Method for evaluating task execution capability of unmanned ship intelligent engine room system based on three-flow coordination theory

Abstract

The application discloses a kind of based on three flow collaborative theory's unmanned ship intelligent engine room system task execution ability evaluation method, method includes: determining each parameter of supporting unmanned ship intelligent engine room system operation, from all parameters unmanned ship intelligent engine room system parameter set is formed;Based on neural network, each parameter in the first weight of unmanned ship intelligent engine room system parameter set and in the fourth weight of the parameter set of flow represented by itself;Based on historical data, the second weight of each parameter and the fifth weight of each parameter in the parameter set of flow represented by itself are determined;The third weight is obtained by correcting the second weight based on the first weight of parameter;The sixth weight is obtained by correcting the fifth weight based on the fourth weight of parameter;From the current state data of the unmanned ship intelligent engine room system, the parameter value corresponding to each parameter is extracted, and the task execution ability of the unmanned ship intelligent engine room system is determined in combination with the third weight and the sixth weight of each parameter.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of unmanned ships, in particular to an unmanned ship intelligent cabin system task execution capability evaluation method based on three-flow coordination theory. BACKGROUND

[0002] In recent years, with the application of intelligent ship technology and the upgrading of marine equipment technology, the development of unmanned ships has become a new direction of marine engineering development.

[0003] During the execution of the task, due to the task characteristics of long voyage time and long voyage distance, the external communication and external support of the unmanned ship are severely limited, and the self-operation and maintenance during the whole task cycle must be relied on, which has an urgent need for intelligent cabin technology. Through various self-monitoring, self-operation and maintenance control means to complete the task execution decision optimization of the unmanned ship, it is of great significance to reduce the possibility of functional failure caused by system and equipment failure, system performance degradation leading to combat capability decline or even fault return, maintain the integrity of the power and power system function, and improve the task completion degree.

[0004] As a key link of the intelligent cabin, the accuracy of the task execution capability evaluation result will directly affect the optimization effect of the task planning decision and operation and maintenance control decision of the unmanned ship. Therefore, since the concept of unmanned ship was proposed, the task execution capability evaluation method has been the focus of the research of the intelligent cabin technology of the unmanned ship. In recent years, with the development and application of big data, artificial intelligence and other technologies, the task execution capability evaluation method has developed rapidly, and the related analysis and research have been continuously deepened, and gradually developed into various evaluation methods based on physical model, knowledge model, data-driven model and hybrid model. However, most of the existing execution capability evaluation methods are focused on the equipment level, lack of consideration of the interaction relationship and dependency between devices and components in the system, and a comprehensive and scientific task execution capability evaluation index system is not established for the whole system. The weight setting of the index cannot adapt to the change of the system operation mode and cannot be dynamically adjusted, cannot be self-adapted with the change of the system life cycle, and cannot support accurate evaluation of the overall task completion capability of the system. SUMMARY

[0005] Therefore, the present application provides an unmanned ship intelligent cabin system task execution capability evaluation method based on three-flow coordination theory, which can solve the problem of measuring the overall health running level of the unmanned ship intelligent cabin system and its support degree for the task matching of the unmanned ship.

[0006] In order to solve the above technical problems, the present application is implemented as follows.

[0007] An unmanned ship intelligent cabin system task execution capability evaluation method based on three-flow coordination theory, comprising:

[0008] Step S1: determining various parameters representing material flow, energy flow, information flow supporting the operation of the unmanned ship intelligent engine room system, and forming an unmanned ship intelligent engine room system parameter set from all parameters;

[0009] Step S2: inputting the historical data of the operation of the unmanned ship intelligent engine room system and each parameter in the unmanned ship intelligent engine room system parameter set into the trained neural network model to obtain the first weight of each parameter in the unmanned ship intelligent engine room system parameter set and the fourth weight in the parameter set of the flow represented by each parameter;

[0010] Step S3: determining the second weight of each parameter in the unmanned ship intelligent engine room system parameter set and the fifth weight of each parameter in the parameter set of the flow represented by each parameter based on the historical data of the operation of the unmanned ship intelligent engine room system; correcting the second weight based on the first weight of the parameter to obtain the third weight; correcting the fifth weight based on the fourth weight of the parameter to obtain the sixth weight;

[0011] Step S4: extracting the parameter value corresponding to each parameter from the current state data of the unmanned ship intelligent engine room system, and determining the task execution capability of the unmanned ship intelligent engine room system in combination with the third weight and the sixth weight of each parameter.

[0012] Preferably, the step S2 comprises: forming a material flow parameter set by parameters representing material flow, forming an energy flow parameter set by parameters representing energy flow, and forming an information flow parameter set by parameters representing information flow; all parameters in the material flow parameter set, the information flow parameter set, and the energy flow parameter set together form the unmanned ship intelligent engine room system parameter set; inputting the historical data of the operation of the unmanned ship intelligent engine room system and each parameter in the unmanned ship intelligent engine room system parameter set into the trained neural network model to obtain the first weight of each parameter in the unmanned ship intelligent engine room system parameter set and the fourth weight in the parameter set of the flow represented by each parameter.

[0013] Preferably, the step S3: determining the second weight of each parameter in the unmanned ship intelligent engine room system parameter set and the fifth weight of each parameter in the parameter set of the flow represented by each parameter based on the historical data of the operation of the unmanned ship intelligent engine room system; correcting the second weight based on the first weight of the parameter to obtain the third weight; correcting the fifth weight based on the fourth weight of the parameter to obtain the sixth weight, comprises:

[0014] Step S31: constructing a historical efficiency matrix of the unmanned ship intelligent engine room system based on the historical data of the operation of the unmanned ship intelligent engine room system, and constructing a historical evaluation matrix based on the historical efficiency matrix;

[0015] Step S32: determining a second weight of each parameter in the unmanned ship intelligent machine cabin system parameter set and a fifth weight of each parameter in the parameter set of the flow represented by the parameter based on the historical evaluation matrix; correcting the second weight based on the first weight of the parameter in the unmanned ship intelligent machine cabin system parameter set to obtain a third weight of the parameter in the unmanned ship intelligent machine cabin system parameter set; correcting the fifth weight based on the fourth weight of the parameter in the flow parameter set to obtain a sixth weight of the parameter in the flow parameter set.

