A polar route navigability evaluation method based on strong synergistic effect of multi-source data

Abstract

The application belongs to the field of new generation information technology, and particularly relates to a polar navigation channel navigability evaluation method based on strong synergistic effect of multi-source data, which comprises the following steps: data preprocessing and grid division; comprehensive evaluation index system construction: an index system covering five dimensions of sea ice, meteorology, ocean, channel and policy management is constructed, specifically including 17 indexes of sea ice density, sea ice thickness, air temperature, wind speed, low cloud amount, precipitation, snowfall, liquid water amount, sea current flow rate, sea surface temperature, sea wave height, sea surface pressure, channel depth, channel width, channel intersection point, port facility and national territorial sea, and dimensionless treatment is performed on each index; weight calculation and optimization algorithm; navigability evaluation system and navigability index construction. The application overcomes the narrowness of the traditional technology evaluation dimension, realizes comprehensive characterization of the multi-factor coupling effect of the polar navigation environment, and effectively improves the comprehensiveness and objectivity of the evaluation result.

Description

Technical Field

[0001] This invention belongs to the field of next-generation information technology, specifically relating to a method for evaluating the navigability of polar waterways based on the strong synergistic effect of multi-source data. Background Technology

[0002] Against the backdrop of global warming, Arctic sea ice continues to melt, and the northwestern section of the polar shipping route, as the potential shortest route connecting the Atlantic and Pacific Oceans, is increasingly demonstrating its navigation potential and strategic value. Remote sensing observations show that the distribution of sea ice in this region exhibits significant spatiotemporal heterogeneity and interannual fluctuations, constituting the main natural constraint affecting navigation capabilities.

[0003] In research on ship navigation safety, polar channel risk assessment is a key indicator. Currently, the mainstream technical approach mainly relies on active and passive microwave satellite remote sensing to obtain information such as sea ice concentration, thickness, and type, and combines this with Automatic Identification System (AIS) data to construct a risk assessment model based on the physical properties of sea ice. These methods typically set empirical thresholds based on single factors such as sea ice concentration to classify risk levels, or use sea ice numerical forecasting models to predict future ice conditions to determine navigation feasibility.

[0004] Existing technologies have extracted navigation windows for the northeastern section of polar routes using sea ice concentration data from 2005 to 2014 and analyzed their changing characteristics. They have also explored the navigation situation of the northwestern section of polar routes using sea ice concentration data. Existing technologies have studied the changes in sea ice thickness and concentration in Arctic routes from 1979 to 2020, finding that the navigation capacity of the routes was relatively strong in 2012 and 2020, while the summer navigation suitability of the East Siberian Sea and Laptev Sea varied significantly. They have also analyzed the ice conditions and navigation status of the northeastern section of polar routes in 2014 based on sea ice concentration data. Furthermore, using sea ice concentration and type data, they have studied the changes in the navigation environment of the northeastern section of polar routes from 2005 to 2015. Some studies have further introduced sea ice thickness parameters to attempt to improve the accuracy of assessing the risks of ship icebreaking navigation. Existing technologies have assessed the summer navigability of trans-Arctic routes by integrating sea ice thickness data with Arctic accessibility models. Results show that navigation conditions in polar routes are generally poor, with short navigation windows. Studies have also examined sea ice thickness variations in the northwestern section of polar routes, indicating that current sea ice thickness still poses significant risks to shipping. However, most of these studies evaluate the overall navigability of routes solely from the perspective of sea ice physical properties, neglecting the combined effects of other environmental factors. To address this deficiency, many scholars have begun to combine sea ice factors with other environmental elements, constructing navigation evaluation systems to comprehensively assess the navigability of various Arctic routes. For example, existing technologies have integrated multi-source data such as sea ice and meteorology, and used the fuzzy set objective weighting method to assess the navigability of the northwestern section of the Arctic shipping route in summer from 2002 to 2020. Other studies have used Arctic sea ice concentration observations, combined with data on wind speed, temperature, and water depth, to analyze and determine the navigation risk of the northwestern section of the Arctic shipping route in summer. Additionally, by using system dynamics and monthly average variation data of wind, fog, waves, and sea ice, the navigation risk of the northwestern section of the Arctic shipping route from 2015 to 2021 was assessed. Although existing studies have incorporated natural factors such as climate, visibility, water depth, and wind and waves into the construction of the Arctic shipping route navigation evaluation system, the navigation environment of the Arctic shipping route has complex natural and social attributes, and its feasibility and safety are influenced by multiple factors. Existing methods still have significant limitations, particularly in treating sea ice physical elements as isolated assessment objects and failing to fully consider their interactions within complex coupled systems. They also lack comprehensive integration of environmental factors such as rainfall, snowfall, and air pressure, as well as waterway traffic risks, navigational aids, and national policies. Consequently, they struggle to address the complexity and uncertainty of actual waterway environments and cannot meet the demands for high-precision, high-reliability navigation safety warnings and route planning. Furthermore, traditional methods exhibit a clear imbalance between subjective and objective factors in the weighting of navigational assessment systems: the analytic hierarchy process (AHP), which overly relies on expert experience, is prone to human bias, while standard objective weighting methods fail to adequately consider the conflicts and variability among indicators, resulting in insufficient scientific rigor and stability in weight allocation and weakening the reliability of model evaluation results.Furthermore, existing navigation analysis frameworks generally neglect the nonlinear synergistic effects among multiple environmental factors such as sea ice, meteorology, and oceanography. They typically employ simple data overlay methods, which fail to effectively quantify the interconnected impacts of various influencing factors on navigation conditions. This results in models struggling to capture the dynamic coupling mechanisms of the polar navigation environment and exhibiting insufficient generalization capabilities.

[0005] These shortcomings often lead to a disconnect between existing navigationability analysis results and actual navigation decision-making needs, making it difficult to support the analysis and evaluation of high-standard polar waterway navigationability. Therefore, it is necessary to develop a new method for evaluating polar waterway navigationability to improve the comprehensiveness and accuracy of waterway capacity assessment. Summary of the Invention

[0006] To further systematically assess the navigability of polar routes, the inventors believe that in addition to sea ice conditions, it is necessary to comprehensively consider various factors such as meteorological factors, marine environment, route geographical features, and policy management. Sea ice is a fundamental condition determining the navigation window of polar routes. Sea ice concentration and sea ice thickness are two key indicators: concentration reflects the spatial distribution and aggregation pattern of sea ice, directly affecting the range of navigable areas; sea ice thickness determines the physical difficulty of ship navigation. Generally, sea ice thickness less than 30 cm has little impact on navigation, while when the thickness exceeds 2 meters, navigation significantly decreases, and effective navigation conditions are basically unavailable. Dynamic monitoring of sea ice parameters using remote sensing technology has become an important technical means to assess navigability periods. Meteorological factors further restrict the actual navigation safety and efficiency of the routes. Polar route areas are characterized by low temperatures, high wind speeds, abundant cloud cover, and frequent precipitation (snow), making overall meteorological conditions quite harsh. Low temperatures can easily lead to icing and equipment malfunctions on the hull, while strong winds and low clouds reduce visibility, increasing the difficulty of ship handling and hazard avoidance. Snowfall and rain can affect the reliability of deck operations and navigation equipment, collectively constituting the main meteorological risks to navigation. Marine environmental factors are closely related to meteorological conditions, jointly influencing the external environment for navigation. Ocean current speed directly affects ship speed and track control; sea surface temperature indirectly reflects the formation and dissipation trends of sea ice; wave height directly affects hull stability and structural load; and changes in sea surface air pressure can predict the evolution of weather systems, providing important references for short-term navigation decisions. These factors constitute the marine indicator system for assessing the navigation environment. Channel geography determines the physical basis and route structure of navigation. The depth, width, and intersection layout of channels affect the ship's passage capacity and safety from different dimensions. Insufficient water depth limits ship tonnage, narrow channel width limits the feasibility of meeting and avoidance maneuvers, and complex intersections may increase the probability of navigational conflicts. Therefore, relying on accurate hydrographic mapping and route planning is crucial for improving the reliability of navigation. Policy and management factors, as human-controlled factors, play a decisive role in the actual openness of polar shipping routes. The coverage and service capacity of port facilities directly affect the level of logistical support after navigation; the more complete and densely distributed the facilities, the better the navigation support conditions. Meanwhile, national territorial sea jurisdiction brings legal and administrative constraints.

