An intelligent waterway management method and system based on digital twin

Abstract

The present invention discloses an intelligent waterway management method and system based on digital twin. The method includes: acquiring GIS data and historical monitoring data of a target waterway to construct an intelligent waterway digital twin model; importing waterway monitoring data for a preset time period for dynamic simulation to determine and update the data update strategy of the model in real time; performing real-time monitoring of ships in the target waterway through the model to identify the behavior states of the ships; combining ship behavior, meteorological and hydrological data to evaluate the navigation risk and generate a risk assessment result; and optimizing the data update strategy according to the assessment result. This method improves the intelligence of waterway management and the safety of ship navigation.

Description

Technical Field

[0001] The present invention relates to the technical field of digital twins, and particularly to an intelligent waterway management method and system based on digital twins. Background Art

[0002] With the development of the global shipping industry, the safety and shipping efficiency of ocean waterways have received increasing attention. In particular, the measurement and management of deep-water waterways have become a key task to ensure navigation safety and improve waterway passage efficiency. For this reason, the International Maritime Organization (IMO) and the International Hydrographic Organization (IHO) have proposed the S-100 series of standards to support the modernization and standardization of digital charts and related services. Among them, the S-102 standard is a three-dimensional bathymetric data product standard under the S-100 framework, aiming to provide more accurate bathymetric information to meet the navigation needs of complex waterways.

[0003] However, the existing S-102 standard still faces several challenges and technical bottlenecks. First, the amount of bathymetric measurement data for deep-water waterways is huge. Especially in the processing of high-resolution and wide-coverage underwater terrain data, the amount of data actually used is less than 1% of the total measurement data, resulting in a huge amount of redundant data. Therefore, how to efficiently utilize this data has become an urgent problem to be solved. Second, the existing standard lacks intelligent means for identifying the behavior status of ships, making it difficult to discover and handle potential navigation risks in a timely manner. Finally, the data update strategy is relatively single and cannot flexibly adjust the data update frequency according to the dynamic changes of the waterway and the results of risk assessment, resulting in low management efficiency and inability to meet the requirements of modern shipping industry for efficient and safe management.

[0004] Based on this background, there is an urgent need to propose an intelligent waterway management method and system based on digital twin technology. By optimizing the three-dimensional bathymetric data product and information service system, an integrated and dynamic waterway management model is established, which can update waterway data in real time, monitor the ship status, and conduct navigation risk assessment and management. This will greatly improve the intelligent level of waterway management, ensure shipping safety, and improve waterway passage efficiency. Summary of the Invention

[0005] In order to solve at least one of the above technical problems, the present invention proposes an intelligent waterway management method and system based on digital twins.

[0006] The first aspect of the present invention provides an intelligent waterway management method based on digital twins, including:

[0007] Obtaining GIS data and historical waterway monitoring data of the target waterway, and constructing an intelligent waterway digital twin model according to the GIS data and historical waterway monitoring data;

[0008] Obtain the waterway monitoring data for a preset time period, import it into the intelligent waterway digital twin model for waterway dynamic simulation, determine the data update strategy of the intelligent waterway digital twin model according to the waterway dynamic simulation, and perform real-time data update on the intelligent waterway digital twin model according to the data update strategy;

[0009] Perform real-time monitoring of ships in the target waterway according to the intelligent waterway digital twin model, and identify the behavior states of ships in the target waterway;

[0010] Evaluate the navigation risks of ships based on the behavior states and the meteorological data and hydrological data at the locations where the ships are sailing, and obtain the navigation risk assessment results;

[0011] Optimize the data update strategy according to the navigation risk assessment results to obtain an optimized data update strategy plan.

[0012] In this solution, the obtaining of the GIS data and historical waterway monitoring data of the target waterway, and the construction of the intelligent waterway digital twin model according to the GIS data and historical waterway monitoring data are specifically as follows:

[0013] Obtain the GIS data of the target waterway, and the GIS data includes the terrain data, landform data, waterway facility location information, spatial coordinates, length, width, and depth data of the target waterway;

[0014] Extract the boundaries, shorelines, and waterway centerlines of the waterway according to the GIS data of the target waterway, and extract the transverse and longitudinal section information of the waterway according to the terrain data of the target waterway and the boundaries, shorelines, and waterway centerlines;

[0015] Perform spatial interpolation operations on the transverse and longitudinal section information based on the linear interpolation method, fit the continuous curve changes of the transverse and longitudinal sections of the target waterway, construct a three-dimensional geometric model of the target waterway according to the fitted transverse and longitudinal section information, and perform landform texture mapping on the three-dimensional geometric model according to the landform data, and map the waterway facility geometric model into the three-dimensional geometric model;

[0016] Obtain the historical waterway monitoring data of the target waterway, and the historical waterway monitoring data includes the meteorological data, ship AIS data, and hydrological data of the target waterway;

[0017] Perform time synchronization analysis on the meteorological data and hydrological data to determine the hydrological impacts of different meteorological conditions on the target waterway, and obtain meteorological-hydrological impact data;

[0018] Construct a weather environment simulation model and a hydrodynamic model for the target waterway based on the meteorological data and hydrological data, and determine the synchronous change relationship between the weather environment simulation model and the hydrodynamic model according to the meteorological-hydrological impact data;

[0019] Construct an intelligent waterway digital twin model based on digital twin technology according to the three-dimensional geometric model, the weather environment simulation model, the hydrodynamic model, and the synchronous change relationship between the weather environment simulation model and the hydrodynamic model;

[0020] Import the ship AIS data into the intelligent waterway digital twin model for data fitting, and update the ship dynamics of the target waterway in real time to obtain a complete intelligent waterway digital twin model.