[0016] Preferably, the step S31 comprises:

[0017] Step S311: obtaining S historical observation samples from the historical data, and constructing a historical efficacy value matrix X based on the S historical observation samples S :

[0018]

[0019] wherein S represents the number of historical observation samples, and N represents the number of parameters in the unmanned ship intelligent machine cabin system parameter set; x ij represents an efficacy value of an observation value of an i-th parameter in the parameter set in a j-th observation sample, and a calculation formula thereof is

[0020]

[0021] wherein is obtained from a maximum value of the i-th parameter in the S historical observation samples, is obtained from a minimum value of the i-th parameter in the S historical observation samples; v i,j is calculated by

[0022]

[0023] wherein c i,j represents an observation value of an i-th parameter in the unmanned ship intelligent machine cabin system parameter set in a j-th observation sample; K i,j represents a theoretical target value corresponding to the i-th parameter in the unmanned ship intelligent machine cabin system parameter set in the j-th observation sample;

[0024] Step S312: performing normalization processing on the historical efficacy value matrix X S :

[0025]

[0026] and further obtaining a historical evaluation matrix X′ S :

[0027]

[0028] wherein x′ i,j represents the historical evaluation value of the i-th parameter in the unmanned ship intelligent engine room system parameter set in the j-th historical observation sample;

[0029] The step S32 comprises:

[0030] Step S321: based on the historical evaluation matrix X′ S , the information entropy of each parameter is calculated:

[0031]

[0032] wherein J=1 / ln(S); only when x′ ij =0, take H i represents the information entropy of the i-th parameter in the unmanned ship intelligent engine room system parameter set;

[0033] Step S322: for each parameter in the unmanned ship intelligent engine room system parameter set, the following operations are performed:

[0034] determine the corresponding flow of the parameter, the corresponding flow being one of a material flow, an information flow, and an energy flow, and determine the number of the parameter in the corresponding flow parameter set, and calculate the fifth weight of the parameter in the flow:

[0035]

[0036] wherein w 物质流,Ei1 represents the fifth weight of the i1-th parameter in the material flow parameter set in the material flow, w 信息流,Ei1 represents the fifth weight of the i2-th parameter in the information flow parameter set in the information flow, w 能量流,Ei1 represents the fifth weight of the i3-th parameter in the energy flow parameter set in the energy flow; H 物质流,i1 represents the information entropy of the i1-th parameter in the material flow parameter set, H 信息流,i2 represents the information entropy of the i2-th parameter in the information flow parameter set, H 能量流,i3 represents the information entropy of the i3-th parameter in the energy flow parameter set; N 物质流 represents the number of parameters in the material flow parameter set, N 信息流 represents the number of parameters in the information flow parameter set, N 能量流 represents the number of parameters in the energy flow parameter set; N 物质流 +N 信息流 +N 能量流 =N;

[0037] The second weight w Bi of the parameter in the unmanned ship intelligent engine room system parameter set is calculated:

[0038]

[0039] Step S323: For each parameter in the unmanned ship intelligent machine cabin system parameter set, the following calculation is performed: let the first weight of the parameter in the unmanned ship intelligent machine cabin system parameter set be w Ai , and the second weight be w Bi ,

[0040] If , let Otherwise, let ω′ i = w Bi ;

[0041] After the calculation of all parameters in the unmanned ship intelligent machine cabin system parameter set is completed, the third weight w Ci of each parameter in the unmanned ship intelligent machine cabin system parameter set is calculated.

[0042]

[0043] The third weight of each parameter in the unmanned ship intelligent machine cabin system parameter set satisfies:

[0044]

[0045] Step S324: For each parameter in the unmanned ship intelligent machine cabin system parameter set, the following operation is performed:

[0046] Determine the flow corresponding to the parameter, which is one of a material flow, an information flow, and an energy flow.

[0047] If the parameter is a material flow parameter, assume that the parameter is the i1th parameter in the material flow parameter set, let the fourth weight of the parameter in the material flow parameter set be w 物质流,Di1 , and the fifth weight be w 物质流,Ei1 , if , let Otherwise, let ω′ 物质流,i1 = w 物质流,Ei1 ;

[0048] If the parameter is an information flow parameter, assume that the parameter is the i2th parameter in the information flow parameter set, let the fourth weight of the parameter in the information flow parameter set be w 信息流,Di2 , and the fifth weight be w 信息流,Ei2 , if , let Otherwise, let ω′ 信息流,i2 = w 信息流,Ei2 ;

[0049] If the parameter is an energy flow parameter, assuming the parameter is the ith3 parameter in the energy flow parameter set, let the fourth weight of the parameter in the energy flow parameter set be w 能量流,Di3 , and the fifth weight of the parameter in the energy flow parameter set be w 能量流,Ei3 ; Otherwise, let ω′ 能量流,i3 = w 能量流,Ei3 ;

[0050] Step S325: After calculating the fifth weight of all parameters, calculate the sixth weight of the parameter in the flow parameter set:

[0051] If the parameter is a material flow parameter, assuming the parameter is the ith1 parameter in the material flow parameter set, the sixth weight of the parameter in the material flow parameter set w 物质流,Fi1 :

[0052]

[0053] If the parameter is an information flow parameter, assuming the parameter is the ith2 parameter in the information flow parameter set, the sixth weight of the parameter in the information flow parameter set w 信息流,Fi2 :

[0054]

[0055] If the parameter is an energy flow parameter, assuming the parameter is the ith3 parameter in the energy flow parameter set, the sixth weight of the parameter in the energy flow parameter set w 能量流,Fi3 :

[0056]

[0057] Step S326: Calculate the flow parameter set weight of the material flow parameter set, the information flow parameter set, and the energy flow parameter set respectively, denoted as W 物质流 , W 信息流 , and W 能量流 ; wherein the flow parameter set weight W 物质流 of the material flow parameter set is equal to the arithmetic sum of the third weights of the parameters in the material flow parameter set in the unmanned ship intelligent machine room system parameter set; the flow parameter set weight W 信息流 of the information flow parameter set is equal to the arithmetic sum of the third weights of the parameters in the information flow parameter set in the unmanned ship intelligent machine room system parameter set; and the flow parameter set weight W 能量流 of the energy flow parameter set is equal to the arithmetic sum of the third weights of the parameters in the energy flow parameter set in the unmanned ship intelligent machine room system parameter set.

[0058] ​Preferably, the step S4: extracting the parameter value corresponding to each parameter from the current state data of the unmanned ship intelligent machine cabin system, determining the unmanned ship intelligent machine cabin system task execution capability in combination with the third weight and the sixth weight of each parameter, comprises:

[0059] Step S41: extracting the parameter value corresponding to each parameter from the current state data of the unmanned ship intelligent machine cabin system, establishing the current efficacy value vector X of the unmanned ship intelligent machine cabin system parameter set c :

[0060]

[0061] Wherein, x c,i represents the efficacy value of the current value of the i-th parameter in the system parameter set, and the calculation formula is

[0062]

[0063] Wherein, is the maximum value of the i-th parameter in S observation samples, is the minimum value of the i-th parameter in S observation samples; v c,i The calculation method is

[0064]

[0065] Wherein, c c,i represents the current observation value of the i-th parameter in the system parameter set; K c,i represents the theoretical target value corresponding to the current observation value of the i-th parameter in the parameter set;

[0066] Step S42: normalizing the current efficacy value vector:

[0067]

[0068] Get the current evaluation vector X c ′:

[0069]

[0070] Wherein, x c ′,i represents the current evaluation value of the i-th parameter in the system parameter set;

[0071] Step S43: based on the historical evaluation matrix X′ S and the sixth weight of each parameter in the corresponding flow parameter set, the historical order degree of each flow is calculated, and the calculation method is

[0072]

