A method for assessing ice navigation risk and resilience of a ship

Abstract

The present application relates to a kind of ship ice area navigation risk and resilience evaluation method, method includes the following steps: S1, obtain current sea ice environment analysis result;S2, based on historical accident data, identify arctic ice area ship navigation accident scene and risk factor;S3, based on the sea ice environment analysis result and identified risk factor, the probability index and consequence index of each accident scene that ship ice area navigation occurs are calculated, risk index is obtained;S4, obtain the current state data of ship;S5, based on current state data and risk index, risk control scheme is obtained, and risk control scheme is carried out resilience evaluation and sorting, and resilience evaluation result is obtained.Compared with prior art, the present application has the advantages of improving the comprehensiveness and accuracy of ship ice area navigation risk management.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of ship ice navigation, and in particular to a ship ice navigation risk and resilience assessment method. BACKGROUND

[0002] With the global warming and the development of international trade, the commercial value of the Arctic route is increasingly prominent. However, the special and complex natural environment of the Arctic region brings new challenges to ship ice navigation. The extreme weather conditions, complex sea ice environment, low reliability of navigation facilities and limited rescue infrastructure greatly increase the risk of maritime accidents in the Arctic waters. The risk management system targeted by the Arctic water navigation ship development is necessary and urgent.

[0003] In the field of risk management, the concept of resilience is increasingly valued. Although resilience has no uniform definition, it can be regarded as a basic property of infrastructure systems. From the perspective of risk, 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, the performance of a system can be considered to be resilient if it can adjust its functions before, during or after an event, thereby maintaining the required operation under expected and unexpected conditions. Based on this point of view, four capabilities of assessing resilient systems are further proposed:

[0004] (1) Learning: the ability to learn from historical events in polar ice navigation;

[0005] (2) Prediction: the ability to predict long-term risks existing in the system of ship ice navigation in the Arctic route;

[0006] (3) Monitoring: the ability to monitor the ship itself and the environment during the process of ship ice navigation in the Arctic route and to identify dangers;

[0007] (4) Response: the ability to respond to emergencies during the process of ship ice navigation in the Arctic route.

[0008] The dependency relationship between the four capabilities and the environment is shown in Figure 1 .

[0009] There are four main learning methods for resilient systems.

[0010] (1) Learning→Prediction→Monitoring→Response;

[0011] (2) Learning→Prediction→Response;

[0012] (3) Learning→Monitoring→Response;

[0013] (4) Learning→Response.

[0014] From the internal perspective of the system, the resilience system construction generally starts from the learning system, passes through the intermediate links such as the prediction system and the monitoring system, and ends with the coping system, and circulates repeatedly, and the specific operation steps are as shown in (1). But also according to the actual situation of the system, the intermediate links can be omitted as shown in (2) (3) (4).

[0015] The existing research and system still have the following shortcomings:

[0016] (1) Lack of systematicness: Most researches focus on specific types of accidents or risk factors, lacking comprehensive and systematic analysis of ship ice area navigation risk.

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

[0018] (3) Low integration: The existing risk management methods are often independent, which is difficult to effectively integrate with other systems of the ship.

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

[0020] The purpose of the present application is to provide a ship ice area navigation risk and resilience assessment method to improve the comprehensiveness and accuracy of ship ice area navigation risk management.

[0021] The purpose of the present application can be achieved by the following technical solutions:

[0022] A ship ice area navigation risk and resilience assessment method, the method comprising the following steps:

[0023] S1, acquiring satellite remote sensing data and field observation data of the sea ice area, analyzing the sea ice environment characteristics of the current channel ice area, and classifying and grading the results to obtain the current sea ice environment analysis result;

[0024] S2, based on historical accident data, identifying the Arctic ice area ship navigation accident scene and risk factors;

[0025] S3, based on the sea ice environment analysis result and the identified risk factors, calculating the probability index and consequence index of each accident scenario of ship ice area navigation, and obtaining the risk index;

[0026] S4, acquiring the current state data of the ship;

[0027] S5, based on the current state data and the risk index, obtaining the risk control scheme, and performing resilience assessment and sorting on the risk control scheme to obtain the resilience assessment result.

