Method for evaluating navigation risk and toughness of ship in ice area

Abstract

The invention relates to a ship ice region navigation risk and toughness evaluation method. The method comprises the following steps: S1, obtaining a current sea ice environment analysis result; s2, based on historical accident data, identifying ship navigation accident scenes and risk factors in the Arctic ice region; s3, based on the sea ice environment analysis result and the identified risk factors, calculating a probability index and a consequence index of each accident scene occurring in ship navigation in the ice region to obtain a risk index; s4, acquiring current state data of the ship; and S5, obtaining a risk control scheme based on the current state data and the risk index, and carrying out toughness evaluation and sorting on the risk control scheme to obtain a toughness evaluation result. Compared with the prior art, the method has the advantages of improving the comprehensiveness and accuracy of ship ice region navigation risk management and the like.

Description

Technical Field

[0001] The present invention relates to the field of ship navigation in ice areas, and in particular to a method for assessing the risk and resilience of ship navigation in ice areas. Background Art

[0002] With global warming and the development of international trade, the commercial value of Arctic shipping routes has become increasingly prominent. However, the special and complex natural environment of the Arctic region has brought new challenges to ships sailing in ice areas. Factors such as extreme weather conditions, complex sea ice environment, low-reliability navigation facilities and limited rescue infrastructure have greatly increased the risk of maritime accidents in Arctic waters. It is necessary and urgent to develop a risk management system for ships sailing in Arctic waters.

[0003] In the field of risk management, the concept of resilience is gaining increasing attention. Although there is no unified definition of resilience, it can be considered a basic attribute of infrastructure systems. From a risk perspective, resilience is defined as the ability of a system to withstand disruptive events and recover within an acceptable time, cost, and risk range. From the perspective of resilience engineering, if a system can adjust its functions before, during, or after an event to maintain the required operation under expected and unexpected conditions, then the performance of the system can be considered resilient. Based on this view, four capabilities for evaluating resilient systems are further proposed:

[0004] (1) Learning: The ability to draw lessons from historical events in polar ice navigation;

[0005] (2) Forecasting: The ability to predict the long-term risks to ships’ ice navigation systems in Arctic shipping routes;

[0006] (3) Monitoring: The ability of ships to monitor themselves and their environment and identify hazards while sailing in ice areas on Arctic routes;

[0007] (4) Response: The ability to respond to emergencies while ships are sailing in ice areas on the Arctic route.

[0008] The dependencies between these four capabilities and the environment are as follows: Figure 1 shown.

[0009] There are four main ways in which resilient systems learn.

[0010] (1) Learn → predict → monitor → respond;

[0011] (2) Learn → Predict → Respond;

[0012] (3) Learn → Monitor → Respond;

[0013] (4) Learn → Cope.

[0014] From the perspective of the system, the construction of a resilient system generally starts with the learning system, goes through intermediate links such as the prediction system and the monitoring system, and ends with the response system, repeating the cycle. The specific operation steps are shown in (1). However, the intermediate links can also be omitted according to the actual situation of the system, as shown in (2)(3)(4).

[0015] The existing research and systems still have the following deficiencies:

[0016] (1) Lack of systematicity: Most studies focus on specific types of accidents or risk factors, lacking a comprehensive and systematic analysis of the risks of ships sailing in ice areas.

[0017] (2) Poor real-time performance: Existing risk assessment methods are mostly static analysis, which is difficult to adapt to the rapidly changing ship ice environment.

[0018] (3) Low integration: Existing risk management methods are often independent and difficult to be effectively integrated with other ship systems.

[0019] (4) Poor scalability: As Arctic navigation technology and environment continue to change, the existing system is difficult to flexibly adapt to new risks and challenges. Summary of the invention

[0020] The purpose of the present invention is to provide a method for assessing the risk and resilience of ship navigation in ice areas in order to improve the comprehensiveness and accuracy of risk management of ship navigation in ice areas.

[0021] The purpose of the present invention can be achieved through the following technical solutions:

[0022] A method for assessing the risk and resilience of ship navigation in ice areas, the method comprising the following steps:

[0023] S1. Obtain satellite remote sensing data and field observation data of the sea ice area, analyze the sea ice environment characteristics of the current channel ice area, and classify and rate the results to obtain the current sea ice environment analysis results;

[0024] S2. Identify accident scenarios and risk factors for ships sailing in the Arctic ice zone based on historical accident data;

[0025] S3. Calculate the probability index and consequence index of each accident scenario that may occur when the ship sails in ice areas based on the sea ice environment analysis results and the identified risk factors to obtain a risk index;

[0026] S4, obtaining the current status data of the ship;

[0027] S5. Obtain risk control plans based on current status data and risk index, and conduct resilience assessment and ranking of risk control plans to obtain resilience assessment results.

