A method for monitoring the comprehensive performance of ships

Abstract

This invention discloses a method for monitoring the comprehensive performance of ships, including: a speed performance monitoring process and a cost-effectiveness monitoring process. The speed performance monitoring process includes: constructing the ship's total resistance, torque coefficient, and thrust coefficient using three sets of polynomials containing unknowns to be identified; dividing the value ranges of the three sets of unknowns into several value intervals; using an improved genetic algorithm, finding the optimal solution in each value interval of the unknowns, and taking the smallest optimal solution as the final solution; calculating the ship's real-time parameters in the ship's mathematical model based on the values ​​of the three sets of unknowns corresponding to the final solution, thus completing the ship's speed performance monitoring; the cost-effectiveness monitoring process includes: calculating cost-effectiveness based on the degree of change in naval coefficients, the degree of change in monitoring system functional indicators, and the cost of the monitoring system. This invention can better obtain ship speed performance indicators and more accurately and dynamically obtain cost-effectiveness.

Description

Technical Field

[0001] This invention relates to the field of ship monitoring technology, and in particular to a method for monitoring the comprehensive performance of ships. Background Technology

[0002] Currently, there are several challenges in improving the speed performance of ships. While data is obtained from sensors and other equipment, it cannot be readily converted into the necessary speed performance data for improvement. Traditional systems typically only measure total power and speed, without monitoring changes in hull resistance at different speeds, making it difficult to analyze the optimal balance between speed and power. Furthermore, traditional systems lack the capability for complex modeling and analysis of monitoring data, making it difficult to transform raw data into effective indicators describing speed performance.

[0003] Furthermore, ensuring a good cost-effectiveness ratio for each component unit also presents a challenge. Improving cost-effectiveness typically involves multiple objectives, such as equipment lifespan and maintenance costs. Traditional systems only support single-objective optimization, neglecting the balance between multiple objectives. Traditional systems usually manage equipment through fixed maintenance cycles, rather than dynamically optimizing based on cost-effectiveness. For example, scheduled maintenance of the main unit may be performed prematurely rather than based on actual operating conditions, increasing maintenance costs. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a method for monitoring the comprehensive performance of ships, thereby solving the technical problems of poor monitoring effect of rapid performance indicators and inability to effectively monitor the cost-effectiveness ratio of dynamic performance.

[0005] This invention provides a method for monitoring the comprehensive performance of ships, including: a speed performance monitoring process and a cost-effectiveness monitoring process;

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

[0007] Step A1: Construct a mathematical model of the ship and optimize it. Specifically, the optimization is as follows:

[0008] The total resistance of a ship is optimized by constructing a total resistance coefficient of the ship using a quaternary fourth-degree polynomial containing a set of unknowns to be identified.

[0009] The torque coefficient is constructed by a ternary quadratic polynomial containing another set of unknowns to be identified;

[0010] The thrust coefficient is constructed by a ternary quadratic polynomial containing the third set of unknowns to be identified.

[0011] Step A2: Obtain real-time ship data and divide the value ranges of the three sets of unknowns to be identified into several value intervals;

[0012] Step A3: Using an improved genetic algorithm, based on real-time ship data and a ship mathematical model, the optimal solution is obtained simultaneously in each value range of the unknown to be identified, and the smallest optimal solution is taken as the final solution.

[0013] Step A4: Calculate the real-time parameters of the ship in the ship's mathematical model by taking the values ​​of the three sets of unknowns to be identified corresponding to the final solution, and complete the monitoring of the ship's speed performance.

[0014] The cost-effectiveness monitoring process includes:

[0015] Cost efficiency is calculated using the following formula:

[0016] I = EF / W

[0017] In the formula, E represents the degree of change in the naval coefficient; F represents the degree of change in the functional indicators of the monitoring system; and W represents the cost of the monitoring system.

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

[0019]

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

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

[0022]

[0023] In the formula, a1 and a2 are the first set of unknowns to be identified; V is the speed, g is the gravitational acceleration, and L is the characteristic length of the ship.

