Ship comprehensive performance monitoring method

Abstract

The invention discloses a ship comprehensive performance monitoring method. The method comprises a rapidity performance monitoring process and a cost performance monitoring process. Wherein the rapidity performance monitoring process comprises the following steps: respectively constructing the total resistance, torque coefficient and thrust coefficient of the ship through three groups of polynomials containing unknown numbers to be identified; dividing the value ranges of the three groups of unknown numbers to be identified into a plurality of value intervals; solving an optimal solution in each value interval of the unknown number to be identified through an improved genetic algorithm, and taking the minimum optimal solution as a final solution; and through values of three groups of unknown numbers to be identified corresponding to the final solution, calculating each real-time parameter of the ship in the ship mathematical model, and completing ship rapidity performance monitoring. And the cost performance monitoring process comprises the step of calculating the cost performance through the change degree of the navy coefficient, the change degree of the function index of the monitoring system and the cost of the monitoring system. According to the method, the ship rapidity performance index can be better obtained, and the cost efficiency can be more accurately and dynamically obtained.

Description

Technical Field

[0001] The invention relates to the technical field of ship monitoring, and in particular to a method for monitoring the comprehensive performance of a ship. Background Art

[0002] At present, there are some challenges in improving the rapid performance of ships. Ship-related data is obtained through sensors and other equipment, but it cannot be converted into the required rapid performance data to improve rapid performance. Traditional systems usually only measure total power and speed, but do not monitor the changes in hull resistance at different speeds, making it difficult to analyze the best matching point between speed and power. Traditional systems lack the ability to perform complex modeling and analysis on monitoring data, making it difficult to convert raw data into effective indicators that describe rapid performance.

[0003] In addition, there is also the problem of how to ensure that each component unit has a good cost-effectiveness ratio. Improving the cost-effectiveness ratio usually involves multiple goals, such as equipment life and maintenance costs. Traditional systems only support single-goal optimization and ignore multi-goal balance. Traditional systems usually manage equipment through fixed maintenance cycles rather than dynamically optimizing based on cost-effectiveness ratios. For example, regular maintenance of the host may be carried out in advance rather than based on the actual operating status, which increases maintenance costs. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a method for monitoring the comprehensive performance of a ship to solve the technical problems in the prior art of poor monitoring of rapid performance indicators and inability to achieve good dynamic monitoring cost-effectiveness.

[0005] The present invention provides a ship comprehensive performance monitoring method, including: a rapid performance monitoring process and a cost-effectiveness monitoring process;

[0006] The rapid performance monitoring process includes the following steps:

[0007] Step A1: Construct a ship mathematical model and optimize the ship mathematical model. The specific optimization is as follows:

[0008] The total resistance of the ship is optimized by constructing the total resistance coefficient of the ship through a quaternary quartic polynomial containing a set of unknown variables to be identified;

[0009] constructing torque coefficients through a three-variable quadratic polynomial containing another set of unknowns to be identified;

[0010] Construct thrust coefficients through a three-variable quadratic polynomial containing a third set of unknowns to be identified;

[0011] Step A2: obtaining real-time ship data, and dividing the value ranges of the three groups of unknown numbers to be identified into several value intervals;

[0012] Step A3: Using the improved genetic algorithm, according to the real-time data of the ship and the mathematical model of the ship, the optimal solution in each value interval of the unknown number to be identified is obtained at the same time, and the smallest optimal solution is taken as the final solution;

[0013] Step A4: by taking the values ​​of the three groups of unknown numbers to be identified corresponding to the final solution, the real-time parameters of the ship in the ship mathematical model are calculated to complete the rapid performance monitoring of the ship;

[0014] Cost effectiveness monitoring process, including:

[0015] The cost effectiveness is calculated by the following formula:

[0016] I=EF / W

[0017] Where E is the change degree of the navy coefficient; F is the change degree of the monitoring system functional indicators; W is the cost of the monitoring system.

