precision position sequence is analyzed for its timeliness to determine whether it meets the real-time updating requirements of the system, realizing closed-loop correction of the whole-process trajectory data. Through comparison of the real-time corrected position sequence and its consistency with the historical trajectory, the system smoothes the trajectory by weighted average method, outputs a smoothed and continuous corrected trajectory, and then verifies the trajectory accuracy to ensure that it meets the set precision threshold. Taking the case of a container ship entering a port area and signal interference causing data drift as an example, the system can correct the trajectory obtained by integrating AIS, scheduling, historical and other data to a trajectory close to the actual movement state through the above multi-dimensional dynamic correction and smoothing mechanism, achieving significant improvement in trajectory continuity and data reliability.

[0019] Step S3: predicting a future transport path of the ship through the particle filtering algorithm based on the corrected position sequence, and generating a prediction path set; The generation of the prediction path set includes: obtaining the corrected position sequence, obtaining the continuous ship movement state through data cleaning and interpolation processing; extracting dynamic characteristics from the ship movement state through the particle filtering algorithm to determine an initial prediction set of the future transport path; adjusting the weight of the particle filtering algorithm through environmental impact factors to obtain an optimized prediction path set; if the path prediction accuracy of the prediction path set is lower than the preset threshold, updating the particle state through iteration to obtain a new prediction path set; calculating the evaluation index of the path optimization target based on the optimized prediction path set to determine the optimal transport path; updating the ship movement state through real-time data input to obtain a dynamically adjusted prediction path set; and outputting the final prediction path set through the evaluation result of the path prediction accuracy.

[0020] Specifically, by data cleaning and interpolation on the corrected ship position sequence, missing values and abnormal points are eliminated to form a time-continuous motion state input sequence. Based on the sequence, the particle filter algorithm establishes a large number of particle individuals, each of which represents a possible future state change path of the ship. The system extracts dynamic characteristics of each particle such as speed, heading, acceleration, etc. and introduces different environmental factors such as real-time wind, wave height, ocean current, etc. into the particle weight adjustment link to realize dynamic weight distribution and probability modeling of path possibility in the particle filtering process. If the path prediction accuracy of the generated prediction path set is lower than the system set threshold, the system automatically iteratively adjusts the particle state, resamples and evolves the particle distribution, and continuously optimizes the prediction path set structure until the overall accuracy meets the requirements. According to the optimized path set, the system comprehensively evaluates multiple target indicators such as transportation timeliness, path safety, fuel consumption, etc. to select the optimal transportation path. On this basis, the system dynamically updates the motion state by real-time collection of the latest ship and environmental data, further corrects and supplements the particle distribution, and realizes adaptive adjustment of the prediction path set. The path prediction accuracy evaluation result is directly used as the criterion for path set output threshold, and the path set output with high confidence is called by subsequent three-dimensional visualization, scheduling optimization and other modules. Taking the transoceanic transportation of a certain cargo ship in complex sea conditions as an example, the system can push multiple optional paths according to the corrected trajectory and environmental changes, and automatically select the optimal path combined with the port operation plan, overcoming the problems of limited prediction accuracy of single model and untimely response to environmental changes in the prior art, forming a closed-loop system of data flow, model prediction and decision optimization, realizing high-reliability prediction of ship transportation path, and providing effective technical support for intelligent port scheduling and risk control. The introduction of the above particle filtering algorithm and the dynamic adjustment mechanism of the environment, the technical features and the actual data and needs in the application scenario are closely combined to form an integrated solution that supports each other in function, realizes the automation and intelligentization of path prediction, and improves the overall transportation safety and operational efficiency.

