Ship security intelligent management method and system based on multi-source perception

Abstract

The invention provides a ship security intelligent management method and system based on multi-source perception, and relates to the technical field of ship monitoring, and the ship security intelligent management method based on multi-source perception comprises the following steps: collecting perception data of a ship in navigation; performing data integration processing on the collected sensing data; performing data analysis on the integrated perception data, and constructing a digital twin model of the ship; detecting the state of the ship by using a digital twin model of the ship, and making an optimal security decision scheme after detecting a risk; and specific security countermeasures are formulated and executed. The security risk response speed can be greatly improved, possible loss can be reduced, different security decision results are simulated, the optimal security decision scheme is selected, the decision quality can be improved, and possible misjudgment can be avoided.

Description

Technical Field

[0001] The present invention relates to the field of ship monitoring technology, and in particular to a ship security intelligent management method and system based on multi-source perception. Background Art

[0002] With the continuous development of my country's shipping industry and technological breakthroughs in the construction of intelligent ships, the issue of ship navigation safety has become increasingly prominent and has become the core focus of the shipping industry. Major shipping countries around the world have invested a lot of resources and energy in ship navigation safety, but maritime traffic accidents still occur from time to time, and their serious consequences not only threaten the lives of crew members, but also have an impact on the marine environment that cannot be ignored. In recent years, with the rapid progress of the shipping industry and navigation technology, the shipping industry is developing towards large-scale, high-speed and professional trends. At the same time, the density of maritime ships is also continuing to grow. While such development improves the efficiency of ship transportation, it also puts forward higher and more stringent requirements for ensuring maritime navigation safety and maintaining a clean marine environment.

[0003] At present, the intelligent security management of ships is a system that combines advanced technology and security strategies. Its main purpose is to ensure the safe operation of ships and protect the safety of crew members. With the development of ship technology, the amount of data generated by ships has increased dramatically. Traditional intelligent security management often lacks the ability to process large amounts of complex data, which limits the in-depth understanding of the ship's status and timely security response. As a result, ships cannot accurately identify various potential risks and determine appropriate response priorities in complex route environments, especially when resources are limited. This makes it difficult to avoid or respond to emergencies during the ship's navigation process.

[0004] Currently, no effective solution has been proposed for the problems in the related technologies. Summary of the invention

[0005] In view of this, the present invention provides a ship security intelligent management method and system based on multi-source perception to solve the problem mentioned above that traditional security intelligent management often lacks the ability to process large amounts of complex data, and cannot accurately identify various potential risks and determine appropriate response priorities.

[0006] In order to solve the above problems, the specific technical solutions adopted by the present invention are as follows: According to one aspect of the invention, a method for intelligent management of ship security based on multi-source perception is provided, and the method for intelligent management of ship security based on multi-source perception comprises the following steps: S1. Collect the ship's perception data during navigation through monitoring sensors pre-deployed on the ship; S2, perform data integration processing on the collected perception data based on knowledge graph technology; S3, parsing the integrated perception data through the syntax tree, and building a digital twin model of the ship based on the parsed data; S4. Use the digital twin model of the ship to detect the ship's status and formulate the best security decision-making plan after detecting risks; S5. Develop and implement specific security response measures based on the best security decision-making plan.

[0007] Preferably, the following steps are included before collecting the sensory data of the ship during navigation by monitoring sensors pre-deployed in the ship: S11. Collect geographic information data of the ship security area and ship structure information data and perform preprocessing. The geographic information data includes terrain data and security area boundaries. The preprocessing includes data cleaning and format conversion. S12. Based on the security requirements of the ship, select monitoring sensors suitable for ship navigation, and divide the monitoring sensors into active and passive types; S13, deploying the installation positions of the monitoring sensors through a sensor position deployment algorithm; S14. Perform coverage and connectivity analysis on the deployment results of the monitoring sensors. If the preset requirements are not met, adjust the location of the monitoring sensors until the preset requirements are met.

[0008] Preferably, based on the security requirements of the ship, selecting monitoring sensors suitable for ship navigation and dividing the monitoring sensors into active and passive types includes the following steps: S121. Analyze the security requirements of the ship during navigation and determine the ship security parameters. The security requirements include anti-theft, fire warning, water level monitoring and weather monitoring. The ship security parameters include monitoring range, response speed and monitoring accuracy. S122, determining the navigation environment of the ship, and selecting monitoring sensors suitable for the navigation of the ship according to the analyzed security requirements; S123. Divide sensor resources into active sensors and passive sensors according to the working principle and function of the monitoring sensor; S124. Determine the number of clusters to be divided according to the number and type of monitoring sensors, and allocate the monitoring sensors to each cluster.

