An import energy sea transportation channel dynamic tracking and monitoring method and system based on multi-source data and channel-specific model

Abstract

This invention discloses a method and system for dynamic tracking and monitoring of imported energy shipping channels based on multi-source data and channel-specific models, relating to the field of maritime energy transportation safety monitoring technology. The method includes: collecting multiple types of data to form multi-source data through a hierarchical acquisition strategy, wherein the multi-source data includes basic data, ship-specific data, and channel-specific data for each channel, including ship-intensive channels, ice-covered navigation channels, canal navigation channels, and typhoon-affected channels; setting up ship-channel adaptability assessment sub-models corresponding to each channel; calculating ship-channel adaptability assessment indices for each channel based on the multi-source data; comparing the ship-channel adaptability assessment indices for each channel with corresponding preset threshold values ​​to determine the current risk status of ships in the corresponding channel and issue early warnings.

Description

Technical Field

[0001] This invention belongs to the field of maritime energy transportation safety monitoring technology, and in particular relates to a dynamic tracking and monitoring method and system for imported energy maritime transportation channels based on multi-source data and channel-specific models. Background Technology

[0002] my country's seaborne crude oil and liquefied natural gas (LNG) imports account for a significant proportion of its total imports. The core transport channels exhibit distinct characteristics of "concentrated sources, fixed routes, and diverse risks": some crude oil routes traverse narrow waterways, facing constraints such as dense ship traffic and limited water depth; some northern crude oil routes are covered by sea ice in winter, and the extreme cold environment can easily lead to signal attenuation in equipment; some LNG routes rely on artificial canals for navigation, and are affected by lock size limitations and scheduling delays; some Southern Hemisphere LNG routes are frequently affected by typhoons, with high typhoon waves posing a significant threat to ship navigation safety.

[0003] However, existing channel monitoring technologies cannot meet the aforementioned differentiated risk requirements, with the following specific shortcomings: (1) Data fusion is limited in dimension and lacks comprehensive coverage of elements. Existing monitoring methods mainly rely on AIS data, which can only capture basic dynamic information of ships and ignore the three key elements of marine environmental data, channel-specific data and ship status data. This results in a limited dimension of risk identification and makes it difficult to effectively warn of potential safety hazards such as ship grounding.

[0004] (2) Insufficient dynamic tracking accuracy and failure to consider the characteristics of the ship channel. The existing technology uses a general ship tracking model, which does not fully consider the specific characteristics of LNG / crude oil ships and the interaction with the channel environment. It cannot quantify the differences in navigation resistance of ships in specific channel environments, resulting in a significant lag in the identification of abnormal speeds.

[0005] (3) Lack of channel adaptability assessment and generalized risk identification. Existing technologies do not design specific assessment models for the core risks of different channels, lack targeted risk thresholds and judgment formulas, and cannot accurately identify channel-specific risks such as ship collisions, power overload in ice areas, and size over-limit.

[0006] (4) The data preprocessing is crude and abnormal interference is not effectively corrected. AIS data has a jump problem in the signal blind zone. The existing technology only uses simple interpolation correction, which leads to a large position deviation. There is a spatiotemporal misalignment error between meteorological and hydrological grid data and the real-time position of ships, which does not achieve accurate alignment and directly affects the accuracy of risk assessment.

[0007] (5) The risk warning mechanism is imperfect and the output form is singular. The existing technology has not established a hierarchical early warning system and can only output basic risk warnings. It lacks risk level determination, targeted emergency suggestions and multi-terminal collaborative push mechanism, resulting in the lag in shore-based and ship-based response and making it difficult to meet the needs of rapid handling of sudden risks.