[0007] In summary, the navigability of polar waterways is not determined by a single factor, but rather by a complex system of five categories of factors: sea ice properties, meteorological conditions, marine environment, waterway geographical features, and policy management. The core of the assessment method proposed in this invention lies in integrating these multi-source parameters to form a multi-level, coupled, and quantifiable navigability index system. This system enables comprehensive judgment and window prediction of waterway navigability status, providing a scientific basis for ship navigation decisions and route optimization.

[0008] In other words, the technical problem that this invention aims to solve is that existing polar navigation assessment methods rely too heavily on a single dimension of sea ice products and subjective interpretation analysis, making it difficult to effectively integrate multi-source environmental factors, resulting in an imbalance in weight allocation and thus forming a technical bottleneck.

[0009] This invention first provides a polar waterway navigationability evaluation method based on the strong synergistic effect of multi-source data, including: Step 1: Data preprocessing and grid division: First, collect multi-source environmental data, including remote sensing images, meteorological reanalysis data, waterway geographic information, and policy data; based on GIS software, divide the waterway into uniform grid units, and extract the various indicator values ​​of each unit to provide a standardized data foundation for subsequent analysis; Step 2: Construction of a comprehensive evaluation index system: Construct an index system covering five dimensions: sea ice, meteorology, oceanography, waterway, and policy management. Specific sea ice factors include sea ice concentration and sea ice thickness; meteorological factors include air temperature, wind speed, low cloud cover, precipitation, snowfall, and liquid water volume; and oceanographic factors include ocean current velocity, sea surface temperature, wave height, and sea surface pressure. The five dimensions include 17 indicators: channel factors (depth, width, intersections) and policy management factors (port facilities, territorial waters); each indicator is dimensionless to standardize the data within the range of 0 to 1, eliminating the influence of dimensions; Step 3: Weight calculation and optimization algorithm: an improved fuzzy hierarchical analysis method is used for subjective weighting, and an improved CRITIC method is used for objective weighting. Finally, a linear weighted combination algorithm is used to optimize the subjective and objective weights; Step 4: Navigability evaluation system and navigation index construction: the navigation index NPNI is calculated by weighted summation; the navigation index is divided into multiple levels using an algorithm; Step 5: the navigation evaluation system and navigation index constructed in Step 4 are used to evaluate the navigationability of polar waterways.

[0010] In one specific implementation, step 1 divides the waterway into uniform 8km×8km grid cells; step 3: weight calculation and optimization algorithm: subjective weighting is performed using an improved fuzzy hierarchical analysis method, a fuzzy complementary judgment matrix is ​​constructed through expert scoring, and a consistency check is performed to ensure the rationality of the weights; objective weighting is performed using an improved CRITIC method, and the coefficient of variation and entropy weight method are introduced to enhance the analysis of index conflict and variability; finally, the subjective and objective weights are optimized through a linear weighted combination algorithm, and the weighting coefficients are determined using a distance function to generate a scientifically balanced comprehensive weight; step 4: construction of navigation evaluation system and navigation index: from the five dimensions mentioned in step 2, 1 Seven indicators were weighted subjectively using the improved fuzzy hierarchical analysis method and the improved CRITIC method objectively using a linear weighting algorithm, as described in step 3, to construct a polar navigationability evaluation system, namely the P-NAS system. Based on the P-NAS system, a navigationability index, namely NPNI, was proposed and calculated. This index was achieved through a weighted summation method, that is, the weight of each indicator was multiplied by its standardized value and then summed. The algorithm was applied to divide the navigationability index into multiple levels. Experiments were conducted using multi-scale assessment. By analyzing interannual and monthly data, a spatiotemporal distribution map of navigationability was generated and verified with actual navigation data, thereby realizing the construction of an effective navigationability evaluation system.

[0011] In one specific implementation, the improved fuzzy hierarchical analysis method subjective weighting in step 3 includes comparing influencing factors at the same level pairwise using a pairwise comparison method and a 0.1~0.9 scaling method, i.e., labeling them as 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, and 0.9, to construct a fuzzy complementary judgment matrix. , where if r ij ∈[0.1,0.5), indicating r j The importance of the factor is greater than r i Factors; if r ij ∈(0.5,0.9], indicating r i The importance of the factor is greater than r j Factors; r ij =0.5 indicates r i Factors and r j These factors are equally important.

[0012] In one specific implementation, step 3 further includes a step of calculating weights, namely, calculating the weights of the fuzzy complementary judgment matrix. Perform row and normalization processing to construct the weight vector of R. The weights W of the fuzzy complementary judgment matrix are solved using formula (1). i ,

[0013] (1).

[0014] In one specific implementation, step 3 further includes a consistency check step, namely, performing a consistency check on the judgment process to verify the weight W. i Whether it is reasonable; calculate the compatibility index I(A,B) and feature matrix W* based on the judgment matrix, as shown in formula (2) and formula (3):

[0015] (2)

[0016] (3)

[0017] Where A and B are both fuzzy complementary judgment matrices. When the compatibility index value I(A,W*) is less than a certain threshold of 0.1, the judgment matrix is ​​considered to satisfy the consistency test, that is, the weight allocation is reasonable.

[0018] In one specific implementation, step 3 further includes a hierarchical ranking calculation step, which calculates the importance of all factors at the lowest level relative to the target level to obtain a weight ranking; A is set as the upper level, B as the lower level, and factor B... i The formula for calculating the total ranking weight of the hierarchy is shown in formula (4):

[0019] (4)

[0020] Where a j For factor A j The total ranking weight of the hierarchy, b ij For factor B i For A j The hierarchical single-order weight.

