Water transport vessel operation status monitoring method and system based on waterway information fusion

Abstract

The present invention discloses a method and system for monitoring the operation status of a water transport vessel based on waterway information fusion, including: obtaining multi-source data of a target waterway, including real-time ship navigation data and waterway environment data, and performing data value assessment; fusing the multi-source data according to the assessment results, generating comprehensive status monitoring data of the target waterway, and constructing a water transport vessel monitoring platform; using the platform to perform real-time monitoring of the ship in the target waterway to obtain operation status data; and assessing the environmental adaptability of the ship based on the data, thereby optimizing the allocation strategy of monitoring resources. The present invention improves the accuracy of water transport vessel monitoring and resource utilization efficiency, and is suitable for intelligent waterway management.

Description

Technical Field

[0001] The present invention relates to the technical field of ship monitoring, and in particular to a method and system for monitoring the operating status of a water transport vessel based on waterway information fusion. Background Art

[0002] The current water transport monitoring system mainly relies on the ship automatic identification system (AIS) and some waterway monitoring equipment to obtain ship navigation data and environmental information, which makes it difficult to achieve comprehensive and accurate monitoring of the ship's operating status in complex environments. This traditional single data source monitoring method has certain limitations in the breadth, depth and real-time nature of data acquisition, and cannot fully meet the safety needs of modern water transport channels.

[0003] Dynamic changes in the waterway environment, such as hydrology, meteorology, tides and other factors, will pose a potential threat to the safe navigation of water-borne vessels. Therefore, it is difficult to achieve ideal results by relying solely on ship navigation data to monitor shipping status. In order to improve the accuracy and efficiency of monitoring, multi-source data fusion based on waterway information has become an effective means to solve this problem. Multi-source data fusion not only includes dynamic information such as the real-time position and speed of the ship, but also environmental data such as meteorological data and hydrological information. By integrating these multi-source data, a complete and dynamic shipping status monitoring system can be built.

[0004] In the process of multi-source data integration, the collection frequency, response speed and importance of different data will affect the real-time and accuracy of the monitoring system. The existing monitoring system lacks an effective evaluation and dynamic allocation mechanism for data value, resulting in uneven allocation of system resources and affecting the response efficiency of monitoring. In addition, the existing technology is difficult to evaluate the adaptability and navigation risks of water transport ships in a specific waterway environment in real time, and it is impossible to dynamically predict and warn the navigation status of ships.

[0005] In order to solve the above problems, a method and system for monitoring the operation status of water transport vessels based on waterway information fusion is urgently needed, which can establish a water transport vessel monitoring platform through value assessment and dynamic fusion of multi-source data, and improve the response speed and accuracy of monitoring data. At the same time, the system should also have the functions of environmental adaptability assessment and risk distribution identification, so as to optimize the monitoring resource allocation strategy and realize real-time and intelligent monitoring of the operation status of water transport vessels. Summary of the invention

[0006] In order to solve at least one of the above technical problems, the present invention proposes a method and system for monitoring the operating status of a water transport vessel based on waterway information fusion.

[0007] The first aspect of the present invention provides a method for monitoring the operation status of a water transport vessel based on waterway information fusion, comprising:

[0008] Acquire multi-source data of the target waterway, the multi-source data including real-time ship navigation data and waterway environment data, and perform data value assessment on the multi-source data to obtain a data value assessment result;

[0009] The multi-source data are integrated according to the data value assessment result to form comprehensive status monitoring data of the target waterway, and a water transport vessel monitoring platform of the target waterway is constructed according to the comprehensive status monitoring data;

[0010] Performing real-time monitoring of water transport vessels in the target waterway according to the water transport vessel monitoring platform, obtaining operation status monitoring data of water transport vessels in the target waterway, and performing environmental adaptability assessment on water transport vessels in the target waterway according to the operation status monitoring data, to obtain environmental adaptability data of water transport vessels;

[0011] The monitoring resource allocation strategy of the water transport vessel monitoring platform for the water transport vessel is determined according to the environmental adaptability data.

[0012] In this solution, the multi-source data of the target waterway is obtained, and the multi-source data includes real-time ship navigation data and waterway environment data. The multi-source data is evaluated for data value to obtain the data value evaluation result, which is specifically:

[0013] Acquire real-time navigation data of ships in the target channel based on the ship AIS system, wherein the real-time navigation data of ships includes the ship's position, speed, heading, ship name, and draft depth;

[0014] Acquiring channel environmental data of a target channel based on an environmental sensor, wherein the channel environmental data includes meteorological data and hydrological data;

[0015] The real-time ship navigation data and the channel environment data are calibrated as multi-source data of the target channel, and the multi-source data are aligned according to the acquisition time;

[0016] Obtain data access frequency information of each data feature of multi-source data during the operation status monitoring of water transport vessels in the target waterway within a preset time period;

[0017] The data demand activity of each data feature for monitoring the operating status of water transport vessels is evaluated according to the data access frequency information, and the data value of each data feature of the multi-source data is evaluated according to the data demand activity to obtain a data value evaluation result.

[0018] In this solution, the multi-source data is integrated according to the data value assessment result to form the comprehensive status monitoring data of the target waterway, and a water transport vessel monitoring platform of the target waterway is constructed according to the comprehensive status monitoring data, specifically:

[0019] According to the data value assessment result, the multi-source data is divided into data value levels to obtain data value level information of each data feature of the multi-source data;

[0020] According to the data value level information, the data response speed requirement information of each data feature in the process of monitoring the ship operation status is obtained;

[0021] Constructing data response speed partitions according to the data value level information and the data response speed requirement information, and importing the multi-source data into the data response speed partitions according to the data response speed requirement information to perform data fusion, so as to form comprehensive status monitoring data of the target waterway;

[0022] Building a water transport vessel monitoring platform for the target waterway based on big data technology according to the comprehensive status monitoring data;

[0023] A water transport vessel operation status monitoring test is performed according to the water transport vessel monitoring platform, an actual data response speed of the comprehensive status monitoring data is obtained according to the monitoring test, and a data response strategy of the water transport vessel monitoring platform is optimized according to the actual data response speed.