[0028] Furthermore, the specific steps of S3 are:

[0029] Select historical accident data corresponding to the sea ice environment analysis results and risk factors, calculate the probability index of each accident scenario in the data based on the selected historical accident data, and calculate the consequence index based on the severity of the consequences of each accident scenario, and add the probability index and the consequence index to obtain the risk index.

[0030] Furthermore, the risk index obtained by adding the probability index and the consequence index is specifically:

[0031] Assume that the logarithmic risk index of the emergency event A in the accident scenario is RI A The logarithmic risk index of the jth sub-scenario with the occurrence of emergency A as the condition is RI j , calculate the logarithmic probability index PI of emergency event A A and logarithmic consequence index SI A and the logarithmic probability index PI of the jth sub-scenario j and logarithmic consequence index SI j , the logarithmic risk index of the emergency event A is obtained by adding the logarithmic probability index PIAA and the logarithmic consequence index SIA, and the logarithmic probability index PI of the jth sub-scenario is j and logarithmic consequence index SI j The logarithmic risk index of the jth sub-scenario is RI j .

[0032] Furthermore, the logarithmic probability index PI of the emergency event A is A for:

[0033] PI A =6+log 10 p A , P.I. A ∈[0,7]

[0034] where p A is the probability of event A occurring.

[0035] Furthermore, the logarithmic risk index PI of the sub-scenario is j for:

[0036]

[0037] in, In an emergency A The probability of the jth sub-event when occurs.

[0038] Furthermore, based on the current status data and risk index, the risk control plan is as follows:

[0039] Identify key risk factors and potential accident types under the current navigation status based on current status data and risk index, and generate corresponding risk control plans based on key risk factors and potential accident types.

[0040] Furthermore, the resilience assessment and ranking of the risk control solutions are carried out. The specific steps to obtain the resilience assessment results are as follows:

[0041] The risk absorption capacity, recovery capacity and adaptability of the risk control plan are calculated, and the resilience assessment results of the risk control plan are obtained based on the risk absorption capacity, recovery capacity and adaptability, and the plan is ranked according to the resilience assessment results. The resilience assessment results are the sum of the risk absorption capacity, recovery capacity and adaptability.

[0042] Furthermore, the risk absorption capacity is:

[0043] R 吸收能力 =[F A (t0)-F j (t d )] / (t d -t e )

[0044] Among them, F A (t0), F j (t d ) are the emergency event A and the jth sub-scenario at time t0 and t d The safety level of the system, t e ,t d They represent the time before an emergency occurs and the time when the safety level remains stable after an emergency occurs.

[0045] Furthermore, the recovery capability is:

[0046] R 恢复能力 =[F j (t r )-F j (t d )] / (t r -t s )

[0047] Among them, F j (t r ), F j (t d ) represent the jth sub-scenario at time t r and t d , t s and t r The table shows the moments when the safety level remains stable before and after the adoption of short-term emergency control measures.

[0048] Furthermore, the adaptability is:

[0049] R 适应能力 =[F j (t f )-F j (t r )](t f -t α )

[0050] Among them, F j (t f ), F j (t r ) represent the jth sub-scenario at time t f and t r The safety level of the system at time t α and t f They respectively represent the moments when the safety level remains stable before and after long-term preventive measures are taken.

[0051] Compared with the prior art, the present invention has the following beneficial effects:

[0052] The present invention proposes a method for comprehensively evaluating the overall resilience level of a system by calculating the three dimensions of absorption capacity, recovery capacity and adaptability. By analyzing the risk index of scenarios under emergencies, resilience assessment results including risk absorption capacity, recovery capacity and adaptability are obtained. This not only improves the comprehensiveness and accuracy of risk management, but also significantly enhances the safety of Arctic shipping. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 The dependencies between the capabilities of the resilience system;

[0054] Figure 2 It is the state transition diagram of the safety level of the navigation system of ships in ice areas;

[0055] Figure 3 is a flow chart of the present invention;

[0056] Figure 4 It is the overall system architecture diagram of the present invention;

[0057] Figure 5 To identify the accident scenarios and risk influencing factors of ships sailing in the Arctic ice zone through text mining methods;

[0058] Figure 6 This is a schematic diagram of part of the knowledge base of risk factors for ship navigation accidents in Arctic ice areas;

[0059] Figure 7 A Bayesian network topology diagram for the risk of ice navigation for ships in the Arctic route;

[0060] Figure 8 The marginal distribution of the Bayesian network model combined with RCM;

[0061] Fig. 9 The present invention is a flowchart of the system operation. DETAILED DESCRIPTION

[0062] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0063] The present invention proposes a method for assessing the risk and resilience of ship navigation in ice areas, the method comprising the following steps:

[0064] S1. Obtain satellite remote sensing data and field observation data of the sea ice area, analyze the sea ice environment characteristics of the current channel ice area, and classify and rate the results to obtain the current sea ice environment analysis results;

[0065] S2. Identify accident scenarios and risk factors for ships sailing in the Arctic ice zone based on historical accident data;

[0066] S3. Calculate the probability index and consequence index of each accident scenario that may occur when the ship sails in ice areas based on the sea ice environment analysis results and the identified risk factors to obtain a risk index;

[0067] S4, obtaining the current status data of the ship;

[0068] S5. Obtain risk control plans based on current status data and risk index, and conduct resilience assessment and ranking of risk control plans to obtain resilience assessment results.