[0021] Step S4: Constructing a ship motion trajectory in a three-dimensional virtual scene, distinguishing real position data and calculated position data by color coding, and generating a visual trajectory model; The generating of the visualized trajectory model comprises: obtaining real position data and calculated position data from the set of predicted paths, obtaining a set of position data in a unified format through data fusion processing, obtaining a ship movement trajectory through trajectory generation algorithm by analyzing the set of position data in a unified format, determining an initial trajectory coordinate set, constructing a three-dimensional trajectory model in a three-dimensional virtual scene according to the initial trajectory coordinate set through scene rendering, obtaining a rendered trajectory framework, distinguishing real position data and calculated position data through color coding for the rendered trajectory framework, generating a color-coded trajectory, and obtaining the visualized trajectory model; if a trajectory point in the visualized trajectory model deviates from the set of predicted paths by more than a preset threshold, recalculating the trajectory point coordinates through position data analysis, updating the visualized trajectory model; adjusting the visualized trajectory model through trajectory dynamic updating technology according to real-time ship position data, obtaining a dynamically updated three-dimensional trajectory model; and performing light and shadow processing and perspective optimization on the dynamically updated three-dimensional trajectory model through scene rendering technology, and obtaining a final visualized trajectory model.

[0022] Specifically, the above technical solution processes the real position data and the calculated position data from the set of predicted paths into a unified data format, forms a consistent-structure position data set after data fusion, analyzes the data set through trajectory generation algorithm, automatically generates a continuous ship movement trajectory and determines a trajectory coordinate set, calls scene rendering technology in a three-dimensional virtual scene, constructs a three-dimensional trajectory model according to the trajectory coordinate set, significantly distinguishes real position and calculated position through color coding, forms an intuitive and identifiable color-coded trajectory, realizes the construction of the visualized trajectory model, and ensures that the model accurately reflects the actual and predicted dynamics of the ship. In the case of deviation of the trajectory point from the set of predicted paths, the trajectory point coordinates are automatically recalculated and the visualized trajectory model is updated in real time by analyzing the position data of the deviated point, ensuring that the model accurately reflects the actual and predicted dynamics of the ship. After input of real-time ship position data, the trajectory dynamic updating technology automatically adjusts the three-dimensional trajectory model, continuously completes and corrects the trajectory according to new data flow, and meets the requirement of dynamic change of ship position data. Scene rendering such as light and shadow processing and perspective optimization further improves the readability and spatial level of the trajectory model, enabling decision makers to intuitively understand the relationship between the ship running trajectory and the predicted path from multiple angles. In actual application, if a ship calculates multiple future paths through a prediction algorithm during signal interruption, the system uniformly fuses and visualizes the real and calculated trajectory data, and management personnel can accurately distinguish the deviation between the current trajectory and the prediction based on the three-dimensional visualized model, assisting in dynamically adjusting the port scheduling plan. This technical means cooperates the algorithm features of data fusion, trajectory generation, dynamic updating, three-dimensional rendering and color coding with the technical features of three-dimensional modeling and real-time scene application, forms a full-process closed loop of shipping logistics trajectory visualization, solves the technical problems of non-intuitive presentation of the trajectory and insufficient decision support data in the signal interruption scenario, and improves the timeliness and reliability of port scheduling decisions.

[0023] Step S5: Through the visualization trajectory model, combined with the confidence of the estimated position sequence, the decision support view is generated through transparency mapping; Wherein, the decision support view generated by the transparency mapping includes: through the visualization trajectory model, the position sequence data is obtained; the position sequence is smoothed through Kalman filtering to obtain smoothed trajectory data; according to the smoothed trajectory data, the confidence of each position point is calculated through Bayesian inference to obtain the position confidence sequence; the position confidence sequence is mapped to the visualization transparency value through the transparency mapping function to obtain the transparency mapping data; through the visualization rendering, the trajectory view is generated according to the transparency mapping data to obtain the preliminary decision support view; if the trajectory point transparency in the preliminary decision support view is lower than the preset threshold, the missing position data is supplemented through the interpolation algorithm to obtain the optimized trajectory view; according to the optimized trajectory view, the trajectory change trend is extracted through sequence analysis to obtain the trajectory trend data; through the view superposition method, the trajectory trend data is fused with the optimized trajectory view to obtain the final decision support view.