[0021] In this solution, the waterway monitoring data for a preset time period is imported into the intelligent waterway digital twin model for waterway dynamic simulation, and the data update strategy of the intelligent waterway digital twin model is determined according to the waterway dynamic simulation. Specifically:

[0022] Preset the data monitoring period of the intelligent waterway digital twin model, obtain the waterway monitoring data for the current data monitoring period, import the waterway monitoring data into the intelligent waterway digital twin model for waterway dynamic simulation, and divide the target waterway into N sub-regions based on geographical location;

[0023] Obtain the waterway monitoring simulation change data of each sub-region during the waterway dynamic simulation, and determine the value range of each data feature of the waterway monitoring simulation change data;

[0024] Preset the data segmentation width of each data feature value range, and perform equal-width discretization operation on the regional range of each data feature according to the data segmentation width to obtain the data distribution interval of each data feature;

[0025] Calculate the data occurrence frequency of each data distribution interval of each data feature according to the waterway monitoring simulation change data, and calculate the probability distribution of each data distribution interval according to the data occurrence frequency;

[0026] Analyze the probability distribution based on the information entropy evaluation method, calculate the information entropy of each data feature of each sub-region, and evaluate the data redundancy of each data feature of each sub-region according to the information entropy;

[0027] Construct a data feature-redundancy distribution map of each data feature in each sub-region of the target waterway according to the data redundancy of each data feature of each sub-region;

[0028] Based on the data feature - redundancy distribution map, comprehensively evaluate the redundant data of the channel simulation monitoring in each sub - region to obtain the comprehensive redundancy evaluation result of each sub - region in the current data monitoring period;

[0029] Obtain the data update frequency of each sub - region in the current data monitoring period, and preset the first comprehensive redundancy threshold and the second comprehensive redundancy threshold;

[0030] According to the comprehensive redundancy evaluation result, if the comprehensive redundancy of a sub - region in the current data monitoring period is less than the first comprehensive redundancy threshold, increase the data update frequency in the next data monitoring period; if the comprehensive redundancy is greater than or equal to the first comprehensive redundancy threshold and less than the second comprehensive redundancy threshold, maintain the existing data update frequency in the next data monitoring period; if the comprehensive redundancy is greater than or equal to the second comprehensive redundancy threshold, reduce the data update frequency in the next data monitoring period, so as to obtain the data update strategy of the intelligent channel digital twin model.

[0031] In this solution, the real - time monitoring of ships in the target channel according to the intelligent channel digital twin model and the identification of the behavior states of ships in the target channel are specifically as follows:

[0032] Obtain the historical behavior state data of ships, extract the navigation features of different behavior states according to the historical behavior state data of ships, and construct a navigation feature database with the navigation features of different behavior states;

[0033] Based on the decision - tree algorithm, construct a ship behavior state recognition model. Use the information gain feature selection criterion as the data division criterion of the ship behavior state recognition model. Import the navigation features in the navigation feature database as the model input and the behavior state as the model output into the ship behavior state recognition model to construct a decision tree, and perform training operations on the model according to the constructed decision tree;

[0034] Obtain the real - time ship navigation data in the target channel according to the intelligent channel digital twin model. The real - time ship navigation data includes the navigation speed, direction, and position of ships in the target channel at each data update frequency;

[0035] Import the real - time ship navigation data into the ship behavior state recognition model to perform real - time recognition of the behavior states of ships in the target channel, and obtain the recognition result.

[0036] In this solution, the evaluation of ship navigation risks according to the meteorological data and hydrological data of the behavior state and the position where the ship is sailing to obtain the navigation risk evaluation result is specifically as follows:

[0037] Obtain the basic information of the ships in the target waterway, the meteorological data and hydrological data of the location where the ships are sailing according to the intelligent waterway digital twin model, and evaluate the sailing suitability of the location where the ships are sailing according to the basic information of the ships, the meteorological data and hydrological data of the location where the ships are sailing, so as to obtain the sailing suitability evaluation result;

[0038] Identify whether there is any abnormal sailing behavior of the sailing ships according to the behavior state, and the abnormal sailing behaviors include speeding, dangerous approach between ships, deviation from the waterway, and anchoring;

[0039] If there is no abnormal sailing behavior, conduct a risk assessment on the ships in the target waterway according to the sailing suitability evaluation result. If there is an abnormal sailing behavior, then conduct a risk assessment on the ships in the target waterway according to the abnormal sailing behavior and the sailing suitability evaluation result to obtain the sailing risk assessment result.

[0040] In this solution, optimize the data update strategy according to the sailing risk assessment result to obtain an optimized data update strategy solution, specifically:

[0041] Preset a sailing risk threshold, and mark the ships greater than the sailing risk threshold according to the sailing risk assessment result to obtain marked ships;

[0042] Determine the position information of the marked ships according to the real-time ship sailing data, determine the sub-region where the marked ships are located in the target waterway according to the position information of the marked ships, and mark it as the area to be optimized for the data update strategy;

[0043] Make a secondary adjustment to the data update frequency of the area to be optimized for the data update strategy;

[0044] Obtain the ship quantity information in the area to be optimized for the data update strategy, and determine the network transmission competition state of the ships in the area to be optimized for the data update strategy according to the ship quantity information;

[0045] Evaluate the data transmission delay of the ships in the area to be optimized for the data update strategy according to the network transmission competition state, obtain the network transmission speed requirement for the adjusted data update frequency of the area to be optimized for the data update strategy, and determine the data transmission delay acceptance threshold according to the network transmission speed requirement;

[0046] Construct a data transmission buffer for each sub-region. If the data transmission delay in the area to be optimized for the data update strategy is greater than the data transmission delay acceptance threshold, import the AIS data, meteorological data, and hydrological data of the unmarked ships in the area to be optimized for the data update strategy into the data transmission buffer, and wait for the AIS data transmission of the marked ships to be completed before transmitting the data in the data transmission buffer to obtain the optimized data update strategy solution for each sub-region of the target waterway.

[0047] The second aspect of the present invention also provides an intelligent waterway management system based on digital twin. The system includes: a memory and a processor. The memory includes a program for the intelligent waterway management method based on digital twin. When the program for the intelligent waterway management method based on digital twin is executed by the processor, the following steps are implemented:

[0048] Obtain the GIS data and historical waterway monitoring data of the target waterway, and construct an intelligent waterway digital twin model according to the GIS data and historical waterway monitoring data;

[0049] Obtain the waterway monitoring data of a preset time period and import it into the intelligent waterway digital twin model for waterway dynamic simulation. Determine the data update strategy of the intelligent waterway digital twin model according to the waterway dynamic simulation, and perform real-time data update on the intelligent waterway digital twin model according to the data update strategy;

[0050] Perform real-time monitoring on the ships in the target waterway according to the intelligent waterway digital twin model, and identify the behavior states of the ships in the target waterway;

[0051] Evaluate the navigation risk of the ship according to the behavior state, meteorological data, and hydrological data of the position where the ship is sailing, and obtain a navigation risk assessment result;

[0052] Optimize the data update strategy according to the navigation risk assessment result to obtain an optimized data update strategy plan.