[0069] The above steps can correspond to the following modules:

[0070] a) Sea ice environment analysis module, used to analyze the characteristics of the Arctic sea ice environment; b) Accident learning module, used to learn maritime accident scenarios and related risk influencing factors; c) Risk prediction module, used to predict navigation risks; d) Status monitoring module, used to monitor the navigation status of ships in real time; e) Risk response module, used to respond to risk prevention and control measures; Among them, the accident learning module, risk prediction module, status monitoring module and risk response module correspond to the four capabilities of learning, prediction, monitoring and response of resilience engineering respectively.

[0071] Sea ice environment analysis module a) includes analyzing the sea ice environment characteristics of the current Arctic ice navigation environment, including sea ice density, sea ice thickness, sea ice type, etc. and classifying them according to relevant guidelines and specifications.

[0072] Sea ice environment analysis module: used to analyze the characteristics of the Arctic sea ice environment. This module mainly has three types of sea ice environment characteristics, namely sea ice density, sea ice thickness and sea ice scale.

[0073] The sea ice density is divided into 6 categories: open waters (sea ice density <1 / 10), waters with little drift ice (1 / 10-3 / 10), waters with drift ice (4 / 10-6 / 10), dense ice (7 / 10-8 / 10), tight ice (9 / 10) and solid ice (10 / 10). The sea ice thickness is divided into 6 categories: new ice (sea ice thickness ≤10cm), initial ice (11-30cm), thin first-year ice (31-70cm), medium first-year ice (71-120cm), thick first-year ice (121-250cm) and multi-year ice (>250cm). The sea ice scale is divided into 7 categories: small floating ice (span <100m), medium floating ice (100-500m), large floating ice (500-2000m), super large floating ice (2000-10000m) and giant floating ice (>10000m).

[0074] b) The accident learning module includes learning 6 types of Arctic ice zone ship navigation accident scenarios and 6 types of risk influencing factors, specifically including: Arctic ice zone ship navigation accident scenarios: collision, grounding, mechanical failure, fire / explosion, ship-ice collision and ship ice entrapment; risk influencing factors: weather, sea conditions, sea ice conditions, mechanical equipment failure, navigation equipment failure, human factors and organizational factors.

[0075] Accident scenario learning module: corresponds to the "learning" ability in resilience engineering, used to learn historical ship ice navigation experience and accident data, identify common maritime accident scenarios and related risk influencing factors. This module considers six accident scenarios for ship ice navigation in Arctic waterways: collision, grounding, mechanical failure, fire / explosion, ship-ice collision and ship ice entrapment. At the same time, six risk influencing factors are identified and classified: bad weather, adverse sea conditions, mechanical equipment failure, navigation equipment failure, human factors and organizational factors. This module can use machine learning algorithms (such as decision trees or random forests) or text mining and other technologies to extract key risk factors from historical ship ice navigation data.

[0076] c) The risk prediction module uses Bayesian network risk assessment technology to calculate the risk of navigation accidents. The risk calculation formula is: P(Risk|Evidence) = P(Evidence|Risk) × P(Risk) / P(Evidence) where P(Risk|Evidence) is the probability of risk given evidence, P(Evidence|Risk) is the probability of observing evidence when risk exists, P(Risk) is the prior probability of risk, and P(Evidence) is the probability of observing evidence.

[0077] Risk prediction module: reflects the "prediction" capability in resilience engineering and is used to perform quantitative risk calculations on the navigation system of ships in the Arctic ice zone. This module can be implemented through the following technical solutions, including the following steps:

[0078] (1) Probability analysis: Use historical data to calculate the probability of various accident scenarios that may occur when ships sail through ice areas on the Arctic route. When historical data is not available or insufficient, use techniques such as fault trees, event trees, or Bayesian networks to calculate the probability.

[0079] (2) Consequence analysis: Analyze the severity of the consequences based on the severity, timeliness, and chain reaction of the accident.

[0080] (3) Quantitative risk calculation: Risk estimation takes into account both probability and consequence, and can be represented by a risk matrix. By establishing a mapping relationship between the probability and consequence assessment results and the standardized probability index and consequence index in the risk matrix, the risk calculation results are expressed using the risk matrix. If special circumstances such as environmental damage and passenger ship accidents are considered, the definition of the table can be appropriately adjusted. The risk index (RI) is calculated according to formula (1):

[0081] Risk Index (RI) = Probability Index (PI) + Consequence Index (SI) (1) Where: Probability Index (PI) - Definition as shown in Table 1; Consequence Index (SI) - Definition as shown in Table 2.