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

[0029] The historical accident data corresponding to the sea ice environment analysis result and the risk factor are selected, the probability index of each accident scenario in the data is calculated based on the selected historical accident data, the consequence index is calculated based on the severity of the consequence of each accident scenario, and the risk index is obtained by adding the probability index and the consequence index.

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

[0031] Let the logarithmic risk index of the emergency A in the accident scenario be RIA A , the logarithmic risk index of the jth sub-scenario under the condition of the occurrence of the emergency A be RIA j , the logarithmic probability index of the emergency A be PIA A , the logarithmic consequence index of the emergency A be SIA A , the logarithmic probability index of the jth sub-scenario be PIA j , and the logarithmic consequence index of the jth sub-scenario be SIA j . j j j .

[0032] Further, the logarithmic probability index of the emergency A PIA A is:

[0033] PIA A = 6 + log 10 p A , PIA A ∈ [0, 7]

[0034] where p A is the probability of the occurrence of the emergency A.

[0035] Further, the logarithmic risk index of the jth sub-scenario PIA j is:

[0036]

[0037] where, is the probability of the jth sub-event under the condition of the occurrence of the emergency e A .

[0038] Further, the risk control scheme based on the current state data and the risk index is specifically:

[0039] ​​According to the current state data and the risk index, a key risk factor and a potential accident type in a current navigation state are identified, and a corresponding risk control scheme is generated according to the key risk factor and the potential accident type.

[0040] Further, the risk control scheme is evaluated and sorted for resilience, and the specific steps of the resilience evaluation result are as follows:

[0041] The risk absorption capacity, recovery capacity and adaptability of the risk control scheme are calculated, the resilience evaluation result of the risk control scheme is obtained based on the risk absorption capacity, the recovery capacity and the adaptability, and the resilience evaluation result is sorted according to the resilience evaluation result, and the resilience evaluation result is the sum of the risk absorption capacity, the recovery capacity and the adaptability.

[0042] Further, the risk absorption capacity is:

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

[0044] Wherein, F A (t0), F j (t d ) are the safety levels of the emergency A and the jth sub-scenario at time t0, t d , respectively, t e , t d represent the time before the emergency occurs and the time when the safety level is stable after the emergency occurs, respectively.

[0045] Further, the recovery capacity is:

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

[0047] Wherein, F j (t r ), F j (t d ) represent the safety levels of the jth sub-scenario at time t r and t d , respectively, t s and t r represent the time before the short-term emergency control measures are taken and the time when the safety level is stable after the short-term emergency control measures are taken.

[0048] Further, the adaptability is:

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

[0050] Wherein, F j (t f ), F j (t r ) respectively represent the safety level of the system at time t f And t r , t α And t f Respectively represent the time when the safety level is stable before and after taking long-term prevention measures.

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

[0052] The present application proposes a method for comprehensively evaluating the overall resilience level of the system by calculating the absorption capacity, recovery capacity and adaptability, and by analyzing the risk index of the scene under the emergency, the resilience evaluation result including the absorption risk capacity, recovery capacity and adaptability is obtained, which not only improves the comprehensiveness and accuracy of risk management, but also significantly enhances the safety of Arctic shipping. BRIEF DESCRIPTION OF DRAWINGS

[0053] Figure 1 The dependence relationship between the resilience system capacity;

[0054] Figure 2 The state transition diagram of the safety level of the ice area ship navigation system;

[0055] Figure 3 The flow chart of the present application;

[0056] Figure 4 The overall architecture diagram of the system of the present application;

[0057] Figure 5 The process of identifying the ice area ship navigation accident scene and risk influencing factors by text mining method;

[0058] Figure 6 The partial knowledge base diagram of the risk factors of the Arctic ice area ship navigation accident;

[0059] Figure 7 The Bayesian network topology diagram of the ice area ship navigation risk of the Arctic shipping route;

[0060] Figure 8 to marginalize the Bayesian network model of RCM;

[0061] Figure 9 to run a flowchart of the system described in the invention. DETAILED DESCRIPTION

[0062] The invention will be described in detail below with reference to the accompanying drawings and specific embodiments. The embodiments are implemented on the premise of the technical solutions of the invention, and detailed implementation modes and specific operation processes are given, but the protection scope of the invention is not limited to the following embodiments.