[0021] In one specific implementation, the improved CRITIC method objective weighting in step 3 includes the following steps:

[0022] 1) Forming the original indicator data matrix:

[0023] Assuming there are n samples to be evaluated and m evaluation indicators, forming the original data matrix shown in formula (5), where x ij This represents the value of the j-th evaluation index for the i-th sample;

[0024] (5)

[0025] 2) Dimensionless data:

[0026] For positive indicators, the corresponding elements after positive transformation are shown in formula (6):

[0027] (6)

[0028] in, This represents the maximum value of the positive index column vector. This represents the minimum value of the positive index column vector; This represents the elements in the positive index column vector; This represents the corresponding element after the positive index has been dimensionless;

[0029] For negative indices, the corresponding elements after negativeing ​​are shown in formula (7):

[0030] (7)

[0031] in, This represents the maximum value of the negative index column vector, and vice versa. This represents the minimum value of the negative index column vector; This represents the elements in the negative index column vector; This represents the corresponding element after the dimensionless transformation of the negative index;

[0032] Air temperature, sea surface temperature, sea surface pressure, channel depth, channel width and port facilities are positive indicators; sea ice concentration, sea ice thickness, wind speed, low cloud cover, precipitation, snowfall, liquid water volume, ocean current velocity, wave height, channel intersection and national territorial waters are negative indicators; the above positive and negative indicators are respectively processed by formula (6) and formula (7) to make the range of indicator values ​​between [0,1];

[0033] 3) Calculate the variability of the indicators:

[0034] The variability of the index is calculated using the coefficient of variation as shown in formula (8);

[0035] (8)

[0036] (9)

[0037] in This represents the dimensionless data. S represents the mean of the column vectors, and in formula (8) S j Cov represents the standard deviation of a column vector. j This represents the coefficient of variation of a column vector.

[0038] In one specific implementation, the improved CRITIC method objective weighting in step 3 further includes the following steps:

[0039] 4) Conflicts in the calculation of indicators:

[0040] Using the correlation coefficient r ijThe correlation between indicators is indicated. If the correlation between two indicators is stronger, then the conflict between the indicator and other indicators is smaller, and the more the evaluation content is repeated, the lower the weight of the indicator should be. The conflict calculation method is as shown in formula (10) to formula (11).

[0041] (10)

[0042] (11)

[0043] In the formula r ij R represents the correlation coefficient of the j-th indicator for the i-th sample. j This indicates the conflict of the j-th indicator;

[0044] 5) Calculating information entropy using the entropy weight method:

[0045] Entropy is a measure of the information content of an indicator. The smaller the information entropy of an indicator, the more information the indicator value provides, and its weight should be greater. The information entropy E is calculated using formulas (12) to (14). j Information utility value D j ;where P ij It is the feature weight of the i-th sample under the j-th indicator, i.e., the contribution, which is an intermediate variable;

[0046] (12)

[0047] (13)

[0048] (14)

[0049] 6) Calculate the objective weight w based on the improved CRITIC method j As shown in formula (15):

[0050] (15).

[0051] In one specific implementation, the linear weighting algorithm in step 3 performs subjective and objective weighting as shown in formula (16):

[0052] w = αw1 + βw2 (16)

[0053] In the formula, α is the weighting coefficient of subjective weight, β is the weighting coefficient of objective weight, and α+β=1, w1 is the subjective weight, and w2 is the objective weight.

[0054] The distance function is used to ensure that the weighting coefficients of the weights conform to the difference span. Let the distance function of w1 and w2 be d(w1,w2). Calculate formula (17)-formula (18) to obtain the weighting coefficients α and β. According to formula (19), the final weight w is obtained.

[0055] (17)

[0056] (18)

[0057] (19).

[0058] In one specific implementation, the airworthiness index construction in step 4 includes the following steps as shown in formula (20):

[0059] (20)

[0060] In the formula, NPNI is the airworthiness index, i represents each influencing indicator, n is the total number of indicators, and C i Let be the parameter of the i-th influencing indicator. Let be the weight of the i-th influencing indicator;

[0061] The K-Means clustering algorithm was used to divide the NPNI value range into five distinct levels from high to low: excellent, good, average, poor, and very poor.

[0062] This invention possesses at least the following advantages: 1. It overcomes the limitations of narrow evaluation dimensions in traditional technologies, constructing a comprehensive evaluation index system covering five dimensions: sea ice, meteorology, oceanography, navigation, and policy. By integrating 17 heterogeneous parameters, it achieves a comprehensive characterization of the multi-factor coupling effect of the polar navigation environment, effectively improving the comprehensiveness and objectivity of the assessment results. 2. Addressing the potential subjective biases and objective limitations of traditional weighting methods, this invention utilizes an improved fuzzy hierarchical analysis method and an improved CRITIC method, employing a linear weighting algorithm for weighting. This method balances expert experience with the inherent laws of data, enhancing the accuracy and robustness of the evaluation system. 3. This invention breaks through the limitations of existing technologies in simply superimposing factors, constructing a P-NAS evaluation system based on a "strong synergistic effect" strategy. This effectively quantifies the nonlinear interactions between sea ice, meteorology, oceanography, and other elements, thereby more accurately capturing the dynamic coupling mechanism of the polar environment and improving the generalization ability of navigation assessment. 4. This invention quantifies the assessment results into five distinct risk levels by proposing the Navigability Index (NPNI), thereby improving the quantitative level of navigation spatial differentiation characteristics and providing an intuitive and quantitative basis for waterway safety management. Attached Figure Description

[0063] Figure 1 This is a flowchart of the airworthiness evaluation technology based on the P-NAS system.

[0064] Figure 2 Figure 1 shows the interannual airworthiness analysis and evaluation charts for September 2015 and September 2016, where Figure 2a is the airworthiness analysis and evaluation chart for September 2015 and Figure 2b is the airworthiness analysis and evaluation chart for September 2016.

[0065] Figure 3 Figure 1 shows the interannual airworthiness analysis and evaluation charts for September 2017 and September 2018, where Figure 2a is the airworthiness analysis and evaluation chart for September 2017 and Figure 2b is the airworthiness analysis and evaluation chart for September 2018.

[0066] Figure 4 Figure 1 shows the interannual airworthiness analysis and evaluation charts for September 2019 and September 2020, where Figure 2a is the airworthiness analysis and evaluation chart for September 2019 and Figure 2b is the airworthiness analysis and evaluation chart for September 2020.

[0067] Figure 5 Figure 1 shows the interannual airworthiness analysis and evaluation charts for September 2021 and September 2022, where Figure 2a is the airworthiness analysis and evaluation chart for September 2021 and Figure 2b is the airworthiness analysis and evaluation chart for September 2022.

[0068] Figure 6 This represents the change in the percentage of polar waterways with varying navigability levels from 2015 to 2022.

[0069] Figure 7 Figure 1 shows the airworthiness analysis and evaluation based on the P-NAS system at the lunar scale from January to February 2022. Figure 2a shows the airworthiness analysis and evaluation map for January 2022, and Figure 2b shows the airworthiness analysis and evaluation map for February 2022.

[0070] Figure 8 Figure 1 shows the airworthiness analysis and evaluation based on the P-NAS system at the lunar scale from March to April 2022. Figure 2a shows the airworthiness analysis and evaluation map for March 2022, and Figure 2b shows the airworthiness analysis and evaluation map for April 2022.

[0071] Figure 9 Figure 1 shows the airworthiness analysis and evaluation at the lunar scale based on the P-NAS system from May to June 2022. Figure 2a shows the airworthiness analysis and evaluation map for May 2022, and Figure 2b shows the airworthiness analysis and evaluation map for June 2022.