[0008] In summary, existing technologies cannot meet the monitoring requirements of "all elements, high precision, scenario-specific, and strong response" for imported energy shipping channels. There is an urgent need for a dynamic tracking and monitoring method that integrates multi-source data, channel-specific characteristics, and hierarchical early warning. Summary of the Invention

[0009] To address the aforementioned technical problems, this invention proposes a dynamic tracking and monitoring method for imported energy shipping channels based on multi-source data and a channel-specific model, comprising: Multi-source data is collected by using a hierarchical acquisition strategy. The multi-source data includes basic data, ship-specific data, and channel-specific data for each channel. The channels include ship-intensive channels, ice-covered navigation channels, canal navigation channels, and typhoon-affected channels. Each of the aforementioned channels is assigned a corresponding ship-channel adaptability assessment sub-model, and the ship-channel adaptability assessment index for each channel is calculated based on the multi-source data. The ship-channel compatibility assessment index for each channel is compared with the corresponding preset index threshold to determine the current risk status of the ship in the corresponding channel and issue an early warning.

[0010] Furthermore, after collecting multiple types of data to form multi-source data through a hierarchical acquisition strategy, the process also includes: standardizing the multi-source data, Kalman filtering for noise reduction, and inverse distance weighted interpolation for spatiotemporal alignment.

[0011] Furthermore, the ship-channel adaptability assessment sub-model for setting up the ship-intensive channel includes: Set up a sub-model for ship encounter risk assessment: in, For the shortest meeting distance, The x-coordinate of the current location of the ship. The x-coordinate of the other ship's location. The vertical coordinate of the current position of the ship. The vertical coordinate of the location of the other vessel is given. To minimize the time of meeting, The current speed of the vessel. The speed of the other vessel; when Less than the safe distance threshold or If the time frame is less than the encounter time threshold, it is considered a high encounter risk. Sub-model for assessing the adaptability of waterway space: in, For saturation, This represents the real-time number of vessels navigating the waters. The average length of navigable vessels. For the safety distance ratio, The average distance between adjacent ships. The width of the waterway; when Above the saturation threshold or When the distance is below the safety clearance ratio threshold, it is considered a risk of insufficient waterway adaptability.

[0012] Furthermore, the vessel-channel adaptability assessment sub-model for setting up the canal navigation channel includes: Canal size adaptability assessment sub-model: in: For the ship's length deviation rate, The actual length of the current vessel. The length limit for ships at the canal locks For draft deviation rate, The current draft of the vessel. The draft limit for canal locks; when >0 or When the value is greater than 0, it is considered a risk of exceeding the size limit; Sub-model for evaluating scheduling delay adaptability: in, The delay impact coefficient, To estimate the duration of the delay, To plan the sailing time; when If the delay exceeds the delay threshold, it is considered a scheduling delay risk.

[0013] Furthermore, the vessel-channel adaptability assessment sub-model for setting up the ice-covered navigation channel includes: Sub-model for assessing total navigation resistance in ice-covered areas: in, The total navigation resistance in ice-covered areas, For hydrostatic resistance, For wave resistance, For ocean current resistance, Adds drag to sea ice; when When the deviation from the current vessel's design maximum resistance exceeds the resistance deviation threshold, it is determined to be a risk of overload during ice-covered navigation; Sub-model for monitoring the condition of cryogenic equipment and insulation layers: The operating temperature of the ship's AIS equipment was collected. Temperature of the cabin insulation layer ,when or At that time, it was determined to be a risk of excessively low temperature, among which, This refers to the critical temperature for equipment failure. This is the critical temperature for insulation failure.

[0014] Furthermore, the ship-channel adaptability assessment sub-model for setting up the typhoon impact channel includes: Typhoon wave load and ship attitude assessment sub-model: Based on the simulated typhoon wave parameters, calculate the current ship's roll angle. With the pitch angle of the hull When the hull roll angle Or the aforementioned pitch angle of the hull If the current vessel's critical angle threshold for stability is exceeded, it is considered a typhoon risk. Sub-model for assessing cabin pressure adaptability: in, For cabin pressure deviation, For the real-time pressure of the cabin, This represents the average standard pressure. when If the pressure deviation is greater than or equal to the pressure deviation threshold and the effective wave height is greater than or equal to the typhoon wave height threshold, it is considered a risk of abnormal pressure.