[0024] Specifically, after obtaining the ship trajectory position sequence data, the above technical solution performs dynamic smoothing processing on all collected trajectory points through the Kalman filtering algorithm, so that the noise and mutations in the original trajectory are effectively weakened, thereby obtaining smooth trajectory data with strong continuity and conforming to the actual trajectory change rule. On this basis, the system further applies the Bayesian inference method to estimate the confidence probability of each smoothed trajectory point, and according to the historical observation, sensor state and environmental data of the current point, the Kalman filtering algorithm is used to predict the position and state of the current point by using the historical observation and the dynamics model of the control input ship, and according to the measurement accuracy of the sensor and the influence of the environmental data, a predicted state estimation value is calculated, and the prediction uncertainty, that is, the prediction error, is also obtained. The confidence value of each trajectory point is obtained, and a position confidence sequence is formed. The system takes the sequence as input, performs normalization processing through the transparency mapping function, directly converts the confidence level into the transparency form of the visualized trajectory, and the trajectory points with high confidence level present high opacity, and the trajectory points with low confidence level present high transparency. Through the visualization rendering technology, the system projects the trajectory into a three-dimensional virtual scene or a two-dimensional interface in a color and transparency combination mode, generates an intuitive and usable preliminary decision support view, identifies which trajectory sections have high credibility and which sections need attention. If the transparency of part of the trajectory points is lower than the system preset threshold, it means that the data credibility of this section is insufficient, and the interpolation algorithm is automatically called to supplement the missing points or optimize the abnormal points according to the context spatiotemporal sequence, so as to enhance the continuity of the trajectory. The completed trajectory is subjected to overall trend extraction through sequence analysis, so as to realize intelligent identification of the trajectory change trend. The trajectory change trend and the optimized trajectory view are combined by using the view superposition method, and a decision support view is output, which can directly reflect the trajectory confidence distribution and motion trend. Taking the case that a ship sails to the nearshore area and encounters signal interference, resulting in a decrease in the confidence of the positioning data in part of the section, as an example, the system automatically displays the trajectory of this section as high transparency, and fills in the missing information through the interpolation algorithm. The optimized trajectory is superimposed with the overall trend, which helps the port logistics scheduling center to master the ship dynamics in real time. The technical solution cooperates the trajectory data processing algorithm with the visualization presentation means, constructs a decision support system integrating trajectory reliability intuitive expression and trend insight, significantly improves the intelligent level of port scheduling and ship safety management, overcomes the problems in the prior art that the trajectory credibility is difficult to quantitatively and intuitively express, and the abnormal sections are difficult to locate and complete, and has outstanding substantial features and practical engineering application value.

[0025] Step S6: According to the decision support view, update the port scheduling plan, and generate an optimized unloading time sequence.

[0026] Specifically, the above step S6 generates trajectory confidence, position prediction, cargo handling demand and other multi-dimensional data through the decision support view, and real-time associates port scheduling system operation state, logistics ship in and out of port dynamics, berth utilization and other business data, automatically compares and analyzes the ship arrival time, berthing time, departure time, operation time and other parameters calculated by the trajectory model with the current scheduling plan, evaluates the resource allocation, equipment availability, berth arrangement and other multi-factor constraints of the handling task, and through the optimization method such as genetic algorithm, iteratively solves the ship queue, operation sequence and resource allocation scheme for multiple rounds to form a new handling time sequence; the data are efficiently synchronized and fed back, the ship predicted path and confidence data are intuitively displayed to the scheduling system through the decision support view, each handling operation node is dynamically adjusted under the guidance of the algorithm, the scheduling instruction is distributed to various operation terminals in the port in real time, and the scheduling plan is quickly reconstructed according to the real-time changes of the actual dynamics and the predicted trajectory of the ship, thereby improving the port operation efficiency and resource utilization; taking the container ship berthing as an example, after the system automatically collects the predicted arrival time and berth arrangement, the optimal operation sequence is determined, the scheduling resources are allocated to the berth and equipment combination with the highest operation efficiency, and sudden events such as equipment failure and abnormal weather are adjusted in real time, thereby reducing the waiting time of the ship and improving the handling efficiency; in the technical features, the trajectory calculation and scheduling optimization algorithm are closely combined in function, and jointly support the real-time optimization of the port scheduling plan; the technical features and algorithm features work together to solve the actual problems of port dynamic scheduling and cargo handling time sequence optimization under the condition of ship signal interruption, and achieve the beneficial technical effects of improving port operation efficiency, reducing resource idling, and optimizing overall logistics time efficiency.