[0009] Preferably, deploying the installation positions of monitoring sensors by using a sensor position deployment algorithm comprises the following steps: S131, dividing the monitoring area of ​​the ship into several sub-areas, and establishing a monitoring sensor deployment model; S132, maximizing the coverage of all sub-areas as the optimization goal, and taking the connectivity of monitoring sensors between clusters as a constraint condition; S133, initializing a particle swarm of monitoring sensor positions, and iteratively optimizing the monitoring sensor positions through a sensor position deployment algorithm until a monitoring sensor deployment scheme that satisfies the constraint conditions and maximizes coverage is found; S134. Deploy sensors on the ship based on the monitoring sensor deployment plan and perform security monitoring on the ship.

[0010] Preferably, the data integration processing of the collected perception data based on the knowledge graph technology includes the following steps: S21, performing abnormal data processing on the collected perception data; S22. Use knowledge graph technology to combine the processed perception data with pre-defined ontologies and rules, annotate and classify the perception data, and establish the correlation between data; S23. Integrate the perception data of different monitoring sensors through data fusion technology, and store the integrated data in a structured knowledge base.

[0011] Preferably, parsing the integrated perception data through a syntax tree, and constructing a digital twin model of the ship according to the parsed data includes the following steps: S31, performing syntax analysis and lexical analysis on the integrated perception data through a syntax parsing tool to generate an abstract syntax tree; S32. Based on the generated abstract syntax tree, a control flow graph is constructed and risk source analysis is performed; S33. Build a digital twin model of the ship based on the abstract syntax tree, control flow graph and risk source analysis results.

[0012] Preferably, performing syntax analysis and lexical analysis on the integrated perception data by a syntax parsing tool to generate an abstract syntax tree comprises the following steps: S311, performing lexical analysis on the integrated perception data and dividing it into a number of word units; S312, performing grammatical analysis on the divided word units according to preset grammatical rules, and combining them into a grammatical structure that satisfies the preset grammatical rules; S313. Use the combined grammatical structure as a node and construct an abstract syntax tree with the nodes.

[0013] Preferably, constructing a control flow graph and performing risk source analysis based on the generated abstract syntax tree includes the following steps: S321, traversing the nodes in the abstract syntax tree by a recursive method, and constructing a control flow graph according to the node type; S322. Analyze the risk sources of the ship during the route based on the constructed control flow graph, where the risk sources include at least one of mechanical failure, human behavior or environmental factors; S323. Determine the security risk events of the ship according to the analyzed risk sources, and conduct risk assessment based on the security risk events; S324. Determine the priority of ship security based on the assessment results, and mark the risk sources on the control flow diagram based on the priority.

[0014] Preferably, the digital twin model of the ship is used to detect the ship status, and the best security decision plan is formulated after the risk is detected, including the following steps: S41. Monitor the perception data of the ship in real time through the deployed monitoring sensors and input it into the digital twin model of the ship; S42. Use real-time monitoring perception data in the digital twin model of the ship to simulate the current security status and behavior of the ship; S43. Perform risk detection on the current security status and behavior of the ship through the early warning mechanism preset in the digital twin model of the ship; S44. Based on the risk detection results, different security decisions are simulated through the digital twin model of the ship, and the prediction results of different decisions are compared to select the best security decision plan.

[0015] According to another aspect of the present invention, a ship security intelligent management system based on multi-source perception is provided, the ship security intelligent management system based on multi-source perception comprises: a perception data collection module, a perception data integration module, a perception data analysis module, a decision scheme simulation module and a decision scheme execution module, and the perception data collection module, the perception data integration module, the perception data analysis module, the decision scheme simulation module and the decision scheme execution module are connected in sequence; A perception data collection module, used to collect the perception data of the ship during navigation through monitoring sensors pre-deployed in the ship; The perception data integration module is used to integrate the collected perception data based on the knowledge graph technology; The perception data parsing module is used to parse the integrated perception data through a syntax tree and build a digital twin model of the ship based on the parsed data; The decision-making simulation module is used to detect the ship's status using the ship's digital twin model and develop the best security decision-making plan after detecting risks; The decision-making execution module is used to formulate and implement specific security response measures based on the best security decision-making plan.

[0016] The beneficial effects of the present invention are: 1. The present invention integrates and processes data through knowledge graph technology, which can realize comprehensive monitoring and in-depth understanding of the ship status. It can not only enhance the perception ability of complex navigation environment and ship status, but also improve the accuracy of security decision-making. The integrated perception data is parsed through the syntax tree, and the digital twin model of the ship is constructed according to the parsed data, which can accurately reflect the real-time status of the ship and provide a basis for real-time monitoring and early warning. The digital twin model of the ship is used to detect the security status of the ship in real time. When security risks are detected, early warnings are issued in time, which can greatly improve the response speed to security risks and reduce possible losses. By simulating different security decision-making results and selecting the best security decision-making plan, it is helpful to improve the quality of decision-making and avoid possible misjudgments.