[0015] Furthermore, risk is classified into multiple risk levels based on the aforementioned ship-channel compatibility assessment indicators. These risk levels include red alert, yellow alert, and orange alert. The risk classification process includes: Obtain the ship-channel compatibility assessment index for each of the aforementioned channels. Set corresponding high-risk thresholds The duration of the risk is and risk change rate ,but: when and When the duration of high risk is greater than or equal to the high-risk duration threshold, or the rate of risk change is... When the mutation threshold is exceeded, the risk level is designated as a red alert. when and When the duration of medium risk is within the threshold range, the risk level is an orange alert, wherein the upper limit of the threshold range of medium risk is less than the threshold of high risk. when Less than and and When the difference is less than or equal to the threshold, the risk level is a yellow warning.

[0016] This invention also proposes a dynamic tracking and monitoring system for imported energy shipping channels based on multi-source data and a channel-specific model, comprising: The data acquisition module is used to collect multiple types of data to form multi-source data through a hierarchical acquisition strategy. The multi-source data includes basic data, ship-specific data, and channel-specific data for each channel. The channels include ship-intensive channels, ice-covered navigation channels, canal navigation channels, and typhoon-affected channels. The calculation index module is used to set up a ship-channel adaptability assessment sub-model corresponding to each of the channels, and calculate the ship-channel adaptability assessment index for each channel based on the multi-source data. The early warning module is used to compare the ship-channel compatibility assessment index of each channel with the corresponding preset index threshold, thereby determining the current risk status of the ship in the corresponding channel and issuing an early warning.

[0017] Furthermore, after collecting multiple types of data to form multi-source data through a hierarchical acquisition strategy, the process also includes: standardizing the multi-source data, Kalman filtering for noise reduction, and inverse distance weighted interpolation for spatiotemporal alignment.

[0018] Furthermore, the ship-channel adaptability assessment sub-model for setting up the ship-intensive channel includes: Set up a sub-model for ship encounter risk assessment: in, For the shortest meeting distance, The x-coordinate of the current location of the ship. The x-coordinate of the other ship's location. The vertical coordinate of the current position of the ship. The vertical coordinate of the location of the other vessel is given. To minimize the time of meeting, The current speed of the vessel. The speed of the other vessel; when Less than the safe distance threshold or If the time frame is less than the encounter time threshold, it is considered a high encounter risk. Sub-model for assessing the adaptability of waterway space: in, For saturation, This represents the real-time number of vessels navigating the waters. The average length of navigable vessels. For the safety distance ratio, The average distance between adjacent ships. The width of the waterway; when Above the saturation threshold or When the distance is below the safety clearance ratio threshold, it is considered a risk of insufficient waterway adaptability.

[0019] Compared with the prior art, the present invention has the following advantages and technical effects: 1. Comprehensive Data Fusion and Application: Breaking through the barriers to collecting multi-source heterogeneous data, it achieves hierarchical collaboration integrating "ship dynamics, marine environment, channel characteristics, and operational status." The data coverage fully meets the requirements of full-process monitoring and can accurately capture differentiated risk factors in different channels.

[0020] 2. High-precision trajectory tracking and early warning: By introducing a multi-channel adaptability model and performing in-depth data cleaning and optimization, the accuracy of ship position monitoring is significantly improved. It can predict abnormal speeds 3-4 hours in advance, gaining crucial time to prevent major risks such as ship grounding and abnormal cabin pressure.

[0021] 3. Precise Risk Assessment Model: A dedicated assessment indicator and dynamic threshold system has been developed for different channel types. Currently, the accuracy rate of core risk identification has stabilized above 95%, and the overall false alarm rate is controlled within 5%, achieving precise quantification of risk identification.

[0022] 4. Agile Early Warning Response Mechanism: A "channel-based, level-based" early warning triggering and push mechanism has been established. The response time from the generation of early warning information to delivery to users has been shortened to less than 1 minute, significantly improving the emergency response and rapid handling capabilities for sudden risks.

