Decision-making method suitable for underwater vehicle magnetic field control strategy design

Abstract

The invention relates to a decision-making method suitable for underwater vehicle magnetic field control strategy design, and the method comprises the steps: determining a threat source weight through expert subjective weighting and objective experience weighting according to an obtained multi-target control degaussing strategy set, and determining an optimal combination weight in combination with a moment estimation theory; and evaluation and decision-making of the degaussing effect are further carried out based on an enhanced grey correlation analysis method of an optimal sequence technology. The method is suitable for underwater vehicle magnetic field control, and can better balance the influence of data correlation and weight, thereby improving the accuracy and stability of degaussing strategy decision.

Description

Technical Field

[0001] The invention belongs to the technical field of underwater vehicle magnetic field control and discloses a decision-making method suitable for designing a magnetic field control strategy for an underwater vehicle. Background Art

[0002] Due to the use of magnetic materials, corrosion and anti-corrosion effects, underwater vehicles will excite magnetic fields in the surrounding seawater and air, and the magnetic field has become an important signal source threatening the safety of underwater vehicles. According to the characteristics of the magnetic field, fixed magnetic field control, magnetic field compensation system, cathodic protection system optimization technology, active shaft grounding device and other technologies have been applied to control various magnetic field sources.

[0003] In the context of modern adversarial forces, underwater vehicles face threats from various magnetic detection technologies, which requires their onboard magnetic field control systems to have stronger adaptability. The decision-making of magnetic field control strategies is an indispensable part of magnetic field control technology. When underwater vehicles face complex threat sources, they can quickly and accurately select appropriate magnetic field control strategies from the strategy set and put them into magnetic field control operation to further protect themselves from the magnetic field more reasonably and effectively. Advanced magnetic field control technology is widely used in underwater vehicles and ships to achieve magnetic field control and provide protection. A set of predefined magnetic field control strategies helps the magnetic field control system respond quickly, and the decision-making process of the magnetic field control strategy further improves the demagnetization efficiency of underwater vehicles and ensures their safe navigation.

[0004] The existing magnetic field control strategy for underwater vehicles usually considers the control of a single target magnetic field source with the smallest magnetic field intensity through the least square method, single-objective evolutionary algorithm, etc. In addition, the existing methods ignore the coordinated control of various field sources. When facing the threats of different magnetic detection technologies, the magnetic field control targets that need to be paid attention to, such as spatial position and characteristic control physical quantities, also need to be adaptively adjusted. In recent years, some people have tried to use multiple targets as control targets at the same time on the existing technology, and provide a strategy set with non-dominated properties through a multi-objective evolutionary algorithm, and then select the most suitable solution for the current situation through expert decision-making or objective information. However, subjective methods such as expert decision-making have limitations such as subjective bias and misjudgment, while objective methods only rely on data and ignore the disadvantages of expert experience. Therefore, in the field of magnetic field control of underwater vehicles, there is an urgent need for a demagnetization strategy decision method that can integrate subjective suggestions and objective information. At present, there is no demagnetization strategy decision method that can be applied to multiple control targets. Summary of the invention

[0005] The present invention discloses a decision-making method applicable to the design of a magnetic field control strategy for an underwater vehicle, and the decision-making method applicable to the design of a magnetic field control strategy for an underwater vehicle comprises the following steps:

[0006] Step 1, establishing a magnetic field control strategy set combination, where the magnetic field control strategy set combination is a combination of control parameters;

[0007] Step 2: According to the threat source situation in the adversarial environment, a subjective weighting method based on expert experience is used to obtain subjective weights;

[0008] Step 3, according to the threat source situation in the confrontation environment, an objective weighting method based on historical objective information is performed to obtain objective weights;

[0009] Step 4: Combine the subjective weights and objective weights to calculate the optimal combination weights based on the moment estimation theory.

[0010] Furthermore, in step 4, the following steps are also included:

[0011] Step 41, determining the discriminant of the subjective weight and the objective weight based on the subjective weight set and the objective weight set;

[0012] Step 42, using the moment estimation theory, the important coefficients of the subjective weight and the objective weight are determined respectively;

[0013] Step 43, determining the optimal weight combination according to the principle of minimum deviation in the optimal weight combination;

[0014] Step 44, the optimal combination weight is evaluated by the TOPSIS-grey correlation analysis method to determine the best magnetic field control strategy solution suitable for the current confrontation environment.

