CN113609573B - Fuzzy comprehensive evaluation method and device based on normal cloud model - Google Patents
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Abstract
The invention relates to a fuzzy comprehensive evaluation method and a device based on a normal cloud model, wherein the method comprises the following steps: acquiring a plurality of operation indexes to be evaluated in a ship system; forming an evaluation index set according to various operation indexes; constructing a corresponding evaluation level set according to the evaluation index set, wherein the evaluation level set is used for representing evaluation result levels of various operation indexes in the evaluation index set; constructing a first cloud model of the evaluation index set, and determining corresponding first cloud model parameters; constructing a second cloud model of the evaluation level set, and determining corresponding second cloud model parameters; determining corresponding cloud similarity according to the first cloud model and the second cloud model; and determining a comprehensive evaluation vector according to the cloud similarity, the weight set and a preset fuzzy operator so as to judge the result. The invention utilizes the cloud model to carry out classification evaluation on various operation indexes in the ship system so as to effectively judge the integrity of the ship combat readiness.
Description
Technical Field
The invention relates to the technical field of ships, in particular to a fuzzy comprehensive evaluation method and device based on a normal cloud model.
Background
The essence of the combat readiness assessment is that a mapping relation between an accurate test quantitative index and a fuzzy qualitative concept is established, and the assessment is an uncertainty assessment problem. As an important method in the category of fuzzy mathematics, the fuzzy comprehensive evaluation method quantitatively characterizes the membership degree and the membership relation between the evaluation object and the qualitative concept through the membership degree, and can well express the uncertainty through quantitative numerical values to obtain a more scientific evaluation result. However, membership functions of the ship combat readiness integrity evaluation indexes are not unified and standardized, and are determined by expert experience, so that large subjective influence is introduced, generalization is not strong, and evaluation results are not accurate. In summary, how to provide an efficient and accurate fuzzy comprehensive evaluation method is a problem to be solved.
Disclosure of Invention
In view of the foregoing, it is necessary to provide a fuzzy comprehensive evaluation method and device based on a normal cloud model, which are used for solving the problem that the ship combat readiness evaluation process in the prior art is not standard.
The invention provides a fuzzy comprehensive evaluation method based on a normal cloud model, which comprises the following steps:
acquiring a plurality of operation indexes to be evaluated in a ship system;
forming an evaluation index set according to a plurality of operation indexes;
constructing a corresponding evaluation level set according to the evaluation index set, wherein the evaluation level set is used for representing evaluation result levels of various operation indexes in the evaluation index set;
constructing a first cloud model of the evaluation index set, and determining corresponding first cloud model parameters;
constructing a second cloud model of the evaluation level set, and determining corresponding second cloud model parameters;
determining corresponding cloud similarity according to the first cloud model and the second cloud model;
and determining a comprehensive evaluation vector according to the cloud similarity, a weight set and a preset fuzzy operator to judge a result, wherein the weight set is determined according to the calculated weights corresponding to the operation indexes.
Further, the forming an evaluation index set according to the plurality of operation indexes includes:
layering a plurality of operation indexes, and determining the index number of each layer;
dividing the operation index of each layer into a plurality of index subsets according to the attribution relation;
and determining the evaluation index set according to a plurality of index subsets.
Further, the constructing a corresponding evaluation level set according to the evaluation index set includes:
determining an evaluation result level corresponding to the operation index according to the evaluation index set;
and forming the evaluation grade set according to the evaluation result grade.
Further, the constructing the first cloud model of the evaluation index set, and determining the corresponding first cloud model parameters includes:
inputting the operation indexes in the evaluation index set into a reverse cloud generator respectively to generate a corresponding first reverse expected value, a first reverse variance value and a first reverse super-entropy value;
and respectively inputting the first reverse expected value, the first reverse variance value and the first reverse super-entropy value into a forward cloud generator to generate N corresponding first forward cloud drops, wherein the N first forward cloud drops form the first cloud model.
Further, the constructing a second cloud model of the evaluation level set, and determining corresponding second cloud model parameters includes:
dividing the operation index into a plurality of state intervals according to the standard limit value corresponding to the operation index;
for different state intervals, randomly generating a plurality of groups of index data corresponding to the running index;
inputting the multiple groups of index data into the reverse cloud generator to generate a corresponding second reverse expected value, a second reverse variance value and a second reverse super-entropy value;
and inputting the second reverse expected value, the second reverse variance value and the second reverse super-entropy value to the forward cloud generator to generate N corresponding second forward cloud drops, wherein the second N forward cloud drops form the second cloud models corresponding to different state intervals.