[0082] Table 1 Probability Index (PI) Definition

[0083]

[0084]

[0085] Table 2 Definition of consequence index (SI)

[0086]

[0087] Combining the probability index and consequence index of the accident scenarios of ship navigation in the Arctic ice area, the risk index matrix of ship navigation in the Arctic ice area is obtained, as shown in Table 3. The risk index matrix can be divided into three areas: high-risk area, low-risk area and the critical area between the two.

[0088] Table 3 Risk Index (RI) Definition

[0089]

[0090]

[0091] When ships are sailing in the Arctic ice zone, various risk factors caused by extreme environmental conditions are intertwined and superimposed, making the evolution of accidents in the Arctic ice zone more complicated and difficult to control. According to the probability index in Table 1, the emergency event A(e) of the risk scenario of ships sailing in the Arctic ice zone is defined as follows: A )'s logarithmic probability index PI A for:

[0092] PI A =6+log 10 p A , P.I. A ∈[0,7] (2)

[0093] The logarithmic probability index PI of the sub-events that evolve after the occurrence of emergency event A j (j=1,2,...,M) can be defined as:

[0094]

[0095] Among them, p A is the probability of the sudden event A occurring, ps j |e A is in e A The probability of the jth sub-event when . According to the scale of probability index described in Table 1, PI is defined A and PI j According to the risk criteria described in the FSA (MSC, 2013), if the probability of occurrence p A / p j Less than 10 -6 , then define PI A / PI j =0, this risk scenario (p A / p j <10 -6 ) can be ignored.

[0096] According to Table 2, the logarithmic severity index (SI) of the jth sub-event of emergency event A is A / SI j ) is the threshold value of SI A / SIj∈[0,4]. A / SI j =0 means the outcome of the event is safe.

[0097] Combining Table 3, formula (2) and formula (3), the logarithmic risk index (RI) of emergency event A is A ) can be defined as:

[0098] RI A =PI A +SIA , R.I. A ∈[0,11] (4)

[0099] The logarithmic risk index (RI) of the jth sub-scenario conditioned on the occurrence of emergency event A j ) can be calculated as follows:

[0100] RI j =PI j +SI j , R.I. j ∈[0,11], j=1,2,…,M (5)

[0101] Among them, RI A / RI j The upper limit of 11 is equivalent to PI A / PI j and SI A / SI j The sum of the upper limits of the critical values. A / RI j The lower the value, the higher the security level. A / RI j =0 means that the safety level of the ship navigation system in the Arctic ice area can be ignored and no risk prevention and control measures are required.

[0102] d) The state monitoring module includes monitoring of 6 aspects of navigation status, including: weather conditions, channel conditions, hull structure, navigation attitude, power system and ship stability. Specifically including: weather conditions: monitoring wind speed, wind direction, visibility, temperature, etc.; channel conditions: monitoring channel depth, channel width, flow velocity and flow direction, etc.; hull structure: monitoring hull stress, hull deformation, ice pressure distribution and integrity of key structural parts, etc.; navigation attitude: monitoring the position, heading, speed, roll, pitch and bow of the ship, etc.; power system: monitoring main engine power, fuel consumption, thruster load, ice resistance and ice making system operation status, etc.; ship stability: monitoring center of gravity position, heel angle, pitch angle and liquid tank level, etc.

[0103] Condition monitoring module: corresponds to the "monitoring" capability in resilience engineering, and is used to monitor the navigation status of ships sailing in ice areas in real time. The module monitors data related to weather conditions, channel conditions, hull structure, navigation attitude, power system and ship stability.

[0104] e) The risk response module includes 6 long-term preventive response measures and 3 short-term emergency control measures, including preventive measures: ship collision prevention measures, ship grounding prevention measures, ship ice entrapment prevention measures, ship-ice collision prevention measures, ship machinery failure prevention measures and ship fire / explosion prevention measures; consequence control measures: hull damage / oil spill response measures, hull damage / personnel rescue and evacuation measures, ship control / rescue / salvage measures.

[0105] Risk response module: reflects the "response" capability in resilience engineering, and is used to propose risk control options for specific navigation risks. This module includes six long-term preventive measures: ship collision prevention, ship grounding prevention, ship ice entrapment prevention, ship-ice collision prevention, mechanical failure prevention, and fire / explosion prevention; as well as three short-term emergency control measures: hull damage / oil spill response, hull damage / personnel rescue and escape, and ship control / rescue / salvage. Among them, long-term preventive measures are proposed based on the real-time changing risk level of the ship after the accident, and are used to improve the overall safety level of the ship system sailing in the Arctic ice zone. Its effectiveness is obtained through expert opinions. The effectiveness of each short-term emergency control measure on the accident is achieved by adjusting the numerical value of the accident consequences in the risk prediction module.