[0015] Furthermore, in step 41, the subjective weight set and the objective weight set are:

[0016]

[0017] W s is the subjective weight set obtained in step 2, W b represents the objective weight set obtained in step 3, k represents the sum of weighting methods, m represents the total number of weight indicators, j represents the sequence number of weight indicators, l represents the number of subjective weight vectors, and w sj represents the subjective weight vector, w bj represents the objective weight vector;

[0018] According to the moment estimation theory, the corresponding subjective weight and objective weight discriminant are calculated:

[0019]

[0020] E(W sj ) represents the expected value of subjective weight, E(W bj ) represents the expected value of the objective weight, α j represents the subjective judgment index, βj It is an objective judgment indicator.

[0021] Furthermore, in step 42, using the moment estimation theory, the important coefficients α and β of the subjective weight and the objective weight are determined respectively:

[0022]

[0023]

[0024] α is the importance coefficient of subjective weight, and β is the importance coefficient of objective weight.

[0025] Furthermore, in step 43, the optimal weight combination W j It is obtained by combining the subjective weight based on expert experience with the objective weight based on influencing factors:

[0026]

[0027] H represents the weight optimization target, w j represents the optimal combination weight.

[0028] Furthermore, in step 44, a positive ideal decision matrix D is constructed. + and negative ideal decision matrix D - :

[0029]

[0030] represents the attribute value of the jth evaluation index of the i-th positive ideal solution (1≤i≤n, 1≤j≤m), represents the attribute value of the jth evaluation index of the i-th negative ideal solution; represents the most ideal value in each column of the matrix, represents the positive and negative ideal decision vectors, represents the positive and negative decision vector;

[0031] The grey relationship coefficient of the jth evaluation index of the i-th scheme is the positive ideal grey relationship coefficient and negative ideal grey relation coefficient The calculation method is:

[0032]

[0033] ρ represents the resolution coefficient, represents the absolute minimum value, is the absolute maximum value;

[0034] The positive ideal grey relational degree of the i-th solution Negative ideal grey relational degree of the i-th solution Determined by grey correlation coefficient:

[0035]

[0036] C i Represents the grey correlation coefficient of the i-th solution;

[0037] Under the same optimal weight combination, the gray proximity C between different decision strategies i The larger it is, the closer the scheme is to the ideal magnetic field control strategy.

[0038] The beneficial effects achieved by the present invention are:

[0039] The decision-making method proposed in the present invention can effectively guide the design and decision-making of underwater vehicle target magnetic field control strategy, as well as the evaluation of underwater target magnetic field characteristic control effect;

[0040] The decision-making method proposed in the present invention combines the advantages of indicator correlation analysis and comprehensive evaluation, can flexibly select the optimal solution under different circumstances, can more comprehensively understand the relationship between different indicators, and provide a more accurate basis for decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is a schematic diagram of the magnetic field control technology process;

[0042] Figure 2 This is the comparison of the results of TOPSIS-GRA, SAW and GRA when simulating a large threat source above: (a) SAW. (b) GRA. (c) TOPSIS-GRA;

[0043] Figure 3 This is the comparison of the results of TOPSIS-GRA, SAW and GRA when simulating a large threat source below: (a) SAW. (b) GRA. (c) TOPSIS-GRA;

[0044] Figure 4 This is the comparison of the results of TOPSIS-GRA, SAW and GRA when simulating the threats from above and below at the same time: (a) SAW. (b) GRA. (c) TOPSIS-GRA. DETAILED DESCRIPTION

[0045] The present invention will be further described below in conjunction with specific embodiments, and the advantages and features of the present invention will become clearer as the description proceeds. However, these embodiments are exemplary only and do not constitute any limitation to the scope of the present invention. It should be understood by those skilled in the art that the details and forms of the technical solution of the present invention may be modified or replaced without departing from the spirit and scope of the present invention, but these modifications and replacements all fall within the scope of protection of the present invention.

[0046] The comprehensive goal of underwater vehicle magnetic field control is to prevent aerial magnetic detection, underwater magnetic detection and underwater weapons, and to ensure the normal operation of the underwater vehicle itself, which includes multiple technical and tactical indicators. At the same time, there are many sources of underwater vehicle magnetic fields, and different technical measures need to be taken to control the magnetic field, and corresponding control indicators are proposed for different magnetic field sources. When there are many control indicators, when comprehensively evaluating the magnetic field control effect of underwater vehicles, it is necessary to first determine the weight of each evaluation indicator before a comprehensive score can be made. If the traditional weight is determined based on subjective, objective or subjective and objective combination principles, there are certain limitations, and it is difficult to obtain results that satisfy all parties. It is necessary to propose a weight determination method that can take into account both subjective and objective factors, and compare and score the pros and cons of each scheme.