Further, the plurality of status intervals include a normal status interval, an attention status interval, an abnormal status interval, and a serious status interval, wherein different status intervals are divided according to the range of the operation index.
Further, the determining, according to the first cloud model and the second cloud model, a corresponding cloud similarity includes:
determining a relative distance between the first cloud model and the second cloud model according to the first cloud model parameters and the second cloud model parameters;
determining corresponding membership according to the relative distance;
and determining the cloud similarity according to the membership degree.
Further, determining a comprehensive evaluation vector according to the cloud similarity, a weight set and a preset fuzzy operator to perform result judgment, wherein the weight set determines according to the calculated weights corresponding to the operation indexes, and the method comprises the following steps:
multiplying the weight set, the fuzzy operator and the cloud similarity in sequence to determine a corresponding primary evaluation vector;
and multiplying the weight set, the fuzzy operator and the primary evaluation vector in sequence to determine a corresponding comprehensive evaluation vector.
Further, the determining of the weight set includes:
for each operation index, performing cooperative game calculation on the operation index according to an analytic hierarchy process, an entropy weight process and a gray correlation degree process to determine corresponding weights;
each of the weights constitutes the set of weights.
The invention also provides a fuzzy comprehensive evaluation device based on the normal cloud model, which comprises a processor and a memory, wherein the memory is stored with a computer program, and when the computer program is executed by the processor, the fuzzy comprehensive evaluation method based on the normal cloud model is realized.
Compared with the prior art, the invention has the beneficial effects that: firstly, effectively acquiring various operation indexes; then, aiming at the operation indexes, a corresponding evaluation index set is formed, so that the data processing among the sets can be conveniently carried out subsequently; then, constructing a corresponding evaluation grade set, so as to effectively feed back the corresponding evaluation result grade of each operation index; then, forming a first cloud model according to a plurality of operation indexes in the evaluation index set, and feeding back data characteristics of the data to be evaluated; further, forming a second cloud model serving as a reference according to the evaluation level set, and feeding back standard data characteristics of different state intervals; finally, the difference between the two cloud models is fed back through the cloud similarity of the two cloud models, so that subsequent grade classification can be conveniently carried out through comparison; and finally, carrying out fuzzy operation according to fuzzy operators through cloud similarity of each operation index and a weight set corresponding to the operation index, thereby realizing efficient fuzzy comprehensive evaluation. In summary, the cloud model is used as a qualitative and quantitative conversion model, so that the internal relevance of randomness and ambiguity is fully reflected, the evaluation set is divided more gently, the method can be directly used for single evaluation of indexes, the problem that membership functions are difficult to determine can be reasonably avoided by utilizing cloud similarity to replace membership, and various operation indexes in a ship system are evaluated in a classified mode by utilizing the cloud model, so that the integrity of the ship combat readiness is effectively judged.
Drawings
FIG. 1 is a schematic flow chart of fuzzy comprehensive evaluation based on a normal cloud model;
FIG. 2 is a schematic flow chart of forming an evaluation index set according to the present invention;
FIG. 3 is a schematic flow chart of forming an evaluation level set according to the present invention;
fig. 4 is a schematic flow chart of forming a first cloud model according to the present invention;
FIG. 5 is a schematic diagram of a cloud model according to the present invention;
FIG. 6 is a second schematic diagram of a cloud model according to the present invention;
fig. 7 is a schematic flow chart of forming a second cloud model according to the present invention;
FIG. 8 is a schematic diagram of a second cloud model provided by the present invention;
FIG. 9 is a schematic diagram of parameters of a second cloud model according to the present invention;
fig. 10 is a schematic flow chart of determining cloud similarity according to the present invention;
fig. 11 is a schematic flow chart of determining a comprehensive evaluation vector according to the present invention.
Detailed Description
Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form a part hereof, and together with the description serve to explain the principles of the invention, and are not intended to limit the scope of the invention.
