Method for analyzing influence of hydrometeorological environment on underwater multi-mode machine behaviors

Abstract

A method for analyzing the influence of a hydrometeorological environment on underwater multi-modal machine behaviors comprises the following steps: 1) analyzing the types of marine environment influence factors according to the machine behaviors, and establishing an evaluation index system; 2) reading machine behaviors set by a user, participation in evaluation environment types, different corresponding numerical value ranges and evaluation weights of various marine environment elements; (3) marine environment data of the evaluated sea area are read according to the marine environment type, data preprocessing is conducted, and consistency is ensured; and 4) adopting a gradient function as an evaluation membership function to obtain a quantitative evaluation result. The method has the advantages that a dynamic fuzzy comprehensive evaluation algorithm is adopted, the influence analysis of the marine environment on the underwater multi-mode machine behavior is combined, a fuzzy comprehensive evaluation model is established, and the marine environment is dynamically evaluated. The method supports a user to dynamically select element types participating in evaluation of the marine environment, can configure the evaluation weight of each element and the value of each level interval, can evaluate the influence of the marine environment on the underwater multi-modal machine behavior in a customized manner, and is high in availability and practicability.

Description

Technical Field

[0001] The present invention relates to the field of analysis of the impact of the marine environment on the behavior of underwater multimodal machines, and in particular to a method for analyzing the impact of the hydrological and meteorological environment on the behavior of underwater multimodal machines. Background Art

[0002] The marine environment has a multifaceted impact on underwater multimodal machines. These impacts not only affect their operational performance and concealment, but also directly impact their safety and operational success. For example, strong currents can make it difficult to operate underwater multimodal machines. Severe marine weather conditions (such as storms) can severely impact underwater multimodal machines during surface operations, and strong surface disturbances can also be transmitted to deeper waters, affecting their stability and maneuverability. Therefore, to ensure the effective operation and safety of underwater multimodal machines, it is necessary to fully consider and adapt to various marine environmental factors. Summary of the Invention

[0003] In order to address the deficiencies in the prior art, the present invention provides a method for analyzing the impact of the hydrological and meteorological environment on the behavior of underwater multimodal machines based on a dynamic fuzzy evaluation algorithm.

[0004] The technical solution of the present invention is to implement a method for analyzing the impact of the hydrological and meteorological environment on the behavior of underwater multimodal machines, which is characterized by comprising the following steps:

[0005] 1) Based on the five behaviors of underwater multimodal robots, analyze the types of marine environmental impact factors under different behavioral states and establish an environmental impact assessment indicator system;

[0006] The five behaviors of the underwater multimodal machine, i.e., the five different working states of the underwater multimodal machine, include surface operation, surface communication, underwater detection, underwater operation, and equipment transportation;

[0007] The analysis of the types of factors affecting the marine environment under different behavior states and the establishment of an environmental impact assessment index system include analyzing various factors affecting the marine environment under five different behavior states of underwater multimodal vehicles. After comprehensive analysis, the following six marine environmental indicators are determined: sea surface wind, cloud environment, strong convection, visibility, waves, and currents;

[0008] 2) Read the user-set evaluation parameters for underwater multimodal machine behavior, the type of marine environment involved in the evaluation, the corresponding numerical ranges of each marine environmental factor at different evaluation levels, and the evaluation weights of various marine environmental factors;

[0009] The various types of marine environments involved in the assessment include the user being able to check a variety of marine environmental indicators in the system software and freely choose the type of marine environment involved in the assessment;

[0010] The different assessment levels include assessment levels 1-5, representing five assessment levels: safe, relatively safe, relatively dangerous, dangerous, and very dangerous;

[0011] 3) According to the marine environment type set by the user, read the marine environmental data of the assessment area and perform data preprocessing to ensure the consistency of the assessment data with the marine environment evaluation indicators;

[0012] 4) The gradient function is used as the evaluation membership function, and the quantitative evaluation results are obtained based on the fuzzy comprehensive evaluation model.