[0017] 2. The present invention can provide accurate basic information for subsequent sensor deployment by collecting geographic information data of ship security areas and ship structure information data and performing preprocessing, thereby improving the accuracy and reliability of security. Based on the ship's security needs, monitoring sensors suitable for ship navigation are selected to ensure that security management can meet various security needs in a targeted manner, thereby improving flexibility and adaptability. The installation positions of monitoring sensors are deployed through sensor location deployment algorithms, thereby ensuring the maximization of monitoring coverage and the connectivity of monitoring sensors, improving the monitoring efficiency and accuracy of security management, and avoiding possible monitoring blind spots and omissions. It has important practical value for ship security management.

[0018] 3. The present invention uses a syntax parsing tool to perform lexical analysis and syntax analysis on the integrated perception data to generate an abstract syntax tree, which can effectively analyze and process complex perception data and improve the efficiency and accuracy of data parsing. Based on the abstract syntax tree, a control flow graph is constructed and risk source analysis is performed, which can identify the possible risk sources of the ship during the route and provide support for risk management and decision-making. Through risk source analysis, the ship's security risk events can be determined and risk assessment can be performed. The priority of ship security can be determined based on the assessment results, and the control flow graph can be labeled with risk sources, which is conducive to scientific risk management and decision-making. The digital twin model can provide real-time status feedback of the ship in actual operation, providing strong support for the operation management of the ship. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings: Figure 1is a flow chart of a method for intelligent management of ship security based on multi-source perception according to an embodiment of the present invention; Figure 2 It is a principle block diagram of a ship security intelligent management system based on multi-source perception according to an embodiment of the present invention.

[0020] In the figure: 1. Perception data collection module; 2. Perception data integration module; 3. Perception data analysis module; 4. Decision plan simulation module; 5. Decision plan execution module. DETAILED DESCRIPTION

[0021] In order to enable those skilled in the art to better understand the technical solutions in this application, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this application.

[0022] According to an embodiment of the present invention, a method and system for intelligent management of ship security based on multi-source perception are provided.

[0023] The present invention will now be further described with reference to the accompanying drawings and specific embodiments. Figure 1 As shown, according to one embodiment of the present invention, a ship security intelligent management method based on multi-source perception is provided, and the ship security intelligent management method based on multi-source perception includes the following steps: S1. Collect the ship's perception data during navigation through monitoring sensors pre-deployed on the ship; As a preferred embodiment, the following steps are included before collecting the sensory data of the ship during navigation by monitoring sensors pre-deployed in the ship: S11. Collect geographic information data of the ship security area and ship structure information data and perform preprocessing. The geographic information data includes terrain data and security area boundaries. The preprocessing includes data cleaning and format conversion. It should be noted that information such as water depth and seabed topography can be obtained through surveying and mapping equipment and satellite remote sensing, and ship structure information can be obtained through ship design blueprints and structural layout drawings.

[0024] Data cleaning means deleting duplicate or erroneous data, processing missing values, smoothing noise, and identifying and processing outliers. Format conversion means unifying data from different sources into one format for easy analysis and processing, including changing data types, units, and encoding methods.

[0025] S12. Based on the security requirements of the ship, select monitoring sensors suitable for ship navigation, and divide the monitoring sensors into active and passive types; As a preferred implementation, based on the security requirements of the ship, selecting monitoring sensors suitable for ship navigation and dividing the monitoring sensors into active and passive types includes the following steps: S121. Analyze the security requirements of the ship during navigation and determine the ship security parameters. The security requirements include anti-theft, fire warning, water level monitoring and weather monitoring, etc. The ship security parameters include monitoring range, response speed and monitoring accuracy, etc.; S122, determining the navigation environment of the ship, and selecting monitoring sensors suitable for the navigation of the ship according to the analyzed security requirements; S123. Divide sensor resources into active sensors and passive sensors according to the working principle and function of the monitoring sensor; Specifically, active sensors are sensors that need to actively emit signals and receive reflected signals, such as radar and sonar; while passive sensors measure by receiving signals naturally emitted by the object being measured or the environment, such as infrared sensors and temperature sensors.

[0026] S124. Determine the number of clusters to be divided according to the number and type of monitoring sensors, and allocate the monitoring sensors to each cluster.