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

[0025]

[0026] In the formula, J is the advance coefficient; β0, β1, and β2 are the second group of unknowns to be identified.

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

[0028] K T =β3 + β4 × J + β5 × J 2

[0029] In the formula, J is the advance 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] In the formula, a1 and a2 are the first group of unknowns to be identified; V is the ship's speed, g is the gravitational acceleration, L is the ship's characteristic length; J is the advance coefficient; β0, β1, and β2 are the second group of unknowns to be identified; β3, β4, and β5 are the third group of unknowns to be identified; ω is the half-flow fraction; ρ is the fluid density; S is the wetted surface area; P s For the host power, η s For shaft system efficiency, η R t represents the relative rotational efficiency; n is the rotational speed; D is the propeller diameter; and t is the thrust deduction fraction.

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

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

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

[0038] Furthermore, the specific formula for calculating cost-effectiveness is as follows:

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

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

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

[0042] Sensor performance is obtained through fuzzy comprehensive evaluation function:

[0043]

[0044] In the formula, η is the membership degree value; U1 is the weight of sensor equipment indicators, and U2 is the weight of sensor equipment cost; U 11 For the accuracy weight of the vibration sensor; U 12 Weighting for pressure sensor accuracy; U 13 For accelerometer accuracy weights; U 14 For the accuracy weight of the flow velocity sensor; U 15 Weights for the accuracy of the water depth sensor; U 16 For water quality sensor accuracy weights; U 17 Weighting of ultrasonic anemometer accuracy; U 18 Weighting for water temperature sensor accuracy; U 19 Weighting of the accuracy of other monitoring equipment; U 21 Divided into vibration sensor cost weights, U 22 As a cost weight for pressure sensors, U 23 As a cost weight for accelerometers, U 24 As a cost weight for flow rate sensors, U 25 As a cost weight for depth sensors, U 26 As a cost weight for water quality sensors, U 27 As a cost weight for ultrasonic anemometers, U 28 As a cost weight for water temperature sensors, U 29 Weight the cost of other monitoring equipment.

[0045] Furthermore, the method for obtaining the data transmission performance in the collection performance is as follows:

[0046] Data transmission efficiency is obtained through fuzzy comprehensive evaluation functions:

[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] In the formula, η is the membership degree value; U3 is the weight of the data transmission equipment index; U4 is the weight of the data transmission equipment cost; U 31 For data transmission rate weighting; U 32 Network latency weight; U 33For data transmission reliability weights; U 34 For system compatibility weights; U 41 Cost weighting for hardware and software wireless communication; U 42 Weighting of network service fees; U 43 Weighting of data security technology costs; U 44 To maintain and upgrade cost weights.

[0049] Furthermore, the method for obtaining the intelligent control efficiency in the aforementioned level of intelligence is as follows:

[0050] Intelligent control performance is obtained through fuzzy comprehensive evaluation functions:

[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 degree value; U5 is the weight of the intelligent control equipment index; U6 is the weight of the intelligent control cost; U 51 For automation level weights; U 52 For accuracy weighting; U 53 For reliability weights; U 54 Weighted by response speed; U 55 For system integration weights; U 61 Weighted by energy consumption cost; U 62 Weighting for insurance and risk management expenses; U 63 To maintain and upgrade cost weights.

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

[0054] Step B1: Set several relative importance levels between the two weights;

[0055] Step B2: Construct a matrix table, with each weight in the fuzzy comprehensive evaluation function serving as the table header and index. The element values ​​in the matrix table are the relative importance level values ​​between the two weights corresponding to the row and column.

[0056] Step B3: Normalize the matrix table using the following formula:

[0057]

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

[0059] Step B4: Normalize the eigenvectors of the normalized square matrix table using the following formula:

[0060]

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

[0062] The beneficial effects of this invention are:

[0063] This invention enables ships to acquire relevant data in real time, perform identification and analysis, and calculate relevant data on ship resistance and propulsion. At the same time, it uses a fuzzy comprehensive evaluation method to ensure that the overall system and each unit have a good cost-effectiveness ratio, thereby improving the speed of ships while reducing costs. Compared with other related monitoring systems in the same field, it has significant effects in these two aspects.