[0023] 5. High compatibility and engineering practicality: Data sources are all based on public or compliant channels, and model parameters strictly adhere to industry-standard practices and ship design specifications. Without relying on dedicated hardware development, it possesses a solid foundation for engineering implementation and is easy to deploy and promote within existing systems. Attached Figure Description

[0024] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings: Figure 1 This is a flowchart of the method in Embodiment 1 of the present invention; Figure 2 This is a system structure diagram of Embodiment 2 of the present invention. Detailed Implementation

[0025] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0026] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0027] Example 1 like Figure 1 As shown, this embodiment proposes a dynamic tracking and monitoring method for imported energy shipping channels based on multi-source data and a channel-specific model, including: Step S1: Collect multiple types of data to form multi-source data through a hierarchical acquisition strategy. The multi-source data includes basic data, ship-specific data, and channel-specific data for each channel. The channels include ship-intensive channels, ice-covered navigation channels, canal navigation channels, and typhoon-affected channels. Preferably, to explain the basic data, ship-specific data, and channel-specific data for each channel, this embodiment provides an example as shown in Table 1. Furthermore, the parameters of all formulas in this embodiment are derived from the basic data or the channel-specific data for each channel, and are not limited to those in Table 1, as detailed below: Table 1 Specifically, after collecting multiple types of data to form multi-source data through a hierarchical acquisition strategy, the process also includes: standardizing the multi-source data, Kalman filtering for noise reduction, and inverse distance weighted interpolation for spatiotemporal alignment.

[0028] Preferably, in this embodiment, the multi-source data is standardized using Z-score, including: in, For multi-source data The first sample Dimensionally standardized data (for example, The value range is usually in [-3, 3], and values ​​outside this range are considered outliers. For multi-source data The first sample Dimensional raw data (for example, historical raw data within one month). For the first The mean of the original data. For the first The standard deviation of the original data.

[0029] Preferably, to address the issue of AIS data abrupt changes in signal blind zones, this embodiment employs a Kalman filter's "prediction-update" mechanism to dynamically correct multi-source data, including the following steps: 1. Ship condition prediction in, For a moment The prior state vector of the ship at time, which includes time ship longitude at that time (x-coordinate of the ship's location), time ship latitude at that time (Vertical coordinate of the ship's location), time The speed of the ship at that time ,time The ship's course ,Right now , For matrix transpose, This is the state transition matrix, used to describe the ship's state transition from time [time]. At the time The pattern of change in the motion state, For a moment The optimal state vector of the ship at that time. This is the control matrix (in this embodiment, the value is 0). For a moment The control quantity at that time (in this embodiment, the value is 0).

[0030] State transition matrix As shown below: in, For a moment The course at that time The sampling interval is denoted as .

[0031] 2. Calculate the error covariance matrix of the prior state vector. The calculation of the error covariance of the prior state vector provides a basis for calculating the Kalman gain in step 3. The specific formula is as follows: in, For a moment The error covariance matrix of the prior state vector. For a moment The error covariance matrix of the optimal state vector at time. The process noise covariance matrix can be fitted using historical data. For example, in this embodiment .

[0032] 3. Calculate the Kalman gain In this embodiment, to balance the reliability of the prior state vector and the observed data, the Kalman gain is calculated using the following formula: in, For a moment The time-varying Kalman gain indicates that the larger the value, the more reliable the observation data. For the observation matrix, To observe the noise covariance matrix, settings are configured based on the AIS device's accuracy; an example in this embodiment is shown below. .

[0033] 4. Ship status update This embodiment uses Kalman gain to fuse the prior state vector and observation data to obtain the time step. The optimal state vector of the ship at that time is given by the following formula: in, For a moment The optimal state vector of the ship at that time. For a moment Time observation vector (which can be historical raw data collected by AIS equipment).

[0034] 5. Update the error covariance matrix of the prior state vector. Calculate and update time. The error covariance of the optimal state vector at time step 1 provides a basis for prediction of the next time step. The specific formula is as follows: in, For a moment The error covariance matrix of the optimal state vector at time. It is an identity matrix.