[0024] In this solution, the water transport vessel operation status monitoring test is performed according to the water transport vessel monitoring platform, the actual data response speed of the comprehensive status monitoring data is obtained according to the monitoring test, and the data response strategy of the water transport vessel monitoring platform is optimized according to the actual data response speed, specifically:

[0025] Acquire monitoring task information of the operation status of water transport vessels in a target waterway within a preset time period, and determine data monitoring items required for monitoring the operation status of water transport vessels according to the monitoring task information;

[0026] Constructing a data access task for a water transport vessel monitoring platform according to the data monitoring project, performing a water transport vessel operation status monitoring test on the water transport vessel monitoring platform according to the data access task, and obtaining the data acquisition time of each data feature of the comprehensive status monitoring data during the monitoring test;

[0027] Determine the actual data response speed of each data feature in the comprehensive status monitoring data according to the data acquisition time, compare the actual data response data with the data response requirement information, and judge the response deviation between the actual response speed of each data feature in the comprehensive status monitoring data and the data response requirement;

[0028] Marking data features with response deviations greater than a preset value as data features to be optimized for data response;

[0029] Obtaining a data response speed partition where the data feature to be optimized for data response is located, and if the data response speed partition is not a highest response speed partition, determining a response speed compensation value of the data feature to be optimized for data response according to the response deviation;

[0030] Calculate the difference between the data response speed partition where the data feature to be optimized for data response is located and the highest data response speed partition; if the difference is greater than the response speed compensation value, adjust the data response speed partition where the data feature to be optimized for data response is located according to the response speed information of each data response speed partition and the response speed compensation value to obtain a first data response optimization strategy;

[0031] If the difference is less than the response speed compensation value or the data response speed partition where the data feature to be optimized for data response is located is the highest response speed partition, construct a data pointer of the data feature to be optimized for data response, and obtain data reusability information of the data feature to be optimized for data response;

[0032] Determine the reusability range of the data feature to be optimized for data response according to the data reusability information, point the data feature to be optimized for data response to the data monitoring item within the reusability range according to the data pointer, and obtain a second data response optimization strategy;

[0033] The data response strategy of the water transport vessel monitoring platform is optimized according to the first data response optimization strategy and the second data response optimization strategy.

[0034] In this solution, the water transport vessel monitoring platform is used to monitor the water transport vessel in the target waterway in real time, and the operation status monitoring data of the water transport vessel in the target waterway is obtained. The environmental adaptability of the water transport vessel in the target waterway is evaluated according to the operation status monitoring data to obtain the environmental adaptability data of the water transport vessel, which is specifically:

[0035] Obtain historical operation status data and historical environmental adaptability data of water transport vessels in the target waterway, introduce fuzzy logic algorithm to build an environmental adaptability evaluation model, and transform the historical operation status data and historical environmental adaptability data into fuzzy sets through language variables as input variables and output variables of the environmental adaptability evaluation model respectively;

[0036] Constructing a membership function for each language variable based on a triangular function, and importing the input variable, the output variable, and the membership function into the environmental adaptability assessment model for model training;

[0037] Real-time monitoring of ships in the target waterway is performed according to the water transport ship monitoring platform to obtain operation status monitoring data of the water transport ships in the target waterway, wherein the operation status monitoring data includes ship status data and navigation environment data;

[0038] The operating status monitoring data is imported into the trained environmental adaptability assessment model to perform environmental adaptability assessment on water transport vessels, and obtain environmental adaptability assessment results for water transport vessels.

[0039] In this solution, the monitoring resource allocation strategy of the water transport vessel monitoring platform for the water transport vessel is determined according to the environmental adaptability data, specifically:

[0040] Assessing the navigation risk of water transport vessels in the target waterway according to the environmental adaptability data to obtain navigation risk data;

[0041] Introducing a K-means clustering algorithm, randomly selecting K initial cluster centers from the navigation risk data according to the K-means clustering algorithm, calculating the Euclidean distance from each data point in the navigation risk data to each initial cluster center, and assigning each data point to the cluster with the nearest initial cluster center;

[0042] Calculate the mean of all data points in each cluster, use the mean as the new cluster center, and iteratively calculate the Euclidean distance of each data point to the new cluster center, and assign the closest cluster until the cluster center no longer changes. Stop the clustering operation and get the navigation risk clustering result.

[0043] Identify the navigation risk distribution of water transport vessels in the target waterway according to the navigation risk clustering result, and construct a navigation risk distribution map;

[0044] Divide the target waterway into risk level areas according to the navigation risk distribution map to obtain navigation risk level information for each position in the target waterway;

[0045] The resource inclination rate for monitoring the operating status of water transport vessels at each location in the target waterway is determined according to the navigation risk level information, and the monitoring resource allocation ratio of the water transport vessel monitoring platform to the water transport vessels is determined according to the resource inclination rate to obtain a monitoring resource allocation strategy.