[0073] Wherein, OD物质流,j , OD 信息流,j , OD 能量流,j respectively represent the historical order degree of the material flow, the historical order degree of the information flow, and the historical order degree of the energy flow corresponding to the jth historical observation sample in the S historical observation samples; w 物质流,Fi1 represents the sixth weight of the ith1 parameter in the material flow parameter set in the material flow parameter set, w 信息流,Fi2 represents the sixth weight of the ith2 parameter in the information flow parameter set in the information flow parameter set, w 能量流,Fi3 represents the sixth weight of the ith3 parameter in the energy flow parameter set in the energy flow parameter set; it is recorded that the corresponding number of the ith1 parameter in the material flow parameter set in the unmanned ship intelligent cabin system parameter set is m1, the corresponding number of the ith2 parameter in the information flow parameter set in the unmanned ship intelligent cabin system parameter set is m2, and the corresponding number of the ith3 parameter in the energy flow parameter set in the unmanned ship intelligent cabin system parameter set is m3; on this basis, x′ m1,j , that is, the historical evaluation value of the ith1 parameter in the material flow parameter set in the jth historical observation sample, x′ m2,j , that is, the historical evaluation value of the ith2 parameter in the information flow parameter set in the jth historical observation sample, x′ m3,j , that is, the historical evaluation value of the ith3 parameter in the energy flow parameter set in the jth historical observation sample;

[0074] Step S44: based on the current evaluation vector X c ′ and the sixth weight of each parameter in the corresponding flow parameter set, the current order degree of each flow is calculated, and the calculation method is

[0075]

[0076] wherein OD 物质流,c , OD 信息流,c , OD 能量流,c respectively represent the current order degree of the material flow, the current order degree of the information flow, and the current order degree of the energy flow, it is recorded that the corresponding number of the ith1 parameter in the material flow parameter set in the unmanned ship intelligent cabin system parameter set is m1, the corresponding number of the ith2 parameter in the information flow parameter set in the unmanned ship intelligent cabin system parameter set is m2, and the corresponding number of the ith3 parameter in the energy flow parameter set in the unmanned ship intelligent cabin system parameter set is m3; on this basis, x c ′,m1, that is, the current evaluation value of the ith1 parameter in the material flow parameter set, x c ′,m2, that is, the current evaluation value of the ith2 parameter in the information flow parameter set, x c ′,m3, that is, the current evaluation value of the ith3 parameter in the energy flow parameter set;

[0077] Step S45: respectively calculating the system order degree corresponding to the jth historical observation sample in the S historical observation samples and the current order degree of the unmanned ship intelligent cabin system, and the calculation method is

[0078]

[0079] Wherein, W 物质流 , W 信息流 , W 能量流 Respectively, the flow parameter set weight corresponding to the material flow parameter set, the information flow parameter set and the energy flow parameter set; SOD j Indicates the historical order degree of the unmanned ship intelligent cabin system corresponding to the jth historical observation sample in the S historical observation samples; SOD c Indicates the current order degree of the unmanned ship intelligent cabin system;

[0080] Step S46: let

[0081] That is, SOD max The maximum value of the system order degree corresponding to each historical observation sample in the S historical observation samples;

[0082] Step S47: according to the current order degree SOD c of the unmanned ship intelligent cabin system, the current task execution capability SE of the unmanned ship intelligent cabin system is calculated and determined as

[0083]

[0084] Wherein, R represents the design performance redundancy ratio of the unmanned ship intelligent cabin system.

[0085] The unmanned ship intelligent cabin system task execution capability evaluation device based on three flow coordination theory provided by the application comprises:

[0086] Parameter determination module: configured to determine each parameter supporting the operation of the unmanned ship intelligent cabin system representing the material flow, energy flow and information flow, and to form the unmanned ship intelligent cabin system parameter set by all parameters;

[0087] Weight module: configured to input the historical data of the unmanned ship intelligent cabin system operation and each parameter in the unmanned ship intelligent cabin system parameter set into the trained neural network model, so as to obtain the first weight of each parameter in the unmanned ship intelligent cabin system parameter set and the fourth weight in the parameter set of the flow represented by each parameter;

[0088] The weight correction module is configured to determine second weights of each parameter in the parameter set of the unmanned ship intelligent cabin system and fifth weights of each parameter in the parameter set of the flow represented by each parameter based on historical data of operation of the unmanned ship intelligent cabin system, correct the second weights based on the first weights of the parameters to obtain third weights, and correct the fifth weights based on the fourth weights of the parameters to obtain sixth weights;

[0089] The calculation module is configured to extract parameter values corresponding to each parameter from current state data of the unmanned ship intelligent cabin system, and determine the task execution capability of the unmanned ship intelligent cabin system by combining the third weights and the sixth weights of each parameter.

[0090] The present application proposes an unmanned ship intelligent cabin system task execution capability evaluation method based on three-flow coordination theory from the perspective of supporting the task execution of the unmanned ship intelligent cabin system. The method determines the mutual influence of material flow, energy flow and information flow based on historical data of the unmanned ship intelligent cabin system, and determines a comprehensive and comprehensive task execution capability evaluation index system and weights. The present application provides a basis for subsequent order issuance, material supply, task content planning and optimization, and operation mode adjustment.

[0091] Advantages:

[0092] (1) The present application can improve the ability of the unmanned ship intelligent cabin system to support the stable and reliable operation of the power system, and improve the task completion capability of the unmanned ship;

[0093] (2) The present application can improve the real-time monitoring capability of the intelligent cabin system based on the task demand of the unmanned ship, and improve the autonomous perception capability of the unmanned ship;

[0094] (3) The present application can improve the evaluation capability of the overall health operation level of the unmanned ship intelligent cabin system and the matching degree of task support, and reduce the direct asset loss and indirect task loss caused by task failure. BRIEF DESCRIPTION OF DRAWINGS

[0095] Figure 1 The present application is a flowchart of the unmanned ship intelligent cabin system task execution capability evaluation method based on three-flow coordination theory;

[0096] Figure 2 The present application is a structure block diagram of the unmanned ship intelligent cabin system task execution capability evaluation device based on three-flow coordination theory. DETAILED DESCRIPTION

[0097] The present application will be described in detail below in combination with the drawings and examples.

[0098] The present application proposes an unmanned ship intelligent cabin system task execution capability evaluation method based on three-flow coordination theory.

[0099] As Figure 1 indicated, the application provides a method for evaluating the task execution capability of an unmanned ship intelligent engine room system based on a three-flow synergy theory, comprising the following steps:

[0100] Step S1: determining each parameter representing a material flow, an energy flow and an information flow supporting the operation of the unmanned ship intelligent engine room system, and forming an unmanned ship intelligent engine room system parameter set from all the parameters;

[0101] Step S2: inputting the historical data of the operation of the unmanned ship intelligent engine room system and each parameter in the unmanned ship intelligent engine room system parameter set into a trained neural network model to obtain a first weight of each parameter in the unmanned ship intelligent engine room system parameter set and a fourth weight of each parameter in the parameter set of the flow it represents;

[0102] Step S3: determining a second weight of each parameter in the unmanned ship intelligent engine room system parameter set and a fifth weight of each parameter in the parameter set of the flow it represents based on the historical data of the operation of the unmanned ship intelligent engine room system; correcting the second weight based on the first weight of the parameter to obtain a third weight; correcting the fifth weight based on the fourth weight of the parameter to obtain a sixth weight;

[0103] Step S4: extracting the parameter value corresponding to each parameter from the current state data of the unmanned ship intelligent engine room system, and determining the task execution capability of the unmanned ship intelligent engine room system in combination with the third weight and the sixth weight of each parameter.