[0064] This invention incorporates identification software that includes mathematical models and improved genetic algorithms to obtain and analyze changes in hull resistance at different speeds, and to analyze the optimal matching point between speed and power, thereby laying the foundation for subsequent improvements in speed performance.

[0065] This invention proposes to divide the range of values ​​of the identified unknowns into intervals. The genetic algorithm adopts a parallel strategy, which uses a divide-and-conquer approach to process the identified unknowns in each interval simultaneously and selects a more suitable optimal solution. Compared with the basic genetic algorithm, the improved result is closer to the global optimal solution.

[0066] This invention uses a fuzzy comprehensive evaluation function to obtain and analyze the cost-effectiveness ratio of each part, thereby reducing costs compared to other systems. Attached Figure Description

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

[0068] Figure 1 This is a flowchart illustrating a specific embodiment of the present invention. Detailed Implementation

[0069] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0070] The present invention will be further illustrated below with reference to specific embodiments. Those skilled in the art should understand that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. Modifications to the present invention in various equivalent forms all fall within the scope defined by the appended claims.

[0071] like Figure 1 As shown, the present invention provides a method for monitoring the comprehensive performance of a ship, including: a speed 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] Precession coefficient:

[0077] The total resistance, thrust coefficient, and torque coefficient of the ship in the mathematical model were optimized, specifically as follows:

[0078] The total resistance of a ship is optimized by constructing a total resistance coefficient of the ship using a quaternary fourth-degree polynomial containing a set of unknowns to be identified.

[0079] The formula for total ship resistance is:

[0080]

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

[0082] Ship's total resistance coefficient C T The formula is:

[0083]

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

[0085] The torque coefficient is constructed by a ternary quadratic polynomial containing another set of unknowns to be identified;

[0086] The formula for the torque coefficient is:

[0087]

[0088] In the formula, J is the advance coefficient; β0, β1, and β2 are the second group of unknowns to be identified;

[0089] The thrust coefficient is constructed by a ternary quadratic polynomial containing the third set of unknowns to be identified.

[0090] The formula for the thrust coefficient is:

[0091] K T =β3 + β4 × J + β5 × J 2

[0092] In the formula, J is the advance coefficient; β3, β4, and β5 are the third group of unknowns to be identified;

[0093] Step A2: Obtain real-time ship data and divide the value ranges of the three sets of unknowns to be identified into several value intervals;

[0094] The process of determining the range of values ​​for the three sets of unknowns to be identified is as follows:

[0095] Determining the range of values ​​for the first set of unknowns a1 and a2:

[0096] Based on the size of the captain's rank (e.g., 'Fu Rude'), ships can be classified into the following categories:

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

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

[0099]

[0100] The formula for total ship resistance can be modified as follows:

[0101]

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

[0103]

[0104] In the formula, v is the kinematic viscous system; Re is the Reynolds number;

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

[0106] As can be seen from the above, the total resistance coefficient of the ship, C... T Corresponding to the speed V, the range of speed V is related to the range of the Froude number Fr; theoretically, two different ship total resistance coefficients C can be obtained using two different speeds V. T ;

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

[0108]

[0109] Two ship total resistance coefficients C were obtained by using two different ship speeds V. T It 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 value. For example, if the theoretical values ​​of the first group of unknowns to be identified are 2 and 3, then the actual value range is (1.9, 2.1) and (2.9, 3.1).

[0110] The process for determining the value ranges of the second and third groups of unknowns is as follows:

[0111] Consult the corresponding propeller design drawings based on the ship's propeller model, and select three groups of J and 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] It can solve for the theoretical values ​​of two sets of unknowns to be identified, and set the actual range of values ​​to be within ±0.1 of the theoretical values ​​obtained.