[0046] The second aspect of the present invention further provides a water transport vessel operation status monitoring system based on waterway information fusion, the system comprising: a memory, a processor, the memory comprising a water transport vessel operation status monitoring method program based on waterway information fusion, the water transport vessel operation status monitoring method program based on waterway information fusion when executed by the processor, implements the following steps:

[0047] Acquire multi-source data of the target waterway, the multi-source data including real-time ship navigation data and waterway environment data, and perform data value assessment on the multi-source data to obtain a data value assessment result;

[0048] The multi-source data are integrated according to the data value assessment result to form comprehensive status monitoring data of the target waterway, and a water transport vessel monitoring platform of the target waterway is constructed according to the comprehensive status monitoring data;

[0049] Performing real-time monitoring of water transport vessels in the target waterway according to the water transport vessel monitoring platform, obtaining operation status monitoring data of water transport vessels in the target waterway, and performing environmental adaptability assessment on water transport vessels in the target waterway according to the operation status monitoring data, to obtain environmental adaptability data of water transport vessels;

[0050] The monitoring resource allocation strategy of the water transport vessel monitoring platform for the water transport vessel is determined according to the environmental adaptability data.

[0051] The present invention discloses a method and system for monitoring the operation status of a water transport vessel based on waterway information fusion, including: obtaining multi-source data of a target waterway, including real-time ship navigation data and waterway environment data, and performing data value assessment; fusing the multi-source data according to the assessment results, generating comprehensive status monitoring data of the target waterway, and constructing a water transport vessel monitoring platform; using the platform to perform real-time monitoring of the ship in the target waterway to obtain operation status data; and assessing the environmental adaptability of the ship based on the data, thereby optimizing the allocation strategy of monitoring resources. The present invention improves the accuracy of water transport vessel monitoring and resource utilization efficiency, and is suitable for intelligent waterway management. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 A flow chart of a method for monitoring the operating status of a water transport vessel based on waterway information fusion according to the present invention is shown;

[0053] Figure 2 A flow chart showing the construction of a water transport vessel monitoring platform for a target waterway according to the present invention is shown;

[0054] Figure 3 A flow chart showing the method of obtaining environmental adaptability data of a water transport vessel according to the present invention is shown;

[0055] Figure 4 The block diagram of the water transport vessel operation status monitoring system based on waterway information fusion of the present invention is shown. DETAILED DESCRIPTION

[0056] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0057] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0058] Figure 1 The flowchart of the method for monitoring the operating status of a water transport vessel based on waterway information fusion of the present invention is shown.

[0059] like Figure 1 As shown, the first aspect of the present invention provides a method for monitoring the operation status of a water transport vessel based on waterway information fusion, comprising:

[0060] S102, acquiring multi-source data of the target waterway, the multi-source data including real-time ship navigation data and waterway environment data, and performing data value assessment on the multi-source data to obtain a data value assessment result;

[0061] S104, fusing the multi-source data according to the data value assessment result to form comprehensive status monitoring data of the target waterway, and constructing a water transport vessel monitoring platform for the target waterway according to the comprehensive status monitoring data;

[0062] S106, performing real-time monitoring of water transport vessels in the target waterway according to the water transport vessel monitoring platform, obtaining operation status monitoring data of water transport vessels in the target waterway, and performing environmental adaptability assessment on water transport vessels in the target waterway according to the operation status monitoring data to obtain environmental adaptability data of water transport vessels;

[0063] S108, determining a monitoring resource allocation strategy of the water transport vessel monitoring platform for the water transport vessel according to the environmental adaptability data.

[0064] It should be noted that by obtaining multi-source data of the target waterway for data value assessment, and then fusing the multi-source data according to the data value assessment results, the most important and timely data features for ship operation status monitoring in the multi-source data can be identified, thereby optimizing the priority of data processing and resource allocation. By analyzing the activity and response speed requirements of the data, data redundancy can be reduced to ensure that key data is processed and responded to more quickly during the monitoring process. The integrated monitoring data after fusion realizes effective collaboration between data sources, making the monitoring data more consistent, comprehensive and real-time; the water transport ship monitoring platform built based on the comprehensive status monitoring data can realize unified management and real-time monitoring of the ship operation status in the target waterway. The platform integrates multi-source data, supports efficient data interaction and sharing, and provides timely and reliable monitoring information for decision support; through the real-time monitoring function of the platform, real-time data of the ship operation status can be obtained, including position, speed, heading and dynamic status affected by the environment; environmental adaptability assessment can judge the adaptability of the ship under current environmental conditions based on real-time monitoring data and the historical operation of the ship. By evaluating the adaptability data of ships, ships with potential safety hazards can be identified, and early warnings and responses can be given; based on environmental adaptability data, the system can dynamically adjust the allocation of monitoring resources, allocating more resources to high-risk areas or ships with poor adaptability, and achieving accurate monitoring. Such a resource allocation strategy optimizes the performance of the monitoring platform, ensuring a higher response speed and monitoring frequency in the monitoring of key areas and high-risk ships, effectively improving monitoring efficiency and ship safety.

[0065] According to an embodiment of the present invention, the multi-source data of the target waterway is obtained, and the multi-source data includes real-time navigation data of ships and waterway environment data, and data value evaluation is performed on the multi-source data to obtain a data value evaluation result, which is specifically:

[0066] S202, acquiring real-time navigation data of ships in the target channel based on the ship AIS system, wherein the real-time navigation data of the ships includes the ship's position, speed, heading, ship name, and draft depth;

[0067] S204, acquiring channel environmental data of the target channel based on the environmental sensor, wherein the channel environmental data includes meteorological data and hydrological data;

[0068] S206, calibrating the real-time ship navigation data and the channel environment data as multi-source data of the target channel, and performing a data alignment operation on the multi-source data according to the acquisition time;

[0069] S208, obtaining data access frequency information of each data feature of multi-source data during the operation status monitoring of water transport vessels in the target waterway within a preset time period;

[0070] S210, evaluating the data demand activity of each data feature for monitoring the operating status of water transport vessels according to the data access frequency information, and performing data value evaluation on each data feature of the multi-source data according to the data demand activity to obtain a data value evaluation result.

