A situation assessment method and apparatus for offshore emergency rescue

Abstract

The application discloses a situation evaluation method and device for offshore emergency rescue, and the method comprises the following steps: collecting a multi-source data set related to offshore emergency rescue; the multi-source data set comprises but is not limited to a marine environment data subset, a ship dynamic data subset, a personnel condition data subset and a rescue resource data subset; performing data preprocessing on the multi-source data set to obtain a preprocessed multi-source data set; performing situation evaluation processing on the preprocessed multi-source data set to obtain a situation evaluation value.

Description

Technical Field

[0001] This invention relates to the field of maritime emergency rescue, and specifically to a situation assessment method and apparatus for maritime emergency rescue. Background Technology

[0002] With the development of marine development and marine science and technology in my country, the density of ships and marine operations are constantly expanding. However, influenced by natural and human factors, the frequency of marine natural disasters, public health emergencies, and other incidents is also increasing, making maritime dangers unavoidable. To minimize loss of life and property and protect the marine ecological environment and social stability, the development and research of maritime emergency rescue require comprehensive consideration of environmental, personnel, and technological factors, necessitating resource integration, coordination, and key technology research. The maritime emergency rescue environment faces complex and ever-changing situations and is influenced by various complex factors. Therefore, how to accurately quantify and assess the maritime emergency rescue situation to provide a basis for implementing efficient rescue operations is a problem that needs to be solved. Summary of the Invention

[0003] The present invention aims to provide a situation assessment method and apparatus for maritime emergency rescue, so as to solve the problem of inaccurate situation assessment in the prior art for maritime emergency rescue.

[0004] In a first aspect, this invention discloses a situation assessment method for maritime emergency rescue, comprising:

[0005] S1, collect multi-source datasets related to maritime emergency rescue; the multi-source datasets include, but are not limited to, subsets of marine environmental data, subsets of ship dynamic data, subsets of personnel status data, and subsets of rescue resource data;

[0006] S2, perform data preprocessing on the multi-source dataset to obtain the preprocessed multi-source dataset;

[0007] S3, perform situation assessment processing on the preprocessed multi-source dataset to obtain situation assessment values.

[0008] The step of preprocessing the multi-source dataset to obtain a preprocessed multi-source dataset includes:

[0009] S21, perform data cleaning on the multi-source dataset to obtain the first dataset;

[0010] S22, perform format filtering on the first dataset to obtain the second dataset;

[0011] S23, perform pattern matching processing on the second dataset to obtain a preprocessed multi-source dataset.

[0012] The process of performing situation assessment on the preprocessed multi-source dataset to obtain situation assessment values ​​includes:

[0013] S31, For each data subset in the preprocessed multi-source dataset, perform evaluation processing to obtain the corresponding evaluation sub-result and weight value;

[0014] S32, perform a weighted summation of all evaluation sub-results and weight values ​​to obtain the situation assessment value.

[0015] The evaluation process is performed on each data subset in the preprocessed multi-source dataset to obtain the corresponding evaluation sub-result and weight value, including:

[0016] S311, Evaluate the subset of marine environmental data to obtain the evaluation sub-results and weight values ​​corresponding to the subset of marine environmental data;

[0017] S312, evaluate the subset of ship dynamic data to obtain the evaluation sub-results and weight values ​​corresponding to the subset of ship dynamic data;

[0018] S313, evaluate the subset of personnel status data to obtain the evaluation sub-result and weight value corresponding to the subset of personnel status data;

[0019] S314, evaluate the subset of rescue resource data to obtain the evaluation sub-results and weight values ​​corresponding to the subset of rescue resource data.

[0020] The evaluation process of the marine environmental data subset to obtain the evaluation sub-results and weight values ​​corresponding to the marine environmental data subset includes:

[0021] S3111, perform feature transformation processing on all measurement data sequences in the aforementioned marine environmental data subset to obtain a set of feature components, denoted as {z}. i (n),i=1,2,…,N,n=1,2,…,N1},z i (n) represents the nth element of the i-th feature component in the feature component set, N is the total number of feature components, and N1 is the length of the feature component;

[0022] S3112, Perform a first evaluation calculation on the feature classification set to obtain the evaluation sub-results corresponding to the marine environmental data subset;

[0023] S3113, Perform a first weight calculation on the feature classification set to obtain the weight value corresponding to the marine environmental data subset.