[0018] Furthermore, the total resistance formula of the ship is:

[0019]

[0020] In the formula, C T is the total resistance coefficient of the ship; ρ is the fluid density; S is the wet surface area; V is the ship speed.

[0021] Furthermore, the formula for the total resistance coefficient of the ship is:

[0022]

[0023] Where a1 and a2 are the first group of unknowns to be identified; V is the ship speed, g is the acceleration of gravity, and L is the characteristic length of the ship.

[0024] Furthermore, the formula of the torque coefficient is:

[0025]

[0026] Where J is the speed coefficient; β0, β1, β2 are the second group of unknowns to be identified.

[0027] Furthermore, the formula of the thrust coefficient is:

[0028] K T =β3+β4×J+β5×J 2

[0029] Where J is the speed coefficient; β3, β4, and β5 are the third group of unknowns to be identified.

[0030] Furthermore, the objective function in the improved genetic algorithm is:

[0031]

[0032] in,

[0033]

[0034] Where a1 and a2 are the first group of unknowns to be identified; V is the ship speed, g is the acceleration of gravity, L is the characteristic length of the ship; J is the advance coefficient; β0, β1, β2 are the second group of unknowns to be identified; β3, β4, β5 are the third group of unknowns to be identified; ω is the semi-flow fraction; ρ is the fluid density; S is the wetted surface area; P s is the host power, η s is the shafting efficiency, η R is the relative rotational efficiency; n is the rotational speed, D is the propeller diameter; t is the thrust deduction fraction.

[0035] Furthermore, the formula of the system function index is:

[0036] R=X L1 Y L2 Z L3 Q L4 N L5 ,

[0037] In the formula, X is the collection efficiency; Y is the information processing efficiency; Z is the monitoring efficiency; Q is the degree of intelligence; N is the consulting service capability; L1×L2×L3×L4×L5=1

[0038] Furthermore, the specific calculation formula of the cost-effectiveness is:

[0039] I = EF / W = ΔK (ΔX L1 ΔY L2 ΔZ L3 ΔQ L4 ΔN L5 ) / (WP+WI+WM)

[0040] In the formula, △K is the degree of change of the navy coefficient; △X is the degree of change of collection efficiency; △Y is the degree of change of information processing efficiency; △Z is the degree of change of monitoring efficiency; △Q is the degree of change of intelligence; △N is the degree of change of consulting service capability; WP is the purchase cost; WI is the installation cost; WM is the operation and maintenance cost.

[0041] Furthermore, the sensor performance in the monitoring performance is obtained in the following manner:

[0042] Obtain sensor effectiveness through fuzzy comprehensive evaluation function:

[0043]

[0044] Where η is the membership value; U1 is the sensor equipment index weight, U2 is the sensor equipment cost weight; U 11 is the vibration sensor accuracy weight; U 12 is the pressure sensor accuracy weight; U 13 is the accelerometer accuracy weight; U 14 is the flow sensor accuracy weight; U 15 is the water depth sensor accuracy weight; U 16 is the water quality sensor accuracy weight; U 17 U is the accuracy weight of ultrasonic anemometer; 18 is the water temperature sensor accuracy weight; U 19 is the accuracy weight of other monitoring equipment; U 21 Divided into vibration sensor cost weight, U 22 is the cost weight of the pressure sensor, U 23 is the cost weight of the acceleration sensor, U 24 is the cost weight of the flow sensor, U 25 is the cost weight of the water depth sensor, U 26 is the cost weight of water quality sensor, U 27 is the cost weight of ultrasonic anemometer, U 28 is the water temperature sensor cost weight, U 29 Cost weight for other monitoring equipment.