Example 1
The embodiment of the invention provides a fuzzy comprehensive evaluation method based on a normal cloud model, and in combination with fig. 1, fig. 1 is a schematic flow chart of the fuzzy comprehensive evaluation based on the normal cloud model, which comprises steps S1 to S7, wherein:
in step S1, acquiring a plurality of operation indexes to be evaluated in a ship system, wherein the plurality of operation indexes may be indexes for feeding back the ship operation state, such as radar indexes and diesel engine operation indexes;
in step S2, an evaluation index set is formed according to a plurality of operation indexes, and in a specific embodiment of the present invention, taking a two-layer fuzzy comprehensive evaluation model as an example, it is assumed that the first layer has m operation indexes, denoted as u= { U 1 ，u 2 ，L，u m According to the attribution relation, m operation indexes are divided into k subsets, and are recorded as U= { U 1 ，U 2 ，L，U k }；
In step S3, a corresponding evaluation level set is constructed according to the evaluation index set, where the evaluation level set is used to represent evaluation result levels of multiple operation indexes in the evaluation index set, and in a specific embodiment of the present invention, if there are L evaluation result levels corresponding to the operation indexes, the evaluation level isThe set is denoted as v= { V 1 ，v 2 ，L，v l }；
In step S4, a first cloud model of the evaluation index set is constructed, and corresponding first cloud model parameters are determined, wherein after modeling, cloud expectations (Ex), entropy (En) and super entropy (He) are extracted as the first cloud model parameters;
in step S5, a second cloud model of the evaluation level set is constructed, and corresponding second cloud model parameters are determined, wherein the cloud expectations (Ex), entropy (En) and super entropy (He) are extracted as the second cloud model parameters after modeling;
in step S6, corresponding cloud similarity is determined according to the first cloud model and the second cloud model, wherein the affiliation of the two cloud models is determined by calculating the cloud similarity of the two cloud models;
in step S7, a comprehensive evaluation vector is determined according to the cloud similarity, the weight set and a preset fuzzy operator, so as to determine a result, wherein the weight set is determined according to the calculated weights corresponding to the multiple operation indexes, and the cloud similarity, the weight set and the preset fuzzy operator are multiplied to determine a final comprehensive evaluation vector.
In the embodiment of the invention, firstly, various operation indexes are effectively acquired; then, aiming at the operation indexes, a corresponding evaluation index set is formed, so that the data processing among the sets can be conveniently carried out subsequently; then, constructing a corresponding evaluation grade set, so as to effectively feed back the corresponding evaluation result grade of each operation index; then, forming a first cloud model according to a plurality of operation indexes in the evaluation index set, and feeding back data characteristics of the data to be evaluated; further, forming a second cloud model serving as a reference according to the evaluation level set, and feeding back standard data characteristics of different state intervals; finally, the difference between the two cloud models is fed back through the cloud similarity of the two cloud models, so that subsequent grade classification can be conveniently carried out through comparison; and finally, carrying out fuzzy operation according to fuzzy operators through cloud similarity of each operation index and a weight set corresponding to the operation index, thereby realizing efficient fuzzy comprehensive evaluation.
It should be noted that the fuzzy comprehensive evaluation method is an important method for processing fuzzy features in the category of fuzzy mathematics, and is widely applied to the problem of uncertainty of the treatment of the subjective factors. The ship system has a lot of index data information such as radar detection distance which does not accord with random distribution characteristics, and the fuzzy data is difficult to describe and process by a probability method. In the fuzzy theory, the fuzzy characteristics of the uncertain information are described through fuzzy sets, quantitative conversion of the fuzzy qualitative concept is realized through membership functions, and the definition of the fuzzy information is realized through substituting uncertain data into an evaluation model for calculation.
Preferably, as seen in fig. 2, fig. 2 is a schematic flow chart of forming an evaluation index set according to the present invention, and the step S2 includes steps S21 to S23, wherein:
in step S21, layering a plurality of operation indexes, and determining the index number of each layer;
in step S22, the operation indexes of each layer are divided into a plurality of index subsets according to the attribution relation;
in step S23, an evaluation index set is determined from the plurality of index subsets.
As a specific embodiment, the embodiment of the invention performs layering on various operation indexes to effectively form an evaluation index set. In a specific embodiment of the present invention, taking a two-layer fuzzy comprehensive evaluation model as an example, it is assumed that the first layer has m running indexes, denoted as u= { U 1 ，u 2 ，L，u m According to the attribution relation, m operation indexes are divided into k subsets, and are recorded as U= { U 1 ，U 2 ，L，U k }. It is understood that the plurality of operation indexes may be indexes such as radar indexes, diesel engine operation indexes, etc. that feed back the operation state of the ship.