[0024] The expression for the first evaluation calculation is:

[0025]

[0026] Wherein, P1 is the evaluation sub-result corresponding to the subset of marine environmental data, y i (n) is the nth element of the i-th measurement data sequence of the marine environmental data subset. and These are the nth element of the mean sequence of all characteristic components and the nth element of the mean sequence of all measurement data sequences of the marine environmental data subset, respectively.

[0027] The expression for the first weight calculation is:

[0028]

[0029] Where, μ i δ is the mean of the i-th measurement data sequence in the subset of marine environmental data. i Let T be the variance of the i-th feature component of the feature component set. i () represents the i-th Legendre polynomial, and ω1 is the weight value corresponding to the subset of marine environmental data.

[0030] The evaluation process of the subset of ship dynamic data to obtain the evaluation sub-results and weight values ​​corresponding to the subset of ship dynamic data includes:

[0031] The vessel dynamic data subset includes information on whether the vessel is carrying people, the number of people carrying the vessel, the vessel speed, the extent of damage, the remaining time, the distance to the rescue point, and the information on hazardous materials carried.

[0032] S3121, The information on whether the ship is carrying people, the degree of damage, and the information on the hazardous materials carried are quantified and encoded to obtain the ship type value, the degree of damage value, and the hazardous materials carried value, respectively.

[0033] S3122, perform a second evaluation calculation on the ship type value, damage level value, ship speed information, hazardous materials carried value, number of passengers, distance to rescue point and endurance information to obtain the evaluation sub-results corresponding to the ship dynamic data subset;

[0034] S3123, perform a second weight calculation on the ship type value, damage level value, ship speed information, hazardous materials carried value, number of passengers, distance to rescue point and endurance information to obtain the weight value corresponding to the ship dynamic data subset;

[0035] The expression for the second evaluation calculation is:

[0036]

[0037] Wherein, α is the ship type value, κ is the damage level value, λ is the hazardous materials carried value, β is the number of passengers, l is the distance to the rescue point, v is the ship speed information, T is the endurance time information, and P2 is the evaluation sub-result corresponding to the subset of ship dynamic data. and These are rounding down and rounding up, respectively.

[0038] The expression for the second weight calculation is:

[0039]

[0040] Wherein, ω2 is the weight value corresponding to the subset of ship dynamic data.

[0041] According to a second aspect of the present invention, a situation assessment device for maritime emergency rescue is disclosed, the device comprising:

[0042] Memory containing executable program code;

[0043] A processor coupled to the memory;

[0044] The processor calls the executable program code stored in the memory to execute the situation assessment method for maritime emergency rescue.

[0045] In a third aspect, the present invention discloses a computer-storable medium storing computer instructions, which, when invoked by a computer, are used to execute the situation assessment method for maritime emergency rescue.

[0046] In a fourth aspect, the present invention discloses an information data processing terminal, which is used to implement the situation assessment method for maritime emergency rescue.

[0047] The beneficial effects of this invention are as follows:

[0048] The maritime emergency rescue situation assessment method and apparatus of the present invention, by comprehensively considering multiple factors such as environment, personnel, resources and rescue operations, constructs a comprehensive situation assessment index system, and uses data fusion technology to analyze and process multi-source data, can accurately quantify and assess the situation of maritime emergency rescue, provide a strong basis for implementing efficient rescue, help minimize loss of life and property, and protect the marine ecological environment and social stability. Attached Figure Description

[0049] Figure 1 This is a flowchart illustrating the implementation of the method of the present invention. Detailed Implementation

[0050] To better understand the content of this invention, an embodiment is provided here.

[0051] Figure 1 This is a flowchart illustrating the implementation of the method of the present invention.

[0052] In a first aspect, this invention discloses a situation assessment method for maritime emergency rescue, comprising:

[0053] S1, collect multi-source datasets related to maritime emergency rescue; the multi-source datasets include, but are not limited to, subsets of marine environmental data, subsets of ship dynamic data, subsets of personnel status data, and subsets of rescue resource data;

[0054] S2, perform data preprocessing on the multi-source dataset to obtain the preprocessed multi-source dataset;

[0055] S3, perform situation assessment processing on the preprocessed multi-source dataset to obtain situation assessment values;

[0056] The step of preprocessing the multi-source dataset to obtain a preprocessed multi-source dataset includes:

[0057] S21, perform data cleaning on the multi-source dataset to obtain the first dataset;

[0058] S22, perform format filtering on the first dataset to obtain the second dataset;

[0059] S23, perform pattern matching processing on the second dataset to obtain a preprocessed multi-source dataset;

[0060] The process of performing situation assessment on the preprocessed multi-source dataset to obtain situation assessment values ​​includes:

[0061] For each data subset in the preprocessed multi-source dataset, an evaluation process is performed to obtain the corresponding evaluation sub-result and weight value;

[0062] The situation assessment value is obtained by weighted summation of all assessment sub-results and weight values.