[0027] It should be noted that determining the number of clusters generally includes the following steps: Define the cluster's goals and requirements, including monitoring objectives, communication requirements, and energy management; An assessment of the geographical and structural layout, taking into account the size and shape of the vessels, and the specific monitoring needs of each area; Determine the optimal number of clusters, using algorithms or planning tools to assess how to most efficiently divide the clusters to cover all monitoring areas and meet communication and energy requirements; Assign sensors to clusters, and decide which sensors go into which cluster based on the needs of each cluster and the functions of the sensors.

[0028] S13, deploying the installation positions of the monitoring sensors through a sensor position deployment algorithm; Specifically, the main idea of ​​the sensor location deployment algorithm is based on the particle swarm optimization algorithm, which is a global optimization technology that finds the optimal solution to the problem by simulating the social behavior of biological groups such as bird flocks and fish schools.

[0029] As a preferred implementation, deploying the installation positions of monitoring sensors using a sensor position deployment algorithm includes the following steps: S131, dividing the monitoring area of ​​the ship into several sub-areas, and establishing a monitoring sensor deployment model; It should be noted that the physical space of the ship is analyzed and divided into several sub-areas. These sub-areas can be divided according to function (such as the ship's driving area, machinery room, cargo storage area, etc.) or physical location (such as upper, middle, lower, etc.). Then, a deployment model is established to describe the size, shape, location and possible monitoring requirements of each area.

[0030] S132, maximizing the coverage of all sub-areas as the optimization goal, and taking the connectivity of monitoring sensors between clusters as a constraint condition; It should be noted that the optimization goal and constraints are set. The optimization goal is to cover all sub-areas as much as possible, which means that each sub-area is expected to be monitored by at least one sensor. The constraints are to ensure that the sensors in each cluster can communicate effectively, including signal transmission, data exchange and collaborative work capabilities between sensors.

[0031] S133, initializing a particle swarm of monitoring sensor positions, and iteratively optimizing the monitoring sensor positions through a sensor position deployment algorithm until a monitoring sensor deployment scheme that satisfies the constraint conditions and maximizes coverage is found; It should be noted that the main idea of ​​using the particle swarm optimization algorithm to find the best sensor deployment plan is to initialize the particle swarm, which can be generated randomly or using some heuristic methods. Then, through the iterative optimization process, the position and velocity of each particle (i.e., a possible deployment plan) are continuously updated until a deployment plan that meets the constraints and maximizes the coverage is found.

[0032] S134. Deploy sensors on the ship based on the monitoring sensor deployment plan and perform security monitoring on the ship.

[0033] S14. Perform coverage and connectivity analysis on the deployment results of the monitoring sensors. If the preset requirements are not met, adjust the location of the monitoring sensors until the preset requirements are met.

[0034] It should be noted that the coverage and connectivity analysis of the deployed monitoring sensors, coverage refers to the ratio of each sub-area covered by at least one sensor, and connectivity refers to the degree to which all sensors can be interconnected through the wireless communication network. The coverage can be obtained by calculating the coverage range of each sensor and comparing it with each sub-area, and the connectivity needs to consider factors such as the distance between sensors, transmission power, channel conditions, etc.

[0035] When the coverage and connectivity do not meet the preset requirements, it is necessary to adjust the position of the sensor. This process can be achieved in a variety of ways, such as manual adjustment or automatic adjustment by using an optimization algorithm. After adjusting the position, it is necessary to perform coverage and connectivity analysis again until the preset requirements are met.

[0036] Specifically, by collecting geographic information data of the ship security area and ship structure information data and preprocessing them, accurate basic information can be provided for subsequent sensor deployment, improving the accuracy and reliability of security. Based on the ship's security needs, selecting monitoring sensors suitable for ship navigation can ensure that security management can meet various security needs in a targeted manner, improving flexibility and adaptability. By deploying the installation location of the monitoring sensor through the sensor location deployment algorithm, it can ensure the maximization of monitoring coverage and the connectivity of the monitoring sensors, improve the monitoring efficiency and accuracy of security management, and avoid possible monitoring blind spots and omissions. It has important practical value for ship security management.

[0037] S2, perform data integration processing on the collected perception data based on knowledge graph technology; As a preferred implementation, the data integration processing of the collected perception data based on the knowledge graph technology includes the following steps: S21, performing abnormal data processing on the collected perception data; It should be noted that the data collected from monitoring sensors is cleaned, including identifying and processing abnormal data, such as erroneous readings, noise interference, or missing data points.

[0038] S22. Use knowledge graph technology to combine the processed perception data with pre-defined ontologies and rules, annotate and classify the perception data, and establish the correlation between data; It should be noted that the knowledge graph is a structured form of knowledge representation. It represents knowledge by defining entities (such as equipment, sensors, monitoring areas, etc.) and the relationships between entities. By combining the processed data with pre-defined ontologies (i.e., a set of entities and a standardized description of the relationships between them) and rules, the data can be more accurately labeled and classified, and it helps to establish correlations between data, thereby better understanding and interpreting the data.