Preferably, as seen in fig. 3, fig. 3 is a schematic flow chart of forming an evaluation level set according to the present invention, where step S3 includes steps S31 to S32, and the step S31 includes:
in step S31, according to the evaluation index set, determining an evaluation result level corresponding to the operation index;
in step S32, an evaluation level set is formed according to the evaluation result level.
As a specific embodiment, the embodiment of the invention generates an evaluation grade set for the grades of various operation indexes so as to effectively feed back the evaluation result grades of different operation indexes. In a specific embodiment of the present invention, if there are L evaluation result levels corresponding to the operation indexes, the evaluation level set is expressed as v= { V 1 ，v 2 ，L，v l }。
Preferably, as seen in fig. 4, fig. 4 is a schematic flow chart of forming the first cloud model according to the present invention, and the step S4 includes steps S41 to S42, where:
in step S41, the operation indexes in the evaluation index set are input into the reverse cloud generator respectively, and a corresponding first reverse expected value, a first reverse variance value and a first reverse super-entropy value are generated;
in step S42, the first inverse desired value, the first inverse variance value, and the first inverse super-entropy value are input to the forward cloud generator, respectively, to generate N first forward cloud droplets, where the N first forward cloud droplets form a first cloud model.
As a specific embodiment, the embodiment of the invention extracts the data characteristics of the operation index according to the reverse cloud generator, and generates the corresponding first cloud model according to the forward cloud generator.
It should be noted that, the cloud model is the core of the cloud theory, and is a model that combines ambiguity and randomness, and realizes the uncertainty conversion between the qualitative concept of the natural language representation and the quantitative representation. The cloud model image is different from the probability density function and the membership curve of the fuzzy theory, is composed of countless points which are randomly distributed in space and have no fixed rule and no determined boundary, and is very similar to the cloud in the nature, so the theory is named by 'cloud'. Cloud uses expectations (Ex), entropy (En), super entropy (He) as their numerical features. The expected Ex represents the gravity center position of the cloud, and is expressed as the highest point of the cloud in the cloud image, and the corresponding certainty is 1, which indicates that the expected value necessarily belongs to the qualitative concept reflected by the cloud. Entropy En derives from a concept in thermodynamics, characterizing the uncertainty of a qualitative concept. The greater the entropy, the greater the degree of dispersion that appears as a cloud droplet in a cloud image, the greater the span of the cloud. The super-entropy He characterizes the uncertainty of the entropy En and reflects the condensation degree of cloud drops in the domain space. The larger the super entropy, the larger the thickness of the cloud in the cloud image, and the smaller the stability.
As shown in fig. 5 and fig. 6, fig. 5 is a schematic diagram of a cloud model provided by the present invention, fig. 6 is a schematic diagram of a cloud model provided by the present invention, let U be a quantitative domain represented by an accurate numerical value, C be a qualitative concept on U, x be an element generated randomly by a qualitative concept represented by C, if: x-N (Ex, en)' 2 ) Wherein, en' to N (En, he) 2 ) The certainty μi of any x to C is expressed as:
as can be seen from the above equation, the boundary of the normal cloud model is an irregular and discrete point, but the mathematical expectation curve of the normal cloud model is a smooth curve as shown in the following equation:
as shown in fig. 5 and 6, two normal cloud models are made, where the cloud model parameters in fig. 5 are ex=10, en=2, and he=0.1, and the cloud model parameters in fig. 6 are ex=20, en=5, and he=1, and the two cloud models include 2000 cloud droplets. The center of the cloud model of fig. 5 is at x=15, and the center of the cloud model of fig. 6 is at x=20, illustrating that the distribution position of the cloud is determined by the desired Ex; the cloud model of fig. 5 has smaller entropy than the cloud model of fig. 6, cloud drops are closer to the middle position, the cloud model of fig. 6 is more dispersed than the cloud model of fig. 5, and the dispersion degree of the cloud is determined by the entropy En; the cloud model of fig. 5 has smaller super entropy than the cloud model of fig. 6, and has smaller thickness than the cloud model, which means that the condensation degree of cloud droplets is determined by super entropy He.