[0063] The invention proposes a ship ice area navigation risk and resilience assessment method, which comprises the following steps:

[0064] S1, satellite remote sensing data and field observation data of the sea ice area are obtained, the sea ice environment characteristics of the current channel ice area are analyzed, and the results are classified and rated to obtain the current sea ice environment analysis results;

[0065] S2, based on historical accident data, the ship navigation accident scene and risk factors in the Arctic ice area are identified;

[0066] S3, based on the sea ice environment analysis results and the identified risk factors, the probability index and the consequence index of each accident scene of the ship ice area navigation are calculated to obtain the risk index;

[0067] S4, the current state data of the ship is obtained;

[0068] S5, based on the current state data and the risk index, a risk control scheme is obtained, and the risk control scheme is subjected to resilience evaluation and sorting to obtain the resilience evaluation results.

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

[0070] a) sea ice environment analysis module, used for analyzing the Arctic sea ice environment characteristics; b) accident learning module, used for learning the marine accident scene and related risk influencing factors; c) risk prediction module, used for predicting the navigation risk; d) state monitoring module, used for real-time monitoring of the ship navigation state; e) risk response module, used for responding to risk prevention and control measures; wherein the accident learning module, the risk prediction module, the state monitoring module and the risk response module correspond to the learning, prediction, monitoring and response of the four abilities of resilience engineering respectively.

[0071] The sea ice environment analysis module a) includes analyzing the sea ice environment characteristics of the current Arctic channel ice area navigation environment, including sea ice density, sea ice thickness, sea ice type, etc. And according to the relevant guidelines and specifications, it is classified.

[0072] Sea ice environment analysis module: used to analyze the characteristics of the Arctic sea ice environment. This module mainly has 3 kinds of sea ice environment characteristics, which are sea ice density, sea ice thickness and sea ice scale.

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

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

[0075] Accident scenario learning module: corresponding to the "learning" ability in resilience engineering, used to learn historical ship ice area navigation experience and accident data, identify common marine accident scenarios and related risk influencing factors. This module considers 6 Arctic route ship ice area navigation accident scenarios: collision, grounding, mechanical failure, fire / explosion, ship-ice collision and ship ice. At the same time, 6 risk influencing factors are identified and classified: adverse weather, poor sea conditions, mechanical equipment failure, navigation aid failure, human factors and organizational factors. This module can use machine learning algorithms (such as decision tree or random forest, etc.) or text mining technology to extract key risk factors from historical ship ice area navigation data.

[0076] c) The risk prediction module uses Bayesian network risk assessment technology to calculate the risk of navigation accidents. Its risk calculation formula is: P(Risk|Evidence)=P(Evidence|Risk)×P(Risk) / P(Evidence) Where P(Risk|Evidence) is the risk probability given the 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: embodies the "prediction" capability in the resilience engineering, which is used for quantitative risk calculation of the Arctic ice area ship navigation system. The module can be realized by the following technical solutions, including the following steps:

[0078] (1) Probability analysis: the probability of each accident scenario of the Arctic channel ship ice area navigation is calculated by using historical data. When the historical data cannot be obtained or is not sufficient, the probability is calculated by using fault tree, event tree or Bayesian network technology.

[0079] (2) Consequence analysis: the severity of the consequences is analyzed from the severity, timeliness and chain reaction of the accident.

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

[0081] Risk index (RI) = probability index (PI) + consequence index (SI) (1) Wherein: probability index (PI) is defined as shown in Table 1; consequence index (SI) is defined as shown in Table 2.