[0053] The present invention discloses an intelligent waterway management method and system based on digital twin. The method includes: obtaining the GIS data and historical monitoring data of the target waterway, and constructing an intelligent waterway digital twin model; importing the waterway monitoring data of a preset time period for dynamic simulation, determining and real-time updating the data update strategy of the model; performing real-time monitoring on the ships in the target waterway through the model to identify the ship behavior states; combining the ship behavior, meteorological, and hydrological data to evaluate the navigation risk and generate a risk assessment result; optimizing the data update strategy according to the assessment result. The method improves the intelligence of waterway management and the safety of ship navigation. Description of the Drawings

[0054] Figure 1 Shows a flowchart of an intelligent waterway management method based on digital twin of the present invention;

[0055] Figure 2 Shows a flowchart of identifying the behavior states of ships in the target waterway of the present invention;

[0056] Figure 3 Shows a flowchart of obtaining the navigation risk assessment result of the present invention;

[0057] Figure 4 The block diagram of an intelligent waterway management system based on digital twin according to the present invention is shown. Specific embodiments

[0058] In order to more clearly understand the above objects, features and advantages of the present invention, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that, without conflict, the embodiments of the present application and the features in the embodiments can be combined with each other.

[0059] In the following description, many specific details are set forth in order to fully understand the present invention. However, the present invention can also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited by the specific embodiments disclosed below.

[0060] Figure 1 The flowchart of an intelligent waterway management method based on digital twin according to the present invention is shown.

[0061] As Figure 1 shown, the first aspect of the present invention provides an intelligent waterway management method based on digital twin, including:

[0062] S102, obtaining the GIS data and historical waterway monitoring data of the target waterway, and constructing an intelligent waterway digital twin model according to the GIS data and historical waterway monitoring data;

[0063] S104, obtaining the waterway monitoring data of a preset time period and importing it into the intelligent waterway digital twin model for waterway dynamic simulation, determining the data update strategy of the intelligent waterway digital twin model according to the waterway dynamic simulation, and performing real-time data update on the intelligent waterway digital twin model according to the data update strategy;

[0064] S106, performing real-time monitoring on the ships in the target waterway according to the intelligent waterway digital twin model, and identifying the behavior states of the ships in the target waterway;

[0065] S108, evaluating the navigation risks of the ships according to the behavior states and the meteorological data and hydrological data at the positions where the ships are sailing, and obtaining the navigation risk assessment results;

[0066] S110, optimizing the data update strategy according to the navigation risk assessment results to obtain an optimized data update strategy solution.

[0067] It should be noted that by obtaining the Geographic Information System (GIS) data and historical monitoring data of the target waterway, a highly simulated digital twin model of the waterway can be constructed. This model includes the geographical location, physical characteristics of the waterway, and hydrological, meteorological and other factors reflected by historical data, which can comprehensively and accurately reflect the actual waterway conditions; by importing the waterway monitoring data (such as hydrological, meteorological and waterway usage information, etc.) within a specific time period in real time and combining with the digital twin model for dynamic simulation, based on the results of the waterway dynamic simulation, an optimized data update strategy can be formulated, which can avoid unnecessary frequent updates, reduce the computational load and resource waste of the system. For example, when the waterway conditions are relatively stable, the update frequency can be reduced, thereby reducing the resource consumption of computing, storage and communication. This can not only optimize the operation efficiency of the system, but also reduce the operation and maintenance costs, reduce the data redundancy of the system, and avoid the generation of a large amount of low-value data in the system; by monitoring the behavior of ships on the target waterway and assessing the navigation risks through the constructed intelligent waterway digital twin model, based on the risk assessment results, the original data update strategy can be optimized, improving the efficiency and flexibility of waterway monitoring, enabling the waterway management system to make rapid adjustments when the risks change.

[0068] According to an embodiment of the present invention, the obtaining of the GIS data and historical waterway monitoring data of the target waterway, and constructing an intelligent waterway digital twin model according to the GIS data and historical waterway monitoring data are specifically as follows:

[0069] Obtain the GIS data of the target waterway, where the GIS data includes the terrain data, landform data, waterway facility location information, spatial coordinates, length, width, and depth data of the target waterway;

[0070] Extract the boundary, shoreline and waterway center line of the waterway according to the GIS data of the target waterway, and extract the transverse and longitudinal section information of the waterway according to the terrain data of the target waterway and the boundary, shoreline and waterway center line of the waterway;

[0071] Perform spatial interpolation operations on the transverse and longitudinal section information based on the linear interpolation method, fit the continuous curve changes of the transverse and longitudinal sections of the target waterway, construct a three-dimensional geometric model of the target waterway according to the fitted transverse and longitudinal section information, and perform landform texture mapping on the three-dimensional geometric model according to the landform data, and map the waterway facility geometric model into the three-dimensional geometric model;

[0072] Obtain the historical waterway monitoring data of the target waterway, where the historical waterway monitoring data includes the meteorological data, ship AIS data, and hydrological data of the target waterway;

[0073] Perform time synchronization analysis on the meteorological data and hydrological data to determine the hydrological impact of different meteorological conditions on the target waterway, and obtain meteorological-hydrological impact data;

[0074] Construct a weather environment simulation model and a hydrodynamic model for the target waterway based on the meteorological data and hydrological data, and determine the synchronous change relationship between the weather environment simulation model and the hydrodynamic model according to the meteorological-hydrological impact data;

[0075] Construct an intelligent waterway digital twin model based on digital twin technology according to the three-dimensional geometric model, the weather environment simulation model and the hydrodynamic model, and the synchronous change relationship between the weather environment simulation model and the hydrodynamic model;

[0076] Import the ship AIS data into the intelligent waterway digital twin model for data fitting, and update the ship dynamics of the target waterway in real time to obtain a complete intelligent waterway digital twin model.