Preferably, as seen in fig. 7, fig. 7 is a schematic flow chart of forming the second cloud model according to the present invention, and the step S5 includes steps S51 to S54, where:
in step S51, the operation index is divided into a plurality of status sections according to the standard limit value corresponding to the operation index;
in step S52, for different state intervals, a plurality of sets of index data corresponding to the operation index are randomly generated;
in step S53, inputting a plurality of sets of index data to the inverse cloud generator, and generating a corresponding second inverse expected value, a second inverse variance value and a second inverse super-entropy value;
in step S54, the second inverse expected value, the second inverse variance value and the second inverse super entropy value are input to the forward cloud generator, so as to generate N corresponding second forward cloud droplets, where the second N forward cloud droplets form second cloud models corresponding to different state intervals.
As a specific embodiment, the embodiment of the invention extracts the data characteristics of random operation indexes of different state intervals according to the reverse cloud generator, and generates a corresponding second cloud model according to the forward cloud generator as a reference.
It should be noted that, the forward cloud generator is mainly used for realizing a qualitative to quantitative mapping function, and the basic principle is that cloud drops are generated in an accurate numerical domain space according to cloud digital characteristics (Ex, en, he), and mainly involves the following five basic steps:
first, a compliance expectancy En and a variance He are generated 2 Is a normal distribution of random numbers;
second, generate a compliance expectancy Ex, variance En 2 Is a normal distribution of random numbers;
third, the membership degree is calculated according to the following formula:
fourth step, (x) i ，μ i (x) A cloud of droplets representing a random realization of the qualitative concept on the precise domain U;
fifth, repeating the first to fourth steps to generate N cloud drops.
It should be noted that, the inverse cloud generator is mainly used for realizing the mapping function from quantitative to qualitative, the basic principle is to calculate and determine the digital characteristics (Ex, en, he) of the cloud model according to a certain amount of accurate data, and mainly involves the following two basic steps:
the first step, according to the sample point xi, calculating the mean value of the sample, the absolute center moment of the first-order sample and the sample variance as shown in the following formula:
second, calculating digital characteristic values Ex, en, he according to the following formula:
preferably, the plurality of status intervals include a normal status interval, an attention status interval, an abnormal status interval, and a serious status interval, wherein the different status intervals are divided according to a range of the operation index. As a specific embodiment, the embodiment of the invention sets a plurality of different state intervals and reflects the data characteristics of the different state intervals.
In a specific embodiment of the present invention, the generating step of the first cloud model is as follows:
the first step: inputting a certain state quantity index to-be-evaluated test data set into a reverse cloud generator to respectively obtain digital characteristic values Ex, en and He of a to-be-evaluated data cloud model;
and a second step of: and inputting the digital characteristic value of the cloud model of the data to be evaluated into a forward cloud generator, and making a cloud model of the data to be evaluated.
In a specific embodiment of the present invention, as seen in conjunction with fig. 8 and fig. 9, fig. 8 is a schematic diagram of a second cloud model provided by the present invention, and fig. 9 is a schematic diagram of parameters of the second cloud model provided by the present invention, where the steps of generating the second cloud model are as follows:
the first step: randomly generating 5000 groups of data in a normal interval of index parameters;
and a second step of: inputting 5000 groups of randomly generated data into a reverse cloud generator to obtain cloud digital characteristic values Ex, en and He of a 'normal' interval;
and a third step of: and inputting the cloud digital characteristic value into a forward cloud generator to obtain the index normal evaluation level cloud model.
Fourth step: repeating the first step to the third step, calculating cloud digital characteristic values of the rest evaluation grades, and drawing all the evaluation grade cloud models in the same domain space to obtain an overall cloud model graph.
Referring to fig. 8 and 9, normal cloud model parameters C (Exj, enj, hej) of each evaluation level of the ship combat system and the comprehensive power system index are respectively determined, where j is the evaluation level, j=1, 2,3, and 4 correspond to the evaluation levels of "normal state interval", "attention state interval", "abnormal state interval", "serious state interval", and the numerical characteristic values of different state intervals are referring to fig. 9.