[0035] Step S2: Set up a ship-channel adaptability assessment sub-model corresponding to each of the channels, and calculate the ship-channel adaptability assessment index for each channel based on the multi-source data; Specifically, the ship-channel compatibility assessment sub-model for setting up the ship-intensive channel includes: Set up a sub-model for ship encounter risk assessment: in, The shortest meeting distance (in kilometers (km)). The x-coordinate of the current location of the ship. The x-coordinate of the other ship's location. The vertical coordinate of the current position of the ship. The vertical coordinate of the location of the other vessel is given. To minimize the time of meeting, The current speed of the vessel is expressed in knots (kn). The speed of the other vessel is given in knots (kn), and 1.852 is a speed unit conversion factor used to convert knots (kn) to kilometers per hour (km / h). when Less than the safe distance threshold or If the time frame is less than the encounter time threshold, it is considered a high encounter risk. Sub-model for assessing the adaptability of waterway space: in, For saturation, This represents the real-time number of vessels navigating the waters. The average length of a vessel in transit (in meters (m)). For the safety distance ratio, The average distance between adjacent ships. The width of the channel is expressed in nautical miles (n mile), and 1852 is a conversion factor for length units, used to convert the width of the channel from nautical miles (n mile) to meters (m). when Above the saturation threshold or When the distance is below the safety clearance ratio threshold, it is considered a risk of insufficient waterway adaptability.

[0036] Specifically, the vessel-channel compatibility assessment sub-model for setting up the canal navigation channel includes: Canal size adaptability assessment sub-model: in: For the ship's length deviation rate, The actual length of the current vessel. The length limit for ships at the canal locks For draft deviation rate, The current draft of the vessel. The draft limit for canal locks; when >0 or When the value is greater than 0, it is considered a risk of exceeding the size limit; Sub-model for evaluating scheduling delay adaptability: in, The delay impact coefficient, To estimate the duration of the delay, To plan the sailing time; when If the delay exceeds the delay threshold, it is considered a scheduling delay risk.

[0037] Specifically, the vessel-channel adaptability assessment sub-model for setting up the ice-covered navigation channel includes: Sub-model for assessing total navigation resistance in ice-covered areas: in, The total navigation resistance in ice-covered areas, For hydrostatic resistance, For wave resistance, For ocean current resistance, Adds drag to sea ice; when When the deviation from the current vessel's design maximum resistance exceeds the resistance deviation threshold, it is determined to be a risk of overload during ice-covered navigation; Preferably, the hydrostatic resistance is calculated. The formula is: in, The density of seawater, The hydrostatic resistance coefficient is... This represents the wetted surface area of ​​the ship's hull.

[0038] Calculate wave resistance The formula is: in, This is the wave resistance coefficient.

[0039] Calculate ocean current resistance The formula is: in, This is the ocean current drag coefficient. This refers to the ocean current velocity.

[0040] Calculate the additional drag from sea ice The formula is: in, This is the ice drag coefficient. This refers to the thickness of the sea ice.

[0041] Sub-model for monitoring the condition of cryogenic equipment and insulation layers: The operating temperature of the ship's AIS equipment was collected. Temperature of the cabin insulation layer ,when or At that time, it was determined to be a risk of excessively low temperature, among which, The critical temperature for equipment failure (for example, it could be...) °C), The critical temperature for insulation failure (for example, it can be...) °C).

[0042] Specifically, the ship-channel adaptability assessment sub-model for setting up the typhoon impact channel includes: Typhoon wave load and ship attitude assessment sub-model: Based on the simulated typhoon wave parameters (for example, typhoon wave parameters can be simulated using MIKE 21 software, which is mainly used to simulate the flow, water quality, waves, and sediment transport processes of two-dimensional free water bodies (horizontal plane) such as rivers, lakes, estuaries, coastlines, and oceans), the current ship's roll angle is calculated. With the pitch angle of the hull When the hull roll angle Or the aforementioned pitch angle of the hull If the current vessel's critical angle threshold for stability is exceeded, it is considered a typhoon risk. Sub-model for assessing cabin pressure adaptability: in, For cabin pressure deviation, For the real-time pressure of the cabin, The standard average pressure (for example, it could be 0.15 MPa). when If the pressure deviation is greater than or equal to the pressure deviation threshold and the effective wave height is greater than or equal to the typhoon wave height threshold, it is considered a risk of abnormal pressure.