[0013] Preferably, in step 2), the numerical ranges corresponding to the various marine environmental factors under the different assessment levels include the numerical range intervals corresponding to the various marine environmental factors under the five assessment levels of safe, relatively safe, relatively dangerous, dangerous, and very dangerous, such as: waves of level 0-2 are safe, waves of level 2-3 are relatively safe, waves of level 3-4 are relatively dangerous, waves of level 4-5 are dangerous, and waves of level 5 and above are very dangerous.

[0014] Preferably, in step 2), the various marine environmental factor assessment weights include the weights of the six aforementioned marine environmental indicators that can be configured by the user in the system software: the larger the weight value, the greater the dominant share in the comprehensive evaluation result; conversely, the smaller the weight, the smaller the impact on the comprehensive evaluation result.

[0015] Preferably, in step 3), the data preprocessing includes that if the data value increases from small to large, the corresponding evaluation level also changes from safe, relatively safe, relatively dangerous, dangerous, and very dangerous, then it can be called consistent; otherwise, the value needs to be negatively processed to ensure that the change in the value is consistent with the change in the evaluation level.

[0016] Preferably, in step 4), the method of obtaining a quantitative evaluation result based on a fuzzy comprehensive evaluation model using a gradient function as the evaluation membership function comprises the following steps:

[0017] S1. Determine the evaluation membership function: Establish the evaluation membership function of each element according to the numerical range corresponding to the different levels of each marine environmental element;

[0018] S2. Substitute various marine environmental data into S1 to establish an evaluation membership function, and obtain the membership of each factor and the overall evaluation matrix R;

[0019]

[0020] Assume that there are m types of marine environmental elements involved in the assessment, of which r m1 Evaluate the membership degree corresponding to level 1 for the mth factor;

[0021] S3. Evaluation weights of various marine environmental factors set by users Perform normalization so that The normalized weight can be obtained

[0022]

[0023] Assume that there are m types of marine environmental elements involved in the assessment, among which Evaluate the weight for the mth factor;

[0024] S4. Perform a fuzzy comprehensive evaluation of the membership and weights of the various factors obtained in S3 to obtain a result vector B, thereby obtaining the hazard level and probability of the underwater multimodal machine behavior under the comprehensive influence of the current various marine environments;

[0025]

[0026] That is, if b1 is the largest in vector B, the underwater operation safety level of the underwater multimodal machine belongs to the first level; where ∨ is the maximum operation, which takes the maximum probability of the jth evaluation level among the m factors.

[0027] S5. Multiply the probability of the belonging level obtained in S4 by the five danger level scores and then calculate the sum, and finally obtain the situation value of the underwater multimodal machine behavior under the comprehensive influence of various current marine environments.

[0028] The present invention achieves the following beneficial effects: It utilizes a dynamic fuzzy comprehensive assessment algorithm, combined with an analysis of the impact of the ocean environment on the behavior of underwater multimodal machines, to establish a fuzzy comprehensive assessment model for dynamic marine environment assessment. This method allows users to dynamically select the types of marine environmental factors to be assessed, configures the assessment weights for each factor, and provides a customized assessment of the impact of the ocean environment on five types of underwater multimodal machine behavior. It offers high usability and practicality. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 is a flow chart of the analysis method of Example 1 of the present invention;

[0030] Figure 2 This is a flow chart of the fuzzy comprehensive evaluation model of the first embodiment of the present invention. DETAILED DESCRIPTION

[0031] The present invention will be further described below with reference to the accompanying drawings:

[0032] like Figure 1 、 2 As shown, the present invention provides a method for analyzing the impact of the hydrological and meteorological environment on the behavior of underwater multimodal machines, comprising the following steps:

[0033] The first step is to analyze the types of marine environmental impact factors under different behavior states based on the five behaviors of underwater multimodal robots and establish an environmental impact assessment indicator system;

[0034] (1) The five different working states of underwater multimodal machines include surface operation, surface communication, underwater detection, underwater operation, and equipment transportation;

[0035] (2) According to the five different behavior states of underwater multimodal robots, various marine environmental influencing factors are analyzed. After comprehensive analysis, a total of six marine environmental indicators are determined, including sea surface wind, cloud environment, strong convection, visibility, waves, and currents.