[0106] Finally, a resilience assessment is conducted. The resilience level of ships sailing in the ice zone of the Arctic route is comprehensively evaluated based on the three dimensions of absorption capacity, recovery capacity and adaptability. Figure 2 As shown. Using the safety level F of the ship navigation system in the Arctic ice area j The corresponding value of (t) is used to describe the system safety level of the th sub-event. The system safety level of the emergency event A can be defined as:

[0107]

[0108] The system safety level of the ,th sub-event can be defined as:

[0109]

[0110] Where M is the number of possible sub-events that may occur from the sudden event A, RI A / 11 is e A The logarithmic risk index (RI A ), RI j / 11 is the logarithmic risk index (RI) of the jth sub-event j ), F A (t0), F j (t d ) is the time t0, t d The security level of the system. j (t d) value, the higher the security level. j (t d )=0 indicates the worst security level of the system, and F j (t d )=1 indicates the best security level of the system.

[0111] like Figure 2 As shown in the figure, the resilience capacity of the Arctic ice-area ship navigation system to absorb risks is defined as:

[0112] R 吸收能力 =[F A (t0)-F j (t d )] / (t d -t e ) (8)

[0113] Where R 吸收能力 is the slope of the absorption capacity curve. The smaller it is, the stronger the system's ability to absorb risks is.

[0114] The recovery capacity of the ship navigation system in the ice zone of the Arctic route is defined as:

[0115] R 恢复能力 =[F j (t r )-F j (t d )](t r -t s ) (9)

[0116] Where R 恢复能力 The slope of the resilience curve is larger, indicating that the system has a stronger ability to recover from risks.

[0117] The adaptability of the ship navigation system in the ice zone of the Arctic route is defined as:

[0118] R 适应能力 =[F j (t f )-F j (t r )](t f -t α ) (10)

[0119] Where R 适应能力 The slope of the adaptability curve is larger, indicating that the system has a stronger ability to adapt after experiencing risks.

[0120] The system corresponding to the present invention also includes a central processing unit: coordinating the operation of the above modules, performing data integration and analysis, and displaying risk assessment results, real-time monitoring data and system resilience level through a visual interface.

[0121] The present invention adopts a modular design, is scalable, and can integrate new risk assessment methods and functional modules. This design allows the system to flexibly add or update modules based on new research results or technological advances to adapt to the ever-changing Arctic navigation environment and emerging risk types. The network interconnection of the present invention realizes data interaction with the ship's integrated navigation system and shore-based support system. This interactive capability allows the system to obtain more comprehensive navigation data, while also being able to promptly transmit risk assessment results and warning information to relevant systems and personnel, thereby improving overall risk management efficiency. The present invention adds risk analysis and management functions for autonomous ships. Taking into account the future development trend of autonomous navigation technology, this system is specially designed with risk assessment and management modules suitable for autonomous ships, which can analyze new risks brought about by autonomous navigation and provide corresponding risk prevention and control strategies.

[0122] This invention systematically applies the core capabilities of resilience engineering (learning, prediction, monitoring and response) to the risk management of ships sailing in ice areas on Arctic routes for the first time. It proposes a complete process including sea ice environment analysis, navigation accident learning, accident risk prediction, navigation status monitoring and risk prevention and control decision-making. At the same time, the invention also proposes a method and formula for comprehensively evaluating the overall resilience level of the system by calculating the three dimensions of absorption capacity, recovery capacity and adaptability. This method not only improves the comprehensiveness and accuracy of risk management, but also significantly enhances the safety of Arctic shipping.

[0123] When conducting the actual evaluation, refer to Figure 3 The steps of the present invention may include:

[0124] Step 1: Arctic sea ice environment data analysis: The sea ice environment analysis module collects and processes satellite remote sensing data and field observation data, analyzes the current sea ice environment characteristics of the Arctic shipping route ice zone, and classifies and rates the results.

[0125] Step 2: Identification of accident scenarios and risk factors for navigation in the Arctic ice zone. The accident learning module is used to extract historical accident data and expert knowledge from the knowledge base to identify accident scenarios and risk factors for navigation in the Arctic ice zone and update the system’s risk identification capabilities.

[0126] Step 3: Risk prediction for navigation in the Arctic ice zone. Substitute the sea ice environment analysis results and the output of the accident learning module into the risk prediction module, and use a variety of risk assessment techniques to quantify the probability of occurrence of various accident scenarios and accident consequences for navigation in the Arctic ice zone.

[0127] Step 4.1: Arctic ice navigation status monitoring: The status monitoring module continuously receives real-time data from onboard sensors and external data sources to monitor key parameters such as the ship's navigation status, weather conditions, and sea ice conditions.