[0104] The material flow, the energy flow and the information flow of the operation of the unmanned ship intelligent engine room system are mutually coupled, mutually influenced and mutually synergistic. In the unmanned ship intelligent engine room system, relevant power, machinery, electrical and electronic equipment are synergistically coordinated under the control of command information to convert fuel-like substances into energy forms such as electrical energy and mechanical energy and deliver them to designated user equipment at the unmanned ship end, while generating corresponding material emissions. To ensure the reliable, stable and high-quality operation and running of the above main process, on the one hand, through the control of control instructions and the driving of energy flow, cooling, lubricating and other substances can be circulated to provide operating condition guarantees for the main process related equipment; on the other hand, based on the real-time feedback of environmental, state and alarm monitoring information, the operating mode and state of the related equipment are adjusted in real time to provide sufficient, sufficient and reliable high-quality operating condition guarantees for the main process related equipment.

[0105] (1) Material flow

[0106] The information flow mainly refers to the flow and circulation of substances such as fuel, cooling medium, lubricating medium and combustion oxidant medium in the unmanned ship intelligent cabin system. The material flow parameters of the unmanned ship intelligent cabin system mainly include fuel pressure / flow, lubricating oil pressure / flow, cooling fresh water pressure / flow / temperature difference before and after cooling, cooling seawater pressure / flow / temperature difference before and after cooling, and internal combustion engine intake charge air pressure / exhaust gas temperature.

[0107] (2) Energy flow

[0108] The energy flow mainly refers to the conversion, transmission, absorption and release of various types of energy such as fuel chemical energy, electric energy and mechanical energy in the unmanned ship intelligent cabin system. The reliable, high-quality and uninterrupted circulation of energy flow is the core requirement and ultimate goal of the unmanned ship intelligent cabin system. The energy flow parameters of the unmanned ship intelligent cabin system mainly include the output shaft power of the prime mover, the output shaft power of the propulsion shaft and the output electric power of the generator.

[0109] (3) Information flow

[0110] The information flow mainly refers to the issuance of various types of control instructions and the feedback of system, device and environment monitoring signals in the unmanned ship intelligent cabin system. The information flow parameters of the unmanned ship intelligent cabin system mainly include the prime mover speed control instruction and monitoring signal, the propulsion shaft speed control instruction and monitoring signal, and the environment temperature.

[0111] The step S1: determining the parameters representing the material flow, energy flow and information flow supporting the operation of the unmanned ship intelligent cabin system, which constitute the parameter system representing the unmanned ship intelligent cabin system.

[0112] Among them, the parameters representing the material flow include fuel pressure, fuel flow, lubricating oil pressure, lubricating oil flow, cooling fresh water pressure, cooling fresh water flow, cooling fresh water temperature difference before and after cooling, cooling seawater pressure, cooling seawater flow, cooling seawater temperature difference before and after cooling, internal combustion engine intake charge air pressure, and internal combustion engine exhaust gas temperature; the parameters representing the energy flow include the output shaft power of the prime mover, the output shaft power of the propulsion shaft, and the output electric power of the generator; and the parameters representing the information flow include the prime mover speed control instruction, the prime mover speed monitoring signal, the propulsion shaft speed control instruction, the propulsion shaft speed monitoring signal, and the environment temperature.

[0113] The step S2 comprises: forming a material flow parameter set by parameters representing a material flow, forming an energy flow parameter set by parameters representing an energy flow, and forming an information flow parameter set by parameters representing an information flow; all parameters in the material flow parameter set, the information flow parameter set and the energy flow parameter set together form the unmanned ship intelligent engine room system parameter set; historical data of the unmanned ship intelligent engine room system and each parameter in the unmanned ship intelligent engine room system parameter set are input into the trained neural network model to obtain a first weight of each parameter in the unmanned ship intelligent engine room system parameter set and a fourth weight of each parameter in the parameter set of the flow represented by the parameter.

[0114] The neural network model is an RNN network model, an LSTM network model or a WaveNet network model.

[0115] The step S3 comprises: determining a second weight of each parameter in the unmanned ship intelligent engine room system parameter set and a fifth weight of each parameter in the parameter set of the flow represented by the parameter based on the historical data of the unmanned ship intelligent engine room system; correcting the first weight to obtain a third weight based on the second weight; correcting the fourth weight to obtain a sixth weight based on the fifth weight, comprising:

[0116] The step S31 comprises: constructing a historical efficiency matrix of the unmanned ship intelligent engine room system based on the historical data of the unmanned ship intelligent engine room system, and constructing a historical evaluation matrix based on the historical efficiency matrix.

[0117] The step S32 comprises: determining a second weight of each parameter in the unmanned ship intelligent engine room system parameter set and a fifth weight of each parameter in the parameter set of the flow represented by the parameter based on the historical evaluation matrix; correcting the first weight to obtain a third weight of the parameter in the unmanned ship intelligent engine room system parameter set based on the second weight; correcting the fourth weight to obtain a sixth weight of the parameter in the parameter set of the flow represented by the parameter based on the fifth weight.

[0118] The step S31 comprises:

[0119] The step S311 comprises: obtaining S historical observation samples from the historical data, and constructing a historical efficiency value matrix X based on the S historical observation samples. S :

[0120]

[0121] Wherein, S represents the number of historical observation samples, N represents the number of parameters in the unmanned ship intelligent engine room system parameter set; x ij represents the efficiency value of the observation value of the i th parameter in the parameter set in the j th observation sample, and the calculation formula is

[0122]

[0123] wherein, the maximum value of the i-th parameter taken from S historical observation samples, the minimum value of the i-th parameter taken from S historical observation samples; v i,j The calculation method is

[0124]

[0125] wherein, c i,j represents the observation value of the i-th parameter in the unmanned ship intelligent cabin system parameter set in the j-th observation sample; K i,j represents the corresponding theoretical target value of the i-th parameter in the unmanned ship intelligent cabin system parameter set in the j-th observation sample;

[0126] Step S312: normalizing the historical efficacy value matrix X S :

[0127]

[0128] Further, the historical evaluation matrix X′ S is obtained:

[0129]

[0130] wherein, x′ i,j represents the historical evaluation value of the i-th parameter in the unmanned ship intelligent cabin system parameter set in the j-th historical observation sample.