[0027] The application also provides a shipping logistics tracking system for implementing the above method, as shown in the accompanying drawings. Figure 2 The system comprises: A generating unit is configured to acquire ship sensor data and external environment data, determine a signal interruption interval through time series analysis, extract a last sailing state of the ship before the interruption from a historical sailing trajectory database according to the signal interruption interval, and generate an initial state vector. A correction unit is configured to calculate a ship position during the signal interruption through state estimation by combining real-time sea state data and weather data based on the initial state vector, obtain a calculated position sequence, and correct the calculated position sequence to obtain a corrected position sequence if a deviation between the calculated position sequence and the historical trajectory exceeds a preset threshold. A prediction unit is configured to predict a future transportation path of the ship through a particle filtering algorithm based on the corrected position sequence, generate a predicted path set, construct a ship motion trajectory in a three-dimensional virtual scene, distinguish real position data and calculated position data through color coding, and generate a visual trajectory model. The mapping unit is configured to generate a decision support view by transparency mapping by visualizing the trajectory model in combination with a confidence of the estimated position sequence.

[0028] The application further provides a computer readable storage medium, which stores instructions, and the instructions are executed by a processor to implement the method.

[0029] In summary, the application obtains ship sensor and external environment data, identifies signal interruption intervals using time series analysis method, extracts the last sailing state of the ship from the historical sailing trajectory database to form an initial state vector, combines real-time sea conditions and weather data, and performs state estimation through Kalman filtering to estimate the position of the ship during signal interruption, and corrects the position when the position estimation deviation is too large by integrating port scheduling and AIS data of nearby ships; the corrected ship position data is then predicted through a particle filtering algorithm to form a plurality of predicted path sets; in a three-dimensional virtual scene, the predicted path and the actual position are combined to generate a visual trajectory model, and the real position and the estimated position are distinguished by color coding to intuitively display the motion trajectory of the ship. At the same time, the confidence of the estimated position sequence is displayed through transparency mapping to further provide a visual view for decision support; the decision support view is combined to optimize the port scheduling plan to generate a new loading and unloading time sequence, thereby improving the operation efficiency of the port; the entire process gradually advances from accurate detection of signal interruption to state estimation, path prediction, three-dimensional visualization, and decision support, forming a closed-loop feedback mechanism to ensure efficient scheduling and accurate decision-making of shipping logistics in complex environments; the above scheme ensures the efficiency and reliability of ship position estimation and future path prediction in complex sea conditions and weather environments during signal interruption, improves the accuracy of logistics ship position estimation and the flexibility of port scheduling, and significantly optimizes the efficiency and safety of the overall shipping logistics.

[0030] The above detailed description further describes the purpose, technical solutions and beneficial effects of the application, and it should be understood that the above description is only a specific embodiment of the application and is not intended to limit the protection scope of the application, and any modification, equivalent replacement, improvement, etc. within the spirit and principles of the application should be included in the protection scope of the application.

Claims

1. A shipping logistics tracking method, characterized in that: The method comprises: Step S1: Obtain ship sensor data and external environment data, and determine the signal interruption interval through time series analysis; based on the signal interruption interval, extract the ship's last navigation state before the interruption from the historical navigation trajectory database to generate an initial state vector; Step S2: Using the initial state vector, combined with real-time sea condition data and weather data, the ship's position during the signal interruption is estimated through state estimation to obtain an estimated position sequence. If the deviation between the estimated position sequence and the historical trajectory exceeds a preset threshold, the estimated position sequence is corrected by integrating port scheduling data and AIS data of nearby ships to obtain a corrected position sequence. Step S3: by correcting the position sequence, the future transport path of the ship is predicted using a particle filter algorithm to generate a set of predicted paths; Step S4: Construct the ship's motion trajectory in the three-dimensional virtual scene, distinguish the real position data from the estimated position data through color coding, and generate a visual trajectory model; Step S5: generating a decision support view through transparency mapping by visualizing the trajectory model and combining it with the confidence of the estimated position sequence; Step S6: Based on the decision support view, the port scheduling plan is updated to generate an optimized loading and unloading time series.