[0039] S23. Integrate the perception data of different monitoring sensors through data fusion technology, and store the integrated data in a structured knowledge base.

[0040] It should be noted that the data collected by different sensors are integrated to provide a more comprehensive monitoring view. The purpose of data fusion is to combine data from different sources to improve the accuracy and reliability of the data. It can be achieved through a variety of data fusion techniques, such as time series analysis, statistical methods or machine learning algorithms. The integrated data is stored in a structured knowledge base for further analysis and decision support.

[0041] S3, parsing the integrated perception data through the syntax tree, and building a digital twin model of the ship based on the parsed data; As a preferred implementation, parsing the integrated perception data through a syntax tree and building a digital twin model of the ship based on the parsed data includes the following steps: S31, performing syntax analysis and lexical analysis on the integrated perception data through a syntax parsing tool to generate an abstract syntax tree; As a preferred implementation, the integrated perception data is subjected to syntax analysis and lexical analysis by a syntax parsing tool, and generating an abstract syntax tree includes the following steps: S311, performing lexical analysis on the integrated perception data and dividing it into a number of word units; It should be noted that the integrated perceptual data is processed through a lexical analyzer, which reads the raw data, identifies the basic elements therein (such as identifiers, keywords, numbers, symbols, etc.), and converts these elements into word units, which can simplify and prepare the data and lay the foundation for subsequent grammatical analysis.

[0042] S312, performing grammatical analysis on the divided word units according to preset grammatical rules, and combining them into a grammatical structure that satisfies the preset grammatical rules; It should be noted that by processing the divided word units according to preset grammatical rules, checking whether the arrangement and combination of word units conform to specific grammatical structures, and identifying various statements and expressions, it is possible to ensure that the data follows specific grammatical rules and form an understandable and operable structure.

[0043] S313. Use the combined grammatical structure as a node and construct an abstract syntax tree with the nodes.

[0044] It should be noted that the result of the grammatical analysis is converted into an abstract syntax tree. In the tree structure of the abstract syntax tree, each node represents a structure identified in the grammatical analysis, which can provide a higher-level, structured data representation to facilitate further processing, such as the construction of control flow graphs, risk analysis, etc.

[0045] S32. Based on the generated abstract syntax tree, a control flow graph is constructed and risk source analysis is performed; As a preferred implementation, based on the generated abstract syntax tree, constructing a control flow graph and performing risk source analysis includes the following steps: S321, traversing the nodes in the abstract syntax tree by a recursive method, and constructing a control flow graph according to the node type; It should be noted that the nodes of the abstract syntax tree are traversed recursively, and a control flow graph is constructed according to the node type (such as assignment, judgment, loop, etc.). The control flow graph is a graphical model used to describe the execution flow of a program. Each node represents an operation or decision, and the edge represents the execution path of the program.

[0046] Specifically, by analyzing the control flow graph, possible risk points can be identified, such as potential incorrect decision paths or failure points, which helps to assess the safety of the ship in various situations, such as dealing with severe weather, mechanical failures or security threats.

[0047] S322. Analyze the risk sources of the ship during the route based on the constructed control flow graph, where the risk sources include at least one of mechanical failure, human behavior or environmental factors; It should be noted that the control flow graph can reveal the key operations and decision points in ship security management. By analyzing these points, potential risk points that may lead to safety problems can be identified.

[0048] S323. Determine the security risk events of the ship according to the analyzed risk sources, and conduct risk assessment based on the security risk events; It should be noted that based on the risk sources previously analyzed based on the control flow diagram, specific events that may lead to safety problems are determined, such as ship loss of control, collision, fire, etc., and then the possibility of each security risk event is evaluated, for example, the frequency or probability of a specific mechanical failure, and the possible losses or impacts of each risk event are evaluated, including financial losses, casualties, environmental impacts, etc.

[0049] S324. Determine the priority of ship security based on the assessment results, and mark the risk sources on the control flow diagram based on the priority.

[0050] Specifically, risk events are classified into high, medium, and low levels based on their likelihood and impact.

[0051] S33. Build a digital twin model of the ship based on the abstract syntax tree, control flow graph and risk source analysis results.

[0052] It should be noted that a digital twin model that reflects the physical and operational characteristics of the ship is constructed by combining the abstract syntax tree, control flow graph and risk source analysis results. This model should be able to simulate the behavior and response of a real ship under different circumstances. The digital twin model can be used to monitor the ship status in real time, predict future behavior, and evaluate the impact of different decisions and operations on ship performance.