[0077] It should be noted that by obtaining the GIS data of the target waterway, the lateral and longitudinal section information of the waterway is extracted. The lateral section information describes the shape of the waterway on a certain cross-section, and the longitudinal section information reflects the undulation of the waterway in the downstream direction. Since the obtained GIS data is discrete, through the linear interpolation method, the discrete lateral and longitudinal section data can be converted into continuous curves to fill the blank areas of the data, so as to provide more comprehensive and detailed waterway geometric information, and then construct a complete three-dimensional geometric model of the waterway; by performing time synchronization analysis on the meteorological data and hydrological data, the influence of different meteorological conditions on hydrological characteristics can be revealed. By determining the synchronous change relationship between the weather environment simulation model and the hydrodynamic model, more accurate dynamic monitoring of the waterway conditions can be realized. Combining the three-dimensional geometric model, the weather environment simulation model and the hydrodynamic model, the constructed intelligent waterway digital twin model can display the real-time state of the waterway; the ship AIS data includes ship type, position, speed, heading, draft depth information; the hydrological data includes water flow velocity, water level depth, water flow direction data, and if the target waterway is a sea lane, wave height data is also included.

[0078] According to an embodiment of the present invention, import the waterway monitoring data of a preset time period into the intelligent waterway digital twin model for waterway dynamic simulation, and determine the data update strategy of the intelligent waterway digital twin model according to the waterway dynamic simulation, specifically:

[0079] Preset the data monitoring period of the intelligent waterway digital twin model, obtain the waterway monitoring data of the current data monitoring period, import the waterway monitoring data into the intelligent waterway digital twin model for waterway dynamic simulation, and divide the target waterway into N sub-regions based on the geographical location;

[0080] Obtain the simulated change data of channel monitoring in the process of channel dynamic simulation for each sub-region, and determine the value range of each data feature of the simulated change data of channel monitoring according to the simulated change data of channel monitoring;

[0081] Preset the data segmentation width of the value range of each data feature, and perform equal-width discretization on the regional range of each data feature according to the data segmentation width to obtain the data distribution interval of each data feature;

[0082] Calculate the data occurrence frequency of each data distribution interval of each data feature according to the simulated change data of channel monitoring, and calculate the probability distribution of each data distribution interval according to the data occurrence frequency;

[0083] It should be noted that the simulated change data of channel monitoring are the meteorological change data of the channel, the ship AIS change data, and the hydrological change data in the process of channel dynamic simulation. And when there are ships sailing in the sub-region, there will be ship AIS change data in the sub-region; the data feature is the data item included in the simulated change data of channel monitoring; the equal-width discretization operation is to equally divide the value range of the time series data of the simulated change data of channel monitoring into several intervals. For example, assuming that the water level change range is from 0 to 10 meters, it can be divided into 5 intervals: 0−2, 2−4, 4−6, 6−8, 8−10; the probability distribution is the number of times this interval appears divided by the total number of data points. For example, 100 dynamic water level change data points are collected during the channel dynamic simulation, and there are 10 data points in the 0-2 interval, then the probability distribution in the 0-2 interval is 0.1.

[0084] Analyze the probability distribution based on the information entropy evaluation method, calculate the information entropy of each data feature of each sub-region, and evaluate the data redundancy of each data feature of each sub-region according to the information entropy;

[0085] Construct a data feature-redundancy distribution map of each data feature in each sub-region of the target channel according to the data redundancy of each data feature of each sub-region;

[0086] It should be noted that a high information entropy indicates that the data distribution in this region is relatively uniform, the amount of information is large, and the changes are relatively complex, which means that the data redundancy in this region is low and a higher update frequency and sampling accuracy are required; a low information entropy indicates that the data in this region is concentrated in a few intervals, the amount of information is small, and the changes are small, which means that the data redundancy in this region is high and the data update frequency can be reduced; the data feature-redundancy distribution map is the distribution of the data redundancy of each data feature in the target channel.

[0087] According to the data feature - redundancy distribution diagram, comprehensively evaluate the redundant degrees of the channel simulation monitoring change data of each sub - region to obtain the comprehensive redundancy evaluation results of each sub - region in the current data monitoring period;

[0088] Obtain the data update frequency of each sub - region in the current data monitoring period, and preset the first comprehensive redundancy threshold and the second comprehensive redundancy threshold;

[0089] According to the comprehensive redundancy evaluation results, if the comprehensive redundancy of a sub - region in the current data monitoring period is less than the first comprehensive redundancy threshold, increase the data update frequency in the next data monitoring period; if the comprehensive redundancy is greater than or equal to the first comprehensive redundancy threshold and less than the second comprehensive redundancy threshold, maintain the existing data update frequency in the next data monitoring period; if the comprehensive redundancy is greater than or equal to the second comprehensive redundancy threshold, reduce the data update frequency in the next data monitoring period, so as to obtain the data update strategy of the intelligent channel digital twin model.

[0090] It should be noted that during the monitoring of the target waterway by the intelligent waterway digital twin model, a large amount of data is often required to update the real-time data of the model to reflect the latest waterway conditions. However, since the data in most areas of the target waterway remains unchanged within a certain period of time, continuously updating this data will greatly cause redundant data updates. Therefore, by determining the data monitoring period and dividing the target waterway into N sub-regions, the waterway monitoring data of the current data monitoring period is obtained and imported into the intelligent waterway digital twin model for waterway dynamic simulation. Through the information entropy analysis method, the data information entropy of each sub-region is evaluated according to the waterway monitoring simulation change data of each sub-region, and then the data redundancy of each sub-region is judged. Finally, a data update strategy for the intelligent waterway digital twin model is constructed based on the data redundancy; for different waterway environments and dynamic situations, the focus and frequency of data monitoring are automatically adjusted. This means that there will be a higher frequency of data monitoring in sub-regions with large data changes, while the monitoring frequency can be reduced in relatively stable regions, thus achieving optimal allocation of resources; in some sub-regions that are greatly affected by the environment (such as areas with frequent tidal effects, rapid water flow or dense ships), the waterway conditions may change rapidly over time. If the original data update frequency is maintained, the subtle changes in the environment may not be captured in time, resulting in a lag in the response of the waterway management system. In this case, through comprehensive redundancy evaluation, it is found that the redundancy is low, and the system can automatically increase the data update frequency to ensure the real-time and accuracy of waterway monitoring; some sub-regions may be in a relatively stable waterway environment, such as areas with wide waterways, few ships or gentle water flow. In such scenarios, the waterway conditions change little, and the redundancy of the monitoring data is high. If the data is continuously updated at a high frequency, it may waste system resources and increase unnecessary data processing pressure. By evaluating that the redundancy is large, the system can reduce the data update frequency, thus reducing resource consumption; in some sub-regions, the waterway changes are between drastic and stable, and the data redundancy is neither low nor high, being in an intermediate state. In this case, frequently adjusting the data update frequency may instead lead to system instability. By evaluating that the redundancy is between the first threshold and the second threshold, the system can choose to maintain the existing data update frequency to maintain the balance and stability of monitoring without causing unnecessary resource fluctuations; the comprehensive redundancy is obtained by multiplying the redundancy of each data feature by the sum of the corresponding weights.