2. The method according to claim 1, wherein Determining the signal interruption interval by time series analysis includes: Sensor data and environmental data are obtained through ship sensors and external environment collection equipment, and the time point of signal interruption in the data stream is determined by time series analysis method; if the sensor data or environmental data is lower than the preset threshold, the sliding window algorithm is used to detect continuous abnormal points to obtain the starting time point of the signal interruption; based on the starting time point of the signal interruption, the duration of the interruption is obtained by the time difference calculation method to determine the end time point of the signal interruption; the sensor data and environmental data are weightedly integrated through data fusion technology to generate a fused data set; if the fluctuation of environmental data in the fused data set exceeds the preset threshold, the decision tree algorithm is used to analyze the correlation between environmental data and signal interruption to determine the interruption interval classification; based on the interruption interval classification result, the interruption interval and the normal interval are separated by the time series segmentation method to obtain the interruption interval sequence; the interruption interval sequence is feature extracted through time series analysis to generate the time distribution characteristics of the interruption interval.

3. The method according to claim 1, wherein The generating of the initial state vector includes: obtaining the last recorded navigation data of the ship before the signal is interrupted from a historical navigation trajectory database to obtain a record set including navigation speed, heading and position; if the record set is not empty, sorting according to the last recorded time, extracting the record with the latest timestamp, and obtaining the last navigation state; converting the navigation speed, heading and position in the last navigation state into a standardized initial state vector through a vector conversion method; and smoothing the initial state vector through a Kalman filter algorithm to obtain an optimized state vector.

4. The method according to claim 1, wherein The method of calculating the ship position during the signal interruption through state estimation to obtain an estimated position sequence includes: obtaining an initial state vector, real-time sea condition data and real-time weather data, and obtaining a synchronized state data set through data time synchronization processing; performing state estimation through a Kalman filter algorithm based on the synchronized state data set in combination with a ship motion model to obtain a preliminary state estimate value; if the deviation between the preliminary state estimate value and the sensor data after fusion exceeds a preset threshold, adjusting the state estimate through an update step of the Kalman filter algorithm to obtain a corrected state estimate value; based on the corrected state estimate value, integrating the dynamic influence of the environment, and calculating the position at the next moment through a prediction step of the Kalman filter algorithm to obtain a single-step position prediction value; based on the single-step position prediction value, in combination with the ship motion model, iteratively executing the prediction and update steps of the Kalman filter algorithm to obtain a continuous position sequence; if the time span of the continuous position sequence exceeds the signal interruption duration, verifying the sequence consistency through data time synchronization processing to obtain a final estimated position sequence; based on the final estimated position sequence, integrating the real-time sea condition data and weather data, optimizing the sequence smoothness, and obtaining a smoothed estimated position sequence.

5. The method according to claim 1, wherein The method of correcting the estimated position sequence to obtain a corrected position sequence includes: if the deviation between the estimated position sequence and the historical trajectory exceeds a preset threshold, calculating the deviation value by comparing the estimated position sequence with the historical trajectory to obtain a deviation distribution feature; optimizing the estimated position sequence by fusing port scheduling data and AIS data of nearby ships through a Kalman filter algorithm to obtain a preliminary corrected position sequence; adjusting the position sequence deviation through a particle filter algorithm based on the preliminary corrected position sequence in combination with the ship's motion state to obtain an optimized corrected position sequence; if the consistency between the optimized corrected position sequence and the historical trajectory is lower than a preset threshold, extracting the timestamp in the port scheduling data and the speed information in the AIS data through multi-source data integration, updating the position sequence to obtain a high-precision position sequence; analyzing the real-time performance of the data based on the high-precision position sequence to determine whether the position sequence meets the real-time update requirements to obtain a real-time corrected position sequence; and smoothing the position sequence through a weighted average method based on the real-time corrected position sequence in combination with trajectory consistency to obtain a final corrected position sequence.