[0045] Furthermore, the method for obtaining the data transmission efficiency in the collection efficiency is:

[0046] Obtain data transmission efficiency through fuzzy comprehensive evaluation function:

[0047] H(CX)=U3·(U 31 ·η 31 +U 32 ·η 32 +U 33 ·η 33 +U 34 ·η 34 )+U4.(U 41 ·η 41 +U 42 ·η 42 +U 43 ·η 43 +U 44 ·η 44 )

[0048] Where η is the membership value; U3 is the data transmission equipment index weight; U4 is the data transmission equipment cost weight; U 31 is the data transmission rate weight; U 32 is the network delay weight; U 33is the data transmission reliability weight; U 34 is the system compatibility weight; U 41 Build cost weights for hardware and software wireless communications; U 42 is the network service fee weight; U 43 is the data security technology cost weight; U 44 Weights for maintenance and upgrade costs.

[0049] Furthermore, the method for obtaining the intelligent control efficiency in the intelligent degree is:

[0050] Obtaining intelligent control effectiveness through fuzzy comprehensive evaluation function:

[0051] H(KZ)=U5·(U 51 ·η 51 +U 52 ·η 52 +U 53 ·η 53 +U 54 ·η 54 )+U6.(U 61 ·η 61 +U 62 ·η 62 +U 63 ·η 63 )

[0052] In the formula, η is the membership value; U5 is the weight of the intelligent control equipment index; U6 is the weight of the intelligent control cost; U 51 is the weight of automation level; U 52 is the precision weight; U 53 is the reliability weight; U 54 is the response speed weight; U 55 is the system integration weight; U 61 is the energy consumption cost weight; U 62 is the weight of insurance and risk management fees; U 63 Weights for maintenance and upgrade costs.

[0053] Furthermore, the method for obtaining each of the weights comprises the following steps:

[0054] Step B1: setting several relative importance level values ​​between two weights;

[0055] Step B2: construct a square matrix table, and each weight in the fuzzy comprehensive evaluation function is used as the table header and index in the square matrix table. The element value in the square matrix table is the relative importance level value between the two weights corresponding to the row and column;

[0056] Step B3: Normalize the matrix table. The specific formula is:

[0057]

[0058] In the formula, M1 is a square matrix table; i and j are rows and columns respectively;

[0059] Step B4: Normalize the eigenvectors of the normalized square matrix table. The specific formula is:

[0060]

[0061] Step B5: Use each element in the feature vector obtained in step B4 as a weight value one by one.

[0062] Beneficial effects of the present invention:

[0063] The present invention enables ships to obtain relevant data about the ships in real time, conduct identification and analysis, and calculate relevant data about the ship's resistance and propulsion. At the same time, the fuzzy comprehensive evaluation method is used to ensure that the cost-effectiveness of the system as a whole and each unit is good, thereby improving the speed of the ship while reducing costs. Compared with other related monitoring systems in the same field, the effects in these two aspects are remarkable.

[0064] The present invention introduces identification software including mathematical models and improved genetic algorithms to obtain and analyze the changes in hull resistance under different speeds, and analyze the best matching point between speed and power, thereby laying a foundation for subsequent rapidity performance improvement.

[0065] The present invention proposes to divide the value range of the identification unknowns into intervals, and the genetic algorithm adopts a parallel strategy. In the parallel strategy, a divide-and-conquer method is used to simultaneously process the identification unknowns in each interval, and a more suitable optimal solution is selected therefrom. Compared with the basic genetic algorithm, the improved result is closer to the global optimal solution.

[0066] The present invention obtains and analyzes the cost-effectiveness ratio of each part through a fuzzy comprehensive evaluation function, thereby reducing costs compared to other systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] The features and advantages of the present invention will be more clearly understood by referring to the accompanying drawings, which are schematic and should not be construed as limiting the present invention in any way. In the accompanying drawings:

[0068] Figure 1 It is a flow chart of a specific embodiment of the present invention. DETAILED DESCRIPTION

[0069] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0070] The present invention is further illustrated below in conjunction with specific embodiments. Those skilled in the art should understand that these embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention, and modifications to various equivalent forms of the present invention fall within the scope defined by the appended claims of this application.