[0053] Specifically, by using a syntax parsing tool to perform lexical analysis and grammatical analysis on the integrated perception data and generate an abstract syntax tree, complex perception data can be effectively analyzed and processed, improving the efficiency and accuracy of data parsing. By constructing a control flow graph based on the abstract syntax tree and performing risk source analysis, the possible risk sources of the ship during the route can be identified, which can provide support for risk management and decision-making. Through risk source analysis, the ship's security risk events can be determined and risk assessment can be conducted. The priority of ship security can be determined based on the assessment results, and the control flow graph can be labeled with risk sources, which is conducive to scientific risk management and decision-making. The digital twin model can provide real-time status feedback of the ship in actual operation, providing strong support for ship operation management.

[0054] S4. Use the digital twin model of the ship to detect the ship's status and formulate the best security decision-making plan after detecting risks; As a preferred implementation method, the digital twin model of the ship is used to detect the ship's status, and the best security decision-making plan is formulated after the risk is detected, including the following steps: S41. Monitor the perception data of the ship in real time through the deployed monitoring sensors and input it into the digital twin model of the ship; Specifically, the real-time monitoring of ship perception data includes the ship's position, speed, hull status, etc.

[0055] S42. Use real-time monitoring perception data in the digital twin model of the ship to simulate the current security status and behavior of the ship; It should be noted that after the perception data of real-time monitoring of the ship is input into the digital twin model of the ship, the digital twin model of the ship updates the status of the ship it represents according to the input data. Through the digital twin model of the ship, the detailed operation status of the ship can be obtained, including the ship's power status, hull structure stress, environmental adaptability, etc.

[0056] S43. Perform risk detection on the current security status and behavior of the ship through the early warning mechanism preset in the digital twin model of the ship; Specifically, setting up the early warning mechanism in the digital twin model of the ship includes the following steps: Early warning rules are defined based on the characteristics of ship operations, safety standards, historical accident data, etc. The early warning rules include safety thresholds of parameters (such as temperature, pressure, speed, status, etc.), standardized processes for operating procedures, and safety standards for environmental conditions.

[0057] Based on the defined warning rules, key parameters that need to be monitored are identified, such as engine temperature, ship speed, weather conditions, etc.

[0058] The early warning rules are then integrated into the digital twin model through programming.

[0059] It should be noted that real-time data, such as the ship's position, speed, hull status, etc., are collected through sensors on the ship. These data are transmitted to the digital twin model in real time and compared and analyzed with the parameters in the early warning mechanism. The early warning mechanism will analyze these data to check whether any parameters exceed the safety range or show abnormal patterns. When they exceed the safety range or show abnormal patterns, it means that the current state of the ship is a risky state.

[0060] S44. Based on the risk detection results, different security decisions are simulated through the digital twin model of the ship, and the prediction results of different decisions are compared to select the best security decision plan.

[0061] It should be noted that when a risk is detected, the specific type and severity of the risk are determined based on the risk detection results, and possible security decisions are set based on the specific type and severity of the risk, including adjusting the ship's path, changing the ship's speed, or starting specific safety procedures, etc. Then, the digital twin model of the ship is used to simulate the possible results of various decisions, and then the simulation results are compared and the effects of various decisions are evaluated. The effects of the decisions specifically include safety levels, as well as corresponding operating efficiency, cost and other factors. Finally, the best security decision plan is selected based on the comparison results.

[0062] S5. Develop and implement specific security response measures based on the best security decision-making plan.

[0063] Specifically, developing and implementing specific security response measures includes the following steps: Break down the best security decision into a series of specific steps or actions, including all the actions that need to be performed, as well as when and in what order they need to be performed.

[0064] Then, based on the planned steps, determine the resources required, including human resources (such as crew or ground support staff), physical resources (such as equipment or tools), and time resources.

[0065] Once response measures have been developed, all relevant personnel need to be notified, including crew members, ship management personnel, and other persons who may be affected by the decision.

[0066] After all resources are ready, start implementing response measures. During the implementation process, monitor the effectiveness of the measures and make adjustments based on actual conditions.

[0067] like Figure 2 As shown, according to another embodiment of the present invention, a ship security intelligent management system based on multi-source perception is provided, and the ship security intelligent management system based on multi-source perception includes: a perception data collection module 1, a perception data integration module 2, a perception data analysis module 3, a decision scheme simulation module 4 and a decision scheme execution module 5, and the perception data collection module 1, the perception data integration module 2, the perception data analysis module 3, the decision scheme simulation module 4 and the decision scheme execution module 5 are connected in sequence; The sensing data collection module 1 is used to collect the sensing data of the ship during navigation through monitoring sensors pre-deployed in the ship; The perception data integration module 2 is used to perform data integration processing on the collected perception data based on the knowledge graph technology; The perception data parsing module 3 is used to parse the integrated perception data through a syntax tree and build a digital twin model of the ship based on the parsed data; Decision-making scheme simulation module 4 is used to detect the ship status using the digital twin model of the ship and formulate the best security decision-making scheme after detecting risks; The decision-making scheme execution module 5 is used to formulate and execute specific security response measures based on the best security decision-making scheme.