[0072] Figure 10Figure 1 shows the airworthiness analysis and evaluation based on the P-NAS system at the lunar scale from July to August 2022. Figure 2a shows the airworthiness analysis and evaluation map for July 2022, and Figure 2b shows the airworthiness analysis and evaluation map for August 2022.

[0073] Figure 11 The data represents the monthly-scale airworthiness analysis and evaluation based on the P-NAS system from September to October 2022. Figure a shows the airworthiness analysis and evaluation map for September 2022, and Figure b shows the airworthiness analysis and evaluation map for October 2022.

[0074] Figure 12 The data represents the monthly-scale airworthiness analysis and evaluation based on the P-NAS system for November-December 2022. Figure a shows the airworthiness analysis and evaluation map for November 2022, and Figure b shows the airworthiness analysis and evaluation map for December 2022.

[0075] Figure 13 This represents the change in the percentage of polar waterways with varying navigability levels from January to December 2022. Detailed Implementation

[0076] Strong synergistic effect: Remote sensing collaborative strategies include the following four types: "data comparison", "multi-scale interpretation", "model calibration" and "data fusion application". Among them, the latter three strategies have a "strong synergistic effect".

[0077] This invention achieves data fusion applications through an improved subjective and objective value assignment method and an optimized weighting algorithm, thereby achieving a strong synergistic effect between "multi-scale interpretation" and "data fusion applications".

[0078] Fuzzy Analytical Hierarchy Process (FAHP): Fuzzy Analytical Hierarchy Process (FAHP) is a multi-criteria decision-making method that uses fuzzy numbers to represent the strength of human cognition of things. It overcomes the shortcomings of Analytical Hierarchy Process (AHP), such as the difficulty in verifying the consistency of the judgment matrix and the lack of scientific basis for the consistency criteria. It also does not require normalization of the eigenvectors.

[0079] CRITIC weighting method: This method is an objective weighting method based on data variability and conflict. Variability is usually measured by standard deviation to measure the dispersion or range of change in data distribution, while conflict is measured by the correlation coefficient of indicators to measure whether there is a high positive correlation between indicators.

[0080] Currently, navigational capability assessment methods for Arctic shipping routes mainly rely on single environmental factors or macro-seasonal analysis, making it difficult to comprehensively reflect the complex and ever-changing navigation environment in the polar regions. Existing technical systems often focus on sea ice concentration or limited meteorological indicators, lacking a systematic integration of multi-dimensional factors, resulting in biased and unreliable assessment results. For example, traditional methods only consider single dimensions such as sea ice changes or wind speed, neglecting the interactive effects of channel conditions, policy management, and ocean dynamics, failing to meet the high requirements for accurate navigational capability prediction in polar shipping safety. In general, existing navigational capability assessment methods have the following technical limitations: First, the assessment dimensions are narrow, and the indicator coverage is incomplete. Most studies only focus on single factors such as sea ice or meteorology, failing to incorporate key elements such as channel depth, width, port facilities, and national territorial waters, thus preventing assessment models from comprehensively reflecting the integrated risks of polar shipping routes. For example, while navigation window analysis based on sea ice concentration can provide seasonal trends, it fails to consider the constraints of channel intersections and policy boundaries on navigation safety, causing the assessment results to be out of sync with actual navigational conditions. Second, the weighting methods are highly subjective and lack sufficient objective data support. Traditional Analytic Hierarchy Process (AHP) relies too heavily on expert experience, making it prone to human bias. Objective weighting methods (such as the standard CRITIC method) fail to adequately consider the conflicts and variability among indicators, leading to uneven weight distribution and affecting the accuracy and stability of the assessment results. This imbalance between subjective and objective factors reduces the reliability of assessment models in responding to dynamic changes in the polar environment. Furthermore, existing methods neglect the synergistic effects among multiple factors, resulting in insufficient generalization ability of the models. Current assessment methods fail to effectively quantify the complex interactions between sea ice, meteorology, and oceanographic factors, typically processing data only through simple superposition, failing to fully capture the nonlinear characteristics of the polar environment. Simultaneously, the lack of standardized risk level classification in the assessment system hinders the provision of clear decision support for waterway management, limiting its effectiveness in practical application.

[0081] To address the aforementioned issues, this invention proposes a polar waterway navigationability evaluation method based on the strong synergistic effect of multi-source data, used for comprehensive analysis of polar navigationability. This method ensures comprehensive coverage of elements by constructing a comprehensive indicator system encompassing 17 indicators across five dimensions: sea ice, meteorology, oceanography, waterways, and policy. Regarding weight determination, it combines subjective weighting using an improved fuzzy hierarchical analysis (FAHP) method with objective weighting using an improved CRITIC method, and optimizes the weight allocation through a linear weighted combination algorithm. This method ensures that the model, while integrating expert experience and data patterns, fully reflects the inherent relationships between various factors. Furthermore, this invention proposes a Navigability Index (NPNI), which uses a K-Means clustering algorithm to classify polar waterway navigationability into five levels: excellent, good, average, poor, and very poor, improving the quantitative level and intuitiveness of the evaluation results. This method effectively overcomes the problems of single-dimensionality and subjective bias in traditional evaluation methods, providing a high-precision and highly reliable navigationability evaluation tool for safe navigation in polar waterways through the synergistic integration of multi-source data and a systematic evaluation system.

[0082] Example 1

[0083] This invention uses polar waterways as an example to implement a technical process for navigation evaluation and analysis based on the P-NAS system, such as... Figure 1 As shown. Figure 1 This is a flowchart of the airworthiness evaluation technology based on the P-NAS system.

[0084] This invention takes the polar shipping route area as an example. Based on multi-source geographic information of the polar region, it constructs a Polar Navigability Assessment System (P-NAS) from 17 indicators across five dimensions: sea ice, meteorology, oceanography, shipping routes, and policy. The system employs a combination of subjective weighting using fuzzy hierarchical analysis and objective weighting using an improved CRITIC method, along with a linear weighting algorithm. This constructs the NPNI index, which classifies polar shipping route navigability into five levels. The technical solution of this invention is as follows:

[0085] 1. Sea ice factor assessment indicators

[0086] Based on the changes in the spatiotemporal pattern of sea ice in polar shipping routes, it can be seen that the sea ice dynamics of polar shipping routes change frequently, and the sea ice density gradually weakens from the northwest outwards. As an important channel for polar shipping, the navigability of polar shipping routes is not only affected by sea ice changes, but also closely related to many other factors. Ocean, meteorology, shipping route conditions, and national management all have a significant impact on the navigability level of polar shipping routes. Therefore, this invention, under the premise of fully considering basic factors such as geographical environment, political and legal system, and infrastructure construction, constructs a polar shipping route navigability assessment index system from five aspects: sea ice, meteorology, ocean, shipping route, and policy. Using the polar shipping routes in September of each year from 2015 to 2022 and from January to December 2022 as experiments, the spatiotemporal differentiation characteristics of navigability were analyzed. The polar shipping route navigability assessment index system is shown in Table 1. Table 1 shows the polar shipping route navigability assessment index system.