[0036] The second step is to read the user-set evaluation of underwater multimodal machine behavior, the type of marine environment involved in the evaluation, the corresponding numerical range of each marine environmental factor at different evaluation levels, and the evaluation weights of various marine environmental factors;

[0037] (1) Types of marine environments to be assessed: Users can check six types of marine environmental indicators in the system software and freely choose the type of marine environment to be assessed.

[0038] (2) Assessment levels 1-5 represent five levels: safe, relatively safe, relatively dangerous, dangerous, and very dangerous.

[0039] (3) The corresponding numerical ranges of various marine environmental factors under different assessment levels: the numerical ranges of various marine environmental factors under the five assessment levels of safe, relatively safe, relatively dangerous, dangerous, and very dangerous. For example, waves of level 0-2 are safe, waves of level 2-3 are relatively safe, waves of level 3-4 are relatively dangerous, waves of level 4-5 are dangerous, and waves of level 5 and above are very dangerous.

[0040] (4) Assessment weights of various marine environmental factors: Users can configure the weights of six marine environmental indicators in the system software. The larger the weight value, the greater the dominant share in the comprehensive evaluation results; conversely, the smaller the weight, the smaller the impact on the comprehensive evaluation results.

[0041] The third step is to read the marine environmental data of the assessment area according to the marine environment type set by the user, and perform data preprocessing to ensure the consistency of the assessment data with the marine environmental evaluation indicators;

[0042] The processing process is as follows: if the data value changes from small to large, and the corresponding assessment level also changes from safe, relatively safe, relatively dangerous, dangerous, and very dangerous, then it can be called consistent. Otherwise, the value needs to be negatived to ensure that the change in value is consistent with the change in assessment level.

[0043] The fourth step is to use the gradient function as the evaluation membership function and obtain the quantitative evaluation results based on the fuzzy comprehensive evaluation model, which includes the following steps:

[0044] (1) Determine the evaluation membership function: According to the numerical range corresponding to the different levels of each marine environmental factor, establish the evaluation membership function of each factor;

[0045] (2) Substituting various marine environmental data into (1) to establish the evaluation membership function, the membership of each factor and the total evaluation matrix R can be obtained;

[0046]

[0047] Assume that there are m types of marine environmental elements involved in the assessment, of which r m1 is the membership degree corresponding to the evaluation level 1 of the mth factor.

[0048] (3) Assessment weights of various marine environmental factors set by users Perform normalization so that The normalized weight can be obtained

[0049]

[0050] Assume that there are m types of marine environmental elements involved in the assessment, among which Evaluate the weight for the mth factor;

[0051] (4) The fuzzy comprehensive evaluation of the membership and weights of the various elements obtained in (3) can be used to obtain the result vector B, and then the danger level and the probability of belonging to the level of the underwater multimodal machine behavior under the comprehensive influence of various current marine environments are obtained;

[0052]

[0053] That is, if b1 is the largest in vector B, the underwater operation safety level of the underwater multimodal machine belongs to the first level; where ∨ is the maximum operation, which takes the maximum probability of the jth evaluation level among the m factors.

[0054] (5) The probability of belonging to the level obtained in (4) is multiplied by the five danger level scores and then summed up to finally obtain the situation value of the underwater multimodal machine behavior under the comprehensive influence of various current marine environments.

[0055] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any other manner. Any person skilled in the art may utilize the above-disclosed technical content to modify or modify the present invention into equivalent embodiments. However, any simple modifications, equivalent variations, and modifications to the above embodiments that do not depart from the technical content of the present invention and are based on the technical essence of the present invention remain within the scope of protection of the present invention.