[0071] It should be noted that the meteorological data includes temperature, humidity, wind direction, wind force, precipitation, visibility, etc., the hydrological data includes water level, water flow speed, tide, water flow direction, etc., and the data feature is each data feature in the multi-source data, that is, the data items contained in the multi-source data such as ship position, speed, temperature, humidity, water level, water flow speed, etc.; data access frequency is an important indicator to measure the frequency of data use, which can effectively reflect the activity and demand of data in practical applications. In the water transport monitoring system, the access frequency of different data features can indicate its impact on decision support. For example, if the data access frequency of a certain feature is high, it means that the data has a high reference value for decision makers under certain circumstances. By analyzing the data access frequency, we can better understand which data features are critical in real-time monitoring.

[0072] Figure 2 A flow chart of constructing a water transport vessel monitoring platform for a target waterway according to the present invention is shown.

[0073] According to an embodiment of the present invention, the multi-source data is integrated according to the data value assessment result to form comprehensive status monitoring data of the target waterway, and a water transport vessel monitoring platform of the target waterway is constructed according to the comprehensive status monitoring data, specifically:

[0074] According to the data value assessment result, the multi-source data is divided into data value levels to obtain data value level information of each data feature of the multi-source data;

[0075] According to the data value level information, the data response speed requirement information of each data feature in the process of monitoring the ship operation status is obtained;

[0076] Constructing data response speed partitions according to the data value level information and the data response speed requirement information, and importing the multi-source data into the data response speed partitions according to the data response speed requirement information to perform data fusion, so as to form comprehensive status monitoring data of the target waterway;

[0077] Building a water transport vessel monitoring platform for the target waterway based on big data technology according to the comprehensive status monitoring data;

[0078] A water transport vessel operation status monitoring test is performed according to the water transport vessel monitoring platform, an actual data response speed of the comprehensive status monitoring data is obtained according to the monitoring test, and a data response strategy of the water transport vessel monitoring platform is optimized according to the actual data response speed.

[0079] It should be noted that in the present invention application, the data value is evaluated by evaluating the data access of each data feature in the process of monitoring the operating status of water transport vessels. The higher the data value, the more important the monitoring role played by the data in the process of monitoring the operating status of water transport vessels. Therefore, by determining the data value level of each data feature of multi-source data, the data response speed requirement required for each data value level in the process of monitoring the operating status of water transport vessels is judged. The higher the data value level, the higher the data response speed required in the process of monitoring the operating status of water transport vessels to ensure that the data is monitored in time. Therefore, by constructing data partitions with different response speeds, the multi-source data is imported into the corresponding data response speed partitions according to the data response speed requirement of each feature to be constructed as unified managed comprehensive status monitoring data; the data response speed partition is distributed computing through big data technology, and more distributed nodes are constructed in the partitions with higher data response speeds to improve the data in the partitions with higher data response speeds. Faster data processing capabilities and access speeds; the data response strategy is to assign different data response speeds to different data features for the constructed data response speed partitions.

[0080] According to an embodiment of the present invention, the water transport vessel operation status monitoring test is performed according to the water transport vessel monitoring platform, the actual data response speed of the comprehensive status monitoring data is obtained according to the monitoring test, and the data response strategy of the water transport vessel monitoring platform is optimized according to the actual data response speed, specifically:

[0081] Acquire monitoring task information of the operation status of water transport vessels in a target waterway within a preset time period, and determine data monitoring items required for monitoring the operation status of water transport vessels according to the monitoring task information;

[0082] Constructing a data access task for a water transport vessel monitoring platform according to the data monitoring project, performing a water transport vessel operation status monitoring test on the water transport vessel monitoring platform according to the data access task, and obtaining the data acquisition time of each data feature of the comprehensive status monitoring data during the monitoring test;

[0083] It should be noted that in the process of monitoring water vessels in the target waterway according to the water vessel monitoring platform, the data response speed may be inconsistent with the response speed of the data response speed partition, resulting in data delay and failure to meet the actual monitoring response speed requirements of the ships, resulting in reduced navigation safety of water vessels in the target waterway. Therefore, the data monitoring items required for the monitoring task are determined by obtaining the monitoring task information of the water vessels within the preset time, and then the data access task is constructed to monitor and test the water vessel monitoring platform.

[0084] Determine the actual data response speed of each data feature in the comprehensive status monitoring data according to the data acquisition time, compare the actual data response data with the data response requirement information, and judge the response deviation between the actual response speed of each data feature in the comprehensive status monitoring data and the data response requirement;

[0085] Marking data features with response deviations greater than a preset value as data features to be optimized for data response;

[0086] Obtaining a data response speed partition where the data feature to be optimized for data response is located, and if the data response speed partition is not a highest response speed partition, determining a response speed compensation value of the data feature to be optimized for data response according to the response deviation;

[0087] Calculate the difference between the data response speed partition where the data feature to be optimized for data response is located and the highest data response speed partition; if the difference is greater than the response speed compensation value, adjust the data response speed partition where the data feature to be optimized for data response is located according to the response speed information of each data response speed partition and the response speed compensation value to obtain a first data response optimization strategy;

[0088] It should be noted that the actual data response speed is judged by the data acquisition time of each data feature in the monitoring test process, and the response deviation between the actual response speed of each data feature in the comprehensive status monitoring data and the data response requirement is judged. The response deviation indicates that the data feature is accessed by multiple functions at the same time in the water transport vessel monitoring platform, and the data of the data feature is acquired multiple times, which in turn causes data congestion, making the data response speed of the data feature inconsistent with the response speed of the data response speed partition where it is located. Therefore, it is necessary to optimize and adjust the data response speed partition where the data feature is located, and adjust the data feature to a faster data response speed partition to compensate for the response speed deviation caused by the data feature being accessed by multiple functions, so as to obtain the first data response optimization strategy.