[0091] Figure 2 FIG. shows a flowchart of identifying the behavior state of ships in the target waterway according to the present invention.

[0092] According to an embodiment of the present invention, the real-time monitoring of ships in the target waterway according to the intelligent waterway digital twin model and identifying the behavior state of ships in the target waterway are specifically as follows:

[0093] S202. Obtain the historical behavior state data of the ship, extract the navigation features of different behavior states according to the historical behavior state data of the ship, and construct a navigation feature database with the navigation features of different behavior states;

[0094] S204. Construct a ship behavior state recognition model based on the decision tree algorithm, use the information gain feature selection criterion as the data partitioning criterion of the ship behavior state recognition model, import the navigation features in the navigation feature database as the model input and the behavior state as the model output into the ship behavior state recognition model to construct a decision tree, and perform a training operation on the model according to the constructed decision tree;

[0095] S206. Obtain the real-time ship navigation data in the target waterway according to the intelligent waterway digital twin model, where the real-time ship navigation data includes the navigation speed, direction, and position of the ship in the target waterway at each data update frequency;

[0096] S208. Import the real-time ship navigation data into the ship behavior state recognition model to perform real-time recognition of the behavior state of the ship in the target waterway, and obtain the recognition result.

[0097] It should be noted that the decision tree algorithm is a classification algorithm based on feature selection, which can quickly divide the behavior state of the ship according to the navigation features. By using information gain as the feature selection criterion, different behavior states of the ship (such as normal navigation, deceleration, turning, berthing, etc.) can be effectively identified, improving the accuracy and efficiency of recognition. This enables the waterway manager to timely grasp the dynamic behavior of the ship and perform targeted management and intervention according to the recognition result; based on the real-time ship navigation data obtained from the intelligent waterway digital twin model and combined with the ship behavior state recognition model constructed by the decision tree algorithm, the navigation state of the ship can be dynamically updated and recognized at different monitoring frequencies. By inputting data such as the real-time speed, direction, and position of the ship into the model, the operation status of the ship in the waterway can be tracked in real time; the historical behavior state data includes berthing, acceleration / deceleration, turning, emergency braking, avoidance operation, and dangerous approach between ships; the navigation features include the change features of navigation speed and navigation direction, position change features, and relative position features between ships.

[0098] Figure 3 The flowchart of obtaining the navigation risk assessment result of the present invention is shown.

[0099] According to an embodiment of the present invention, the navigation risk of the ship is evaluated according to the meteorological data and hydrological data of the behavior state and the position where the ship sails, and the navigation risk assessment result is specifically:

[0100] S302. Obtain the basic information of the ships in the target waterway, the meteorological data and hydrological data of the location where the ships are sailing according to the intelligent waterway digital twin model, and evaluate the sailing suitability of the location where the ships are sailing based on the basic information of the ships, the meteorological data and hydrological data of the location where the ships are sailing, so as to obtain the sailing suitability evaluation result;

[0101] S304. Identify whether there is any abnormal sailing behavior of the sailing ships according to the behavior state, where the abnormal sailing behaviors include speeding, dangerous approach between ships, deviation from the waterway, and anchoring;

[0102] S306. If there is no abnormal sailing behavior, conduct a risk assessment on the ships in the target waterway according to the sailing suitability evaluation result. If there is an abnormal sailing behavior, then conduct a risk assessment on the ships in the target waterway according to the abnormal sailing behavior and the sailing suitability evaluation result, so as to obtain the sailing risk assessment result.

[0103] It should be noted that by integrating the basic information of the ships, meteorological data and hydrological data, this method can comprehensively evaluate the sailing environment of the ships. The sailing suitability of the ships reflects the safety of the current location of the ships and the goodness of the sailing conditions. Such comprehensive evaluation can identify potential risk factors in advance (such as bad weather, rapid water flow, etc.). This method can identify whether there is any abnormal sailing behavior based on the behavior state of the ships, such as speeding, dangerous approach between ships, deviation from the waterway or improper anchoring, etc. Once an abnormal behavior is identified, the system will conduct a more detailed risk assessment in combination with the sailing suitability; the basic information of the ships includes the ship type and the draft depth of the ships.

[0104] According to the embodiment of the present invention, optimizing the data update strategy according to the sailing risk assessment result to obtain an optimized data update strategy solution specifically includes:

[0105] Preset a sailing risk threshold, and mark the ships greater than the sailing risk threshold according to the sailing risk assessment result to obtain marked ships;

[0106] Determine the position information of the marked ships according to the real-time ship sailing data, determine the sub-region where the marked ships are located in the target waterway according to the position information of the marked ships, and mark it as the area to be optimized for the data update strategy;

[0107] Make a secondary adjustment to the data update frequency of the area to be optimized for the data update strategy;

[0108] Obtain the ship quantity information in the area to be optimized for the data update strategy, and determine the network transmission competition state of the ships in the area to be optimized for the data update strategy according to the ship quantity information;

[0109] Evaluate the data transmission delay of ships in the area to be optimized for the data update strategy according to the network transmission competition status, obtain the network transmission speed requirements for the adjusted data update frequency in the area to be optimized for the data update strategy, and determine the data transmission delay acceptance threshold according to the network transmission speed requirements;

[0110] Construct a data transmission buffer for each sub-region. If the data transmission delay in the area to be optimized for the data update strategy is greater than the data transmission delay acceptance threshold, import the AIS data, meteorological data, and hydrological data of unmarked ships in the area to be optimized for the data update strategy into the data transmission buffer, and wait until the AIS data transmission of the marked ships is completed before transmitting the data in the data transmission buffer to obtain the optimized data update strategy plan for each sub-region of the target waterway.