6. The method according to claim 1, wherein The method of predicting the future transport path of a ship by using a particle filter algorithm and generating a predicted path set includes: obtaining a corrected position sequence, and obtaining a continuous ship motion state through data cleaning and interpolation processing; extracting dynamic features from the ship motion state through a particle filter algorithm to determine an initial predicted path set for the future transport path; adjusting the weight of the particle filter algorithm based on environmental influencing factors to obtain an optimized predicted path set; if the path prediction accuracy of the predicted path set is lower than a preset threshold, iteratively updating the particle state to obtain a new predicted path set; calculating an evaluation index of a path optimization target based on the optimized predicted path set to determine an optimal transport path; updating the ship motion state through real-time data input to obtain a dynamically adjusted predicted path set; and outputting a final predicted path set based on the evaluation result of the path prediction accuracy.

7. The method according to claim 1, wherein The method for generating a visual trajectory model includes: acquiring real position data and estimated position data from a predicted path set, and obtaining a position data set in a unified format through data fusion processing; generating a ship motion trajectory by parsing the position data set in the unified format and using a trajectory generation algorithm to determine an initial trajectory coordinate set; constructing a three-dimensional trajectory model based on the initial trajectory coordinate set in a three-dimensional virtual scene through scene rendering technology to obtain a rendered trajectory framework; distinguishing the real position data from the estimated position data through color coding in the rendered trajectory framework, generating a color-coded trajectory, and obtaining a visual trajectory model; if a trajectory point in the visual trajectory model deviates from the predicted path set by more than a preset threshold, recalculating the trajectory point coordinates through position data parsing and updating the visual trajectory model; adjusting the visual trajectory model based on the real-time position data of the ship through a trajectory dynamic update technology to obtain a dynamically updated three-dimensional trajectory model; and performing light and shadow processing and perspective optimization on the dynamically updated three-dimensional trajectory model through scene rendering technology to obtain a final visual trajectory model.

8. The method according to claim 1, wherein A decision support view is generated through transparency mapping, including: obtaining position sequence data through a visual trajectory model; smoothing the position sequence to obtain smoothed trajectory data; calculating the confidence of each position point based on the smoothed trajectory data to obtain a position confidence sequence; mapping the position confidence sequence to a visual transparency value through a transparency mapping function to obtain transparency mapping data; generating a trajectory view based on the transparency mapping data through visual rendering technology to obtain a preliminary decision support view; if the transparency of the trajectory point in the preliminary decision support view is lower than a preset threshold, supplementing the missing position data through an interpolation algorithm to obtain an optimized trajectory view; extracting the trajectory change trend based on the optimized trajectory view through a sequence analysis technology to obtain trajectory trend data; and fusing the trajectory trend data with the optimized trajectory view through a view overlay method to obtain a final decision support view.

9. A shipping logistics tracking system, used to implement the method according to any one of claims 1 to 8, characterized in that: The system comprises: A generation unit is used to obtain ship sensor data and external environment data, determine the signal interruption interval through time series analysis; based on the signal interruption interval, extract the ship's last navigation state before the interruption from the historical navigation trajectory database to generate an initial state vector; The correction unit is used to use the initial state vector, combined with real-time sea condition data and weather data, to estimate the ship's position during the signal interruption through state estimation to obtain an estimated position sequence; if the deviation between the estimated position sequence and the historical trajectory exceeds a preset threshold, the estimated position sequence is corrected by fusing port scheduling data and AIS data of nearby ships to obtain a corrected position sequence; The prediction unit is used to predict the future transportation path of the ship by correcting the position sequence and using the particle filter algorithm to generate a set of predicted paths. The ship's motion trajectory is constructed in a three-dimensional virtual scene, and the real position data and the estimated position data are distinguished by color coding to generate a visual trajectory model. The mapping unit is used to generate a decision support view through transparency mapping by visualizing the trajectory model and combining it with the confidence of the estimated position sequence.

10. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instructions are executed by a processor, the method according to any one of claims 1 to 8 is implemented.

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