[0128] Step 4.2: Based on real-time monitoring data, use the risk prediction module to dynamically update the risk assessment results to ensure the timeliness and accuracy of the risk assessment.

[0129] Step 5: Propose risk prevention and control measures. According to the accident scenarios and risk factors of ships sailing in the Arctic ice zone, propose corresponding risk prevention and control measures, and formulate a risk prevention and control plan composed of risk prevention and control measures. Evaluate the effectiveness of risk prevention and control measures based on historical data and expert opinions.

[0130] Step 6.1: Use the resilience assessment module to comprehensively assess the resilience level of ships sailing in the ice zone of the Arctic route from the three dimensions of absorption capacity, recovery capacity and adaptability, and calculate the resilience index of the system. This is used to calculate the resilience level of ships sailing in the ice zone of the Arctic route.

[0131] Step 6.2: The resilience level ranking after selecting each risk prevention and control plan is presented on the user interface through the central processing unit to provide decision support for operators.

[0132] Step 7: Display risk assessment results, real-time monitoring data, risk prevention and control recommendations, and system resilience levels through the central processing unit’s visual interface, and record user feedback for subsequent improvements.

[0133] Step 8: Store new data, analysis results, and user feedback into the knowledge base for future learning and analysis.

[0134] Step 9: Continue to perform steps 1 to 5 to maintain the timeliness and effectiveness of risk assessment and management, and adjust the frequency of the cycle as needed.

[0135] The overall system structure diagram of the present invention is as follows: Figure 4 shown.

[0136] In the overall system of the present invention, the actual workflow of each module is as follows:

[0137] The sea ice environment analysis module of the present invention mainly analyzes the sea ice environment of the Arctic waterway ice zone based on satellite remote sensing data and field observation data. The module adopts the following steps:

[0138] 1. Data collection: Obtain sea ice information from multi-source satellite remote sensing data (such as MODIS, SAR, etc.) and field observation stations;

[0139] 2. Data preprocessing: perform geometric correction, radiation correction and other preprocessing on the original data;

[0140] 3. Sea ice parameter extraction: Extract sea ice density, sea ice thickness, sea ice scale and other parameters from the collected data;

[0141] 4. Sea ice environment classification: The collected parameters are classified according to the established rules and input into the system.

[0142] Sea ice environment analysis module. The sea ice environment analysis module mainly analyzes the sea ice environment in the Arctic shipping lanes based on satellite remote sensing data and field observation data. This module uses the following steps:

[0143] 1. Data collection: Obtain sea ice information from multi-source satellite remote sensing data (such as MODIS, SAR, etc.) and field observation stations;

[0144] 2. Data preprocessing: perform geometric correction, radiation correction and other preprocessing on the original data;

[0145] 3. Sea ice parameter extraction: Extract sea ice density, sea ice thickness, sea ice scale and other parameters from the collected data;

[0146] 4. Sea ice environment classification: The collected parameters are classified according to the established rules and input into the system.

[0147] The accident learning module is mainly based on historical accident data and expert knowledge, and uses text mining methods to identify accident scenarios and risk influencing factors of ship navigation in the Arctic ice area, such as Figure 5 The specific implementation steps of this module are as follows:

[0148] 1. Data collection: Collect historical accident reports, navigation logs and other data of ships sailing in polar ice areas;

[0149] 2. Data preprocessing: Text preprocessing of raw data includes data cleaning, spell checking, stemming, word form restoration, and setting stop words;

[0150] 3. Accident scenario identification: Use text mining technology and the confidence analysis software Nvivo to identify accident scenarios and risk factors for ship navigation in the Arctic ice zone from historical data.

[0151] 4. Knowledge base construction: Integrate the identified accident scenarios and risk factors into the knowledge base such as Figure 6 As shown, the first line is the accident investigation report number, and the dark grid represents the risk factors involved in the accident investigation report.

[0152] : Risk prediction module. In this embodiment, the risk prediction module uses Bayesian network risk assessment technology to conduct a comprehensive analysis of the ship-ice collision and ship ice entrapment accident scenarios in Arctic navigation risks. The implementation steps are as follows:

[0153] 1. Network structure learning: Based on historical data and expert knowledge, construct a Bayesian network structure such as Figure 7The colored part on the left represents the six risk factors: weather, sea conditions, sea ice conditions, mechanical equipment failure, navigation equipment failure, human factors and organizational factors. The red part is the accident scene node, and the white part is the consequence evolution node after the accident.