Preferably, as seen in fig. 10, fig. 10 is a schematic flow chart of determining cloud similarity according to the present invention, and step S6 includes steps S61 to S63, where:
in step S61, determining a relative distance between the first cloud model and the second cloud model according to the first cloud model parameter and the second cloud model parameter;
in step S62, corresponding membership degrees are determined according to the relative distances;
in step S63, cloud similarity is determined according to membership.
As a specific embodiment, the embodiment of the invention feeds back the difference between the cloud similarity of the two cloud models, thereby facilitating the subsequent classification of the grades of different operation indexes through comparison.
Preferably, as seen in fig. 11, fig. 11 is a schematic flow chart of determining a comprehensive evaluation vector according to the present invention, and the step S7 includes steps S71 to S72, where:
in step S71, multiplying the weight set, the fuzzy operator and the cloud similarity in sequence to determine a corresponding primary evaluation vector;
in step S72, the weight set, the blurring operator and the primary evaluation vector are multiplied in order to determine a corresponding comprehensive evaluation vector.
As a specific embodiment, the embodiment of the invention carries out fuzzy operation according to fuzzy operators through cloud similarity of each operation index and a weight set corresponding to the operation index, thereby realizing efficient fuzzy comprehensive evaluation.
In a specific embodiment of the present invention, it is assumed that the first layer of the evaluation index set has m indices, denoted as u= { U 1 ，u 2 ，L，u m According to the attribution relation, m operation indexes are divided into k subsets, and are recorded as U= { U 1 ，U 2 ，L，U k The evaluation level set is denoted as v= { V } 1 ，v 2 ，L，v l Assume that the weight set of the running index of the first layer is w= { W 1 ，W 2 ，L，W k } k×m T Multiplying the weight set, the fuzzy operator and the cloud similarity in sequence, and determining a corresponding primary evaluation vector B through fuzzy operation, wherein the primary evaluation vector B is expressed by the following formula:
wherein o is a fuzzy operator, and R is a cloud similarity matrix;
assume that the index weight of the second layer is w= { w 1 ，w 2 ，L，w k } 1×k The final calculated comprehensive evaluation vector is represented by the following formula:
and judging the comprehensive evaluation vector to determine the grade result of the evaluation object (operation index).
Preferably, the weight set is determined by performing cooperative game calculation on the operation index according to a analytic hierarchy process, an entropy weight process and a gray correlation process. As a specific embodiment, the embodiment of the invention obtains a more accurate weight set by utilizing various algorithms and ensures accurate weight distribution of each operation index.
Example 2
The embodiment of the invention provides a fuzzy comprehensive evaluation device based on a normal cloud model, which comprises a processor and a memory, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the fuzzy comprehensive evaluation method based on the normal cloud model is realized.
The invention discloses a fuzzy comprehensive evaluation method and a fuzzy comprehensive evaluation device based on a normal cloud model, which are characterized in that firstly, various operation indexes are effectively acquired; then, aiming at the operation indexes, a corresponding evaluation index set is formed, so that the data processing among the sets can be conveniently carried out subsequently; then, constructing a corresponding evaluation grade set, so as to effectively feed back the corresponding evaluation result grade of each operation index; then, forming a first cloud model according to a plurality of operation indexes in the evaluation index set, and feeding back data characteristics of the data to be evaluated; further, forming a second cloud model serving as a reference according to the evaluation level set, and feeding back standard data characteristics of different state intervals; finally, the difference between the two cloud models is fed back through the cloud similarity of the two cloud models, so that subsequent grade classification can be conveniently carried out through comparison; and finally, carrying out fuzzy operation according to fuzzy operators through cloud similarity of each operation index and a weight set corresponding to the operation index, thereby realizing efficient fuzzy comprehensive evaluation.
According to the technical scheme, the cloud model is used as a qualitative and quantitative conversion model, so that the internal relevance of randomness and ambiguity is fully reflected, the evaluation set is divided more gently, the method can be directly used for single evaluation of indexes, the problem that membership functions are difficult to determine can be reasonably avoided by utilizing cloud similarity to replace membership, and various operation indexes in a ship system are evaluated in a classified mode by utilizing the cloud model, so that the integrity of the ship combat readiness is effectively judged.
The present invention is not limited to the above-mentioned embodiments, and any changes or substitutions that can be easily understood by those skilled in the art within the technical scope of the present invention are intended to be included in the scope of the present invention.