[0071] like Figure 1 As shown, the present invention provides a method for monitoring the comprehensive performance of a ship, including: a rapid performance monitoring process and a cost-effectiveness monitoring process;

[0072] The rapid performance monitoring process includes the following steps:

[0073] Step A1: Constructing a mathematical model of the ship:

[0074] Thrust: T = P T / V A =P s η s η R η0 / (V / (1-ω))

[0075] Effective thrust: T P =T(1-t)

[0076] Speed ​​coefficient:

[0077] The total resistance, thrust coefficient and torque coefficient of the ship in the ship mathematical model are optimized. The specific optimization is as follows:

[0078] The total resistance of the ship is optimized by constructing the total resistance coefficient of the ship through a quaternary quartic polynomial containing a set of unknown variables to be identified;

[0079] The total resistance formula of a ship is:

[0080]

[0081] In the formula, C T is the total resistance coefficient of the ship; ρ is the fluid density; S is the wet surface area; V is the ship speed.

[0082] Total resistance coefficient of the ship C T The formula is:

[0083]

[0084] In the formula, a1 and a2 are the first group of unknowns to be identified; V is the speed, g is the acceleration of gravity, and L is the characteristic length of the ship;

[0085] constructing torque coefficients through a three-variable quadratic polynomial containing another set of unknowns to be identified;

[0086] The formula for torque coefficient is:

[0087]

[0088] Where, J is the speed coefficient; β0, β1, β2 are the second group of unknowns to be identified;

[0089] Construct thrust coefficients through a three-variable quadratic polynomial containing a third set of unknowns to be identified;

[0090] The formula for thrust coefficient is:

[0091] K T =β3+β4×J+β5×J 2

[0092] Where, J is the speed coefficient; β3, β4, β5 are the third group of unknowns to be identified;

[0093] Step A2: obtaining real-time ship data, and dividing the value ranges of the three groups of unknown numbers to be identified into several value intervals;

[0094] The process of determining the value range of the three groups of unknown numbers to be identified is:

[0095] Determination of the value range of the first group of unknown numbers a1 and a2 to be identified:

[0096] According to the size of the captain's Froude number, ships can be divided into the following categories:

[0097] Low-speed ship: Fr<0.2; Medium-speed ship: 0.2≤Fr≤0.3; High-speed ship: Fr>0.3;

[0098] The range of V values ​​can be obtained through the range of Fr values, where:

[0099]

[0100] The total resistance formula of a ship can be changed to:

[0101]

[0102] Among them, the total resistance coefficient of the ship is C T The friction coefficient C f and the residual resistance coefficient C rcomposition,

[0103]

[0104] Where, v is the kinematic viscosity coefficient; Re is the Reynolds number;

[0105] Residual resistance coefficient C r The residual resistance coefficient Cr value corresponding to the Froude number can be obtained by consulting the Cr table of ship types;

[0106] From the above, we can know that the total resistance coefficient of the ship is C T Corresponding to the speed V, the range of the speed V is related to the value range of the Froude number Fr; theoretically, two ship total resistance coefficients C can be obtained through two different speeds V. T ;

[0107] According to the total resistance coefficient of the ship C T And the formula for the first set of unknowns to be identified:

[0108]

[0109] The total resistance coefficients C of the two ships are obtained by two different speeds V and T We can calculate the theoretical values ​​of the first group of unknowns to be identified and set the actual value range of the first group of unknowns to be identified to be within ±0.1 of the theoretical values. For example, if the theoretical values ​​of the first group of unknowns to be identified are 2 and 3, respectively, then the actual value range is (1.9, 2.1) and (2.9, 3.1).

[0110] The process of determining the value range of the second group of unknowns to be identified and the third group of unknowns to be identified is:

[0111] According to the ship propeller model, refer to the corresponding propeller design atlas and select three groups of J, K T , K Q Substitute the value into the torque coefficient formula:

[0112]

[0113] Thrust coefficient formula:

[0114] K T =β3+β4×J+β5×J 2

[0115] The values ​​of two sets of unknown variables to be identified can be solved in theory, and the actual value range is set to within ±0.1 of the theoretical value.