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

[0117] Step A3: Using an improved genetic algorithm, based on real-time ship data and a ship mathematical model, the optimal solution is obtained simultaneously in each value range of the unknown to be identified, 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] In the formula, a1 and a2 are the first group of unknowns to be identified; V is the ship's speed, g is the gravitational acceleration, L is the ship's characteristic length; J is the advance coefficient; β0, β1, and β2 are the second group of unknowns to be identified; β3, β4, and β5 are the third group of unknowns to be identified; ω is the half-flow fraction; ρ is the fluid density; S is the wetted surface area; P s For the host power, η s For shaft system efficiency, η R The relative rotational efficiency is given by n; the rotational speed is given by D; the propeller diameter is given by D; the thrust derating fraction is given by t; and the number of samplings is given by N.

[0123] The constraints in genetic algorithms mainly consider the equality constraints of torque and the upper and lower limits of design variables. The specific formulas are as follows:

[0124]

[0125] Penalty functions can be used to transform constrained problems into unconstrained problems, thus simplifying the computation process. A penalty function can be used to transform a problem into an unconstrained one, and the penalty value of the established penalty function is:

[0126]

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

[0128]

[0129] The genetic algorithm employs a parallel strategy, using several partitioned intervals of the unknowns to be identified as constraints. The algorithm simultaneously searches for the optimal solution within each partitioned interval, maximizing the total ship resistance R. 总The optimal solution whose difference from the thrust T is closest to zero is taken as the final optimal solution.

[0130] Step A4: Calculate the real-time parameters of the ship in the ship's mathematical model by taking the values ​​of the three sets of unknowns to be identified corresponding to the final solution, and complete the monitoring of the ship's speed performance.

[0131] The cost-effectiveness monitoring process includes:

[0132] Cost efficiency is calculated using the following formula:

[0133] I = EF / W

[0134] In the formula, E represents the degree of change in the naval coefficient; F represents the degree of change in the functional indicators of the monitoring system; and W represents the cost of the monitoring system.

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

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

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

[0138] The specific formula for calculating cost-effectiveness is as follows:

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

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

[0141] The sensor performance in the monitoring performance is obtained through the following methods:

[0142] Sensor performance is obtained through fuzzy comprehensive evaluation function:

[0143]

[0144] In the formula, η is the membership degree value; U1 is the weight of sensor equipment indicators, and U2 is the weight of sensor equipment cost; U 11For the accuracy weight of the vibration sensor; U 12 Weighting for pressure sensor accuracy; U 13 For accelerometer accuracy weights; U 14 For the accuracy weight of the flow velocity sensor; U 15 Weights for the accuracy of the water depth sensor; U 16 For water quality sensor accuracy weights; U 17 Weighting of ultrasonic anemometer accuracy; U 18 Weighting for water temperature sensor accuracy; U 19 Assign accuracy weights to other monitoring devices.

[0145] U 21 Divided into vibration sensor cost weights, U 22 As a cost weight for pressure sensors, U 23 As a cost weight for accelerometers, U 24 As a cost weight for flow rate sensors, U 25 As a cost weight for depth sensors, U 26 As a cost weight for water quality sensors, U 27 As a cost weight for ultrasonic anemometers, U 28 As a cost weight for water temperature sensors, U 29 Weight the cost of other monitoring equipment.

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

[0147] Data transmission efficiency is obtained through fuzzy comprehensive evaluation functions:

[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] In the formula, η is the membership degree value; U3 is the weight of the data transmission equipment index; U4 is the weight of the data transmission equipment cost; U 31 For data transmission rate weighting; U 32 Network latency weight; U 33 For data transmission reliability weights; U 34For system compatibility weights; U 41 Cost weighting for hardware and software wireless communication; U 42 Weighting of network service fees; U 43 Weighting of data security technology costs; U 44 To maintain and upgrade cost weights.