[0043] It should be noted that the ship-channel adaptability assessment index for each of the aforementioned channels is an index obtained through each of the above sub-models, such as the shortest encounter distance. wait.

[0044] Step S3: Compare the ship-channel compatibility assessment index of each channel with the corresponding preset index threshold to determine the current risk status of the ship in the corresponding channel and issue an early warning.

[0045] Specifically, risk is classified into multiple risk levels based on the aforementioned ship-channel compatibility assessment indicators. These risk levels include red alert, yellow alert, and orange alert. The risk classification process includes: Obtain the ship-channel compatibility assessment index for each of the aforementioned channels. Set corresponding high-risk thresholds The duration of the risk is and risk change rate ,but: when and When the duration of high risk is greater than or equal to the high-risk duration threshold, or the rate of risk change is... When the mutation threshold is exceeded, the risk level is red alert, which may lead to major accidents such as ship sinking, explosion, and large-scale leakage. The priority response level is level one. when and When the duration of medium risk is within the threshold range, the risk level is orange alert, which means that high-risk hazards need to be paid close attention to and the priority response level is level two. The upper limit of the duration of medium risk threshold range is less than the duration of high risk threshold. when Less than and and When the difference is less than or equal to the threshold, the risk level is a yellow warning, that is, it is close to the high risk threshold but there is no immediate hidden danger, and the priority response level is level three.

[0046] Preferably, different warning levels are pushed to corresponding terminals: red warnings are pushed to ships, shore-based dispatch centers, channel management agencies, and emergency command teams; orange warnings are pushed to ships, shore-based dispatch centers, and channel management agencies; and yellow warnings are pushed to ships and shore-based dispatch centers. Receiving terminals must provide feedback on the handling results within a specified time and automatically record the handling process, forming a closed-loop process of "monitoring-early warning-handling-feedback," providing data support for subsequent model optimization.

[0047] Preferably, a corresponding early warning message is generated (for example, a red warning generates a red early warning message). Each level of early warning message includes basic information consisting of the ship's identifier, location, and timestamp, the risk type (e.g., abnormal cabin pressure risk), and quantitative indicator values ​​(e.g., total navigation resistance in ice zones). ) and deviation rate (e.g., length deviation rate) The risk information consists of risk information, the forecast information consists of risk trend and duration predictions, and the emergency advice consists of channel-specific operational suggestions.

[0048] Example 2 like Figure 2 As shown, this embodiment proposes a dynamic tracking and monitoring system for imported energy shipping channels based on multi-source data and a channel-specific model, including: The data acquisition module is used to collect multiple types of data to form multi-source data through a hierarchical acquisition strategy. The multi-source data includes basic data, ship-specific data, and channel-specific data for each channel. The channels include ship-intensive channels, ice-covered navigation channels, canal navigation channels, and typhoon-affected channels. Specifically, after collecting multiple types of data to form multi-source data through a hierarchical acquisition strategy, the process also includes: standardizing the multi-source data, Kalman filtering for noise reduction, and inverse distance weighted interpolation for spatiotemporal alignment.

[0049] The calculation index module is used to set up a ship-channel adaptability assessment sub-model corresponding to each of the channels, and calculate the ship-channel adaptability assessment index for each channel based on the multi-source data. Specifically, the ship-channel compatibility assessment sub-model for setting up the ship-intensive channel includes: Set up a sub-model for ship encounter risk assessment: in, For the shortest meeting distance, The x-coordinate of the current location of the ship. The x-coordinate of the other ship's location. The vertical coordinate of the current position of the ship. The vertical coordinate of the location of the other vessel is given. To minimize the time of meeting, The current speed of the vessel. The speed of the other vessel; when Less than the safe distance threshold or If the time frame is less than the encounter time threshold, it is considered a high encounter risk. Sub-model for assessing the adaptability of waterway space: in, For saturation, This represents the real-time number of vessels navigating the waters. The average length of navigable vessels. For the safety distance ratio, The average distance between adjacent ships. The width of the waterway; when Above the saturation threshold or When the distance is below the safety clearance ratio threshold, it is considered a risk of insufficient waterway adaptability.