[0116] Taking the first group of unknown numbers to be identified as an example, where the actual value range of a1 is (1.9, 2.1), it can be divided into intervals: (1.90, 1.93), (1.94, 1.97), (1.98, 2.10). The specific division method can be more detailed according to the actual situation and actual accuracy requirements.

[0117] Step A3: Using the improved genetic algorithm, according to the real-time data of the ship and the mathematical model of the ship, the optimal solution in each value interval of the unknown number to be identified is obtained at the same time, and the smallest optimal solution is taken as the final solution;

[0118] The objective function in the genetic algorithm is:

[0119]

[0120] in,

[0121]

[0122] Where a1 and a2 are the first group of unknowns to be identified; V is the ship speed, g is the acceleration of gravity, L is the characteristic length of the ship; J is the advance coefficient; β0, β1, β2 are the second group of unknowns to be identified; β3, β4, β5 are the third group of unknowns to be identified; ω is the semi-flow fraction; ρ is the fluid density; S is the wetted surface area; P s is the host power, η s is the shafting efficiency, η R is the relative rotation efficiency; n is the rotation speed, D is the propeller diameter; t is the thrust reduction fraction; N is the number of sampling times;

[0123] The constraints in the genetic algorithm mainly consider the equality constraints of torque and the constraints of the upper and lower limits of design variables. The specific formula is:

[0124]

[0125] The penalty function can be used to transform some problems with constrained conditions into unconstrained problems, thereby simplifying the calculation process. The penalty function can be used to transform into an unconstrained problem. The penalty value of the established penalty function is:

[0126]

[0127] Then the fitness function in the genetic algorithm is:

[0128]

[0129] The genetic algorithm adopts a parallel strategy, taking several partition intervals of unknown numbers to be identified as the restriction conditions of the genetic algorithm. The genetic algorithm simultaneously searches for the optimal solution in the partition intervals and makes the total resistance R 总The optimal solution whose difference with the thrust T is closest to zero is taken as the final optimal solution.

[0130] Step A4: by taking the values ​​of the three groups of unknown numbers to be identified corresponding to the final solution, the real-time parameters of the ship in the ship mathematical model are calculated to complete the rapid performance monitoring of the ship;

[0131] Cost effectiveness monitoring process, including:

[0132] The cost effectiveness is calculated by the following formula:

[0133] I=EF / W

[0134] Where E is the change degree of the navy coefficient; F is the change degree of the monitoring system functional indicators; W is the cost of the monitoring system.

[0135] The formula for the system function index is:

[0136] R=X L1 Y L2 Z L3 Q L4 N L5 ,

[0137] In the formula, X is the collection efficiency; Y is the information processing efficiency; Z is the monitoring efficiency; Q is the degree of intelligence; N is the consulting service capability; L1×L2×L3×L4×L5=1

[0138] The specific calculation formula for cost effectiveness is:

[0139] I = EF / W = ΔK (ΔX L1 ΔY L2 ΔZ L3 ΔQ L4 ΔN L5 ) / (WP+WI+WM)

[0140] In the formula, △K is the degree of change of the navy coefficient; △X is the degree of change of collection efficiency; △Y is the degree of change of information processing efficiency; △Z is the degree of change of monitoring efficiency; △Q is the degree of change of intelligence; △N is the degree of change of consulting service capability; WP is the purchase cost; WI is the installation cost; WM is the operation and maintenance cost.

[0141] The sensor performance in the monitoring performance is obtained as follows:

[0142] Obtain sensor effectiveness through fuzzy comprehensive evaluation function:

[0143]

[0144] Where η is the membership value; U1 is the sensor equipment index weight, U2 is the sensor equipment cost weight; U 11is the vibration sensor accuracy weight; U 12 is the pressure sensor accuracy weight; U 13 is the accelerometer accuracy weight; U 14 is the flow sensor accuracy weight; U 15 is the water depth sensor accuracy weight; U 16 is the water quality sensor accuracy weight; U 17 U is the accuracy weight of ultrasonic anemometer; 18 is the water temperature sensor accuracy weight; U 19 It is the accuracy weight of other monitoring equipment.