[0150] The method for obtaining the intelligent control efficiency in the level of intelligence is as follows:

[0151] Intelligent control performance is obtained through fuzzy comprehensive evaluation functions:

[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 degree value; U5 is the weight of the intelligent control equipment index; U6 is the weight of the intelligent control cost; U 51 For automation level weights; U 52 For accuracy weighting; U 53 For reliability weights; U 54 Weighted by response speed; U 55 For system integration weights; U 61 Weighted by energy consumption cost; U 62 Weighting for insurance and risk management expenses; U 63 To maintain and upgrade cost weights.

[0154] Weights U xy The method for obtaining it includes the following steps:

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

[0156] Step B2: Construct a matrix table as shown in Table 1 below. The weights in the fuzzy comprehensive evaluation function serve as the table header and index. The element values ​​in the matrix table are the relative importance level values ​​between the two weights corresponding to the row and column.

[0157]

[0158] Table 1

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

[0160] Step B3: Normalize the matrix table using the following formula:

[0161]

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

[0163] Step B4: Normalize the eigenvectors of the normalized square matrix table using the following formula:

[0164]

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

[0166] For example:

[0167] The matrix table constructed in step B2 is shown in Table 2 below:

[0168]

[0169] Table 2

[0170] After normalizing Table 2 according to step B3, we obtain Table 3 below:

[0171]

[0172] Table 3

[0173] The eigenvectors in Table 3 are 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 It is 0.206; U 12 It is 0.143; U 13 It is 0.264; U 14 It is 0.087; U 15 It is 0.175; U16 It is 0.125.

[0177] Membership calculation requires the use of graded evaluation to quantify the indicators. Therefore, an evaluation system with six grades is used to determine the membership of each indicator. These grades are: excellent, good, relatively good, average, poor, and very poor, with corresponding membership values ​​of 1.0, 0.8, 0.6, 0.4, 0.2, and 0.

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

Claims

1. A method of monitoring the overall performance of a marine vessel, characterized by, The application relates to a ship performance monitoring system and a ship cost performance monitoring system. The ship performance monitoring system comprises a rapidity performance monitoring process and a cost performance monitoring process. The rapidity performance monitoring process comprises the following steps: Step A1: constructing a ship mathematical model and optimizing the ship mathematical model, wherein the optimization is as follows: a polynomial containing a group of to-be-identified unknown numbers is used to construct a ship total resistance coefficient to optimize a ship total resistance; a polynomial containing another group of to-be-identified unknown numbers is used to construct a torque coefficient; a polynomial containing a third group of to-be-identified unknown numbers is used to construct a thrust coefficient, wherein a ship total resistance formula is as follows: ; In the formula, C T is the total resistance coefficient of the ship; p is the fluid density; S is the wet surface area; and V is the sailing speed. a ship total resistance coefficient formula is as follows: ; wherein a1 and a2 are the first group of to-be-identified unknown numbers; V is a speed; g is a gravity acceleration; and L is a ship characteristic length; a torque coefficient formula is as follows: ; wherein J is a speed coefficient; and beta0, beta1 and beta2 are the second group of to-be-identified unknown numbers; a thrust coefficient formula is as follows: ; wherein J is a speed coefficient; and beta3, beta4 and beta5 are the third group of to-be-identified unknown numbers; Step A2: obtaining ship real-time data and dividing the value ranges of the three groups of to-be-identified unknown numbers into a plurality of value intervals; Step A3: simultaneously obtaining optimal solutions in the value intervals of the to-be-identified unknown numbers by using an improved genetic algorithm according to the ship real-time data and the ship mathematical model, and taking the minimum optimal solution as a final solution, wherein a target function in the improved genetic algorithm is as follows: ; wherein ; where a1, a2 are the first group of unknowns to be identified; V is the speed, g is the gravity acceleration, 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; is the semi-flow fraction; is the fluid density; S is the wet surface area; P S is the main engine power, is the shafting efficiency, is the relative rotation efficiency; n is the rotational speed, D is the diameter of the propeller; t is the thrust reduction fraction; Step A4: calculating each real-time parameter of the ship in the ship mathematical model by using the values of the three groups of to-be-identified unknown numbers corresponding to the final solution, and completing ship rapidity performance monitoring. The cost performance monitoring process comprises the following steps: Cost performance is calculated by using the following formula: ; wherein E is a change degree of a navy coefficient; F is a change degree of a monitoring system function index; W is a monitoring system cost; Delta K is a change degree of the navy coefficient; Delta X is a change degree of collection efficiency; Delta Y is a change degree of information processing efficiency; Delta Z is a change degree of monitoring efficiency; Delta Q is a change degree of intelligent degree; Delta N is a change degree of consulting service capability; WP is a purchase cost; WI is an installation cost; and WM is an operation and maintenance cost. A system function index formula is as follows: ; wherein X is collection efficiency; Y is information processing efficiency; Z is monitoring efficiency; Q is intelligent degree; N is consulting service capability; and L1*L2*L3*L4*L5=1.