[0131] Step S32: determining the second weight of each parameter in the unmanned ship intelligent cabin system parameter set and the fifth weight of each parameter in the parameter set of the flow represented by each parameter based on the historical evaluation matrix; correcting the second weight based on the first weight of the parameter in the unmanned ship intelligent cabin system parameter set to obtain the third weight of the parameter in the unmanned ship intelligent cabin system parameter set; correcting the fifth weight based on the fourth weight of the parameter in the flow parameter set to obtain the sixth weight of the parameter in the flow parameter set, comprising:

[0132] Step S321: calculating the information entropy of each parameter based on the historical evaluation matrix X′ S :

[0133]

[0134] wherein, J=1 / ln(S); only when x′ ij =0, take H iinformation entropy of the i-th parameter in the unmanned ship intelligent engine room system parameter set;

[0135] Step S322: For each parameter in the unmanned ship intelligent engine room system parameter set, the following operations are performed:

[0136] determining the flow corresponding to the parameter, the corresponding flow being one of a material flow, an information flow, and an energy flow, and determining the number of the parameter in the corresponding flow parameter set, calculating the fifth weight of the parameter in the flow:

[0137]

[0138] wherein w 物质流,Ei1 represents the fifth weight of the i1-th parameter in the material flow parameter set in the material flow, w 信息流,Ei1 represents the fifth weight of the i2-th parameter in the information flow parameter set in the information flow, w 能量流,Ei1 represents the fifth weight of the i3-th parameter in the energy flow parameter set in the energy flow; H 物质流,i1 represents the information entropy of the i1-th parameter in the material flow parameter set, H 信息流,i2 represents the information entropy of the i2-th parameter in the information flow parameter set, H 能量流,i3 represents the information entropy of the i3-th parameter in the energy flow parameter set; N 物质流 represents the number of parameters in the material flow parameter set, N 信息流 represents the number of parameters in the information flow parameter set, N 能量流 represents the number of parameters in the energy flow parameter set; N 物质流 +N 信息流 +N 能量流 =N;

[0139] calculating the second weight w Bi of the parameter in the unmanned ship intelligent engine room system parameter set:

[0140]

[0141] Step S323: For each parameter in the unmanned ship intelligent engine room system parameter set, the following calculation is performed: let the first weight of the parameter in the unmanned ship intelligent engine room system parameter set be w Ai , and the second weight be w Bi ,

[0142] if then let otherwise, let ω′ i =w Bi ;

[0143] After the calculation of all parameters in the unmanned ship intelligent engine room system parameter set is completed, the third weight w of each parameter in the unmanned ship intelligent engine room system parameter set is calculated Ci :

[0144]

[0145] The third weight of each parameter in the unmanned ship intelligent engine room system parameter set satisfies:

[0146]

[0147] Step S324: For each parameter in the unmanned ship intelligent engine room system parameter set, the following operations are performed:

[0148] Determine the corresponding flow of the parameter, which is one of the material flow, information flow and energy flow;

[0149] If the parameter is a material flow parameter, assume that the parameter is the i1th parameter in the material flow parameter set, and denote the fourth weight of the parameter in the material flow parameter set as w 物质流,Di1 , the fifth weight of the parameter in the material flow parameter set as w 物质流,Ei1 , if then let otherwise, let ω′ 物质流,i1 = w 物质流,Ei1 ;

[0150] If the parameter is an information flow parameter, assume that the parameter is the i2th parameter in the information flow parameter set, and denote the fourth weight of the parameter in the information flow parameter set as w 信息流,Di2 , the fifth weight of the parameter in the information flow parameter set as w 信息流,Ei2 , if then let otherwise, let ω′ 信息流,i2 = w 信息流,Ei2 ;

[0151] If the parameter is an energy flow parameter, assume that the parameter is the i3th parameter in the energy flow parameter set, and denote the fourth weight of the parameter in the energy flow parameter set as w 能量流,Di3 , the fifth weight of the parameter in the energy flow parameter set as w 能量流,Ei3 , if then let otherwise, let ω′ 能量流,i3 = w 能量流,Ei3 ;

[0152] Step S325: After the calculation of the fifth weight of all parameters is completed, the sixth weight of the parameter in the flow parameter set is calculated:

[0153] If the parameter is a material flow parameter, assuming that the parameter is the ith1 parameter in the material flow parameter set, the sixth weight w 物质流,Fi1 :

[0154]

[0155] If the parameter is an information flow parameter, assuming that the parameter is the ith2 parameter in the information flow parameter set, the sixth weight w 信息流,Fi2 :

[0156]

[0157] If the parameter is an energy flow parameter, assuming that the parameter is the ith3 parameter in the energy flow parameter set, the sixth weight w 能量流,Fi3 :

[0158]

[0159] Further:

[0160]

[0161] Step S326: the corresponding flow parameter set weight is calculated for the material flow parameter set, the information flow parameter set and the energy flow parameter set, respectively, which are denoted as W 物质流 , W 信息流 , W 能量流 ; wherein the flow parameter set weight W 物质流 of the material flow parameter set is equal to the arithmetic sum of the third weights of each parameter in the material flow parameter set in the unmanned ship intelligent engine room system parameter set; the flow parameter set weight W 信息流 of the information flow parameter set is equal to the arithmetic sum of the third weights of each parameter in the information flow parameter set in the unmanned ship intelligent engine room system parameter set; and the flow parameter set weight W 能量流 of the energy flow parameter set is equal to the arithmetic sum of the third weights of each parameter in the energy flow parameter set in the unmanned ship intelligent engine room system parameter set.

[0162] Further, W 物质流 + W 信息流 + W 能量流 = 1.

[0163] In the present application, the analysis of historical data by the neural network model is combined to correct the weight of the parameter, which can comprehensively utilize the advantages of entropy value calculation and neural network calculation.

[0164] The step S4: the parameter value corresponding to each parameter is extracted from the current state data of the unmanned ship intelligent engine room system, and the third weight and the sixth weight of each parameter are combined to determine the task execution capability of the unmanned ship intelligent engine room system, including:

[0165] Step S41: Extracting parameter values corresponding to each parameter from the current state data of the unmanned ship intelligent engine room system, and establishing a current efficiency value vector X of the parameter set of the unmanned ship intelligent engine room system c :

[0166]

[0167] Wherein, x c,i represents the efficiency value of the current value of the i-th parameter in the system parameter set, and the calculation formula is

[0168]

[0169] Wherein, is the maximum value of the i-th parameter in S observation samples, is the minimum value of the i-th parameter in S observation samples; v c,i The calculation method is

[0170]

[0171] Wherein, c c,i represents the current observation value of the i-th parameter in the system parameter set; K c,i represents the theoretical target value corresponding to the current observation value of the i-th parameter in the parameter set;

[0172] Step S42: Normalizing the current efficiency value vector:

[0173]

[0174] Get the current evaluation vector X c ′:

[0175]

[0176] Wherein, x c ′,i represents the current evaluation value of the i-th parameter in the system parameter set;

[0177] Step S43: Based on the historical evaluation matrix X′ S and the sixth weight of each parameter in the corresponding flow parameter set, the historical order degree of each flow is calculated, and the calculation method is

[0178]