[0063] The evaluation process is performed on each data subset in the preprocessed multi-source dataset to obtain the corresponding evaluation sub-result and weight value, including:

[0064] The marine environmental data subset is evaluated to obtain the evaluation sub-results and weight values ​​corresponding to the marine environmental data subset;

[0065] The subset of ship dynamic data is evaluated to obtain the evaluation sub-results and weight values ​​corresponding to the subset of ship dynamic data.

[0066] The subset of personnel status data is evaluated to obtain the evaluation sub-results and weight values ​​corresponding to the subset of personnel status data.

[0067] The subset of rescue resource data is evaluated to obtain the evaluation sub-results and weight values ​​corresponding to the subset of rescue resource data.

[0068] The evaluation process of the marine environmental data subset to obtain the evaluation sub-results and weight values ​​corresponding to the marine environmental data subset includes:

[0069] The marine environmental data subset includes measurement data sequences of wind speed, wind direction, air pressure, temperature, wave height, ocean current speed, water temperature, and salinity;

[0070] For all measurement data sequences in the aforementioned marine environmental data subset, feature transformation processing is performed to obtain a set of feature components, which is denoted as {z}. i (n),i=1,2,…,N,n=1,2,…,N1},z i (n) represents the nth element of the i-th feature component in the feature component set, N is the total number of feature components, and N1 is the length of the feature component;

[0071] The first evaluation calculation process is performed on the feature classification set to obtain the evaluation sub-results corresponding to the marine environmental data subset;

[0072] The first weight calculation process is performed on the feature classification set to obtain the weight value corresponding to the marine environment data subset;

[0073] The expression for the first evaluation calculation is:

[0074]

[0075] Wherein, P1 is the evaluation sub-result corresponding to the subset of marine environmental data, y i (n) is the nth element of the i-th measurement data sequence of the marine environmental data subset. and These are the nth element of the mean sequence of all characteristic components and the nth element of the mean sequence of all measurement data sequences of the marine environmental data subset, respectively.

[0076] The first assessment calculation process, based on a formula constructed using covariance and standard deviation, quantifies the synergistic relationship between various marine environmental elements by calculating the linear correlation between different measurement data sequences (wind speed, wave height, etc.) and characteristic components. For example, it can reveal the close correlation between strong wind speed and high wave height, providing data support for assessing environmental complexity. The formula is essentially a correlation coefficient calculation, with the results normalized to the [-1, 1] interval for easy comparison of assessment results under different combinations of environmental elements. The closer the value is to 1, the stronger the synergistic trend among marine environmental elements, and the more likely a complex and dangerous rescue environment is to form. By eliminating data bias through mean and normalizing using standard deviation, the influence of noise and abnormal fluctuations in the measurement data can be effectively suppressed, ensuring that the assessment results truly reflect the actual state of the marine environment.

[0077] The expression for the first weight calculation is:

[0078]

[0079] Where, μ i δ is the mean of the i-th measurement data sequence in the subset of marine environmental data. i Let T be the variance of the i-th feature component of the feature component set. i () represents the i-th Legendre polynomial, and ω1 is the weight value corresponding to the subset of marine environmental data.

[0080] The first weight calculation process uses the product of the data mean and variance as input to the Legendre polynomial, comprehensively considering both the central tendency and dispersion of the data. For example, environmental factors with high mean and large variance (such as extreme wave height fluctuations) will receive higher weights after polynomial transformation, highlighting their importance in environmental situation assessment. Utilizing the nonlinear characteristics of the Legendre polynomial, differentiated weight calculations are performed for different combinations of data features. Small changes in mean or variance can significantly alter the weight magnitude after polynomial mapping, allowing the weight allocation to more accurately reflect the actual impact of environmental factors on the rescue situation. By adjusting the order and parameters of the Legendre polynomial, it can flexibly adapt to the marine environmental characteristics of different sea areas and seasons, ensuring that the weight calculation meets the needs of specific rescue scenarios and improving the versatility of the assessment model.