[0089] If the difference is less than the response speed compensation value or the data response speed partition where the data feature to be optimized for data response is located is the highest response speed partition, construct a data pointer of the data feature to be optimized for data response, and obtain data reusability information of the data feature to be optimized for data response;

[0090] Determine the reusability range of the data feature to be optimized for data response according to the data reusability information, point the data feature to be optimized for data response to the data monitoring item within the reusability range according to the data pointer, and obtain a second data response optimization strategy;

[0091] The data response strategy of the water transport vessel monitoring platform is optimized according to the first data response optimization strategy and the second data response optimization strategy.

[0092] It should be noted that when the partitions with the highest data response speed in the water transport vessel monitoring platform cannot make up for the data response speed deviation of the data feature to be optimized for data response, or the data response speed partition where the data feature to be optimized for data response is located is the partition with the highest response speed, at this time, no data response speed partition meets the response speed required by the data feature to be optimized for data response. At this time, it is considered that the data of the data feature to be optimized for data response is overloaded, that is, it is repeatedly acquired by too many functions, resulting in a significant reduction in the data response speed. Therefore, at this time, the data reusability information of the data feature to be optimized for data response is obtained, and the data reusability information is that by which function the data feature to be optimized for data response is repeatedly accessed or acquired in the water transport vessel monitoring platform; by constructing a data pointer for the data feature to be optimized for data response, according to the data pointer pointing to the data monitoring item within the data reusability range, that is, in the monitoring platform function, and the data response speed partition of the data feature to be optimized for data response is adjusted to the partition with the highest response speed, a second data response optimization strategy is obtained. By constructing data pointer operations, the operation of repeatedly accessing and searching for data features to optimize data response is avoided. Constructing data pointers only requires one data acquisition or access to link data to multiple data monitoring projects, which greatly saves the consumption of data access resources and improves data response speed. Data pointers are references or pointers to specific data locations or storage addresses. Through data pointers, the system can point to previously stored data or data reused by other features without re-acquiring or calculating. This is particularly critical for real-time data monitoring systems, which not only reduces redundant calculations but also optimizes storage.

[0093] Figure 3 A flow chart of obtaining environmental adaptability data of a water transport vessel according to the present invention is shown.

[0094] According to an embodiment of the present invention, the water transport vessel monitoring platform performs real-time monitoring on the water transport vessel in the target waterway, obtains the operation status monitoring data of the water transport vessel in the target waterway, and performs environmental adaptability assessment on the water transport vessel in the target waterway according to the operation status monitoring data to obtain the environmental adaptability data of the water transport vessel, specifically:

[0095] S302, obtaining historical operation status data and historical environmental adaptability data of water transport vessels in the target waterway, introducing a fuzzy logic algorithm to construct an environmental adaptability evaluation model, and converting the historical operation status data and historical environmental adaptability data into fuzzy sets through language variables as input variables and output variables of the environmental adaptability evaluation model respectively;

[0096] S304, constructing a membership function for each language variable based on the triangular function, and importing the input variable, the output variable, and the membership function into the environmental adaptability assessment model for model training;

[0097] S306, real-time monitoring of ships in the target waterway is performed according to the water transport ship monitoring platform to obtain operation status monitoring data of the water transport ships in the target waterway, wherein the operation status monitoring data includes ship status data and navigation environment data;

[0098] S308, importing the operating status monitoring data into the trained environmental adaptability assessment model to perform environmental adaptability assessment on the water transport vessel, and obtaining an environmental adaptability assessment result for the water transport vessel.

[0099] It should be noted that by introducing the fuzzy logic algorithm to construct the environmental adaptability assessment model, the fuzzy logic algorithm can more accurately describe the complex navigation environment and state changes of water transport ships by converting historical data and real-time data into fuzzy sets. In this way, the assessment model can adapt to the changes of various complex waterway conditions, thereby improving the accuracy and reliability of environmental adaptability assessment; since the fuzzy logic algorithm can flexibly construct the membership range through the triangular membership function, the model can be easily adjusted to meet different assessment needs. For example, in a waterway with a large change in environmental risk level, the model can adaptively adjust the membership range, so that the model has the ability to assess the adaptability of navigation risks at different levels; by analyzing the ship's environmental adaptability data, it can identify high-risk areas and areas with high adaptability requirements in the waterway, thereby helping the waterway management department to reasonably allocate monitoring resources; the fuzzy set is represented by several linguistic variables, such as: water flow speed: low, medium, high; wind speed: breeze, moderate wind, strong wind; tide: low tide, medium tide, high tide. The membership function is used to describe the degree of membership of an element to a fuzzy set. For example, the membership function of water velocity can be set as follows: Low: Applicable to low water velocity, such as 0~1 m / s. Medium: Medium water velocity, such as 0.8~2 m / s. High: High water velocity, such as greater than 1.5 m / s. The environmental adaptability refers to the ability to measure the operational stability of a ship under specific environmental conditions; the ship status data is the real-time navigation data of the ship.