Claims

1. A method for analyzing the impact of the hydrological and meteorological environment on the behavior of underwater multimodal machines, characterized by: The following steps are involved: 1) Based on the five behaviors of underwater multimodal robots, analyze the types of marine environmental impact factors under different behavioral states and establish an environmental impact assessment indicator system; The five behaviors of the underwater multimodal machine, i.e., the five different working states of the underwater multimodal machine, include surface operation, surface communication, underwater detection, underwater operation, and equipment transportation; The analysis of the types of factors affecting the marine environment under different behavior states and the establishment of an environmental impact assessment index system include analyzing various factors affecting the marine environment under five different behavior states of underwater multimodal vehicles. After comprehensive analysis, the following six marine environmental indicators are determined: sea surface wind, cloud environment, strong convection, visibility, waves, and currents; 2) Read the user-set evaluation parameters for underwater multimodal machine behavior, the type of marine environment involved in the evaluation, the corresponding numerical ranges of each marine environmental factor at different evaluation levels, and the evaluation weights of various marine environmental factors; The various types of marine environments involved in the assessment include the user being able to check a variety of marine environmental indicators in the system software and freely choose the type of marine environment involved in the assessment; The different assessment levels include assessment levels 1-5, representing five assessment levels: safe, relatively safe, relatively dangerous, dangerous, and very dangerous; 3) According to the marine environment type set by the user, read the marine environmental data of the assessment area and perform data preprocessing to ensure the consistency of the assessment data with the marine environment evaluation indicators; 4) The gradient function is used as the evaluation membership function, and the quantitative evaluation results are obtained based on the fuzzy comprehensive evaluation model.

2. The method for analyzing the impact of the hydrological and meteorological environment on the behavior of underwater multimodal machines according to claim 1, characterized in that: In step 2), the numerical ranges corresponding to the various marine environmental factors under the different assessment levels include the numerical range intervals corresponding to the various marine environmental factors under the five assessment levels of safe, relatively safe, relatively dangerous, dangerous, and very dangerous, such as: waves of level 0-2 are safe, waves of level 2-3 are relatively safe, waves of level 3-4 are relatively dangerous, waves of level 4-5 are dangerous, and waves of level 5 and above are very dangerous.

3. The method for analyzing the impact of the hydrological and meteorological environment on the behavior of underwater multimodal machines according to claim 2, characterized in that: In step 2), the weights of the various marine environmental factors assessments, including the weights of the six aforementioned marine environmental indicators, can be configured by the user in the system software: the larger the weight value, the greater the dominant share in the comprehensive evaluation results; On the contrary, the smaller the weight, the smaller the impact on the comprehensive evaluation results.

4. The method for analyzing the impact of the hydrological and meteorological environment on the behavior of underwater multimodal machines according to claim 3 is characterized by: In step 3), the data preprocessing includes that if the data value increases from small to large, the corresponding evaluation level also changes from safe, relatively safe, relatively dangerous, dangerous, and very dangerous, then it can be called consistent; otherwise, the value needs to be negatively processed to ensure that the change in value is consistent with the change in evaluation level.

5. The method for analyzing the impact of the hydrological and meteorological environment on the behavior of underwater multimodal machines according to claim 4, characterized in that: In step 4), the method of obtaining a quantitative evaluation result based on a fuzzy comprehensive evaluation model using a gradient function as an evaluation membership function comprises the following steps: S1. Determine the evaluation membership function: Establish the evaluation membership function of each element according to the numerical range corresponding to the different levels of each marine environmental element; S2. Substitute various marine environmental data into S1 to establish an evaluation membership function, and obtain the membership of each factor and the overall evaluation matrix R; Assume that there are m types of marine environmental elements involved in the assessment, of which r m1 Evaluate the membership degree corresponding to level 1 for the mth factor; S3. Evaluation weights of various marine environmental factors set by users Perform normalization so that The normalized weight can be obtained Assume that there are m types of marine environmental elements involved in the assessment, among which Evaluate the weight for the mth factor; S4. Perform a fuzzy comprehensive evaluation of the membership and weights of the various factors obtained in S3 to obtain a result vector B, thereby obtaining the hazard level and probability of the underwater multimodal machine behavior under the comprehensive influence of the current various marine environments; That is, if b1 is the largest in vector B, the underwater operation safety level of the underwater multimodal machine belongs to the first level; where ∨ is the maximum operation, which takes the maximum probability of the jth evaluation level among the m factors. S5. Multiply the probability of the belonging level obtained in S4 by the five danger level scores and then calculate the sum, and finally obtain the situation value of the underwater multimodal machine behavior under the comprehensive influence of various current marine environments.

Images