[0081] The evaluation process of the subset of ship dynamic data to obtain the evaluation sub-results and weight values ​​corresponding to the subset of ship dynamic data includes:

[0082] The vessel dynamic data subset includes information on whether the vessel is carrying people, the number of people carrying the vessel, the vessel speed, the extent of damage, the remaining time, the distance to the rescue point, and the information on hazardous materials carried.

[0083] The information on whether the vessel is carrying passengers, the extent of damage, and the hazardous materials carried are quantified and encoded to obtain vessel type value, damage level value, and hazardous materials carried value, respectively.

[0084] A second evaluation calculation is performed on the ship type value, damage level value, ship speed information, hazardous materials carried value, number of passengers, distance to rescue point and endurance information to obtain the evaluation sub-results corresponding to the ship dynamic data subset.

[0085] A second weighting calculation is performed on the ship type value, damage level value, ship speed information, hazardous materials carried value, number of passengers, distance to rescue point value, and endurance time information to obtain the weight values ​​corresponding to the ship dynamic data subset.

[0086] The expression for the second evaluation calculation is:

[0087]

[0088] Wherein, α is the ship type value, κ is the damage level value, λ is the hazardous materials carried value, β is the number of passengers, l is the distance to the rescue point, v is the ship speed information, T is the endurance time information, and P2 is the evaluation sub-result corresponding to the subset of ship dynamic data. and These are rounding down and rounding up, respectively.

[0089] The second assessment calculation process integrates key information such as vessel passenger capacity, damage, speed, and distance. Through nonlinear transformations such as trigonometric and logarithmic functions, discrete or continuous data is uniformly quantified into assessment values. For example, the arcsine function is used to convert distance, speed, and endurance time into rescue accessibility indicators, while the logarithmic function highlights the amplifying effect of hazardous materials and damage on rescue risks. Rounding down and up converts the calculation results into integers, intuitively distinguishing the urgency levels of different vessels. Higher values ​​indicate greater urgency, facilitating rapid priority identification and rational resource allocation by rescue commanders. The function design closely aligns with the needs of maritime rescue scenarios; for instance, αβ / T reflects the endurance pressure of passenger vessels. Emphasizing the potential risks of damaged vessels carrying hazardous materials ensures that assessment results directly inform rescue decisions.

[0090] The expression for the second weight calculation is:

[0091]

[0092] Wherein, ω2 is the weight value corresponding to the subset of ship dynamic data.

[0093] The evaluation process of the subset of rescue resource data to obtain the evaluation sub-results and weight values ​​corresponding to the subset of rescue resource data includes:

[0094] The subset of rescue resource data includes a sequence of technical status indicators for each type of rescue resource and information on the completeness of rescue equipment;

[0095] Obtain the upper and lower limits of each type of data, including the sequence of technical status indicators of rescue resources and the completeness information of rescue equipment; obtain the standard completeness z0 of rescue equipment.

[0096] The sequence of technical status indicators and the completeness information of rescue equipment for each type of rescue resource are represented as corresponding vectors;

[0097] The vectors of all types of rescue resources are subjected to a third-row evaluation calculation to obtain the evaluation sub-results corresponding to the subset of rescue resource data;

[0098] A third weight calculation is performed on the vectors of all types of rescue resources to obtain the weight values ​​corresponding to the subset of rescue resource data;

[0099] The expression for the third evaluation calculation is:

[0100]

[0101] Where M1 and M2 are the number of categories of rescue resources and the number of elements in the vector corresponding to the rescue resources, respectively, A ij Let j be the j-th element of the vector of the i-th type of rescue resource. Let be the upper bound of the j-th element of the vector of rescue resources. P3 is the lower bound of the j-th element of the vector of rescue resources, and P3 is the evaluation sub-result corresponding to the subset of rescue resource data.

[0102] The expression for the third weight calculation is:

[0103]

[0104] Where, ξ i Let ψ be the mean of the vector of the i-th type of rescue resources. i Let ω3 be the variance of the vector of the i-th type of rescue resource, and let ω3 be the weight value corresponding to the subset of rescue resource data.

[0105] The evaluation process of the subset of personnel status data to obtain the evaluation sub-results and weight values ​​corresponding to the subset of personnel status data includes:

[0106] The subset of personnel status data includes quantitative values ​​of personnel health status, quantitative values ​​of skill level, and density level.