[0100] According to an embodiment of the present invention, the monitoring resource allocation strategy of the water transport vessel monitoring platform for the water transport vessel is determined according to the environmental adaptability data, specifically:

[0101] Assessing the navigation risk of water transport vessels in the target waterway according to the environmental adaptability data to obtain navigation risk data;

[0102] Introducing a K-means clustering algorithm, randomly selecting K initial cluster centers from the navigation risk data according to the K-means clustering algorithm, calculating the Euclidean distance from each data point in the navigation risk data to each initial cluster center, and assigning each data point to the cluster with the nearest initial cluster center;

[0103] Calculate the mean of all data points in each cluster, use the mean as the new cluster center, and iteratively calculate the Euclidean distance of each data point to the new cluster center, and assign the closest cluster until the cluster center no longer changes. Stop the clustering operation and get the navigation risk clustering result.

[0104] Identify the navigation risk distribution of water transport vessels in the target waterway according to the navigation risk clustering result, and construct a navigation risk distribution map;

[0105] Divide the target waterway into risk level areas according to the navigation risk distribution map to obtain navigation risk level information for each position in the target waterway;

[0106] The resource inclination rate for monitoring the operating status of water transport vessels at each location in the target waterway is determined according to the navigation risk level information, and the monitoring resource allocation ratio of the water transport vessel monitoring platform to the water transport vessels is determined according to the resource inclination rate to obtain a monitoring resource allocation strategy.

[0107] It should be noted that the navigation risk is assessed based on the environmental adaptability data, and the navigation risk is clustered by introducing a clustering algorithm to identify the distribution of navigation risks of water transport ships in the target waterway, and obtain a navigation risk distribution map. The navigation risk distribution map reveals which position in the target waterway has a higher navigation risk and which position has a lower navigation risk, and then determines the navigation risk level of different positions in the target waterway; the resource inclination rate of the water transport ship operation status monitoring at each position in the target waterway is determined based on the navigation risk level information. The resource inclination rate refers to the priority or proportion of monitoring resources allocated to each area by the monitoring platform according to the navigation risk level of different areas in the target waterway. The resource inclination rate helps the system to provide more monitoring resources to high-risk areas in priority when resources are limited, so as to improve the monitoring accuracy and timeliness of high-risk areas. The resource tilt rate enables the system to deploy more monitoring resources (such as sensors, monitoring frequency, data processing capabilities, and communication network resources) in high-risk areas to ensure more accurate and timely monitoring of these areas. If the risk distribution in the waterway changes over time, the resource tilt rate can dynamically adjust the monitoring resource allocation strategy to maintain the flexibility and adaptability of the monitoring platform. The resource tilt rate is usually proportional to the navigation risk level information, that is, the higher the risk level of the area, the higher the resource tilt rate, and the final monitoring resource allocation strategy is obtained.

[0108] According to an embodiment of the present invention, it also includes:

[0109] Acquire the water transport vessel operation status monitoring data of a preset time length according to the water transport vessel monitoring platform, conduct environmental adaptability assessment on the water transport vessel of the current preset time length of the target waterway according to the water transport vessel operation status monitoring data, and conduct navigation risk assessment to obtain the navigation risk assessment change result within the preset time period;

[0110] Perform clustering operations on the navigation risk assessment results at each time point within a preset time period, integrate the clustering results at each time point, and draw a navigation risk distribution change diagram within the preset time period;

[0111] Building a navigation risk change prediction model based on a decision tree algorithm, and importing the navigation risk change data of each position in the navigation risk distribution change diagram into the navigation risk change prediction model for learning and training;

[0112] Obtaining navigation risk assessment change result data for each position in the target waterway in the current time period and importing it into the navigation risk change prediction model to predict the navigation risk of each position in the target waterway in the future preset time period, and constructing a navigation risk distribution change prediction map;

[0113] Obtaining navigation route information of a water transport vessel in a target waterway, and determining navigation position information of the water transport vessel in a future navigation period according to the navigation route information;

[0114] The navigation route risk of the water transport vessel in the future navigation time period is determined according to the navigation risk distribution change prediction map and the navigation position information of the future navigation time period. If the navigation route risk is greater than the preset risk value, the navigation route segment with the navigation route risk greater than the preset risk value is adjusted according to the navigation risk distribution change prediction map to obtain the optimized navigation route of the water transport vessel in the target waterway.

[0115] It should be noted that since the operating status monitoring data of water transport ships in the target waterway will change over time, the navigation risks at different locations in the target waterway will also change; therefore, the operating status monitoring data of the ship within a preset time length is obtained through the water transport ship monitoring platform. After obtaining the monitoring data, the system simultaneously evaluates the environmental adaptability and navigation risk of the ship, and generates a navigation risk distribution change map through clustering operations and data integration; the navigation risk distribution change map reveals the changes in navigation risks at different locations in the target waterway within a preset time length; the navigation risk change prediction model constructed based on the decision tree algorithm can be used to learn the preset time length. The navigation risk data within the length is used to predict future navigation risks and construct a risk change prediction map. The prediction map can clearly indicate the navigation risk level of each location, so that ship operators can intuitively identify high-risk areas. By analyzing the navigation risk distribution change map, the system can identify upcoming high-risk situations in advance and issue alarms in time, thereby helping ships take preventive measures to reduce the possibility of accidents. For sections of the navigation route where the risk is greater than the preset risk value, the system can generate adjustment plans in real time to guide ships to avoid or bypass high-risk areas and ensure navigation safety. By adjusting the navigation route, ships can travel on safer routes.

[0116] Figure 4 The block diagram of the water transport vessel operation status monitoring system based on waterway information fusion of the present invention is shown.