[0068] In summary, with the help of the above-mentioned technical scheme of the present invention, the present invention integrates and processes the data through the knowledge graph technology, which can realize comprehensive monitoring and in-depth understanding of the ship status, not only can enhance the perception ability of complex navigation environment and ship status, but also can improve the accuracy of security decision-making. The integrated perception data is parsed through the syntax tree, and the digital twin model of the ship is constructed according to the parsed data, which can accurately reflect the real-time status of the ship and provide a basis for real-time monitoring and early warning. The digital twin model of the ship is used to detect the security status of the ship in real time. When the security risk is detected, an early warning is issued in time, which can greatly improve the response speed to the security risk and reduce possible losses. By simulating different security decision results and selecting the best security decision plan, it is helpful to improve the quality of decision-making and avoid possible misjudgment; the present invention collects geographic information data and ship structure information data of the ship security area and performs preprocessing, which can provide accurate basic information for subsequent sensor deployment, improve the accuracy and reliability of security, and select monitoring sensors suitable for ship navigation based on the security needs of the ship to ensure safety. The security management can meet various security needs in a targeted manner, improve flexibility and adaptability, and deploy the installation position of the monitoring sensor through the sensor position deployment algorithm, which can ensure the maximization of the monitoring coverage and the connectivity of the monitoring sensor, improve the monitoring efficiency and accuracy of the security management, avoid possible monitoring blind spots and omissions, and have important practical value for ship security management; the present invention uses a syntax parsing tool to perform lexical analysis and syntax analysis on the integrated perception data to generate an abstract syntax tree, which can effectively analyze and process complex perception data, improve the efficiency and accuracy of data parsing, and construct a control flow graph based on the abstract syntax tree and perform risk source analysis. It can identify the possible risk sources of the ship during the route process, and can provide support for risk management and decision-making. Through risk source analysis, the ship's security risk events can be determined, and risk assessment can be performed. The priority of ship security is determined according to the assessment results, and the control flow graph is marked with risk sources, which is conducive to scientific risk management and decision-making. The digital twin model can provide real-time status feedback of the ship in actual operation, providing strong support for the operation management of the ship.

[0069] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, optical storage, etc.) containing computer-usable program code.

[0070] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A ship security intelligent management method based on multi-source perception, characterized in that: The ship security intelligent management method based on multi-source perception includes the following steps: S1. Collect the ship's perception data during navigation through monitoring sensors pre-deployed on the ship; S2, perform data integration processing on the collected perception data based on knowledge graph technology; S3, parsing the integrated perception data through the syntax tree, and building a digital twin model of the ship based on the parsed data; S4. Use the digital twin model of the ship to detect the ship's status and formulate the best security decision-making plan after detecting risks; S5. Develop and implement specific security response measures based on the best security decision-making plan.

2. According to claim 1, a method for intelligent management of ship security based on multi-source perception is characterized in that: The method of collecting the sensor data of the ship during navigation by using the monitoring sensors pre-deployed in the ship includes the following steps: S11, collecting geographic information data of the ship security area and ship structure information data and performing preprocessing, wherein the geographic information data includes terrain data and security area boundaries, and the preprocessing includes data cleaning and format conversion; S12. Based on the security requirements of the ship, select monitoring sensors suitable for ship navigation, and divide the monitoring sensors into active and passive types; S13, deploying the installation positions of the monitoring sensors through a sensor position deployment algorithm; S14. Perform coverage and connectivity analysis on the deployment results of the monitoring sensors. If the preset requirements are not met, adjust the location of the monitoring sensors until the preset requirements are met.

3. A ship security intelligent management method based on multi-source perception according to claim 2, characterized in that: The method of selecting monitoring sensors suitable for ship navigation based on the ship's security requirements and classifying the monitoring sensors into active and passive types includes the following steps: S121. Analyze the security requirements of the ship during navigation and determine the ship security parameters, wherein the security requirements include anti-theft, fire warning, water level monitoring and weather monitoring, and the ship security parameters include monitoring range, response speed and monitoring accuracy; S122, determining the navigation environment of the ship, and selecting monitoring sensors suitable for the navigation of the ship according to the analyzed security requirements; S123. Divide sensor resources into active sensors and passive sensors according to the working principle and function of the monitoring sensor; S124. Determine the number of clusters to be divided according to the number and type of monitoring sensors, and allocate the monitoring sensors to each cluster.