[0047] When multiple indicators are coordinated and controlled, it is necessary to consider how to determine the indicator weights. There are currently two main methods for determining the weights of multiple indicators: subjective weighting methods and objective weighting methods. Subjective weighting methods, such as the ordinal ranking method and the analytic hierarchy process, reflect the ranking relationship of experts on the importance of different indicators; objective weighting methods, such as the entropy weight method, the principal component analysis method, and the CRITIC rule, determine the weights of indicators based on the relationship between the original data through mathematical theoretical methods, reflecting the relationship between the weights of evaluation indicators and changes in original data. The evaluation results obtained by a single weighting method are greatly affected by the decision maker or the mathematical method used. When different experts have different preferences for weighting method selection, it is difficult for traditional weighting methods to obtain results that most experts can agree on.

[0048] In the magnetic field control strategy decision method, in view of the limited scalability and flexibility of SAW (simple weighting method) and the insufficient standardization and order preservation of GRA (grey relational analysis method), the present invention will introduce the concept of TOPSIS (strategy of prioritization by similarity to ideal solution) to enhance the grey relational analysis method, and will provide a decision method based on improved grey relational analysis, including the following steps:

[0049] Step 1, establishing a magnetic field control strategy set combination;

[0050] The magnetic field control strategy set combination is a combination of control parameters; the control strategy set can be obtained by single-objective optimization or multi-objective optimization, or can be obtained based on expert experience;

[0051] Step 2: According to the threat source situation in the adversarial environment, a subjective weighting method based on expert experience is used to obtain subjective weights;

[0052] The relevant subjective weighting methods can be selected from the order relationship ranking method, hierarchical analysis method, etc.

[0053] Step 3: According to the threat source situation in the confrontation environment, an objective weighting method based on historical objective information is performed to obtain objective weights; relevant objective weighting methods can include entropy weight method, principal component analysis method, CRITIC method, etc.;

[0054] Step 4: Combine the subjective weights and objective weights to calculate the optimal combination weights based on the moment estimation theory. After combining the subjective weight values ​​and the objective weight values, the optimal weights of the evaluation criteria are determined according to the moment estimation theory.

[0055] Step 41, determining the discriminant of the subjective weight and the objective weight based on the subjective weight set and the objective weight set;

[0056]

[0057] Where: W s is the subjective weight set obtained in step 2, W b represents the objective weight set obtained in step 3, k represents the sum of weighting methods, m represents the total number of weight indicators, j represents the sequence number of weight indicators, l represents the number of subjective weight vectors, and w sj represents the subjective weight vector, w bj represents the objective weight vector.

[0058] According to the moment estimation theory, when k samples are drawn from two populations, a j (1≤j≤m) For each indicator attribute, its expected value can be determined and the corresponding discriminant α can be calculated j and β j :

[0059]

[0060] Where: E(W sj ) represents the expected value of subjective weight, E(W bj ) represents the expected value of the objective weight, α j represents the subjective judgment index, β j It is an objective judgment indicator.

[0061] Step 42, using the moment estimation theory, the important coefficients α and β of the subjective weight and the objective weight are determined respectively.

[0062]

[0063] Step 43, according to the principle of minimum deviation in the optimal weight combination, determine the optimal weight combination W j ;

[0064] The optimal weight combination W j It is obtained by combining the subjective weight based on expert experience with the objective weight based on influencing factors.

[0065]

[0066] H represents the weight optimization target, w j represents the optimal combination weight.

[0067] Step 44, the optimal combination weight is evaluated by the TOPSIS-grey correlation analysis method to determine the best magnetic field control strategy solution suitable for the current confrontation environment.

[0068]

[0069] Where: represents the attribute value of the jth evaluation index of the i-th positive ideal solution (1≤i≤n, 1≤j≤m), Represents the attribute value of the i-th evaluation index of the i-th negative ideal solution. represents the most ideal value in each column of the matrix, represents the positive and negative ideal decision vectors, represents the positive and negative decision vector.

[0070] In order to determine the grey relationship coefficient of the jth evaluation index of the i-th scheme, the positive ideal grey relationship coefficient and negative ideal grey relation coefficient The calculation method of is as follows.