[0117] The second aspect of the present invention further provides a water transport vessel operation status monitoring system 4 based on waterway information fusion, the system comprising: a memory 41, a processor 42, the memory comprising a water transport vessel operation status monitoring method program based on waterway information fusion, the water transport vessel operation status monitoring method program based on waterway information fusion when executed by the processor, implements the following steps:

[0118] Acquire multi-source data of the target waterway, the multi-source data including real-time ship navigation data and waterway environment data, and perform data value assessment on the multi-source data to obtain a data value assessment result;

[0119] The multi-source data are integrated according to the data value assessment result to form comprehensive status monitoring data of the target waterway, and a water transport vessel monitoring platform of the target waterway is constructed according to the comprehensive status monitoring data;

[0120] Performing real-time monitoring of water transport vessels in the target waterway according to the water transport vessel monitoring platform, obtaining operation status monitoring data of water transport vessels in the target waterway, and performing environmental adaptability assessment on water transport vessels in the target waterway according to the operation status monitoring data, to obtain environmental adaptability data of water transport vessels;

[0121] The monitoring resource allocation strategy of the water transport vessel monitoring platform for the water transport vessel is determined according to the environmental adaptability data.

[0122] The present invention discloses a method and system for monitoring the operation status of a water transport vessel based on waterway information fusion, including: obtaining multi-source data of a target waterway, including real-time ship navigation data and waterway environment data, and performing data value assessment; fusing the multi-source data according to the assessment results, generating comprehensive status monitoring data of the target waterway, and constructing a water transport vessel monitoring platform; using the platform to perform real-time monitoring of the ship in the target waterway to obtain operation status data; and assessing the environmental adaptability of the ship based on the data, thereby optimizing the allocation strategy of monitoring resources. The present invention improves the accuracy of water transport vessel monitoring and resource utilization efficiency, and is suitable for intelligent waterway management.

[0123] In the 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 schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: 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 components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0124] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0125] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0126] A person of ordinary skill in the art can understand that: all or part of the steps of implementing the above method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiment; and the aforementioned storage medium includes: a mobile storage device, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0127] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

[0128] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for monitoring the operation status of a water transport vessel based on waterway information fusion, characterized in that: The following steps are involved: Acquire multi-source data of the target waterway, the multi-source data including real-time ship navigation data and waterway environment data, and perform data value assessment on the multi-source data to obtain a data value assessment result; The multi-source data are integrated according to the data value assessment result to form comprehensive status monitoring data of the target waterway, and a water transport vessel monitoring platform of the target waterway is constructed according to the comprehensive status monitoring data; Performing real-time monitoring of water transport vessels in the target waterway according to the water transport vessel monitoring platform, obtaining operation status monitoring data of water transport vessels in the target waterway, and performing environmental adaptability assessment on water transport vessels in the target waterway according to the operation status monitoring data, to obtain environmental adaptability data of water transport vessels; Determining a monitoring resource allocation strategy of the water transport vessel monitoring platform for the water transport vessel according to the environmental adaptability data; The water transport vessel monitoring platform is used to monitor the water transport vessel in the target waterway in real time, obtain the operation status monitoring data of the water transport vessel in the target waterway, and evaluate the environmental adaptability of the water transport vessel in the target waterway according to the operation status monitoring data to obtain the environmental adaptability data of the water transport vessel, specifically: Obtain historical operation status data and historical environmental adaptability data of water transport vessels in the target waterway, introduce fuzzy logic algorithm to build an environmental adaptability evaluation model, and transform the historical operation status data and historical environmental adaptability data into fuzzy sets through language variables as input variables and output variables of the environmental adaptability evaluation model respectively; Constructing a membership function for each language variable based on a triangular function, and importing the input variable, the output variable, and the membership function into the environmental adaptability assessment model for model training; Real-time monitoring of ships in the target waterway is performed according to the water transport ship monitoring platform to obtain operation status monitoring data of the water transport ships in the target waterway, wherein the operation status monitoring data includes ship status data and navigation environment data; The operating status monitoring data is imported into the trained environmental adaptability assessment model to perform environmental adaptability assessment on water transport vessels, thereby obtaining an environmental adaptability assessment result for water transport vessels; The monitoring resource allocation strategy of the water transport vessel monitoring platform for the water transport vessel is determined according to the environmental adaptability data, specifically: Assessing the navigation risk of water transport vessels in the target waterway according to the environmental adaptability data to obtain navigation risk data; Introducing a K-means clustering algorithm, randomly selecting K initial cluster centers from the navigation risk data according to the K-means clustering algorithm, calculating the Euclidean distance from each data point in the navigation risk data to each initial cluster center, and assigning each data point to the cluster with the nearest initial cluster center; Calculate the mean of all data points in each cluster, use the mean as the new cluster center, and iteratively calculate the Euclidean distance of each data point to the new cluster center, and assign the closest cluster until the cluster center no longer changes. Stop the clustering operation and get the navigation risk clustering result. Identify the navigation risk distribution of water transport vessels in the target waterway according to the navigation risk clustering result, and construct a navigation risk distribution map; Divide the target waterway into risk level areas according to the navigation risk distribution map to obtain navigation risk level information for each position in the target waterway; The resource inclination rate for monitoring the operating status of water transport vessels at each location in the target waterway is determined according to the navigation risk level information, and the monitoring resource allocation ratio of the water transport vessel monitoring platform to the water transport vessels is determined according to the resource inclination rate to obtain a monitoring resource allocation strategy.