[0111] It should be noted that by marking ships with risks exceeding the threshold and optimizing the data update frequency in their respective sub-regions, this method can focus on monitoring high-risk areas. For these high-risk areas, the system can adjust the data update frequency twice to increase the frequency of data updates, enabling managers to promptly grasp the dynamic changes of ships in these areas and reduce accident risks; by calibrating the area to be optimized, only the sub-regions where high-risk ships are located are optimized for the data update frequency, while other sub-regions maintain the regular update frequency, thereby achieving targeted allocation of resources and improving the efficiency of the overall system; when multiple ships are transmitting data in the target waterway, data transmission may generate competition, resulting in increased delays. By evaluating the network transmission competition status, this method can dynamically adjust the priority of data transmission to ensure that the data transmission of marked ships is not interfered with and reduce the monitoring lag caused by transmission delays. This mechanism effectively optimizes the utilization rate of the network transmission channel and ensures more timely risk monitoring of key ships; according to the number of ships in the area and the network competition situation, adjust the data transmission strategy according to the data transmission delay acceptance threshold. In areas with dense ships and intense network competition, this method can intelligently adjust the data transmission order while ensuring the update frequency, so as to ensure that the transmission speed requirements match the delay threshold, and finally achieve the optimization of the data update strategy for each sub-region. The network transmission competition status refers to the competition phenomenon that occurs when multiple devices or nodes (such as ships) simultaneously attempt to transmit data under limited network resources. This competition will affect the speed and delay of data transmission for each device.

[0112] According to an embodiment of the present invention, before importing the AIS data, meteorological data, and hydrological data of unmarked ships in the area to be optimized for the data update strategy into the data transmission buffer, it further includes:

[0113] Estimate the data volume to be imported into the data transmission buffer according to the ship quantity information, meteorological data, and hydrological data in the area to be optimized for the data update strategy;

[0114] Obtain the capacity information of the data transmission buffer. If the amount of data to be imported into the data transmission buffer is greater than the capacity of the data transmission buffer, convert the data to be imported into a character representation format to obtain character format data;

[0115] Perform a character occurrence frequency statistics on the character format data, construct a priority queue, and import each character in the character format data into the priority queue according to the character occurrence frequency;

[0116] Construct a Huffman tree according to the priority queue, construct Huffman codes for each character according to the Huffman tree, and perform character encoding operations on the character format data according to the Huffman codes to obtain compressed data of the data to be imported;

[0117] Import the compressed data into the data transmission buffer for waiting to be transmitted. When the data in the data transmission buffer is transmitted, perform a decoding and restoration operation on the compressed data according to the Huffman tree.

[0118] It should be noted that since the data capacity of the constructed data transmission buffer is limited, when the amount of data to be imported into the data transmission buffer is greater than the data capacity of the data transmission buffer, if the import is still carried out, data loss will occur; therefore, when the amount of data to be imported into the data transmission buffer is greater than the capacity of the data transmission buffer, by introducing the Huffman coding data compression algorithm to compress the data to be imported, the limited storage space can be utilized more effectively, avoiding data loss caused by insufficient space, and being able to improve the data transmission speed. After the data transmission is completed, the Huffman tree can be used to decode the compressed data to quickly restore the original data, ensuring the integrity and availability of the data; the data to be imported is the AIS data, meteorological data, and hydrological data of non-marked ships in the area of the data update strategy optimization area of the data transmission buffer to be imported.

[0119] Figure 4 Shows a block diagram of an intelligent waterway management system based on digital twin of the present invention.

[0120] The second aspect of the present invention also provides an intelligent waterway management system 4 based on digital twin. The system includes: a memory 41 and a processor 42. The memory includes an intelligent waterway management method program based on digital twin. When the intelligent waterway management method program based on digital twin is executed by the processor, the following steps are implemented:

[0121] Obtain the GIS data and historical waterway monitoring data of the target waterway, and construct an intelligent waterway digital twin model according to the GIS data and historical waterway monitoring data;

[0122] Obtain the waterway monitoring data for a preset time period and import it into the intelligent waterway digital twin model for waterway dynamic simulation. Determine the data update strategy of the intelligent waterway digital twin model according to the waterway dynamic simulation, and perform real-time data update on the intelligent waterway digital twin model according to the data update strategy;

[0123] Perform real-time monitoring of ships in the target waterway according to the intelligent waterway digital twin model, and identify the behavior states of the ships in the target waterway;

[0124] Evaluate the navigation risks of ships based on the behavior states and the meteorological data and hydrological data at the locations where the ships are sailing, and obtain the navigation risk assessment results;

[0125] Optimize the data update strategy according to the navigation risk assessment results to obtain an optimized data update strategy solution

[0126] The present invention discloses an intelligent waterway management method and system based on digital twin. The method includes: obtaining the GIS data and historical monitoring data of the target waterway, and constructing an intelligent waterway digital twin model; importing the waterway monitoring data for a preset time period, performing dynamic simulation, determining and real-time updating the data update strategy of the model; performing real-time monitoring of ships in the target waterway through the model, and identifying the ship behavior states; combining the ship behavior, meteorological and hydrological data, evaluating the navigation risks, and generating risk assessment results; optimizing the data update strategy according to the assessment results. The method improves the intelligence of waterway management and the navigation safety of ships.

[0127] In several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only illustrative. For example, the division of the units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined, or can be integrated into another system, or some features can be ignored, or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed with each other can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical, mechanical, or other forms.

[0128] The units described as separate components above may or may not be physically separated, and the components shown as units may or may not be physical units; they can be located in one place or distributed to multiple network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0129] In addition, each functional unit in the embodiments of the present invention may be all integrated into one processing unit, or each unit may be separately regarded as one unit, or two or more units may be integrated into one unit; the above integrated unit may be implemented in the form of hardware, or in the form of a combination of hardware and software functional units.

[0130] Those of ordinary skill in the art can understand that all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions. The foregoing program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps including the above method embodiments; and the foregoing storage medium includes: removable storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks and other various media that can store program codes.