[0107] The fourth evaluation calculation is performed on the subset of personnel status data to obtain the corresponding evaluation sub-result;

[0108] The fourth weight calculation process is performed on the subset of personnel status data to obtain the corresponding weight values;

[0109] The fourth evaluation calculation process includes:

[0110]

[0111] Where r is the quantitative value of personnel health status, f is the density value, and P4 is the evaluation sub-result of the subset of personnel health status data;

[0112] The fourth weight calculation process includes:

[0113] ω4=exp(-g / f) / 2,

[0114] Among them, g is the quantitative value of skill level, and ω4 is the weight value of the subset of personnel status data;

[0115] The and The upper and lower limits of each type of data, namely the technical status index sequence of rescue resources and the integrity information of rescue equipment, are obtained respectively.

[0116] The mean sequence is obtained by taking the average of each element in all sequences.

[0117] The feature decomposition can be performed using EMD decomposition;

[0118] The feature components can be IMF components;

[0119] The quantitative coding process for whether a vessel is carrying passengers involves encoding the vessel type value (which is 1) when the vessel is carrying passengers and encoding the vessel type value (which is 0) when the vessel is not carrying passengers.

[0120] The damage level information is categorized into four levels: overturned, severely damaged, on fire, moderately damaged, and slightly damaged, with corresponding quantization codes of 1, 2, 3, 4, and 5, respectively.

[0121] The values ​​of the hazardous materials information are respectively for seriously hazardous materials, moderately hazardous materials, and generally hazardous materials, with corresponding quantization codes of 3, 2, and 1.

[0122] The higher the situation assessment value, the more severe the current maritime emergency rescue situation.

[0123] The data cleaning process includes outlier removal, elimination of invalid and redundant data, and ensuring data accuracy and usability.

[0124] The format filtering process includes: checking whether each type of data in the first dataset matches a preset data format, deleting the mismatched data from the first dataset, and obtaining a second dataset;

[0125] The process of performing pattern matching on the second dataset to obtain a preprocessed multi-source dataset includes:

[0126] For each data attribute in the second dataset, using the data collection information of the data as the independent variable and the data value of the data as the dependent variable, autoregressive-moving average modeling is performed to obtain the regression model of the data attribute.

[0127] Using the regression model, the independent variables are calculated and processed to obtain regression data values; it is determined whether the absolute value of the difference between the regression data value and the corresponding dependent variable value is greater than a set first regression discrimination threshold; if it is greater than the first regression discrimination threshold, the data is deleted from the second dataset; if it is less than or equal to the first regression discrimination threshold, the data is not processed.

[0128] The data from all execution patterns matched in the second dataset are fused to obtain a preprocessed multi-source dataset.

[0129] According to a second aspect of the present invention, a situation assessment device for maritime emergency rescue is disclosed, the device comprising:

[0130] Memory containing executable program code;

[0131] A processor coupled to the memory;

[0132] The processor calls the executable program code stored in the memory to execute the situation assessment method for maritime emergency rescue.

[0133] In a third aspect, the present invention discloses a computer-storable medium storing computer instructions, which, when invoked by a computer, are used to execute the situation assessment method for maritime emergency rescue.

[0134] In a fourth aspect, the present invention discloses an information data processing terminal, which is used to implement the situation assessment method for maritime emergency rescue.

[0135] The above description is merely an 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 principle of the present invention should be included within the scope of the claims of the present invention.