[0145] U 21 Divided into vibration sensor cost weight, U 22 is the cost weight of the pressure sensor, U 23 is the cost weight of the acceleration sensor, U 24 is the cost weight of the flow sensor, U 25 is the cost weight of the water depth sensor, U 26 is the cost weight of water quality sensor, U 27 is the cost weight of ultrasonic anemometer, U 28 is the water temperature sensor cost weight, U 29 Cost weight for other monitoring equipment.

[0146] The method for obtaining the data transmission performance in the collection performance is as follows:

[0147] Obtain data transmission efficiency through fuzzy comprehensive evaluation function:

[0148] H(CX)=U3·(U 31 ·η 31 +U 32 ·η 32 +U 33 ·η 33 +U 34 ·η 34 )+U4.(U 41 ·η 41 +U 42 ·η 42 +U 43 ·η 43 +U 44 ·η 44 )

[0149] Where η is the membership value; U3 is the data transmission equipment index weight; U4 is the data transmission equipment cost weight; U 31 is the data transmission rate weight; U 32 is the network delay weight; U 33 is the data transmission reliability weight; U 34is the system compatibility weight; U 41 Build cost weights for hardware and software wireless communications; U 42 is the network service fee weight; U 43 is the data security technology cost weight; U 44 Weights for maintenance and upgrade costs.

[0150] The method for obtaining the intelligent control efficiency in the intelligent degree is:

[0151] Obtaining intelligent control effectiveness through fuzzy comprehensive evaluation function:

[0152] H(KZ)=U5·(U 51 ·η 51 +U 52 ·η 52 +U 53 ·η 53 +U 54 ·η 54 )+U6.(U 61 ·η 61 +U 62 ·η 62 +U 63 ·η 63 )

[0153] In the formula, η is the membership value; U5 is the weight of the intelligent control equipment index; U6 is the weight of the intelligent control cost; U 51 is the weight of automation level; U 52 is the precision weight; U 53 is the reliability weight; U 54 is the response speed weight; U 55 is the system integration weight; U 61 is the energy consumption cost weight; U 62 is the weight of insurance and risk management fees; U 63 Weights for maintenance and upgrade costs.

[0154] Each weight U xy The method for obtaining includes the following steps:

[0155] Step B1: Set several relative importance level values ​​between two weights, for example, 9 levels are provided from 1 to 9, and the level values ​​are 1 to 9;

[0156] Step B2: construct a square matrix table, as shown in Table 1 below, where each weight in the fuzzy comprehensive evaluation function is used as the table header and index in the square matrix table, and the element value in the square matrix table is the relative importance level value between the two weights corresponding to the row and column;

[0157]

[0158] Table 1

[0159] Among them, A1-An corresponds to U xy ; b, c, d, e, f, g, h, i, j, k are relative importance level values, and the inverse number indicates that the two weights have opposite relationships in the relative importance level;

[0160] Step B3: Normalize the matrix table. The specific formula is:

[0161]

[0162] In the formula, M1 is a square matrix table; i and j are rows and columns respectively;

[0163] Step B4: Normalize the eigenvectors of the normalized square matrix table. The specific formula is:

[0164]

[0165] Step B5: Use each element in the feature vector obtained in step B4 as a weight value one by one.

[0166] For example:

[0167] The square matrix table constructed in step B2 is the following Table 2:

[0168]

[0169] Table 2

[0170] After normalizing Table 2 according to step B3, the following Table 3 is obtained:

[0171]

[0172] Table 3

[0173] The eigenvector of Table 3 is M2 = [1.089, 0.811, 1.233, 0.54, 1.131, 1.198] T

[0174] The eigenvectors in Table 3 are normalized according to step B4 to obtain:

[0175] M3=[0.206,0.143,0.264,0.087,0.175,0.125] T

[0176] Then, U 11 is 0.206; U 12 is 0.143; U 13 is 0.264; U 14 is 0.087; U 15 is 0.175; U16 is 0.125.