2. The method of claim 1, wherein, A sensor efficiency in the monitoring efficiency is obtained by using a fuzzy comprehensive evaluation function. A data transmission efficiency in the collection efficiency is obtained by using a fuzzy comprehensive evaluation function. ; wherein, is a membership value; U1is a sensor device index weight, U2a sensor device cost weight; U 11 is a vibration sensor precision weight; U 12 is a pressure sensor precision weight; U 13 is an acceleration sensor precision weight; U 14 is a flow rate sensor precision weight; U 15 is a water depth sensor precision weight; U 16 is a water quality sensor precision weight; U 17 is an ultrasonic wind speed and direction instrument precision weight; U 18 is a water temperature sensor precision weight; U 19 is an other monitoring device precision weight; U 21 is a vibration sensor cost weight; U 22 is a pressure sensor cost weight; U 23 is an acceleration sensor cost weight; U 24 is a flow rate sensor cost weight; U 25 is a water depth sensor cost weight; U 26 is a water quality sensor cost weight; U 27 is an ultrasonic wind speed and direction instrument cost weight; U 28 is a water temperature sensor cost weight; U 29 is an other monitoring device cost weight.

3. The method of claim 1, wherein, An intelligent control efficiency in the intelligent degree is obtained by using a fuzzy comprehensive evaluation function. The weight obtaining method comprises the following steps: ; In the formula, is a membership value; U3 is a data transmission equipment index weight; U4 is a data transmission equipment cost weight; U 31 is a data transmission rate weight;U 32 is a network delay weight;U 33 is a data transmission reliability weight;U 34 is a system compatibility weight;U 41 is a software and hardware wireless communication setup cost weight;U 42 is a network service cost weight;U 43 is a data security technology cost weight; U 44 To maintain and upgrade the cost weight.

4. The method of claim 1, wherein, Step B1: setting a plurality of relative importance level values between two weights; Step B2: constructing a square matrix table, wherein each weight in the fuzzy comprehensive evaluation function is used as a table header and an index in the square matrix table, and an element value in the square matrix table is a relative importance level value between two weights corresponding to a row and a column; ; In the formula, is a membership value; U5 is an intelligent control device index weight; U6 is an intelligent control cost weight; U 51 is an automation level weight; U 52 is an accuracy weight; U 53 is a reliability weight; U 54 is a response speed weight; U 55 is a system integration weight; U 61 is an energy consumption cost weight; U 62 is the insurance and risk management cost weight;U 63 is the maintenance and upgrade cost weight.

5. The method of monitoring the overall performance of a marine vessel according to any one of claims 2-4, characterized in that, Step B3: performing normalization processing on the square matrix table, and a specific formula is as follows: ​ ​ ​ ; In the formula, M1 is a square matrix table; i and j are rows and columns respectively; Step B4: Normalizing the characteristic vector of the normalized square matrix table, and the specific formula is: ; Step B5: Taking each element in the characteristic vector obtained in step B4 as a weight value.

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