[0071]

[0072] In the formula, ρ represents the resolution coefficient, represents the absolute minimum value, is the absolute maximum value.

[0073] The positive ideal grey relational degree of the i-th solution Negative ideal grey relational degree of the i-th solution Determined by grey correlation coefficient:

[0074]

[0075] Where: C i Represents the grey relational coefficient of the ith solution.

[0076] In the presence of a specific threat source, that is, under the same optimal weight combination, the grey correlation coefficient C between different decision strategies is i The larger the value is, the closer the scheme is to the ideal magnetic field control strategy. In other words, this shows that under the current circumstances, the current magnetic field control scheme is the most suitable choice among the existing magnetic field control strategies.

[0077] When the present invention was proposed, the magnetic characteristics and magnetic field control efficiency of underwater vehicles were simulated. A set of adaptable magnetic field control strategy schemes were established for multiple targets, and three threat scenarios were assumed, including air defense magnetic detection, underwater magnetic detection and underwater weapons. The optimization targets included upper peak value, upper peak value, upper gradient value and lower gradient value, etc., forming a set of selectable magnetic field control strategy set combinations. The proposed TOPSIS-GRA method was quantitatively compared to verify its effectiveness and accuracy in magnetic field control strategy. Through simulation scenario experiments, TOPSIS-GRA was comprehensively compared with SAW and GRA.

[0078] Combination Figure 1 , Figure 1 It is represented as a process of magnetic field control algorithm and control technology. Multi-objective heuristic optimization calculation is performed through basic information data of the magnetic field to be controlled to obtain a demagnetization strategy set that meets multiple control objectives. The non-dominated optimal decision-making plan in a suitable complex environment is selected through the method described in the present invention, and the control coil is adjusted according to a preset method. Real-time adjustment and prediction are performed based on electro-magnetic relationships such as Biotsafar's theorem and other factors such as navigation speed, heading, internal magnetic field coefficient, magnetic field extrapolation coefficient, etc.

[0080] Combination Figure 2 , and the more detailed data and their ranking are shown in Table 1.

[0081] Table 1 Correlation and ranking when the upper threat source is large

[0082]

[0083] When faced with significant threats from the above factors, both the SAW and TOPSIS-GRA methods selected Option 3 as the best option, while the GRA method selected Option 2. Further comparison of the correlation between Option 2 and Option 3 shows that they are very close. In this case, it is challenging to determine the pros and cons of the two solutions based on subjective judgment alone.

[0084] Combination Figure 3 , and the more detailed data and their ranking are presented in the form of Table 2.

[0085] Table 2 Correlation and ranking when the threat source at the bottom is large

[0086]

[0087] In this case, all three methods provide different optimal solutions from different analytical perspectives. The SAW method selects solution 6 as the best solution by performing a simple weighted calculation on multiple indicators. The GRA method determines solution 8 as the best choice based on the correlation between indicators. At the same time, the TOPSIS-GRA method gives full play to the advantages of the TOPSIS method by considering the degree of correlation between indicators, and selects solution 7 as the best solution. This solution once again emphasizes the different advantages of various methods in balancing multiple indicators. The SAW method is suitable for direct weighted calculation, while the GRA method emphasizes the correlation between indicators. By comparison, the TOPSIS-GRA method can comprehensively consider both the degree of correlation between indicators and the specific performance of indicators, thereby providing more accurate decision-making criteria for complex decision-making problems.

[0088] Combination Figure 4 , and the more detailed data and their ranking are presented in the form of Table 3.

[0089] Table 3 Correlation and ranking when the upper and lower parts face threat sources at the same time

[0090]

[0091] In this case, both the SAW and TOPSIS-GRA methods came up with the same optimal choice, which is Option 4. Both methods believe that Option 4 is the best decision in this case. However, since the GRA method pays more attention to the correlation between indicators, it still chooses Option 8 as the optimal decision. While the GRA method emphasizes the correlation between indicators, it also leads to insufficient overall ranking ability. In contrast, the TOPSIS-GRA method effectively considers the interrelationships between indicators while maintaining the decision ranking ability.

[0092] It is worth noting that in the decision ranking of all three options, Option 10 is always considered the worst choice. After studying its data, it is found that Option 10 performs the best among a group of magnetic field control strategies, with low downlink peak error and root mean square error values. However, it has obvious deficiencies in uplink peak error and root mean square error, which is the main reason why it is considered the worst option in the overall evaluation.