[0177] The membership calculation requires the use of graded evaluation to quantify the indicators. Therefore, a six-grade evaluation system is used to determine the membership of each indicator. These grades are: excellent, good, good, medium, poor, and bad. The corresponding membership values ​​are 1.0, 0.8, 0.6, 0.4, 0.2, and 0.

[0178] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A method for monitoring comprehensive performance of a ship, characterized in that: include: Rapid performance monitoring process and cost-effectiveness monitoring process; The rapid performance monitoring process includes the following steps: Step A1: Construct a ship mathematical model and optimize the ship mathematical model. The specific optimization is as follows: The total resistance of the ship is optimized by constructing the total resistance coefficient of the ship through a quaternary quartic polynomial containing a set of unknown variables to be identified; constructing torque coefficients through a three-variable quadratic polynomial containing another set of unknowns to be identified; Construct thrust coefficients through a three-variable quadratic polynomial containing a third set of unknowns to be identified; Step A2: obtaining real-time ship data, and dividing the value ranges of the three groups of unknown numbers to be identified into several value intervals; Step A3: Using the improved genetic algorithm, according to the real-time data of the ship and the mathematical model of the ship, the optimal solution in each value interval of the unknown number to be identified is obtained at the same time, and the smallest optimal solution is taken as the final solution; Step A4: by taking the values ​​of the three groups of unknown numbers to be identified corresponding to the final solution, the real-time parameters of the ship in the ship mathematical model are calculated to complete the rapid performance monitoring of the ship; Cost effectiveness monitoring process, including: The cost effectiveness is calculated by the following formula: I=EF / W Where E is the change degree of the navy coefficient; F is the change degree of the monitoring system functional indicators; W is the cost of the monitoring system.

2. The ship comprehensive performance monitoring method according to claim 1, characterized in that: The total resistance formula of the ship is: In the formula, C T is the total resistance coefficient of the ship; ρ is the fluid density; S is the wet surface area; V is the ship speed; The formula for the total resistance coefficient of the ship is: In the formula, a1 and a2 are the first group of unknowns to be identified; V is the speed, g is the acceleration of gravity, and L is the characteristic length of the ship; The formula for the torque coefficient is: Where, J is the speed coefficient; β0, β1, β2 are the second group of unknowns to be identified; The formula for the thrust coefficient is: K T =β3+β4×J+β5×J 2 Where J is the speed coefficient; β3, β4, and β5 are the third group of unknowns to be identified.

3. The ship comprehensive performance monitoring method according to claim 1, characterized in that: The objective function in the improved genetic algorithm is: in, Where a1 and a2 are the first group of unknowns to be identified; V is the ship speed, g is the acceleration of gravity, L is the characteristic length of the ship; J is the advance coefficient; β0, β1, β2 are the second group of unknowns to be identified; β3, β4, β5 are the third group of unknowns to be identified; ω is the semi-flow fraction; ρ is the fluid density; S is the wetted surface area; P s is the host power, η s is the shafting efficiency, η R is the relative rotational efficiency; n is the rotational speed, D is the propeller diameter; t is the thrust deduction fraction.

4. The ship comprehensive performance monitoring method according to claim 1, characterized in that: The formula of the system function index is: R=X L1 Y L2 Z L3 Q L4 N L5 , In the formula, X is the collection efficiency; Y is the information processing efficiency; Z is the monitoring efficiency; Q is the degree of intelligence; N is the consulting service capability; L1×L2×L3×L4×L5=1 5. The ship comprehensive performance monitoring method according to claim 4, characterized in that: The specific calculation formula of the cost-effectiveness is: I=EF / W=ΔK(ΔX L1 ΔY L2 ΔZ L3 ΔQ L4 ΔN L5 ) / (WP+WI+WM) In the formula, △K is the degree of change of the navy coefficient; △X is the degree of change of collection efficiency; △Y is the degree of change of information processing efficiency; △Z is the degree of change of monitoring efficiency; △Q is the degree of change of intelligence; △N is the degree of change of consulting service capability; WP is the purchase cost; WI is the installation cost; WM is the operation and maintenance cost.