2. The method for monitoring the operation status of a water transport vessel based on waterway information fusion according to claim 1 is characterized in that: The multi-source data of the target waterway is obtained, and the multi-source data includes real-time ship navigation data and waterway environment data, and data value evaluation is performed on the multi-source data to obtain a data value evaluation result, specifically: Acquire real-time navigation data of ships in the target channel based on the ship AIS system, wherein the real-time navigation data of ships includes the ship's position, speed, heading, ship name, and draft depth; Acquiring channel environmental data of a target channel based on an environmental sensor, wherein the channel environmental data includes meteorological data and hydrological data; The real-time ship navigation data and the channel environment data are calibrated as multi-source data of the target channel, and the multi-source data are aligned according to the acquisition time; Obtain data access frequency information of each data feature of multi-source data during the operation status monitoring of water transport vessels in the target waterway within a preset time period; The data demand activity of each data feature for monitoring the operating status of water transport vessels is evaluated according to the data access frequency information, and the data value of each data feature of the multi-source data is evaluated according to the data demand activity to obtain a data value evaluation result.

3. The method for monitoring the operation status of a water transport vessel based on waterway information fusion according to claim 1 is characterized in that: The multi-source data are integrated according to the data value assessment result to form comprehensive status monitoring data of the target waterway, and a water transport vessel monitoring platform of the target waterway is constructed according to the comprehensive status monitoring data, specifically: According to the data value assessment result, the multi-source data is divided into data value levels to obtain data value level information of each data feature of the multi-source data; According to the data value level information, the data response speed requirement information of each data feature in the process of monitoring the ship operation status is obtained; Constructing data response speed partitions according to the data value level information and the data response speed requirement information, and importing the multi-source data into the data response speed partitions according to the data response speed requirement information to perform data fusion, so as to form comprehensive status monitoring data of the target waterway; Building a water transport vessel monitoring platform for the target waterway based on big data technology according to the comprehensive status monitoring data; A water transport vessel operation status monitoring test is performed according to the water transport vessel monitoring platform, an actual data response speed of the comprehensive status monitoring data is obtained according to the monitoring test, and a data response strategy of the water transport vessel monitoring platform is optimized according to the actual data response speed.

4. The method for monitoring the operation status of a water transport vessel based on waterway information fusion according to claim 3 is characterized in that: The water transport vessel operation status monitoring test is performed according to the water transport vessel monitoring platform, the actual data response speed of the comprehensive status monitoring data is obtained according to the monitoring test, and the data response strategy of the water transport vessel monitoring platform is optimized according to the actual data response speed, specifically: Acquire monitoring task information of the operation status of water transport vessels in a target waterway within a preset time period, and determine data monitoring items required for monitoring the operation status of water transport vessels according to the monitoring task information; Constructing a data access task for a water transport vessel monitoring platform according to the data monitoring project, performing a water transport vessel operation status monitoring test on the water transport vessel monitoring platform according to the data access task, and obtaining the data acquisition time of each data feature of the comprehensive status monitoring data during the monitoring test; Determine the actual data response speed of each data feature in the comprehensive status monitoring data according to the data acquisition time, compare the actual data response data with the data response requirement information, and judge the response deviation between the actual response speed of each data feature in the comprehensive status monitoring data and the data response requirement; Marking data features with response deviations greater than a preset value as data features to be optimized for data response; Obtaining a data response speed partition where the data feature to be optimized for data response is located, and if the data response speed partition is not a highest response speed partition, determining a response speed compensation value of the data feature to be optimized for data response according to the response deviation; Calculate the difference between the data response speed partition where the data feature to be optimized for data response is located and the highest data response speed partition; if the difference is greater than the response speed compensation value, adjust the data response speed partition where the data feature to be optimized for data response is located according to the response speed information of each data response speed partition and the response speed compensation value to obtain a first data response optimization strategy; If the difference is less than the response speed compensation value or the data response speed partition where the data feature to be optimized for data response is located is the highest response speed partition, construct a data pointer of the data feature to be optimized for data response, and obtain data reusability information of the data feature to be optimized for data response; Determine the reusability range of the data feature to be optimized for data response according to the data reusability information, point the data feature to be optimized for data response to the data monitoring item within the reusability range according to the data pointer, and obtain a second data response optimization strategy; The data response strategy of the water transport vessel monitoring platform is optimized according to the first data response optimization strategy and the second data response optimization strategy.

5. A water transport vessel operation status monitoring system based on waterway information fusion for implementing the monitoring method according to any one of claims 1 to 4, characterized in that: The water transport vessel operation status monitoring system based on waterway information fusion includes a storage and a processor, wherein the storage includes a water transport vessel operation status monitoring method program based on waterway information fusion, and when the water transport vessel operation status monitoring method program based on waterway information fusion is executed by the processor, the following steps are implemented: Acquire multi-source data of the target waterway, the multi-source data including real-time ship navigation data and waterway environment data, and perform data value assessment on the multi-source data to obtain a data value assessment result; The multi-source data are integrated according to the data value assessment result to form comprehensive status monitoring data of the target waterway, and a water transport vessel monitoring platform of the target waterway is constructed according to the comprehensive status monitoring data; Performing real-time monitoring of water transport vessels in the target waterway according to the water transport vessel monitoring platform, obtaining operation status monitoring data of water transport vessels in the target waterway, and performing environmental adaptability assessment on water transport vessels in the target waterway according to the operation status monitoring data, to obtain environmental adaptability data of water transport vessels; Determining a monitoring resource allocation strategy of the water transport vessel monitoring platform for the water transport vessel according to the environmental adaptability data; The water transport vessel monitoring plan in the target waterway is adjusted according to the monitoring resource allocation strategy.

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