[0093] The above experimental results show that the TOPSIS-GRA method combines the advantages of indicator correlation analysis and comprehensive evaluation, and can flexibly select the optimal solution under different circumstances. It can more comprehensively understand the relationship between different indicators and provide a more accurate basis for decision-making. In summary, compared with SAW and GRA, the TOPSIS-GRA method has significant advantages in considering comprehensive performance, adaptability and stability, and is a valuable decision-making method for underwater vehicle magnetic field control strategy.

[0094] The present invention is not limited to the above-mentioned specific implementation modes. A person skilled in the art may implement the present invention in various other specific implementation modes according to the embodiments and the disclosure of the drawings. Therefore, any design that adopts the design structure and concept of the present invention and makes some simple transformations or changes shall fall within the scope of protection of the present invention.

Claims

1. A decision-making method for designing a magnetic field control strategy for an underwater vehicle, characterized in that: The decision-making method for designing a magnetic field control strategy for an underwater vehicle comprises the following steps: Step 1, establishing a magnetic field control strategy set combination, where the magnetic field control strategy set combination is a combination of control parameters; Step 2: According to the threat source situation in the confrontation environment, a subjective weighting method based on expert experience is used to obtain subjective weights; Step 3, according to the threat source situation in the confrontation environment, an objective weighting method based on historical objective information is performed to obtain objective weights; Step 4: Combine the subjective weights and objective weights to calculate the optimal combination weights based on the moment estimation theory.

2. The decision-making method for designing a magnetic field control strategy for an underwater vehicle according to claim 1, characterized in that: In step 4, the following steps are also included: Step 41, determining the discriminant of the subjective weight and the objective weight based on the subjective weight set and the objective weight set; Step 42, using the moment estimation theory, the important coefficients of the subjective weight and the objective weight are determined respectively; Step 43, determining the optimal weight combination according to the principle of minimum deviation in the optimal weight combination; Step 44, the optimal combination weight is evaluated by the TOPSIS-grey correlation analysis method to determine the best magnetic field control strategy solution suitable for the current confrontation environment.

3. The decision-making method for designing a magnetic field control strategy for an underwater vehicle according to claim 2, characterized in that: In step 41, the subjective weight set and the objective weight set are: W s is the subjective weight set obtained in step 2, W b represents the objective weight set obtained in step 3, k represents the sum of weighting methods, m represents the total number of weight indicators, j represents the sequence number of weight indicators, l represents the number of subjective weight vectors, and w sj represents the subjective weight vector, w bj represents the objective weight vector; According to the moment estimation theory, the corresponding subjective weight and objective weight discriminant are calculated: E(W sj ) represents the expected value of subjective weight, E(W bj ) represents the expected value of the objective weight, α j represents the subjective judgment index, β j It is an objective judgment indicator.

4. The decision-making method for designing a magnetic field control strategy for an underwater vehicle according to claim 3, characterized in that: In step 42, using the moment estimation theory, the important coefficients α and β of the subjective weight and the objective weight are determined respectively: α is the importance coefficient of subjective weight, and β is the importance coefficient of objective weight.

5. The decision-making method for designing a magnetic field control strategy for an underwater vehicle according to claim 4, characterized in that: In step 43, the optimal weight combination W j It is obtained by combining the subjective weight based on expert experience with the objective weight based on influencing factors: H represents the weight optimization target, w j represents the optimal combination weight.

6. The decision-making method for designing a magnetic field control strategy for an underwater vehicle according to claim 5, characterized in that: In step 44, construct the positive ideal decision matrix D + and negative ideal decision matrix D - : represents the attribute value of the jth evaluation index of the i-th positive ideal solution (1≤i≤n,1≤j≤m), represents the attribute value of the jth evaluation index of the i-th negative ideal solution; represents the most ideal value in each column of the matrix, represents the positive and negative ideal decision vectors, represents the positive and negative decision vector; The grey relationship coefficient of the jth evaluation index of the i-th scheme is the positive ideal grey relationship coefficient and negative ideal grey relation coefficient The calculation method is: ρ represents the resolution coefficient, represents the absolute minimum value, is the absolute maximum value; The positive ideal grey relational degree of the i-th solution Negative ideal grey relational degree of the i-th solution Determined by grey correlation coefficient: C i Represents the grey correlation coefficient of the i-th solution; Under the same optimal weight combination, the gray proximity C between different decision strategies i The larger it is, the closer the scheme is to the ideal magnetic field control strategy.

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