[0082] Table 1 Definition of probability index (PI)

[0083]

[0084]

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

[0086]

[0087] The probability index and consequence index of the Arctic ice area ship navigation accident scenario are combined to obtain the risk index matrix of the Arctic ice area ship navigation, as shown in Table 3. The risk index matrix can be divided into three regions: high risk region, low risk region and critical region between the two.

[0088] Table 3 Definition of risk index (RI)

[0089]

[0090]

[0091] When ships navigate in the Arctic ice zone, various risk factors caused by extreme environmental conditions intertwine and overlap, making the evolution of accidents in the Arctic ice zone more complex and difficult to control. Based on the probability index in Table 1, the sudden event A(e) of the risk scenario for ship navigation in the Arctic ice zone is defined. A The logarithmic probability exponent PI A for:

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

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

[0094]

[0095] Where, p A It represents the probability of event A occurring, ps j |e A It is in e A Given that the j-th sub-event occurs, the probability of the j-th sub-event is given. Based on the scale of the probability exponent described in Table 1, PI is defined. A and PI j The threshold (maximum logarithmic probability exponent is 7). According to the risk criteria described in FSA (MSC, 2013), if the probability p occurs... A / p j Below 10 -6 Then define PI A / PI j =0, this risk scenario (p A / p j <10 -6 (This can be ignored.)

[0096] According to Table 2, the logarithmic severity index (SI) of the j-th sub-event of emergency A. A / SI j The threshold for SI is A / SIj∈[0,4]. SI A / SI j =0 indicates that 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 It can be defined as:

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

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

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

[0101] where the upper limit 11 of RI A / RI j corresponds to the sum of the upper limit of PI A / PI j and SI A / SI j . The lower the value of RI A / RI j , the higher the safety level. Specifically, RIA A / RI j = 0 means that the safety level of the Arctic ice region ship navigation system can be ignored, and no risk prevention and control measures are needed.

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

[0103] State monitoring module: corresponding to the "monitoring" capability in the resilience engineering, used for real-time monitoring of the navigation state of the ice region navigation ship in the sea ice environment. The objects monitored by this module include weather conditions, channel conditions, ship structure, navigation attitude, power system and ship stability related data.

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

[0105] Risk response module: embodies the "response" capability in resilience engineering, which provides risk control options for specific navigation risks. This module includes 6 long-term prevention measures: ship collision prevention, ship grounding prevention, ship ice entrapment prevention, ship-ice collision prevention, mechanical failure prevention, and fire / explosion prevention; and three short-term emergency control measures: ship damage / oil spill response, ship damage / personnel rescue and escape, and ship control / rescue / recovery. Among them, the long-term prevention measures are proposed according to the real-time changing risk level of the ship after the accident, which is used to improve the overall safety level of the ship system in the Arctic ice area, and its effectiveness is obtained through expert opinions. The effectiveness of each short-term emergency control measure on the accident is realized in the risk prediction module to regulate the numerical value of the accident consequences.

[0106] Finally, the resilience is evaluated, and the resilience level of the ship in the Arctic ice area is evaluated by the absorption capacity, recovery capacity and adaptation capacity as shown in Figure 2 The corresponding value of the system safety level F j (t) is used to describe the system safety level of the jth sub-event. The system safety level of the incident A can be defined as:

[0107]

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

[0109]

[0110] Where M is the number of possible sub-events that may occur from the incident A, RI A / 11 is the normalized value of the log risk index (RI A ) of e A , RI j / 11 is the normalized value of the log risk index (RI j ) of the jth sub-event, F A (t0), F j (t d ) is the safety level of the system at time t0, t d . F j (t dA higher F value indicates a higher level of security. Specifically, F... j (t d ) = 0 indicates the worst-case security level of the system, while F j (t d If ) = 1, it indicates the optimal security level of the system.

[0111] like Figure 2 As shown, the resilience of the Arctic shipping system in ice-covered areas is defined as follows:

[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 The slope of the absorption capacity curve indicates that the smaller the slope, the stronger the system's ability to absorb risks.

[0114] The resilience of the navigation system in the ice zone of the Arctic shipping 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 recovery capability curve indicates that the larger the slope, the stronger the system's ability to recover from risk.