Claims (7)
1. A fuzzy comprehensive evaluation method based on a normal cloud model is characterized by comprising the following steps:
acquiring a plurality of operation indexes to be evaluated in a ship system;
forming an evaluation index set according to a plurality of operation indexes;
constructing a corresponding evaluation level set according to the evaluation index set, wherein the evaluation level set is used for representing evaluation result levels of various operation indexes in the evaluation index set;
constructing a first cloud model of the evaluation index set, and determining corresponding first cloud model parameters;
constructing a second cloud model of the evaluation level set, and determining corresponding second cloud model parameters;
determining corresponding cloud similarity according to the first cloud model and the second cloud model;
providing a preset fuzzy operator, and determining a comprehensive evaluation vector according to the cloud similarity, a weight set and the preset fuzzy operator so as to evaluate the running state of the ship, wherein the weight set is determined according to the calculated weights corresponding to a plurality of running indexes;
the constructing the first cloud model of the evaluation index set, and determining the corresponding first cloud model parameters includes:
respectively inputting the operation indexes in the evaluation index set into a reverse cloud generator to generate a corresponding first reverse expected value, a first reverse variance value and a first reverse super-entropy value;
respectively inputting the first reverse expected value, the first reverse variance value and the first reverse super-entropy value into a forward cloud generator to generate N corresponding first forward cloud drops, wherein the N first forward cloud drops form the first cloud model;
the constructing the second cloud model of the evaluation level set, and determining the corresponding second cloud model parameters includes:
dividing the operation index into a plurality of state intervals according to the standard limit value corresponding to the operation index;
for different state intervals, randomly generating a plurality of groups of index data corresponding to the running index;
inputting the multiple groups of index data into the reverse cloud generator to generate a corresponding second reverse expected value, a second reverse variance value and a second reverse super-entropy value;
inputting the second reverse expected value, the second reverse variance value and the second reverse super-entropy value to the forward cloud generator to generate N corresponding second forward cloud drops, wherein the N second forward cloud drops form the second cloud models corresponding to different state intervals;
the determining, according to the first cloud model and the second cloud model, the corresponding cloud similarity includes:
determining a relative distance between the first cloud model and the second cloud model according to the first cloud model parameters and the second cloud model parameters;
determining corresponding membership according to the relative distance;
and determining the cloud similarity according to the membership degree.
2. The fuzzy synthetic evaluation method of claim 1, wherein the forming an evaluation index set from a plurality of the operation indexes comprises:
layering a plurality of operation indexes, and determining the index number of each layer;
dividing the operation index of each layer into a plurality of index subsets according to the attribution relation;
and determining the evaluation index set according to a plurality of index subsets.
3. The fuzzy synthetic evaluation method of claim 1, wherein constructing a corresponding set of evaluation levels from the set of evaluation metrics comprises:
determining an evaluation result level corresponding to the operation index according to the evaluation index set;
and forming the evaluation grade set according to the evaluation result grade.
4. The fuzzy synthetic evaluation method of claim 1, wherein the plurality of status intervals includes a normal status interval, an attention status interval, an abnormal status interval, and a severe status interval, wherein different status intervals are divided according to a range of the operation index.
5. The fuzzy comprehensive evaluation method based on the normal cloud model according to claim 1, wherein the determining the comprehensive evaluation vector according to the cloud similarity, the weight set and the preset fuzzy operator to perform the result determination, wherein the determining the weight set according to the calculated weights corresponding to the plurality of operation indexes includes:
multiplying the weight set, the fuzzy operator and the cloud similarity in sequence to determine a corresponding primary evaluation vector;
and multiplying the weight set, the fuzzy operator and the primary evaluation vector in sequence to determine a corresponding comprehensive evaluation vector.
6. The method for fuzzy synthetic evaluation based on the normal cloud model of any one of claims 1-5, wherein the determining of the set of weights includes:
for each operation index, performing cooperative game calculation on the operation index according to an analytic hierarchy process, an entropy weight process and a gray correlation degree process to determine corresponding weights;
each of the weights constitutes the set of weights.
7. A fuzzy comprehensive evaluation device based on a normal cloud model, which is characterized by comprising a processor and a memory, wherein the memory is stored with a computer program, and the computer program realizes the fuzzy comprehensive evaluation method based on the normal cloud model according to any one of claims 1-6 when the computer program is executed by the processor.
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