[0087] Table 1

[0088]

[0089] 2. Subjective weighting in fuzzy hierarchical analysis

[0090] Fuzzy Analytical Hierarchy Process (FAHP) is a multi-criteria decision-making method that uses fuzzy numbers to represent the strength of human perception of things. It overcomes the shortcomings of the Analytical Hierarchy Process (AHP), such as the difficulty in verifying the consistency of the judgment matrix and the lack of scientific basis for the consistency criteria. It also eliminates the need for normalization of eigenvectors. The basic calculation idea of ​​FAHP is as follows:

[0091] (1) Constructing a hierarchical structure model

[0092] Based on the hierarchical relationship between factors, a hierarchical structure is established: target layer - criterion layer - indicator layer. The target layer refers to the assessment of polar waterway navigability, the criterion layer consists of influencing factors and decision-making criteria, and the indicator layer refers to the various influencing factors, which belong to the criterion layer.

[0093] (2) Establish fuzzy complementary judgment matrix

[0094] The importance of each indicator is determined by pairwise comparisons, and quantitatively expressed using a scaling method. A fuzzy complementary judgment matrix is ​​constructed by comparing influencing factors at the same level using both pairwise comparison and a 0.1-0.9 scaling method. r ij The values ​​of the fuzzy scaling and their meanings are shown in Table 2. Table 2 shows the fuzzy scaling method and its meaning.

[0095] Table 2

[0096]

[0097] Where, if r ij ∈[0.1,0.5), indicating r j The importance of the factor is greater than r i Factors; if r ij ∈(0.5,0.9], indicating r i The importance of the factor is greater than r j Factors; r ij =0.5 indicates r i Factors and r j These factors are equally important.

[0098] (3) Calculate the weights

[0099] For fuzzy complementary judgment matrix Perform row and normalization processing to construct the weight vector of R. The weights W of the fuzzy complementary judgment matrix are solved using formula (1). i .

[0100] (1)

[0101] (4) Consistency check

[0102] Perform a consistency check on the judgment process to verify the weight W. i Is it reasonable? The compatibility index I(A,B) and feature matrix W* based on the judgment matrix are calculated as shown in formula (2) and formula (3).

[0103] (2)

[0104] (3)

[0105] Here, A and B are both fuzzy complementary judgment matrices. When the compatibility index value I(A,W*) is less than a certain threshold of 0.1, the judgment matrix is ​​considered to satisfy the consistency test, that is, the weight allocation is reasonable.

[0106] (5) Hierarchical sorting calculation

[0107] Calculate the importance of all factors at the lowest level relative to the target level to obtain a weighted ranking. Let A be the upper level, B the lower level, and factor B... i The formula for calculating the total ranking weight of the hierarchy is shown in formula (4):

[0108] (4)

[0109] Where a j For factor Aj The total ranking weight of the hierarchy, b ij For factor B i For A j The hierarchical single-order weight.

[0110] This invention uses the calculation steps of fuzzy hierarchical analysis and employs expert scoring to calculate the subjective weights of the polar waterway navigability assessment indicators. Based on suggestions from relevant researchers, fuzzy complementary judgment matrices for each indicator layer were constructed, and the results are shown in Tables 3-8. Table 3 shows the weight allocation of the polar waterway navigability analysis judgment matrix. Table 4 shows the weight allocation of the sea ice factor judgment matrix. Table 5 shows the weight allocation of the meteorological factor judgment matrix. Table 6 shows the weight allocation of the marine factor judgment matrix. Table 7 shows the weight allocation of the waterway factor judgment matrix. Table 8 shows the weight allocation of the management factor judgment matrix.

[0111] Table 3

[0112]

[0113] Table 4

[0114]

[0115] Table 5

[0116]

[0117] Table 6

[0118]

[0119] Table 7

[0120]

[0121] Table 8

[0122]

[0123] The compatibility indices I(A,W*) of the above judgment matrices are 0.0717, 0.0698, 0.0547, 0.0222, and 0.0750, respectively, all less than 0.10, passing the consistency test. This indicates that the weight allocation of indicators in each criterion layer—sea ice factors, ocean factors, meteorological factors, navigation channel factors, and policy factors—is reasonable. Therefore, the subjective weights of the evaluation indicators are calculated, as shown in Table 9. Table 9 shows the subjective weight values ​​of the evaluation indicators.

[0124] Table 9

[0125]

[0126] 3. Improved CRITIC method for objective weighting

[0127] The CRITIC weighting method is an objective weighting method based on data variability and conflict. Variability is typically measured using standard deviation to indicate the dispersion or range of data distribution, while conflict is measured using the correlation coefficient to indicate whether there is a high positive correlation between indicators. Because the dimensions and orders of magnitude of the indicators in this invention differ significantly, potentially distorting the standard deviation calculation, this invention introduces the coefficient of variation instead of standard deviation. Furthermore, the CRITIC method cannot effectively capture the dispersion or distributional differences between indicators; this invention improves upon this deficiency by using the entropy weighting method.

[0128] (1) Forming the original indicator data matrix

[0129] Assuming there are n samples to be evaluated and m evaluation indicators, forming the original data matrix shown in formula (5), where x ij This represents the value of the j-th evaluation index for the i-th sample.

[0130] (5)

[0131] (2) Dimensionless data

[0132] The main purpose of dimensionless data transformation is to eliminate the influence of dimensions and enable all data to be measured using a unified standard. For positive indicators, the corresponding elements after transformation are shown in formula (6):

[0133] (6)

[0134] in, This represents the maximum value of the positive index column vector, and vice versa. This represents the minimum value of the positive index column vector; This represents the elements in the positive index column vector; This represents the corresponding element after the positive index has been dimensionless.

[0135] For negative indices, the corresponding elements after negativeing ​​are shown in formula (7):

[0136] (7)

[0137] in, This represents the maximum value of the negative index column vector, and vice versa. This represents the minimum value of the negative index column vector; This represents the elements in the negative index column vector; This represents the corresponding element after the dimensionless transformation of the negative index.

[0138] In the polar waterway navigability evaluation system, air temperature, sea surface temperature, sea surface pressure, waterway depth, waterway width, and port facilities are positive indicators; sea ice concentration, sea ice thickness, wind speed, low cloud cover, precipitation, snowfall, liquid water volume, ocean current velocity, wave height, waterway intersections, and national territorial waters are negative indicators. This invention performs dimensionless processing on the above positive and negative indicators using formulas (6) and (7) respectively, so that the range of indicator values ​​is between [0,1].

[0139] (3) Calculate the variability of the index

[0140] In traditional methods, the variability of indicators is represented by the standard deviation. If the standard deviation of the data is larger, it means that the fluctuation is greater and the weight will be higher. However, since there are differences in the dimensions and orders of magnitude between different evaluation indicators, using the standard deviation to measure the variability of these indicators may have certain limitations. In order to more accurately assess the degree of variability of indicators, this invention considers using the coefficient of variation, as shown in formula (8).

[0141] (8)

[0142] (9)

[0143] in This represents the dimensionless data. S represents the mean of the column vectors, and in formula (8) S j Cov represents the standard deviation of a column vector. j This represents the coefficient of variation of a column vector.

[0144] (4) Conflict in the calculation of indicators

[0145] Indicator conflict is based on the correlation between indicators, using the correlation coefficient r. ij This is used to represent the correlation between indicators. The stronger the correlation between two indicators, the less conflict there is between that indicator and other indicators, and the more overlap there is in the evaluation content. In such cases, the weight of the indicator should be reduced. The method for calculating conflict is shown in formula (10-11).