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

[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 this 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 application adopts a modular design, has scalability, and can integrate new risk assessment methods and functional modules. This design allows the system to flexibly add or update modules according to new research results or technological progress, to adapt to the changing Arctic navigation environment and new types of risks. The network interconnection of the present application realizes data interaction with the ship integrated navigation system and the shore-based support system. This interaction capability enables the system to obtain more comprehensive navigation data, while also timely transmitting risk assessment results and warning information to related systems and personnel, improving the overall risk management efficiency. The present application increases the risk analysis and management functions for autonomous navigation ships. Considering the development trend of future autonomous navigation technology, the system specially designs a risk assessment and management module suitable for autonomous ships, which can analyze new risks brought by autonomous navigation and provide corresponding risk prevention and control strategies.

[0122] The present application first systematically applies the core capabilities of resilience engineering (learning, prediction, monitoring, and response) to Arctic shipping ice area navigation risk management. A complete process including sea ice environment analysis, navigation accident learning, accident risk prediction, navigation state monitoring, and risk prevention and control decision is proposed, and a method and formula for comprehensively evaluating the overall resilience level of the system by calculating the absorption capacity, recovery capacity, and adaptability are also proposed. This method not only improves the comprehensiveness and accuracy of risk management, but also significantly enhances the safety of Arctic shipping.

[0123] In actual evaluation, referring to the flowchart of Figure 3 , the steps of the present application can include:

[0124] Step 1: Arctic sea ice environment data analysis. Collect and process satellite remote sensing data and field observation data through the sea ice environment analysis module, analyze the sea ice environment characteristics of the current Arctic shipping ice area, and classify and rate the results.

[0125] Step 2: Identification of Arctic ice area navigation accident scenarios and risk factors. Use the accident learning module to extract historical accident data and expert knowledge from the knowledge base, identify Arctic ice area ship navigation accident scenarios and risk factors, and update the risk identification capability of the system.

[0126] Step 3: Arctic ice area navigation risk prediction. Substitute the sea ice environment analysis results and the output of the accident learning module into the risk prediction module, and use multiple risk assessment techniques to quantitatively calculate the probability of occurrence of each accident scenario and the consequences of accidents in the Arctic shipping ice area.

[0127] Step 4.1: Arctic ice area navigation state monitoring. Continuously receive real-time data from shipboard sensors and external data sources through the state monitoring module, monitor the ship's navigation state, weather conditions, sea ice conditions, and other key parameters.

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

[0129] Step 5: Risk prevention and control measures are proposed. According to the ship navigation accident scene and risk factors in the Arctic ice area, corresponding risk prevention and control measures are proposed, and a risk prevention and control scheme composed of risk prevention and control measures is developed. The effectiveness of the risk prevention and control measures is evaluated according to historical data and expert opinions.

[0130] Step 6.1: The resilience assessment module is used to comprehensively evaluate the resilience level of ships sailing in the Arctic shipping route ice area from the three dimensions of absorption capacity, recovery capacity and adaptation capacity, and calculate the resilience index of the system. And it is used to calculate the resilience level of ships sailing in the Arctic shipping route ice area.

[0131] Step 6.2: The central processing unit sorts the resilience level after selecting each risk prevention and control scheme and presents it on the user interface, providing decision support for operators.

[0132] Step 7: The central processing unit visual interface displays risk assessment results, real-time monitoring data, risk prevention and control suggestions and system resilience level, and records user feedback for subsequent improvement.

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

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

[0135] The overall system structure of the present application is shown in Figure 4 .