6. The ship comprehensive performance monitoring method according to claim 1, characterized in that: The sensor performance in the monitoring performance is obtained in the following manner: Obtain sensor effectiveness through fuzzy comprehensive evaluation function: Where η is the membership value; U1 is the sensor equipment index weight, U2 is the sensor equipment cost weight; U 11 is the vibration sensor accuracy weight; U 12 is the pressure sensor accuracy weight; U 13 is the accelerometer accuracy weight; U 14 is the flow sensor accuracy weight; U 15 is the water depth sensor accuracy weight; U 16 is the water quality sensor accuracy weight; U 17 U is the accuracy weight of ultrasonic anemometer; 18 is the water temperature sensor accuracy weight; U 19 is the accuracy weight of other monitoring equipment; U 21 Divided into vibration sensor cost weight, U 22 is the cost weight of the pressure sensor, U 23 is the cost weight of the acceleration sensor, U 24 is the cost weight of the flow sensor, U 25 is the cost weight of the water depth sensor, U 26 is the cost weight of water quality sensor, U 27 is the cost weight of ultrasonic anemometer, U 28 is the water temperature sensor cost weight, U 29 Cost weight for other monitoring equipment.

7. The ship comprehensive performance monitoring method according to claim 1, characterized in that: The method for obtaining the data transmission efficiency in the collection efficiency is as follows: Obtain data transmission efficiency through fuzzy comprehensive evaluation function: H(CX)=U3·(U 31 ·or 31 +U 32 ·or 32 +U 33 ·or 33 +U 34 ·or 34 )+U4.(U 41 ·or 41 +U 42 ·or 42 +U 43 ·or 43 +U 44 ·or 44 ) Where η is the membership value; U3 is the data transmission equipment index weight; U4 is the data transmission equipment cost weight; U 31 is the data transmission rate weight; U 32 is the network delay weight; U 33 is the data transmission reliability weight; U 34 is the system compatibility weight; U 41 Build cost weights for hardware and software wireless communications; U 42 is the network service fee weight; U 43 is the cost weight of data security technology; U 44 Weights for maintenance and upgrade costs.

8. The ship comprehensive performance monitoring method according to claim 1, characterized in that: The method for obtaining the intelligent control efficiency in the intelligent degree is: Obtaining intelligent control effectiveness through fuzzy comprehensive evaluation function: H(KZ)=U5·(U 51 ·η 51 +U 52 ·η 52 +U 53 ·η 53 +U 54 ·η 54 )+U6.(U 61 ·η 61 +U 62 ·η 62 +U 63 ·η 63 ) In the formula, η is the membership value; U5 is the weight of the intelligent control equipment index; U6 is the weight of the intelligent control cost; U 51 is the weight of automation level; U 52 is the precision weight; U 53 is the reliability weight; U 54 is the response speed weight; U 55 is the system integration weight; U 61 is the energy consumption cost weight; U 62 is the weight of insurance and risk management fees; U 63 Weights for maintenance and upgrade costs.

9. The ship comprehensive performance monitoring method according to any one of claims 6 to 8, characterized in that: The method for obtaining each of the weights comprises the following steps: Step B1: setting several relative importance level values ​​between two weights; Step B2: construct a square matrix table, and each weight in the fuzzy comprehensive evaluation function is used as the table header and index in the square matrix table. The element value in the square matrix table is the relative importance level value between the two weights corresponding to the row and column; Step B3: Normalize the matrix table. The specific formula is: In the formula, M1 is a square matrix table; i and j are rows and columns respectively; Step B4: Normalize the eigenvectors of the normalized square matrix table. The specific formula is: Step B5: Use each element in the feature vector obtained in step B4 as a weight value one by one.

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