[0146] (10)

[0147] (11)

[0148] In the formula r ij R represents the correlation coefficient of the j-th indicator for the i-th sample. j This indicates the conflict of the j-th indicator.

[0149] (5) Calculating information entropy using the entropy weight method

[0150] Entropy is a measure of the amount of information in an indicator. The smaller the information entropy of an indicator, the more information it provides, and its weight should be greater. The information entropy E is calculated using formula (12-14). j Information utility value D j Among them, P ij It is the feature weight of the i-th sample under the j-th indicator, i.e., the contribution, which is an intermediate variable.

[0151] (12)

[0152] (13)

[0153] (14)

[0154] (6) Calculate the objective weight w based on the improved CRITIC method j As shown in formula (15):

[0155] (15)

[0156] Based on the improved CRITIC objective weighting method described above, the collected indicator data are extracted and listed in Table 10. The coefficient of variation, correlation coefficient, and information utility value of each evaluation indicator are calculated to obtain the objective weight value of each indicator. Table 10 shows the objective weight values ​​of the evaluation indicators.

[0157] Table 10

[0158]

[0159] 4. Weighting of linear weighted combinations

[0160] The fuzzy hierarchical analysis method (AHP) determines indicator weights based on expert subjective assessment, while the improved CRITIC method determines indicator weights entirely based on the numerical patterns of factors. Therefore, the weights calculated by the fuzzy hierarchical analysis method are defined as subjective weights, and the weights calculated by the improved CRITIC method are defined as objective weights. Based on this, a linear weighting algorithm is introduced to combine the two methods to obtain a comprehensive subjective-objective weight.

[0161] The linear weighted algorithm calculates the proportion of subjective and objective weights in the overall weight, and then sums them according to this proportion to obtain the final overall weight. This method can reveal the objective data patterns and effectively eliminate data interference with large fluctuations, as shown in formula (16).

[0162] w = αw1 + βw2 (16)

[0163] In the formula, α is the weighting coefficient of subjective weights, β is the weighting coefficient of objective weights, and α+β=1, w1 is the subjective weight, and w2 is the objective weight. Table 11 shows the comprehensive weights of the polar waterway navigability assessment indicators.

[0164] Table 11

[0165]

[0166] To determine the weighting coefficients α and β corresponding to the subjective and objective weights, this invention uses a distance function to ensure that the weighting coefficients of the weights conform to the difference span. Let the distance function of w1 and w2 be d(w1,w2), calculate formula (17)-formula (18), calculate the weighting coefficients α and β, and obtain the final weight w according to formula (19).

[0167] (17)

[0168] (18)

[0169] (19)

[0170] Based on the above formula, this invention obtains the comprehensive weights using fuzzy hierarchical analysis and the improved CRITIC method, as shown in Table 11. Table 11 shows the comprehensive weights of the polar waterway navigability assessment indicators.

[0171] 5. Construction of a comprehensive assessment index for the navigability of polar waterways

[0172] Some branches of polar shipping routes are quite wide. Using the entire branch area as the research scale inevitably leads to inaccurate assessments. This invention comprehensively considers ship navigation requirements and research objectives, dividing the research area into multiple 8km×8km grid assessment units based on a "fishing net tool." Sea ice data, reanalysis data, and other data are extracted monthly for each research area, and values ​​are extracted to each assessment unit using GIS zoning statistical tools.

[0173] The navigability of polar waterways is mainly determined by various indicators among sea ice, ocean, meteorology, waterway, and policy factors. Therefore, this invention uses a polar waterway navigability assessment index system. After dimensionless processing of each assessment indicator to make the data threshold between [0,1], the subjective weighting method of fuzzy hierarchical analysis and the improved CRITIC objective weighting method are used to form the final weight through linear weighted combination weighting, and the polar waterway navigability index is constructed as shown in formula (20):

[0174] (20)

[0175] In the formula, NPNI(x) (Northwest Passage Navigability Index) is the polar passage navigability index, i represents each influencing indicator, n is the total number of indicators, and C i Let be the parameter of the i-th influencing indicator. Let be the weight of the i-th influencing indicator.

[0176] This invention uses September of each year from 2015 to 2022, and January to December 2022, as the time nodes for polar waterway navigability analysis. The polar waterway navigability assessment index is calculated from the data of various indicators using an evaluation system. The K-means clustering algorithm is then used to classify the navigability index into five levels, as shown in Table 12. Table 12 shows the classification of polar waterway navigability assessment levels.

[0177] Table 12

[0178]

[0179] The key points of this invention include at least the following aspects.

[0180] 1. Construction of a Multi-Dimensional Indicator System: This invention breaks through the limitations of traditional methods that rely on a single sea ice factor, and innovatively constructs a comprehensive indicator system of 17 items covering five dimensions: sea ice, meteorology, oceanography, shipping routes, and policy. By comprehensively covering the key elements of the polar navigation environment, it achieves a systematic characterization of the coupling effects of multiple factors, effectively improving the comprehensiveness and objectivity of the assessment.

[0181] 2. Optimized Weight Allocation Method: Addressing the subjective biases and objective limitations of traditional weighting methods, this invention combines an improved Fuzzy Hierarchical Analysis (FAHP) and an improved CRITIC method, optimizing weight allocation through a linear weighted combination algorithm. The improved FAHP utilizes a fuzzy complementary judgment matrix and consistency checks to reduce human error introduced by expert experience; the improved CRITIC method incorporates the coefficient of variation and entropy weighting method to enhance the consideration of indicator conflict and variability. Finally, the weights are determined by a distance function, ensuring a balance between subjective and objective weights.

[0182] 3. Development of a Strong Synergistic Effect System: Based on the "strong synergistic effect" strategy, this invention develops a polar navigationability assessment system (P-NAS). This system quantifies the nonlinear interactions between sea ice, meteorology, and ocean factors through multi-source data fusion. Furthermore, it proposes a navigationability index (NPNI) and uses the K-Means clustering algorithm to classify navigationability into five levels (excellent, good, average, poor, and very poor), achieving the quantification and visualization of the assessment results.

[0183] 4. Refined Spatiotemporal Analysis Framework: This invention divides the study area into 8km×8km grid evaluation units to conduct navigational analysis at annual and monthly scales, and combines it with actual navigation to verify the accuracy of the evaluation system in extracting and analyzing the spatiotemporal features of navigational capabilities.

[0184] This invention aims to protect a polar navigation capability evaluation method based on a P-NAS system constructed using strong synergistic effects of multi-source data. It includes the entire process of multi-source data integration, P-NAS index system construction, weight optimization algorithm, NPNI index calculation, and K-Means clustering algorithm for navigation capability level classification; and a system for implementing this method, including processing modules for data preprocessing, index calculation, weight allocation, model running, and result visualization. The specific steps are as follows:

[0185] Step 1: Data Preprocessing and Grid Division: First, multi-source environmental data were collected, including remote sensing imagery (Sentinel-1 data), meteorological reanalysis data, waterway geographic information, and policy data. Using GIS software, the polar waterway was divided into uniform 8km × 8km grid units, and the values ​​of various indicators for each unit were extracted to provide a standardized data foundation for subsequent analysis.