[0179] Wherein, OD 物质流,j , OD 信息流,j , OD 能量流,jrespectively represent the historical order degree of the material flow, the historical order degree of the information flow, and the historical order degree of the energy flow corresponding to the jth historical observation sample in the S historical observation samples; w 物质流,Fi1 represents the sixth weight of the ith1 parameter in the material flow parameter set in the material flow parameter set, w 信息流,Fi2 represents the sixth weight of the ith2 parameter in the information flow parameter set in the information flow parameter set, w 能量流,Fi3 represents the sixth weight of the ith3 parameter in the energy flow parameter set in the energy flow parameter set; it is recorded that the corresponding number of the ith1 parameter in the material flow parameter set in the unmanned ship intelligent cabin system parameter set is m1, the corresponding number of the ith2 parameter in the information flow parameter set in the unmanned ship intelligent cabin system parameter set is m2, and the corresponding number of the ith3 parameter in the energy flow parameter set in the unmanned ship intelligent cabin system parameter set is m3; on this basis, x′ m1,j is the historical evaluation value of the ith1 parameter in the material flow parameter set in the jth historical observation sample, x′ m2,j is the historical evaluation value of the ith2 parameter in the information flow parameter set in the jth historical observation sample, x′ m3,j is the historical evaluation value of the ith3 parameter in the energy flow parameter set in the jth historical observation sample.

[0180] Step S44: based on the current evaluation vector X c and the sixth weight of each parameter in the corresponding flow parameter set, the current order degree of each flow is calculated, and the calculation method is

[0181]

[0182] wherein, OD 物质流,c , OD 信息流,c , OD 能量流,c respectively represent the current order degree of the material flow, the current order degree of the information flow, and the current order degree of the energy flow; it is recorded that the corresponding number of the ith1 parameter in the material flow parameter set in the unmanned ship intelligent cabin system parameter set is m1, the corresponding number of the ith2 parameter in the information flow parameter set in the unmanned ship intelligent cabin system parameter set is m2, and the corresponding number of the ith3 parameter in the energy flow parameter set in the unmanned ship intelligent cabin system parameter set is m3; on this basis, x c ′,m1 is the current evaluation value of the ith1 parameter in the material flow parameter set, x c ′,m2 is the current evaluation value of the ith2 parameter in the information flow parameter set, x c ′,m3 is the current evaluation value of the ith3 parameter in the energy flow parameter set.

[0183] Step S45: respectively calculating the system order degree corresponding to the jth historical observation sample in the S historical observation samples and the current order degree of the unmanned ship intelligent cabin system, and the calculation method is

[0184]

[0185] Wherein, W 物质流 , W 信息流 , W 能量流 respectively material flow parameter set, information flow parameter set, energy flow parameter set corresponding to the flow parameter set weight; SOD j indicates the historical order degree of the unmanned ship intelligent cabin system corresponding to the jth historical observation sample in the S historical observation samples; SOD c indicates the current order degree of the unmanned ship intelligent cabin system;

[0186] Step S46: let

[0187] That is, SOD max is the maximum value of the system order degree corresponding to each historical observation sample in the S historical observation samples;

[0188] Step S47: according to the current order degree SOD c of the unmanned ship intelligent cabin system, the current task execution capability SE of the unmanned ship intelligent cabin system is calculated and determined as

[0189]

[0190] Wherein, R indicates the design performance redundancy ratio of the unmanned ship intelligent cabin system.

[0191] In the application, R is the system performance redundancy reserved in the early design stage in order to offset the decline of system synergy caused by factors such as aging and wear during long-term operation of the real system. Generally, the value of R can be 0.05-0.1.

[0192] Further, the method comprises the step S6: based on the current task execution capability of the unmanned ship intelligent cabin system, planning and adjusting the subsequent task of the unmanned ship intelligent cabin system, such as reconnaissance, patrol, special operation, navigation, entering and exiting narrow waterway, entering and exiting port, leaving and approaching wharf, etc.

[0193] The application is based on the S historical observation samples collected in the early stage (the more the number of historical observation samples, the more accurate the statistical calculation results based on the historical observation samples. The number of historical observation samples S in the calculation method described above can be 1000-2000), to establish the unmanned ship intelligent cabin system parameter set.

[0194] The application further provides an unmanned ship intelligent engine room system task execution capability evaluation device based on a three-flow coordination theory. Figure 2 As shown in the figure, the device comprises:

[0195] A parameter determination module configured to determine respective parameters of representative material flow, energy flow and information flow supporting operation of the unmanned ship intelligent engine room system, and to form an unmanned ship intelligent engine room system parameter set from all the parameters.

[0196] A weight module configured to input historical data of the unmanned ship intelligent engine room system operation and respective parameters in the unmanned ship intelligent engine room system parameter set into a trained neural network model to obtain a first weight of each parameter in the unmanned ship intelligent engine room system parameter set and a fourth weight of each parameter in the parameter set of the flow represented by the parameter.

[0197] A weight correction module configured to determine a second weight of each parameter in the unmanned ship intelligent engine room system parameter set and a fifth weight of each parameter in the parameter set of the flow represented by the parameter based on the historical data of the unmanned ship intelligent engine room system operation, to correct the second weight based on the first weight of the parameter to obtain a third weight, and to correct the fifth weight based on the fourth weight of the parameter to obtain a sixth weight.

[0198] A calculation module configured to extract parameter values corresponding to respective parameters from current state data of the unmanned ship intelligent engine room system, and to determine the task execution capability of the unmanned ship intelligent engine room system in combination with the third weight and the sixth weight of each parameter.

[0199] The part of the application not described in detail belongs to the known technology of the person skilled in the art.

[0200] The foregoing specific embodiments only describe the design principles of the application, and the shapes and names of the components in the description can be different and are not limited. Therefore, the person skilled in the art can modify or equivalently replace the technical solutions recorded in the foregoing embodiments; and these modifications and replacements do not deviate from the purpose and technical solutions of the application, and should all belong to the protection scope of the application.