4. The method for intelligent ship security management based on multi-source perception according to claim 2 is characterized in that: The step of deploying the installation positions of the monitoring sensors by using the sensor position deployment algorithm comprises the following steps: S131, dividing the monitoring area of ​​the ship into several sub-areas, and establishing a monitoring sensor deployment model; S132, maximizing the coverage of all sub-areas as the optimization goal, and taking the connectivity of monitoring sensors between clusters as a constraint condition; S133, initializing a particle swarm of monitoring sensor positions, and iteratively optimizing the monitoring sensor positions through a sensor position deployment algorithm until a monitoring sensor deployment scheme that satisfies the constraint conditions and maximizes coverage is found; S134. Deploy sensors on the ship based on the monitoring sensor deployment plan and perform security monitoring on the ship.

5. The method for intelligent ship security management based on multi-source perception according to claim 1 is characterized in that: The data integration processing of the collected perception data based on the knowledge graph technology includes the following steps: S21, performing abnormal data processing on the collected perception data; S22. Use knowledge graph technology to combine the processed perception data with pre-defined ontologies and rules, annotate and classify the perception data, and establish the correlation between data; S23. Integrate the perception data of different monitoring sensors through data fusion technology, and store the integrated data in a structured knowledge base.

6. The method for intelligent ship security management based on multi-source perception according to claim 1 is characterized in that: The data parsing of the integrated perception data through the syntax tree and constructing the digital twin model of the ship according to the parsed data includes the following steps: S31, performing syntax analysis and lexical analysis on the integrated perception data through a syntax parsing tool to generate an abstract syntax tree; S32. Based on the generated abstract syntax tree, a control flow graph is constructed and risk source analysis is performed; S33. Build a digital twin model of the ship based on the abstract syntax tree, control flow graph and risk source analysis results.

7. A ship security intelligent management method based on multi-source perception according to claim 6, characterized in that: The process of performing grammatical analysis and lexical analysis on the integrated perception data by using a grammar parsing tool to generate an abstract syntax tree comprises the following steps: S311, performing lexical analysis on the integrated perception data and dividing it into a number of word units; S312, performing grammatical analysis on the divided word units according to preset grammatical rules, and combining them into a grammatical structure that satisfies the preset grammatical rules; S313. Use the combined grammatical structure as a node and construct an abstract syntax tree with the nodes.

8. The method for intelligent ship security management based on multi-source perception according to claim 6 is characterized in that: The construction of a control flow graph and risk source analysis based on the generated abstract syntax tree includes the following steps: S321, traversing the nodes in the abstract syntax tree by a recursive method, and constructing a control flow graph according to the node type; S322. Analyze the risk sources of the ship during the route based on the constructed control flow graph, where the risk sources include at least one of mechanical failure, human behavior or environmental factors; S323. Determine the security risk events of the ship according to the analyzed risk sources, and conduct risk assessment based on the security risk events; S324. Determine the priority of ship security based on the assessment results, and mark the risk sources on the control flow diagram based on the priority.

9. The method for intelligent ship security management based on multi-source perception according to claim 1 is characterized in that: The method of using the digital twin model of the ship to detect the ship status and formulating the best security decision plan after detecting the risk includes the following steps: S41. Monitor the perception data of the ship in real time through the deployed monitoring sensors and input it into the digital twin model of the ship; S42. Use real-time monitoring perception data in the digital twin model of the ship to simulate the current security status and behavior of the ship; S43. Perform risk detection on the current security status and behavior of the ship through the early warning mechanism preset in the digital twin model of the ship; S44. Based on the risk detection results, different security decisions are simulated through the digital twin model of the ship, and the prediction results of different decisions are compared to select the best security decision plan.

10. A ship security intelligent management system based on multi-source perception, used to implement the ship security intelligent management method based on multi-source perception according to any one of claims 1 to 9, characterized in that: The ship security intelligent management system based on multi-source perception includes: a perception data collection module, a perception data integration module, a perception data analysis module, a decision scheme simulation module and a decision scheme execution module, and the perception data collection module, the perception data integration module, the perception data analysis module, the decision scheme simulation module and the decision scheme execution module are connected in sequence; The sensing data collection module is used to collect the sensing data of the ship during navigation through monitoring sensors pre-deployed in the ship; The perception data integration module is used to perform data integration processing on the collected perception data based on the knowledge graph technology; The perception data parsing module is used to parse the integrated perception data through a syntax tree and build a digital twin model of the ship based on the parsed data; The decision-making scheme simulation module is used to detect the ship status using the digital twin model of the ship and formulate the best security decision-making scheme after detecting the risk; The decision-making scheme execution module is used to formulate and execute specific security response measures based on the best security decision-making scheme.

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