[0186] Step 2: Construction of a Comprehensive Evaluation Index System: A comprehensive evaluation index system is constructed, encompassing five dimensions: sea ice, meteorology, oceanography, shipping channels, and policy. Specifically, this system includes 17 indicators: sea ice concentration, sea ice thickness, air temperature, wind speed, low cloud cover, precipitation, snowfall, liquid water volume, ocean current velocity, sea surface temperature, wave height, sea surface pressure, shipping channel depth, shipping channel width, shipping channel intersections, port facilities, and national territorial waters. Each indicator is dimensionless, standardizing the data to the range of 0 to 1 to eliminate the influence of dimensions.

[0187] Step 3: Weight Calculation and Optimization Algorithm: A modified fuzzy hierarchical analysis method is used for subjective weighting. A fuzzy complementary judgment matrix is ​​constructed through expert scoring, and a consistency check is performed to ensure the rationality of the weights. Simultaneously, a modified CRITIC method is used for objective weighting, introducing the coefficient of variation and entropy weighting method to enhance the analysis of index conflict and variability. Finally, a linear weighted combination algorithm is used to optimize the subjective and objective weights, and the weighting coefficients are determined using a distance function to generate a scientifically balanced comprehensive weight.

[0188] Step 4: Construction of Navigability Evaluation System and Navigability Index: Based on the P-NAS evaluation index system, a navigationability index (NPNI) is proposed and calculated. This index is achieved through a weighted summation method, where the weight of each index is multiplied by its standardized value and then summed. The K-Means clustering algorithm is applied to divide the navigationability index into five distinct levels: Excellent, Good, Average, Poor, and Very Poor. Multi-scale assessment experiments are conducted. By analyzing interannual data from September of each year from 2015 to 2022, and monthly data from January to December 2022, a spatiotemporal distribution map of navigationability is generated. This map is then validated using actual navigation data, thus establishing an effective polar waterway navigationability evaluation system.

[0189] The experimental area is located in the northwestern section of the polar shipping route, a crucial maritime passage within the Arctic Circle. This route traverses the Arctic Ocean, extends along the northern coast of North America, and connects the Atlantic and Pacific Oceans. Situated between 75° and 130° west longitude and 65° and 75° north latitude, the area is characterized by numerous islands and complex ice conditions, making it one of the most dangerous shipping routes globally.

[0190] This invention uses multi-source environmental data from mid-September each year from 2015 to 2022 to assess the navigability of polar waterways and analyze their spatiotemporal patterns on an interannual scale. Navigability is classified into five levels using the polar waterway navigability index. A distribution map of polar waterway navigability levels for September each year from 2015 to 2022 is provided. Figures 2-5 Statistics on the proportion of different levels are as follows: Figure 6 As shown. Figure 2 This is a map showing the interannual navigational performance analysis and evaluation based on the P-NAS system in September 2015 and September 2016. Figure 3 This is a map showing the interannual navigational performance analysis and evaluation based on the P-NAS system in September 2017 and September 2018. Figure 4 This is a map showing the interannual navigational performance analysis and evaluation based on the P-NAS system in September 2019 and September 2020. Figure 5 This is a map showing the interannual navigational performance analysis and evaluation based on the P-NAS system in September 2021 and September 2022. Figure 6 This represents the change in the percentage of polar waterways with varying navigability levels from 2015 to 2022.

[0191] To gain a deeper understanding of the changes in navigability of polar routes with seasonal changes, this invention uses 2022 as an example, selecting multi-source data from the middle of each month from January to December to conduct monthly-scale navigability assessments and spatiotemporal pattern analyses of polar routes. The distribution map of polar route navigability levels from January to December 2022 is shown below. Figures 7 to 12 As shown, the change in the area ratio of navigability level is as follows: Figure 13 As shown. Figures 7-12This study aims to evaluate and assess navigation capabilities on a lunar scale based on the P-NAS framework. Figure 7 This study evaluates the navigationability of the lunar scale based on the P-NAS system during January-February 2022. Figure 8 This study evaluates the navigationability of the lunar scale based on the P-NAS system during March-April 2022. Figure 9 This study evaluates the navigationability of a lunar scale based on the P-NAS system during May-June 2022. Figure 10 This study evaluates the navigationability of the lunar scale based on the P-NAS system during July-August 2022. Figure 11 This study evaluates the navigationability of the lunar scale based on the P-NAS system during September-October 2022. Figure 12 This study evaluates the navigationability of a lunar scale based on the P-NAS system during November-December 2022. Figure 13 This represents the change in the percentage of polar waterways with varying navigability levels from January to December 2022.

[0192] In summary, this invention belongs to the field of next-generation information technology, specifically relating to a polar navigation capability evaluation method based on the strong synergistic effect of multi-source data. The method includes: data preprocessing and grid division; construction of a comprehensive evaluation index system: constructing an index system covering five dimensions—sea ice, meteorology, oceanography, navigation, and policy management—specifically including 17 indicators such as sea ice concentration, sea ice thickness, air temperature, wind speed, low cloud cover, precipitation, snowfall, liquid water volume, ocean current velocity, sea surface temperature, wave height, sea surface pressure, channel depth, channel width, channel intersections, port facilities, and national territorial waters, with dimensionless processing of each indicator; weight calculation and optimization algorithm; and construction of the navigation capability evaluation system and navigation capability index. This invention overcomes the limitations of traditional technologies with narrow evaluation dimensions, achieving a comprehensive characterization of the multi-factor coupling effect of the polar navigation environment, and effectively improving the comprehensiveness and objectivity of the evaluation results.