[0154] 2. Parameter learning and data integration: Using the expectation maximization (EM) algorithm, the conditional probability table (CPT) of each node in the network is learned based on historical data, and the real-time monitoring data of the sea ice environment analysis results are integrated into the Bayesian network as observational evidence;

[0155] 3. Probabilistic reasoning and risk calculation: Calculate the posterior probability of a target event (such as a specific type of accident) given observational evidence based on the constructed Bayesian network model;

[0156] 4. Sensitivity analysis: Analyze the impact of different factors on risk probability by changing the probability distribution of input variables;

[0157] 5. Extraction of key risk influencing factors: Based on the results of sensitivity analysis, identify the key risk factors that have the most significant impact on the risk assessment results.

[0158] Condition monitoring module. The condition monitoring module monitors the ship's navigation status and environmental conditions in real time through onboard sensors and external data sources. The specific implementation steps are as follows:

[0159] 1. Data collection: Obtain real-time data from onboard sensors (such as GPS, gyroscope, accelerometer, etc.) and external data sources (such as weather stations, sea ice monitoring stations, etc.);

[0160] 2. Data preprocessing: filtering and denoising the raw data;

[0161] 3. State estimation: Use algorithms such as Kalman filtering to estimate the position, attitude, speed and other states of the ship;

[0162] 4. Anomaly detection: Use statistical methods or machine learning algorithms to detect anomalies in navigation status or environmental conditions;

[0163] 5. Data visualization: Display monitoring results in the form of charts, dashboards, etc.

[0164] Risk response module. The risk response module proposes corresponding risk prevention and control options based on the risk assessment results of the risk prediction module and the real-time data of the status monitoring module. The specific implementation steps are as follows:

[0165] 1. Risk status assessment: Receive risk assessment result data from the risk prediction module and status monitoring module to identify key risk factors and potential accident types under the current navigation status;

[0166] 2. Risk prevention and control measures generation: Generate corresponding risk prevention and control measures based on key risk factors and potential accident types, such as Figure 8 As shown, currently only the schematic diagram of each risk prevention measure for the risk factors before the accident occurs is shown. Risk prevention measures can reduce the probability of each risk factor, thereby reducing the probability of an accident. Emergency control measures can reduce the probability of a serious accident after the accident occurs.

[0167] 3. Calculation of effectiveness of risk prevention and control measures: Taking into account the current navigation conditions, available resources and operational constraints of the ship, combined with key risk factors and potential accident types, the effectiveness of each risk prevention and control measure is determined through expert opinions.

[0168] 4. Decision execution and feedback: The adopted risk prevention and control measures are combined into a risk prevention and control plan and transmitted to relevant personnel or automatic control systems through the ship's communication system to monitor the implementation of the measures, and then make real-time adjustments and feedback based on the resilience assessment results.

[0169] Resilience assessment module. The resilience assessment module comprehensively evaluates the overall resilience level of the system from three dimensions: absorption capacity, recovery capacity, and adaptability. The specific implementation steps are as follows:

[0170] 1. Resilience assessment: The resilience of ships sailing in the ice zone of the Arctic route is assessed based on the obtained resilience calculation formula.

[0171] 2. Screening of risk prevention and control plans: Screen out the risk prevention and control plans with better effects on increasing the resilience of the ship navigation system from the risk prevention and control plans ranked after the resilience assessment results;

[0172] 3. Resilience assessment report generation: Generate an assessment report that includes risk prevention and control plans and scores of various capabilities and return it to the decision-making execution function of the risk response module.

[0173] Central processing unit. The central processing unit is responsible for coordinating the work of each module and providing a user interface. Its main functions include:

[0174] 1. Module scheduling: Schedule the operation of each functional module according to the system operation status and user needs;

[0175] 2. Data management: manage all types of data generated by the system, including storage, retrieval and backup;

[0176] 3. User interface: Provides an intuitive graphical user interface to display risk assessment results, real-time monitoring data and resilience assessment reports;

[0177] 4. System configuration: allows users to configure system parameters, such as risk thresholds, warning levels, etc.;

[0178] 5. Log recording: record system operation logs for subsequent analysis and improvement.

[0179] System operation process. Fig. 9 The overall operation process of this system is demonstrated.

[0180] like Fig. 9 As shown in the figure, the system operation process mainly includes the following steps:

[0181] 1. System initialization: loading historical data, knowledge base and system configuration.

[0182] 2. Sea ice environment analysis: Analyze the current and predicted sea ice environment.

[0183] 3. Risk prediction: predict potential risks based on historical data and current environment.

[0184] 4. Real-time status monitoring: monitor the ship's navigation status and navigation environment.

[0185] 5. Risk assessment: Combine the forecast results and real-time data to evaluate the current risk index according to the formula.

[0186] 6. Propose risk control measures: Propose corresponding risk prevention and control measures based on key risk factors.

[0187] 7. Resilience assessment: Comprehensively evaluate the overall resilience level of the system from three dimensions: absorption capacity, recovery capacity and adaptability.

[0188] 8. Result presentation: Present risk assessment results, control recommendations and resilience assessment reports to users.