[0131] Alternatively, if the above integrated unit of the present invention is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the embodiments of the present invention, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the embodiments of the present invention. And the foregoing storage medium includes: removable storage devices, ROM, RAM, magnetic disks, or optical disks and other various media that can store program codes.

[0132] The above is only the specific implementation manners of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. An intelligent waterway management method based on digital twins, characterized in that: The following steps are involved: Acquire GIS data and historical waterway monitoring data of the target waterway, and build a digital twin model of the intelligent waterway based on the GIS data and historical waterway monitoring data; Acquire waterway monitoring data for a preset time period and import it into the smart waterway digital twin model to perform waterway dynamic simulation, determine a data update strategy for the smart waterway digital twin model based on the waterway dynamic simulation, and perform real-time data update on the smart waterway digital twin model based on the data update strategy; According to the smart waterway digital twin model, real-time monitoring of ships in the target waterway is performed to identify the behavior status of ships in the target waterway; Assess the navigation risk of the ship according to the behavior state and the meteorological data and hydrological data of the location where the ship is sailing, and obtain a navigation risk assessment result; Optimizing the data update strategy according to the navigation risk assessment result to obtain a data update strategy optimization solution; The channel monitoring data acquired in the preset time period is imported into the smart waterway digital twin model to perform dynamic simulation of the channel, and the data update strategy of the smart waterway digital twin model is determined according to the dynamic simulation of the channel, specifically: Preset a data monitoring cycle of the smart waterway digital twin model, obtain waterway monitoring data of the current data monitoring cycle, import the waterway monitoring data into the smart waterway digital twin model to perform waterway dynamic simulation, and divide the target waterway into N sub-areas based on geographical location; Acquire the waterway monitoring simulation change data of each sub-area during the waterway dynamic simulation process, and determine the value range of each data feature of the waterway monitoring simulation change data according to the waterway monitoring simulation change data; Preset a data segmentation width for each data feature value range, and perform an equal-width discretization operation on the region range of each data feature according to the data segmentation width to obtain a data distribution interval for each data feature; Calculate the data occurrence frequency of each data distribution interval of each data feature according to the waterway monitoring simulation change data, and calculate the probability distribution of each data distribution interval according to the data occurrence frequency; Analyzing the probability distribution based on an information entropy evaluation method, calculating the information entropy of each data feature of each sub-region, and evaluating the data redundancy of each data feature of each sub-region according to the information entropy; Constructing a data feature-redundancy distribution map of each data feature in each sub-area of ​​the target waterway according to the data redundancy of each data feature in each sub-area; Performing a comprehensive redundancy evaluation on the waterway simulation monitoring change data of each sub-area according to the data feature-redundancy distribution map, and obtaining a comprehensive redundancy evaluation result of each sub-area in the current data monitoring cycle; Obtaining the data update frequency of each sub-area in the current data monitoring cycle, and presetting a first comprehensive redundancy threshold and a second comprehensive redundancy threshold; According to the comprehensive redundancy evaluation result, if the comprehensive redundancy of the sub-area in the current data monitoring cycle is less than the first comprehensive redundancy threshold, the data update frequency of the next data monitoring cycle is increased; if the comprehensive redundancy is greater than or equal to the first comprehensive redundancy threshold and less than the second comprehensive redundancy threshold, the existing data update frequency is maintained in the next data monitoring cycle; if the comprehensive redundancy is greater than or equal to the second comprehensive redundancy threshold, the data update frequency of the next data monitoring cycle is reduced, so as to obtain the data update strategy of the smart waterway digital twin model.

2. According to claim 1, a digital twin-based intelligent waterway management method is characterized in that: The step of acquiring the GIS data and historical waterway monitoring data of the target waterway and constructing the intelligent waterway digital twin model according to the GIS data and historical waterway monitoring data is as follows: Acquire GIS data of the target waterway, wherein the GIS data includes topographic data, geomorphic data, location information of waterway facilities, spatial coordinates, length, width, and depth data of the target waterway; Extracting the boundary, shoreline and centerline of the channel according to the GIS data of the target channel, and extracting the transverse and longitudinal section information of the channel according to the terrain data of the target channel and the boundary, shoreline and centerline of the channel; Based on the linear interpolation method, spatial interpolation operation is performed on the transverse and longitudinal section information, and continuous curve changes of the transverse and longitudinal sections of the target waterway are fitted, and a three-dimensional geometric model of the target waterway is constructed according to the fitted transverse and longitudinal section information, and the three-dimensional geometric model is subjected to landform texture mapping according to the landform data, and a waterway facility geometric model is constructed and mapped in the three-dimensional geometric model; Acquire historical waterway monitoring data of the target waterway, wherein the historical waterway monitoring data includes meteorological data, ship AIS data, and hydrological data of the target waterway; Performing time synchronization analysis on the meteorological data and the hydrological data to determine the hydrological impact of different meteorological conditions on the target waterway and obtain meteorological-hydrological impact data; Constructing a weather environment simulation model and a hydrodynamic model of the target waterway according to the meteorological data and the hydrological data, and determining a synchronous change relationship between the weather environment simulation model and the hydrodynamic model according to the meteorological-hydrological impact data; Constructing a digital twin model of an intelligent waterway based on digital twin technology according to the three-dimensional geometric model, the weather environment simulation model and the hydrodynamic model, and the synchronous change relationship between the weather environment simulation model and the hydrodynamic model; The ship AIS data is imported into the smart waterway digital twin model for data fitting, and the ship dynamics of the target waterway are updated in real time to obtain a complete smart waterway digital twin model.

3. According to the digital twin-based intelligent waterway management method of claim 1, it is characterized in that: The real-time monitoring of the ships in the target waterway according to the smart waterway digital twin model and the identification of the behavior status of the ships in the target waterway are specifically as follows: Acquire historical behavior state data of the ship, extract navigation characteristics of different behavior states according to the historical behavior state data of the ship, and construct a navigation characteristic database with the navigation characteristics of different behavior states; A ship behavior state recognition model is constructed based on a decision tree algorithm, an information gain feature selection criterion is used as a data division criterion for the ship behavior state recognition model, navigation features in a navigation feature database are used as model inputs, and behavior states are used as model outputs to construct a decision tree for the ship behavior state recognition model, and a training operation is performed on the model according to the constructed decision tree; Acquire real-time ship navigation data in the target waterway according to the smart waterway digital twin model, wherein the real-time ship navigation data includes the navigation speed, direction, and position of the ship in the target waterway at each data update frequency; The real-time ship navigation data is imported into the ship behavior state recognition model, and the behavior state of the ship in the target channel is recognized in real time to obtain a recognition result.