[0050] The early warning module is used to compare the ship-channel compatibility assessment index of each channel with the corresponding preset index threshold, thereby determining the current risk status of the ship in the corresponding channel and issuing an early warning.

[0051] Since the system technical solution of this embodiment 2 is based on the technical solution of embodiment 1, it will not be described again.

[0052] The above are merely preferred embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for dynamic tracking and monitoring of import energy sea lane based on multi-source data and channel-specific model, characterized in that, include: Multi-source data is collected by using a hierarchical acquisition strategy. The multi-source data includes basic data, ship-specific data, and channel-specific data for each channel. The channels include ship-intensive channels, ice-covered navigation channels, canal navigation channels, and typhoon-affected channels. Each of the aforementioned channels is assigned a corresponding ship-channel adaptability assessment sub-model, and the ship-channel adaptability assessment index for each channel is calculated based on the multi-source data. The ship-channel compatibility assessment index for each channel is compared with the corresponding preset index threshold to determine the current risk status of the ship in the corresponding channel and issue an early warning.

2. The method of claim 1, wherein, After collecting multiple types of data to form multi-source data through a hierarchical acquisition strategy, the process also includes: standardizing the multi-source data, Kalman filtering for noise reduction, and inverse distance weighted interpolation for spatiotemporal alignment.

3. The method of claim 1, wherein, The ship-channel compatibility assessment sub-model for the aforementioned ship-intensive channel includes: Set up a sub-model for ship encounter risk assessment: in, For the shortest meeting distance, The x-coordinate of the current location of the ship. The x-coordinate of the other ship's location. The vertical coordinate of the current position of the ship. The vertical coordinate of the location of the other vessel is given. To minimize the time of meeting, The current speed of the vessel. The speed of the other vessel; when Less than the safe distance threshold or If the time frame is less than the encounter time threshold, it is considered a high encounter risk. Sub-model for assessing the adaptability of waterway space: in, For saturation, This represents the real-time number of vessels navigating the waters. The average length of navigable vessels. For the safety distance ratio, The average distance between adjacent ships. The width of the waterway; when Above the saturation threshold or When the distance is below the safe distance ratio threshold, it is judged as a risk of insufficient waterway adaptability.

4. The method for dynamic tracking and monitoring of imported energy shipping channels based on multi-source data and a channel-specific model as described in claim 1, characterized in that, The vessel-channel compatibility assessment sub-model for the canal navigation channel includes: Canal size adaptability assessment sub-model: in: For the ship's length deviation rate, The actual length of the current vessel. The length limit for ships at the canal locks For draft deviation rate, The current draft of the vessel. The draft limit for canal locks; when >0 or When the value is greater than 0, it is considered a risk of exceeding the size limit; Sub-model for evaluating scheduling delay adaptability: in, The delay impact coefficient, To estimate the duration of the delay, To plan the sailing time; when If the delay exceeds the delay threshold, it is considered a scheduling delay risk.

5. The method for dynamic tracking and monitoring of imported energy shipping channels based on multi-source data and a channel-specific model as described in claim 1, characterized in that, The vessel-channel compatibility assessment sub-model for setting up the ice-area navigation channel includes: Sub-model for assessing total navigation resistance in ice-covered areas: in, The total navigation resistance in ice-covered areas, For hydrostatic resistance, For wave resistance, For ocean current resistance, Adding drag to sea ice; when When the deviation from the current vessel's design maximum resistance exceeds the resistance deviation threshold, it is determined to be a risk of overload during ice-covered navigation; Sub-model for monitoring the condition of cryogenic equipment and insulation layers: The operating temperature of the ship's AIS equipment was collected. Temperature of the cabin insulation layer ,when or At that time, it was determined to be a risk of excessively low temperature, among which, This refers to the critical temperature for equipment failure. This is the critical temperature for insulation failure.