[0189] 9. Loop execution: The system runs continuously and updates the evaluation results regularly.

[0190] The preferred specific embodiments of the present invention are described in detail above. It should be understood that a person skilled in the art can make many modifications and changes based on the concept of the present invention without creative work. Therefore, any technical solution that can be obtained by a person skilled in the art through logical analysis, reasoning or limited experiments based on the concept of the present invention on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. A method for assessing the risk and resilience of ship navigation in ice areas, characterized in that: The method comprises the following steps: S1. Obtain satellite remote sensing data and field observation data of the sea ice area, analyze the sea ice environment characteristics of the current channel ice area, and classify and rate the results to obtain the current sea ice environment analysis results; S2. Identify accident scenarios and risk factors for ships sailing in the Arctic ice zone based on historical accident data; S3. Calculate the probability index and consequence index of each accident scenario that may occur when the ship sails in ice areas based on the sea ice environment analysis results and the identified risk factors to obtain a risk index; S4, obtaining the current status data of the ship; S5. Obtain risk control plans based on current status data and risk index, and conduct resilience assessment and ranking of risk control plans to obtain resilience assessment results.

2. A method for assessing the risk and resilience of ship navigation in ice areas according to claim 1, characterized in that: The specific steps of S3 are: Select historical accident data corresponding to the sea ice environment analysis results and risk factors, calculate the probability index of each accident scenario in the data based on the selected historical accident data, and calculate the consequence index based on the severity of the consequences of each accident scenario, and add the probability index and the consequence index to obtain the risk index.

3. A method for assessing the risk and resilience of ship navigation in ice areas according to claim 2, characterized in that: The risk index obtained by adding the probability index and the consequence index is specifically: Assume that the logarithmic risk index of the emergency event A in the accident scenario is RI A The logarithmic risk index of the jth sub-scenario with the occurrence of emergency A as the condition is RI j , calculate the logarithmic probability index PI of emergency event A A and logarithmic consequence index SI A and the logarithmic probability index PI of the jth sub-scenario j and logarithmic consequence index SI j , the logarithmic probability index PI A and logarithmic consequence index SI A Add together to get the logarithmic risk index of emergency event A, which is RI A , the logarithmic probability index PI of the jth sub-scenario j and logarithmic consequence index SI j The logarithmic risk index of the jth sub-scenario is RI j .

4. A method for assessing the risk and resilience of ship navigation in ice areas according to claim 3, characterized in that: The logarithmic probability index PI of the emergency event A A for: PI A =6+log 10 p A ,PI A ∈[0,7] where p A is the probability of event A occurring.

5. A method for assessing the risk and resilience of ship navigation in ice areas according to claim 4, characterized in that: The logarithmic risk index PI of the jth sub-scenario j for: in, In an emergency A The probability of the jth sub-event when occurs.

6. A method for assessing the risk and resilience of ship navigation in ice areas according to claim 1, characterized in that: The specific risk control plan based on the current status data and risk index is as follows: Identify key risk factors and potential accident types under the current navigation status based on current status data and risk index, and generate corresponding risk control plans based on key risk factors and potential accident types.

7. A method for assessing the risk and resilience of ship navigation in ice areas according to claim 1, characterized in that: The specific steps to evaluate and rank the risk control solutions and obtain the resilience evaluation results are as follows: The risk absorption capacity, recovery capacity and adaptability of the risk control plan are calculated, and the resilience assessment results of the risk control plan are obtained based on the risk absorption capacity, recovery capacity and adaptability, and the plan is ranked according to the resilience assessment results. The resilience assessment result is the sum of the risk absorption capacity, recovery capacity and adaptability.

8. A method for assessing the risk and resilience of ship navigation in ice areas according to claim 7, characterized in that: The risk absorption capacity is: R 吸收能力 =[F A (t0)-F j (t d )] / (t d -t e ) Among them, F A (t0), F j (t d ) are the emergency event A and the jth sub-scenario at time t0 and t d The safety level of the system, t e ,t d They represent the time before an emergency occurs and the time when the safety level remains stable after an emergency occurs.

9. A method for assessing the risk and resilience of ship navigation in ice areas according to claim 8, characterized in that: The recovery capability is: R 恢复能力 =[F j (t r )-F j (t d )] / (t r -t s ) Among them, F j (t r ), F j (t d ) represent the jth sub-scenario at time t r and t d , t s and t r The table shows the moments when the safety level remains stable before and after the short-term emergency control measures are adopted.

10. A method for assessing the risk and resilience of ship navigation in ice areas according to claim 9, characterized in that: The adaptability is: R 适应能力 =[F j (t f )-F j (t r )] / (t f -tα) Among them, F j (t f ), F j (t r ) represent the jth sub-scenario at time t f and t r The safety level of the system at time t α and t f They respectively represent the moments when the safety level remains stable before and after long-term preventive measures are taken.

Images