4. According to claim 1, a digital twin-based intelligent waterway management method is characterized in that: The navigation risk of the ship is assessed according to the behavior state and the meteorological data and hydrological data of the location where the ship is sailing, and the navigation risk assessment result is obtained, which is specifically: Obtaining basic information of ships in the target waterway, meteorological data and hydrological data of the location where the ships are sailing according to the smart waterway digital twin model, evaluating the navigation suitability of the location where the ships are sailing according to the basic information of the ships, the meteorological data and hydrological data of the location where the ships are sailing, and obtaining a navigation suitability evaluation result; Identify whether the sailing ship has abnormal sailing behavior according to the behavior state, wherein the abnormal sailing behavior includes speeding, dangerous approach between ships, deviation from the waterway, and anchoring; If there is no abnormal navigation behavior, a risk assessment is performed on the target channel ship according to the navigation suitability assessment result. If there is an abnormal navigation behavior, a risk assessment is performed on the target channel ship according to the abnormal navigation behavior and the navigation suitability assessment result to obtain a navigation risk assessment result.

5. The intelligent waterway management method based on digital twin according to claim 1 is characterized in that: The data updating strategy is optimized according to the navigation risk assessment result to obtain a data updating strategy optimization scheme, which is specifically: Preset a navigation risk threshold, and mark ships with a risk greater than the navigation risk threshold according to the navigation risk assessment result to obtain marked ships; Determine the position information of the marked ship according to the real-time ship navigation data, determine the sub-area where the marked ship is located in the target channel according to the position information of the marked ship, and mark it as the area to be optimized for the data update strategy; Performing a secondary adjustment on the data update frequency of the area to be optimized for the data update strategy; Acquiring information on the number of ships in the area to be optimized for the data update strategy, and determining a network transmission competition state of the ships in the area to be optimized for the data update strategy according to the information on the number of ships; According to the network transmission competition state, the data transmission delay of the ships in the area to be optimized for the data update strategy is evaluated, the network transmission speed requirement required for the adjusted data update frequency in the area to be optimized for the data update strategy is obtained, and the data transmission delay acceptance threshold is determined according to the network transmission speed requirement; A data transmission buffer is constructed for each sub-area. If the data transmission delay in the area to be optimized for the data update strategy is greater than the data transmission delay acceptance threshold, the AIS data, meteorological data, and hydrological data of the unmarked ships in the area to be optimized for the data update strategy are imported into the data transmission buffer. After the AIS data transmission of the marked ships is completed, the data in the data transmission buffer is transmitted to obtain the data update strategy optimization plan for each sub-area of ​​the target waterway.

6. An intelligent waterway management system based on digital twins, characterized in that: The intelligent waterway management system based on digital twins includes a storage device and a processor. The storage device includes an intelligent waterway management method program based on digital twins. When the intelligent waterway management method program based on digital twins is executed by the processor, the following steps are implemented: Acquire GIS data and historical waterway monitoring data of the target waterway, and build a digital twin model of the intelligent waterway based on the GIS data and historical waterway monitoring data; Acquire waterway monitoring data for a preset time period and import it into the smart waterway digital twin model to perform waterway dynamic simulation, determine a data update strategy for the smart waterway digital twin model based on the waterway dynamic simulation, and perform real-time data update on the smart waterway digital twin model based on the data update strategy; According to the smart waterway digital twin model, real-time monitoring of ships in the target waterway is performed to identify the behavior status of ships in the target waterway; Assess the navigation risk of the ship according to the behavior state and the meteorological data and hydrological data of the location where the ship is sailing, and obtain a navigation risk assessment result; Optimizing the data update strategy according to the navigation risk assessment result to obtain a data update strategy optimization solution; The channel monitoring data acquired in the preset time period is imported into the smart waterway digital twin model to perform dynamic simulation of the channel, and the data update strategy of the smart waterway digital twin model is determined according to the dynamic simulation of the channel, specifically: Preset a data monitoring cycle of the smart waterway digital twin model, obtain waterway monitoring data of the current data monitoring cycle, import the waterway monitoring data into the smart waterway digital twin model to perform waterway dynamic simulation, and divide the target waterway into N sub-areas based on geographical location; Acquire the waterway monitoring simulation change data of each sub-area during the waterway dynamic simulation process, and determine the value range of each data feature of the waterway monitoring simulation change data according to the waterway monitoring simulation change data; Preset a data segmentation width for each data feature value range, and perform an equal-width discretization operation on the region range of each data feature according to the data segmentation width to obtain a data distribution interval for each data feature; Calculate the data occurrence frequency of each data distribution interval of each data feature according to the waterway monitoring simulation change data, and calculate the probability distribution of each data distribution interval according to the data occurrence frequency; Analyzing the probability distribution based on an information entropy evaluation method, calculating the information entropy of each data feature of each sub-region, and evaluating the data redundancy of each data feature of each sub-region according to the information entropy; Constructing a data feature-redundancy distribution map of each data feature in each sub-area of ​​the target waterway according to the data redundancy of each data feature in each sub-area; Performing a comprehensive redundancy evaluation on the waterway simulation monitoring change data of each sub-area according to the data feature-redundancy distribution map, and obtaining a comprehensive redundancy evaluation result of each sub-area in the current data monitoring cycle; Obtaining the data update frequency of each sub-area in the current data monitoring cycle, and presetting a first comprehensive redundancy threshold and a second comprehensive redundancy threshold; According to the comprehensive redundancy evaluation result, if the comprehensive redundancy of the sub-area in the current data monitoring cycle is less than the first comprehensive redundancy threshold, the data update frequency of the next data monitoring cycle is increased; if the comprehensive redundancy is greater than or equal to the first comprehensive redundancy threshold and less than the second comprehensive redundancy threshold, the existing data update frequency is maintained in the next data monitoring cycle; if the comprehensive redundancy is greater than or equal to the second comprehensive redundancy threshold, the data update frequency of the next data monitoring cycle is reduced, so as to obtain the data update strategy of the smart waterway digital twin model.

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