[0136] In the overall system of the present application, the actual working process of each module is as follows:

[0137] In the sea ice environment analysis module of the present application, the sea ice environment of the Arctic shipping route ice area is analyzed mainly 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: Geometric correction, radiation correction and other preprocessing are performed 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 of the Arctic Channel ice area based on satellite remote sensing data and field observation data. The module adopts 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: Geometric correction, radiation correction and other preprocessing are performed 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 mainly based on historical accident data and expert knowledge, through the text mining method to identify the Arctic ice area ship navigation accident scene and risk influence factors such as Figure 5 as shown. 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 the polar ice area;

[0149] 2. Data preprocessing: Text preprocessing of the original data includes data cleaning, spelling checking, stem extraction, word form restoration and setting stop words;

[0150] 3. Accident scene identification: Use text mining technology and use the confidence analysis software Nvivo to identify the Arctic ice area ship navigation accident scene and risk factors from historical data.

[0151] 4. Knowledge base construction: The identified accident scene and risk factors are integrated into the knowledge base as shown in Figure 6 , where the first behavior accident investigation report number, the dark grid represents the risk factors involved in the accident investigation report.

[0152] : Risk prediction module. In this embodiment, the risk prediction module adopts the Bayesian network risk assessment technology to comprehensively analyze the ship-ice collision and ship ice-entrapment accident scenes in the Arctic navigation risk. The implementation steps are as follows:

[0153] 1. Network structure learning: Based on historical data and expert knowledge, the Bayesian network structure is constructed as shown in Figure 7The left color part represents six risk factors: weather, sea conditions, sea ice conditions, mechanical equipment failure, navigation aid 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 occurs.

[0154] 2. Parameter learning and data integration: Use the Expectation-Maximization (EM) algorithm to learn the conditional probability table (CPT) of each node in the network based on historical data, and integrate real-time monitoring data of sea ice environment analysis into the Bayesian network as observation evidence;

[0155] 3. Probability inference and risk calculation: Based on the constructed Bayesian network model, calculate the posterior probability of the target event (such as a specific type of accident) under the given observation evidence;

[0156] 4. Sensitivity analysis: By changing the probability distribution of input variables, analyze the influence degree of different factors on risk probability;

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

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

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

[0160] 2. Data preprocessing: Filter, denoise, and other processing of raw data;

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

[0162] 4. Abnormality detection: Use statistical methods or machine learning algorithms to detect abnormalities in navigation state or environmental conditions;

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

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

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

[0166] 2. Risk prevention measures generation: Based on the key risk factors and potential accident types, corresponding risk prevention measures are generated, such as Figure 8 As shown, the current risk prevention measures are only shown for the risk factors before the accident occurs. Through risk prevention measures, the probability of each risk factor occurring can be reduced, thereby reducing the probability of an accident occurring. Emergency control measures can reduce the probability of a serious accident occurring after an accident;

[0167] 3. Risk prevention measures effectiveness calculation: Considering the current navigation conditions, available resources, and operational limitations of the ship, the effectiveness of each risk prevention measure is determined by expert opinions, taking into account key risk factors and potential accident types.

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

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

[0170] 1. Resilience assessment: The resilience of the ship navigating in the Arctic ice area is assessed according to the obtained resilience calculation formula.

[0171] 2. Risk prevention plan screening: The risk prevention plan with better effect on increasing the resilience of the ship navigation system is screened out from the risk prevention plan sorted by the resilience assessment results;

[0172] 3. Resilience assessment report generation: The assessment report containing the risk prevention plan and the scores of each capability is generated and returned to the decision 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: According to the system running state and user demand, the running of each functional module is scheduled;

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

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

[0177] 4. System configuration: Allow users to configure system parameters, such as risk threshold, warning level, etc.

[0178] 5. Logging: Records system operation logs for subsequent analysis and improvement.

[0179] System operation flow. Figure 9 The overall operation flow of the system is shown.

[0180] As Figure 9 shown, the system operation flow mainly includes the following steps:

[0181] 1. System initialization: Load 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 state monitoring: Monitor the ship's navigation state and navigation environment.

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

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

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

[0188] 8. Results display: Show the user the risk assessment results, control suggestions and resilience assessment report.

[0189] 9. Cycle execution: The system continues to run, and the evaluation results are updated regularly.

[0190] The above describes the preferred embodiments of the present application in detail. It should be understood that those skilled in the art can make many modifications and changes without creative labor according to the concept of the present application. Therefore, any technical solution obtained by logical analysis, reasoning or limited experiment based on the existing technology within the scope of the present application should be within the protection scope determined by the claims.