Claims

1. A method for evaluating the mission execution capability of an unmanned surface vessel (USV) intelligent engine room system based on the theory of three-flow collaboration, characterized in that, include: Step S1: Determine the parameters representing the material flow, energy flow, and information flow that support the operation of the unmanned ship intelligent cabin system, and form the parameter set of the unmanned ship intelligent cabin system from all parameters; Step S2: Input the historical data of the operation of the unmanned ship intelligent cabin system and each parameter in the parameter set of the unmanned ship intelligent cabin system into the trained neural network model to obtain the first weight of each parameter in the parameter set of the unmanned ship intelligent cabin system and the fourth weight in the parameter set of the flow they represent. Step S3: Based on the historical data of the operation of the unmanned ship intelligent cabin system, determine the second weight of each parameter in the parameter set of the unmanned ship intelligent cabin system and the fifth weight of each parameter in the parameter set of the flow it represents. The third weight is obtained by correcting the second weight based on the first weight of the parameters; The fifth weight is adjusted based on the fourth weight of the parameters to obtain the sixth weight; Step S4: Extract the parameter values ​​corresponding to each parameter from the current status data of the unmanned ship intelligent cabin system, and determine the task execution capability of the unmanned ship intelligent cabin system by combining the third weight and the sixth weight of each parameter. Step S3: Based on the historical data of the operation of the unmanned ship intelligent cabin system, determine the second weight of each parameter in the parameter set of the unmanned ship intelligent cabin system and the fifth weight of each parameter in the parameter set of the flow it represents. The third weight is obtained by correcting the second weight based on the first weight of the parameters; The fifth weight is adjusted based on the fourth weight of the parameters to obtain the sixth weight, which includes: Step S31: Construct a historical efficiency matrix of the unmanned vessel intelligent cabin system based on the historical data of the unmanned vessel intelligent cabin system operation, and construct a historical evaluation matrix based on the historical efficiency matrix; Step S32: Based on the historical evaluation matrix, determine the second weight of each parameter in the parameter set of the unmanned ship intelligent cabin system and the fifth weight of each parameter in the parameter set of the flow it represents; correct the second weight based on the first weight of the parameter in the parameter set of the unmanned ship intelligent cabin system to obtain the third weight of the parameter in the parameter set of the unmanned ship intelligent cabin system; correct the fifth weight based on the fourth weight of the parameter in the parameter set of the flow it belongs to to obtain the sixth weight of the parameter in the parameter set of the flow it belongs to.

2. The method as described in claim 1, characterized in that, Step S2 includes: forming a material flow parameter set by parameters representing material flow, forming an energy flow parameter set by parameters representing energy flow, and forming an information flow parameter set by parameters representing information flow; all parameters in the material flow parameter set, information flow parameter set, and energy flow parameter set together form the parameter set of the unmanned vessel intelligent cabin system; inputting the historical data of the operation of the unmanned vessel intelligent cabin system and each parameter in the parameter set of the unmanned vessel intelligent cabin system into the trained neural network model to obtain the first weight of each parameter in the parameter set of the unmanned vessel intelligent cabin system and the fourth weight in the parameter set of the flow it represents.

3. The method as described in claim 1, characterized in that, Step S31 includes: Step S311: Obtain S historical observation samples from the historical data, and construct a historical efficacy value matrix X based on the S historical observation samples. S : Where S represents the number of historical observation samples, and N represents the number of parameters in the parameter set of the unmanned ship intelligent cabin system; x i,j The power value of the i-th parameter in the j-th observation sample is represented by the following formula: in, Take the maximum value of the i-th parameter among the S historical observation samples. Taken from the minimum value of the i-th parameter in S historical observation samples; v i,j The calculation method is as follows Among them, c i,j K represents the observed value of the i-th parameter in the j-th observation sample of the parameter set of the unmanned ship intelligent cabin system. i,j This represents the theoretical target value corresponding to the i-th parameter in the j-th observation sample of the parameter set of the unmanned ship intelligent cabin system; Step S312: For the historical efficacy value matrix X S Normalization is performed: This leads to the historical evaluation matrix X′. S : Where, x′ i,j This represents the historical evaluation value of the i-th parameter in the j-th historical observation sample within the parameter set of the unmanned vessel intelligent cabin system. Step S32 includes: Step S321: Based on the historical evaluation matrix X′ S Calculate the information entropy of each parameter: Where J = 1 / ln(S); H i This represents the information entropy of the i-th parameter in the parameter set of the unmanned vessel intelligent cabin system; Step S322: For each parameter in the parameter set of the unmanned vessel intelligent cabin system, perform the following operations: Determine the flow corresponding to the parameter, where the flow is one of material flow, information flow, or energy flow, and determine the parameter's number in the corresponding flow parameter set. Calculate the fifth weight of the parameter in the flow in which it belongs. Among them, w 物质流,Ei1 w represents the fifth weight of the i1th parameter in the material flow parameter set in the material flow. 信息流,Ei2 w represents the fifth weight of the i2th parameter in the information flow parameter set within the information flow. 能量流,Ei3 H represents the fifth weight of the i-th parameter in the energy flow parameter set in the energy flow; 物质流,i1 H represents the information entropy of the i1th parameter in the material flow parameter set. 信息流,i2 H represents the information entropy of the i2th parameter in the information flow parameter set. 能量流,i3 N represents the information entropy of the i-th parameter in the energy flow parameter set; 物质流 N represents the number of parameters in the material flow parameter set. 信息流 N represents the number of parameters in the information flow parameter set. 能量流 N represents the number of parameters in the energy flow parameter set; 物质流 +N 信息流 +N 能量流 =N; Calculate the second weight w of this parameter in the parameter set of the unmanned ship intelligent cabin system. Bi : Step S323: For each parameter in the parameter set of the unmanned vessel intelligent cabin system, perform the following calculation: Let w be the first weight of the parameter in the parameter set of the unmanned vessel intelligent cabin system. Ai The second weight is w Bi , like Then let Otherwise, let ω′ i =w Bi ; After calculating all parameters in the parameter set of the unmanned vessel intelligent engine room system, calculate the third weight w of each parameter in the parameter set of the unmanned vessel intelligent engine room system. Ci : The third weight of each parameter in the parameter set of the unmanned ship intelligent cabin system satisfies: Step S324: For each parameter in the parameter set of the unmanned vessel intelligent cabin system, perform the following operations: Determine the flow corresponding to this parameter, where the corresponding flow is one of the material flow, information flow, or energy flow; If this parameter is a material flow parameter, assuming it is the i1th parameter in the material flow parameter set, let w be the fourth weight of this parameter in the material flow parameter set. 物质流,Di1 The fifth weight of this parameter in the material flow parameter set is w. 物质流,Ei1 ,like Then let Otherwise, let ω′ 物质流,i1 =w 物质流,Ei1 ; If this parameter is an information flow parameter, assuming it is the i-th parameter in the information flow parameter set, let w be the fourth weight of this parameter in the information flow parameter set. 信息流,Di2 The fifth weight of this parameter in the information flow parameter set is w. 信息流,Ei2 ,like Then let Otherwise, let ω′ 信息流,i2 =w 信息流,Ei2 ; If this parameter is an energy flow parameter, assuming it is the i-th parameter in the energy flow parameter set, let w be the fourth weight of this parameter in the energy flow parameter set. 能量流,Di3 The fifth weight of this parameter in the energy flow parameter set is w. 能量流,Ei3 ,like Then let Otherwise, let ω′ 能量流,i3 =w 能量流,Ei3 ; Step S325: After calculating the fifth weight for all parameters, calculate the sixth weight of the parameter in the flow parameter set: If this parameter is a material flow parameter, assuming it is the i1th parameter in the material flow parameter set, then the sixth weight w of this parameter in the material flow parameter set... 物质流,Fi1 : If this parameter is an information flow parameter, assuming it is the i-th parameter in the information flow parameter set, then the sixth weight w of this parameter in the information flow parameter set... 信息流,Fi2 : If this parameter is an energy flow parameter, assuming it is the i-th parameter in the energy flow parameter set, then the sixth weight w of this parameter in the energy flow parameter set... 能量流,Fi3 : Step S326: Calculate the corresponding flow parameter set weights for the material flow parameter set, information flow parameter set, and energy flow parameter set, and denote them as W respectively. 物质流 W 信息流 W 能量流 Among them, the flow parameter set weight W of the material flow parameter set 物质流 The weight W of the flow parameter set is equal to the arithmetic sum of the third weights of each parameter in the material flow parameter set within the unmanned vessel intelligent cabin system parameter set; 信息流 Equals the arithmetic sum of the third weights of each parameter in the information flow parameter set within the parameter set of the unmanned ship intelligent cabin system; the flow parameter set weight W of the energy flow parameter set. 能量流 It equals the arithmetic sum of the third weights of each parameter in the energy flow parameter set within the parameter set of the unmanned ship intelligent cabin system.