6. The method for dynamic tracking and monitoring of imported energy shipping channels based on multi-source data and a channel-specific model as described in claim 1, characterized in that, The ship-channel adaptability assessment sub-model for setting up the typhoon impact channel includes: Typhoon wave load and ship attitude assessment sub-model: Based on the simulated typhoon wave parameters, calculate the current ship's roll angle. With the pitch angle of the hull When the hull roll angle Or the aforementioned pitch angle of the hull If the current vessel's critical angle threshold for stability is exceeded, it is considered a typhoon risk. Sub-model for assessing cabin pressure adaptability: in, For cabin pressure deviation, For the real-time pressure of the cabin, The average standard pressure; when If the pressure deviation is greater than or equal to the pressure deviation threshold and the effective wave height is greater than or equal to the typhoon wave height threshold, it is considered a risk of abnormal pressure.

7. The method for dynamic tracking and monitoring of imported energy shipping channels based on multi-source data and a channel-specific model as described in claim 1, characterized in that, Risk is classified into multiple risk levels based on the aforementioned ship-channel compatibility assessment indicators. These risk levels include red alert, yellow alert, and orange alert. The risk classification process includes: Obtain the ship-channel compatibility assessment index for each of the aforementioned channels. Set corresponding high-risk thresholds The duration of the risk is and risk change rate ,but: when and When the duration of high risk is greater than or equal to the high-risk duration threshold, or the rate of risk change is... When the mutation threshold is exceeded, the risk level is designated as a red alert. when and When the duration of medium risk is within the threshold range, the risk level is an orange alert, wherein the upper limit of the threshold range of medium risk is less than the threshold of high risk. when Less than and and When the difference is less than or equal to the threshold, the risk level is a yellow warning.

8. A dynamic tracking and monitoring system for imported energy shipping channels based on multi-source data and a channel-specific model, characterized in that, include: The data acquisition module is used to collect multiple types of data to form multi-source data through a hierarchical acquisition strategy. The multi-source data includes basic data, ship-specific data, and channel-specific data for each channel. The channels include ship-intensive channels, ice-covered navigation channels, canal navigation channels, and typhoon-affected channels. The calculation index module is used to set up a ship-channel adaptability assessment sub-model corresponding to each of the channels, and calculate the ship-channel adaptability assessment index for each channel based on the multi-source data. The early warning module is used to compare the ship-channel compatibility assessment index of each channel with the corresponding preset index threshold, thereby determining the current risk status of the ship in the corresponding channel and issuing an early warning.

9. The dynamic tracking and monitoring system for imported energy shipping channels based on multi-source data and a channel-specific model as described in claim 8, characterized in that, After collecting multiple types of data to form multi-source data through a hierarchical acquisition strategy, the process also includes: standardizing the multi-source data, Kalman filtering for noise reduction, and inverse distance weighted interpolation for spatiotemporal alignment.

10. The dynamic tracking and monitoring system for imported energy shipping channels based on multi-source data and a channel-specific model as described in claim 8, characterized in that, The ship-channel compatibility assessment sub-model for the aforementioned ship-intensive channel includes: Set up a sub-model for ship encounter risk assessment: in, For the shortest meeting distance, The x-coordinate of the current location of the ship. The x-coordinate of the other ship's location. The vertical coordinate of the current position of the ship. The vertical coordinate of the location of the other vessel is given. To minimize the time of meeting, The current speed of the vessel. The speed of the other vessel; when Less than the safe distance threshold or If the time frame is less than the encounter time threshold, it is considered a high encounter risk. Sub-model for assessing the adaptability of waterway space: in, For saturation, This represents the real-time number of vessels navigating the waters. The average length of navigable vessels. For the safety distance ratio, The average distance between adjacent ships. The width of the waterway; when Above the saturation threshold or When the distance is below the safe distance ratio threshold, it is judged as a risk of insufficient waterway adaptability.

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