Claims

1. A situation assessment method for maritime emergency rescue, characterized in that, include: S1, collect multi-source datasets related to maritime emergency rescue; the multi-source datasets include a subset of marine environmental data, a subset of ship dynamic data, a subset of personnel status data, and a subset of rescue resource data; S2, perform data preprocessing on the multi-source dataset to obtain the preprocessed multi-source dataset; S3, perform situation assessment processing on the preprocessed multi-source dataset to obtain situation assessment values, including: S31, for each data subset in the preprocessed multi-source dataset, perform evaluation processing to obtain the corresponding evaluation sub-result and weight value, including: S311, Evaluate the subset of marine environmental data to obtain the evaluation sub-results and weight values ​​corresponding to the subset of marine environmental data, including: S3111, perform feature transformation processing on all measurement data sequences in the marine environmental data subset to obtain a set of feature components, wherein the set of feature components is represented as... , Let N represent the nth element of the i-th feature component in the feature component set, where N is the total number of feature components. The length of the characteristic component; S3112, Perform a first evaluation calculation on the set of feature components to obtain the evaluation sub-results corresponding to the marine environmental data subset; The expression for the first evaluation calculation is: ， in, The evaluation sub-results corresponding to the aforementioned subset of marine environmental data. This refers to the nth element of the i-th measurement data sequence in the subset of marine environmental data. and These are the nth element of the mean sequence of all characteristic components and the nth element of the mean sequence of all measurement data sequences of the marine environmental data subset, respectively. S3113, Perform a first weight calculation on the feature component set to obtain the weight value corresponding to the marine environment data subset; The expression for the first weight calculation is: ， in, Let be the mean of the i-th measurement data sequence in the subset of marine environmental data. Let be the variance of the i-th feature component in the set of feature components. Let i be the Legendre polynomial of order i. The weight values ​​corresponding to the subset of marine environmental data; S312, evaluate the subset of ship dynamic data to obtain the evaluation sub-results and weight values ​​corresponding to the subset of ship dynamic data, including: The vessel dynamic data subset includes information on whether the vessel is carrying people, the number of people carrying the vessel, the vessel speed, the extent of damage, the remaining time, the distance to the rescue point, and the information on hazardous materials carried. S3121, The information on whether the ship is carrying people, the degree of damage, and the information on the hazardous materials carried are quantified and encoded to obtain the ship type value, the degree of damage value, and the hazardous materials carried value, respectively. S3122, perform a second evaluation calculation on the ship type value, damage level value, ship speed information, hazardous materials carried value, number of passengers, distance to rescue point and endurance information to obtain the evaluation sub-results corresponding to the ship dynamic data subset; S3123, perform a second weight calculation on the ship type value, damage level value, ship speed information, hazardous materials carried value, number of passengers, distance to rescue point and endurance information to obtain the weight value corresponding to the ship dynamic data subset; The expression for the second evaluation calculation is: ， in, For ship type value, This represents the degree of damage. To carry hazardous materials, For passenger quantity information, This is the distance value from the rescue point. For ship speed information, For battery life information, The evaluation sub-result corresponding to the subset of ship dynamic data. and These are rounding down and rounding up, respectively. The expression for the second weight calculation is: ， in, The weight value corresponding to the subset of ship dynamic data; S313, evaluate the subset of personnel status data to obtain the evaluation sub-result and weight value corresponding to the subset of personnel status data; S314, evaluate the subset of rescue resource data to obtain the evaluation sub-result and weight value corresponding to the subset of rescue resource data; S32, perform a weighted summation of all evaluation sub-results and weight values ​​to obtain the situation assessment value.

2. The situation assessment method for maritime emergency rescue as described in claim 1, characterized in that, The step of preprocessing the multi-source dataset to obtain a preprocessed multi-source dataset includes: S21, perform data cleaning on the multi-source dataset to obtain the first dataset; S22, perform format filtering on the first dataset to obtain the second dataset; S23, perform pattern matching processing on the second dataset to obtain a preprocessed multi-source dataset.

3. The situation assessment method for maritime emergency rescue as described in claim 1, characterized in that, The evaluation process of the subset of rescue resource data to obtain the evaluation sub-results and weight values ​​corresponding to the subset of rescue resource data includes: The subset of rescue resource data includes a sequence of technical status indicators for each type of rescue resource and information on the completeness of rescue equipment; Obtain the upper and lower limits of each data category, including the sequence of technical status indicators for rescue resources and the completeness information of rescue equipment; obtain the completeness information of standard rescue equipment. ; The sequence of technical status indicators and the completeness information of rescue equipment for each type of rescue resource are represented as corresponding vectors; A third evaluation calculation is performed on the vectors of all types of rescue resources to obtain the evaluation sub-results corresponding to the subset of rescue resource data; A third weight calculation is performed on the vectors of all types of rescue resources to obtain the weight values ​​corresponding to the subset of rescue resource data.

4. A situation assessment device for maritime emergency rescue, characterized in that, The device includes: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the situation assessment method for maritime emergency rescue as described in any one of claims 1 to 3.

5. A computer-storable medium, characterized in that, The computer storage medium stores computer instructions, which, when invoked by the computer, are used to execute the situation assessment method for maritime emergency rescue as described in any one of claims 1 to 3.

6. An information data processing terminal, characterized in that, The information data processing terminal is used to implement the situation assessment method for maritime emergency rescue as described in any one of claims 1 to 3.

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