[0193] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for evaluating the navigability of polar waterways based on the strong synergistic effect of multi-source data, characterized in that, The method includes the following steps: Step 1: Data Preprocessing and Grid Division: First, collect multi-source environmental data, including remote sensing imagery, meteorological reanalysis data, waterway geographic information, and policy data; based on GIS software, divide the waterway into uniform grid units and extract the various indicator values ​​of each unit to provide a standardized data foundation for subsequent analysis; Step 2: Construction of a Comprehensive Evaluation Index System: An index system covering five dimensions—sea ice, meteorology, oceanography, waterways, and policy management—is constructed. Specific sea ice factors include sea ice concentration and thickness; meteorological factors include air temperature, wind speed, low cloud cover, precipitation, snowfall, and liquid water volume; oceanographic factors include ocean current velocity, sea surface temperature, wave height, and sea surface pressure; waterway factors include waterway depth, waterway width, and waterway intersections; and policy management factors include port facilities and national territorial waters. These five dimensions comprise a total of 17 indicators. Each indicator is dimensionless, standardizing the data to the range of 0 to 1 to eliminate the influence of dimensions. Step 3: Weight Calculation and Optimization Algorithm: Subjective weighting is performed using an improved fuzzy hierarchical analysis method, and objective weighting is performed using an improved CRITIC method. Finally, the subjective and objective weights are optimized using a linear weighted combination algorithm. The improved fuzzy hierarchical analysis method's subjective weighting in step 3 involves comparing influencing factors at the same level pairwise using a pairwise comparison method and a 0.1-0.9 scaling method (labeled as 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, and 0.9) to construct a fuzzy complementary judgment matrix. , where if r ij ∈[0.1,0.5), indicating r j The importance of the factor is greater than r i Factors; if r ij ∈(0.5,0.9], indicating r i The importance of the factor is greater than r j Factors; r ij =0.5 indicates r i Factors and r j The factors are equally important; Step 3 also includes the step of calculating weights, that is, calculating the fuzzy complementary judgment matrix. Perform row and normalization processing to construct the weight vector of R. The weights W of the fuzzy complementary judgment matrix are solved using formula (1). i , （1）； The improved CRITIC method objective weighting described in step 3 includes the following steps: 1) Forming the original indicator data matrix: Assuming there are n samples to be evaluated and m evaluation indicators, forming the original data matrix shown in formula (5), where x ij This represents the value of the j-th evaluation index for the i-th sample; （5） 2) Dimensionless data: For positive indicators, the corresponding elements after positive transformation are shown in formula (6): （6） in, This represents the maximum value of the positive index column vector, and vice versa. This represents the minimum value of the positive index column vector; This represents the elements in the positive index column vector; This represents the corresponding element after the positive index has been dimensionless; For negative indices, the corresponding elements after negativeing ​​are shown in formula (7): （7） in, This represents the maximum value of the negative index column vector, and vice versa. This represents the minimum value of the negative index column vector; This represents the elements in the negative index column vector; This represents the corresponding element after the dimensionless transformation of the negative index; Air temperature, sea surface temperature, sea surface pressure, channel depth, channel width and port facilities are positive indicators; sea ice concentration, sea ice thickness, wind speed, low cloud cover, precipitation, snowfall, liquid water volume, ocean current velocity, wave height, channel intersection and national territorial waters are negative indicators; the above positive and negative indicators are respectively processed by formula (6) and formula (7) to make the range of indicator values ​​between [0,1]; 3) Calculate the variability of the indicators: The variability of the index is calculated using the coefficient of variation as shown in formula (8); （8） （9） in This represents the dimensionless data. S represents the mean of the column vectors, and in formula (8) S j Cov represents the standard deviation of a column vector. j Represents the coefficient of variation of a column vector; The improved CRITIC method objective weighting described in step 3 also includes the following steps: 4) Conflicts in the calculation of indicators: Using the correlation coefficient r ij The correlation between indicators is indicated. If the correlation between two indicators is stronger, then the conflict between the indicator and other indicators is smaller, and the more the evaluation content is repeated, the lower the weight of the indicator should be. The conflict calculation method is as shown in formula (10) to formula (11). （10） （11） In the formula r ij R represents the correlation coefficient of the j-th indicator for the i-th sample. j This indicates the conflict of the j-th indicator; 5) Calculating information entropy using the entropy weight method: Entropy is a measure of the information content of an indicator. The smaller the information entropy of an indicator, the more information the indicator value provides, and its weight should be greater. The information entropy E is calculated using formulas (12) to (14). j Information utility value D j ;where P ij It is the feature weight of the i-th sample under the j-th indicator, i.e., the contribution, which is an intermediate variable; （12） （13） （14） 6) Calculate the objective weight w based on the improved CRITIC method j As shown in formula (15): （15）； Step 4: Construction of the airworthiness evaluation system and airworthiness index: The airworthiness index NPNI is calculated by weighted summation; the airworthiness index is divided into multiple levels by applying an algorithm; The airworthiness index construction in step 4 includes the following steps as shown in formula (20): （20） In the formula, NPNI is the airworthiness index, i represents each influencing indicator, n is the total number of indicators, and C i Let be the parameter of the i-th influencing indicator. Let be the weight of the i-th influencing indicator; Step 5: Use the navigation evaluation system and navigation index constructed in Step 4 to evaluate the navigation of polar waterways.

2. The polar waterway navigation evaluation method based on strong synergistic effects of multi-source data as described in claim 1, characterized in that, Step 1: The waterway is divided into 8km × 8km uniform grid units; Step 3: Weight calculation and optimization algorithm: A modified fuzzy hierarchical analysis method is used for subjective weighting, a fuzzy complementary judgment matrix is ​​constructed through expert scoring, and a consistency check is performed to ensure the rationality of the weights; and a modified CRITIC method is used for objective weighting, introducing the coefficient of variation and entropy weight method to enhance the analysis of index conflict and variability; finally, the subjective and objective weights are optimized through a linear weighted combination algorithm, and the weighting coefficients are determined using a distance function to generate a scientifically balanced comprehensive weight; Step 4: Construction of navigationability evaluation system and navigationability index: From the 17 indicators of the five dimensions mentioned in Step 2, the navigationability evaluation system and navigationability index are constructed. The improved fuzzy hierarchical analysis method (as described in step 3) is used for subjective weighting, and the improved CRITIC method is used for objective weighting. A linear weighting algorithm is then used to construct a polar navigationability evaluation system, namely the P-NAS system. Based on the P-NAS system, a navigationability index, NPNI, is proposed and calculated. This index is achieved through a weighted summation method, where the weight of each index is multiplied by its standardized value and then summed. An algorithm is applied to divide the navigationability index into multiple levels. Multi-scale assessment experiments are conducted, and by analyzing interannual and monthly data, a spatiotemporal distribution map of navigationability is generated. This map is then validated using actual navigation data, thus achieving the construction of an effective navigationability evaluation system.

3. The polar waterway navigation evaluation method based on strong synergistic effects of multi-source data as described in claim 1, characterized in that, Step 3 also includes a consistency check step, which involves checking the consistency of the judgment process to verify the weight W. i Whether it is reasonable; calculate the compatibility index I(A,B) and feature matrix W* based on the judgment matrix, as shown in formula (2) and formula (3): （2） （3） Where A and B are both fuzzy complementary judgment matrices. When the compatibility index value I(A,W*) is less than a certain threshold of 0.1, the judgment matrix is ​​considered to satisfy the consistency test, that is, the weight allocation is reasonable.

4. The polar waterway navigation evaluation method based on strong synergistic effects of multi-source data as described in claim 1, characterized in that, Step 3 also includes a hierarchical ranking calculation step, which calculates the importance of all factors at the lowest level relative to the target level to obtain a weighted ranking; let A be the upper level, B be the lower level, and factor B i The formula for calculating the total ranking weight of the hierarchy is shown in formula (4): （4） Where a j For factor A j The total ranking weight of the hierarchy, b ij For factor B i For A j The hierarchical single-order weight.

5. The polar waterway navigation evaluation method based on strong synergistic effects of multi-source data according to claim 1, characterized in that, The linear weighting algorithm described in step 3 performs subjective and objective weighting as shown in formula (16): w = αw1 + βw2 (16) In the formula, α is the weighting coefficient of subjective weight, β is the weighting coefficient of objective weight, and α+β=1, w1 is the subjective weight, and w2 is the objective weight. The distance function is used to ensure that the weighting coefficients of the weights conform to the difference span. Let the distance function of w1 and w2 be d(w1,w2). Calculate formula (17)-formula (18) to obtain the weighting coefficients α and β. According to formula (19), the final weight w is obtained. （17） （18） （19）。 6. A polar waterway navigation capability evaluation method based on strong synergistic effects of multi-source data according to any one of claims 1 to 5, characterized in that, The K-Means clustering algorithm was used to divide the NPNI value range into five distinct levels from high to low: excellent, good, average, poor, and very poor.

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