4. The method as described in claim 3, characterized in that, Step S4: Extract the parameter values ​​corresponding to each parameter from the current status data of the unmanned vessel intelligent cabin system, and determine the task execution capability of the unmanned vessel intelligent cabin system by combining the third and sixth weights of each parameter, including: Step S41: Extract the parameter values ​​corresponding to each parameter from the current state data of the unmanned vessel intelligent engine room system, and establish the current efficacy value vector X of the parameter set of the unmanned vessel intelligent engine room system. c : Where, x c,i The efficacy value represents the current value of the i-th parameter in the system parameter set, and its calculation formula is: in, Take the maximum value of the i-th parameter among the S historical observation samples. Taken from the minimum value of the i-th parameter in S historical observation samples; v c,i The calculation method is as follows Among them, c c,i K represents the current observed value of the i-th parameter in the system parameter set; c,i This represents the theoretical target value corresponding to the current observed value of the i-th parameter in the parameter set; Step S42: Normalize the current efficacy value vector: Obtain the current evaluation vector X c ′: Where, x c ′,i represents the current evaluation value of the i-th parameter in the system parameter set; Step S43: Based on the historical evaluation matrix X′ S The historical orderliness of each flow is calculated by assigning the sixth weight of each parameter to the corresponding flow parameter set. The calculation method is as follows: Among them, OD 物质流,j OD 信息流,j OD 能量流,j These represent the historical orderliness of the matter flow, information flow, and energy flow corresponding to the j-th historical observation sample among S historical observation samples, respectively; w 物质流,Fi1 w represents the sixth weight of the i1th parameter in the material flow parameter set. 信息流,Fi2 w represents the sixth weight of the i2th parameter in the information flow parameter set. 能量流,Fi3 Let m1 represent the sixth weight of the i3rd parameter in the energy flow parameter set; let m2 represent the corresponding number of the i1th parameter in the material flow parameter set in the unmanned vessel intelligent cabin system parameter set; let m3 represent the corresponding number of the i2th parameter in the information flow parameter set in the unmanned vessel intelligent cabin system parameter set; and let m3 represent the corresponding number of the i3th parameter in the energy flow parameter set in the unmanned vessel intelligent cabin system parameter set. Based on this, x′ m1,j That is, the historical evaluation value of the i1th parameter in the jth historical observation sample of the material flow parameter set, x′ m2,j That is, the historical evaluation value of the i2th parameter in the j-th historical observation sample in the information flow parameter set, x′ m3,j That is, the historical evaluation value of the i3rd parameter in the jth historical observation sample in the energy flow parameter set; Step S44: Based on the current evaluation vector X c ' and the sixth weight of each parameter in the corresponding flow parameter set are used to calculate the current order of each flow. The calculation method is as follows: Among them, OD 物质流,c OD 信息流,c OD 能量流,c Let m1 represent the current orderliness of the material flow, the information flow, and the energy flow, respectively; let m1 be the corresponding number of the i1th parameter in the material flow parameter set in the unmanned vessel intelligent cabin system parameter set, m2 be the corresponding number of the i2th parameter in the information flow parameter set in the unmanned vessel intelligent cabin system parameter set, and m3 be the corresponding number of the i3th parameter in the energy flow parameter set in the unmanned vessel intelligent cabin system parameter set; based on this, x c ′,m1 is the current evaluation value of the i1th parameter in the material flow parameter set, x c ′,m2 is the current evaluation value of the i2th parameter in the information flow parameter set, x c ′,m3 is the current evaluation value of the i3th parameter in the energy flow parameter set; Step S45: Calculate the system orderliness corresponding to the j-th historical observation sample in the S historical observation samples and the current orderliness of the unmanned ship intelligent cabin system, respectively. The calculation method is as follows: Among them, W 物质流 W 信息流 W 能量流 Weights of the flow parameter sets corresponding to the material flow parameter set, information flow parameter set, and energy flow parameter set, respectively; SOD j SOD represents the historical orderliness of the unmanned ship's intelligent cabin system corresponding to the j-th historical observation sample out of S historical observation samples; c This indicates the current orderliness of the unmanned vessel's intelligent cabin system; Step S46: Let That is, SOD max Let S be the maximum value of the system orderliness corresponding to each of the S historical observation samples. Step S47: Based on the current orderliness (SOD) of the unmanned vessel's intelligent engine room system. c The current mission execution capability (SE) of the unmanned vessel's intelligent cabin system is calculated and determined to be... Where R represents the design performance redundancy ratio of the unmanned ship's intelligent cabin system.

5. A device for evaluating the mission execution capability of an unmanned surface vessel intelligent engine room system based on the theory of three-flow collaboration, characterized in that, The method for evaluating the mission execution capability of an unmanned ship intelligent cabin system based on the three-flow cooperative theory as described in any one of claims 1-4 includes: Parameter determination module: configured to determine the various parameters representing the material flow, energy flow, and information flow that support the operation of the unmanned ship intelligent cabin system, and the parameter set of the unmanned ship intelligent cabin system is composed of all parameters; Weighting module: configured to input the historical data of the operation of the unmanned ship intelligent cabin system and each parameter in the parameter set of the unmanned ship intelligent cabin system into the trained neural network model, and obtain the first weight of each parameter in the parameter set of the unmanned ship intelligent cabin system and the fourth weight in the parameter set of the flow they represent. Weight correction module: configured to determine the second weight of each parameter in the parameter set of the unmanned ship intelligent cabin system and the fifth weight of each parameter in the parameter set of the flow it represents, based on the historical data of the operation of the unmanned ship intelligent cabin system; correct the second weight based on the first weight of the parameter to obtain the third weight; correct the fifth weight based on the fourth weight of the parameter to obtain the sixth weight; Calculation module: configured to extract the parameter values ​​corresponding to each parameter from the current status data of the unmanned ship intelligent cabin system, and determine the task execution capability of the unmanned ship intelligent cabin system by combining the third weight and the sixth weight of each parameter.

6. A computer-readable storage medium, characterized in that, The storage medium stores a plurality of instructions; the plurality of instructions are loaded by a processor and executed as described in any one of claims 1-4.

7. An electronic device, characterized in that, The electronic device includes: A processor is used to execute multiple instructions; Memory, used to store multiple instructions; The plurality of instructions are to be stored in the memory and loaded by the processor and executed as described in any one of claims 1-4.

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