Claims

1. A method for assessing ice navigation risk and resilience of a vessel, characterized in that, The method comprises the following steps: S1, acquiring satellite remote sensing data and field observation data of the sea ice area, analyzing the sea ice environment characteristics of the current channel ice area, and classifying and grading the results to obtain a current sea ice environment analysis result; S2, identifying ship navigation accident scenarios and risk factors in the Arctic ice area based on historical accident data; S3, calculating the probability index and consequence index of each accident scenario of ship ice area navigation based on the sea ice environment analysis result and the identified risk factors to obtain a risk index; S4, acquiring current state data of the ship; S5, obtaining a risk control scheme based on the current state data and the risk index, and performing a resilience assessment and sorting on the risk control scheme to obtain a resilience assessment result; the resilience assessment result is the sum of the risk absorption capacity, the recovery capacity and the adaptation capacity; The specific steps of S3 are: Selecting historical accident data corresponding to the sea ice environment analysis result and the risk factors, calculating the probability index of each accident scenario in the selected historical accident data, and calculating the consequence index based on the severity of the consequences of each accident scenario, and adding the probability index and the consequence index to obtain the risk index; The specific steps of adding the probability index and the consequence index to obtain the risk index are: Let the logarithmic risk index of the sudden event A in the accident scenario be... The first condition based on the occurrence of emergency A j The logarithmic risk index for each sub-scenario is Calculate the logarithmic probability exponent of sudden event A. And logarithmic consequences index and the j Log probability exponent of each sub-scenario And logarithmic consequences index The logarithmic probability exponent And logarithmic consequences index The sum of these gives the logarithmic risk index of event A. , will the j Log probability exponent of each sub-scenario And logarithmic consequences index Adding them together gives the first j The logarithmic risk index for each sub-scenario is .

2. A method of assessing ice class ship navigation risk and resilience according to claim 1, characterized in that, The logarithmic probability index of the event A is: wherein is the probability of the occurrence of the incident A.

3. A method of assessing ice navigation risk and resilience for a ship according to claim 2, characterized in that, The log risk index of the first j sub-scenario is: wherein, is the probability of the first sub-event given that the emergency occurred. The probability of the second sub-event given that the first sub-event j occurred is 4. A method of assessing ice navigation risk and resilience of a ship according to claim 1, characterized in that, The specific steps of obtaining the risk control scheme based on the current state data and the risk index are: According to the current state data and the risk index, identifying the key risk factors and potential accident types in the current navigation state, and generating the corresponding risk control scheme according to the key risk factors and the potential accident types.

5. A method of assessing ice navigation risk and resilience of a ship in ice-covered waters according to claim 1, characterized in that, The specific steps of performing a resilience assessment and sorting on the risk control scheme to obtain a resilience assessment result are: Calculating the risk absorption capacity, the recovery capacity and the adaptation capacity of the risk control scheme, obtaining the resilience assessment result of the risk control scheme based on the risk absorption capacity, the recovery capacity and the adaptation capacity, and sorting according to the resilience assessment result.

6. A method of assessing ice class ship navigation risk and resilience according to claim 5, characterised in that, The risk absorption capacity is: wherein, , are the safety levels of the system at the time of the emergency A and the first sub-scenario, respectively, j , , denote the time before the emergency and the time when the safety level is stable after the emergency, respectively.​​ 7. A method of assessing ice navigation risk and resilience of a vessel according to claim 6, characterized in that, The recovery capacity is: wherein, , respectively represent the safety level of the system at time j , t r and t d , and respectively represent the time instants at which the safety level is stable before and after the short-term emergency control measures are taken.

8. A method of assessing ice navigation risk and resilience of a vessel according to claim 7, characterized in that, The adaptation capacity is: wherein, , respectively represent the safety level of the system at time j , t f and t r , and respectively represent the time instants at which the safety level is stable before and after the effect of the long-term preventive measure has been taken, respectively.

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