CN113609573A - 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 fuzzy comprehensive evaluation device based on a normal cloud model, wherein the method comprises the following steps: acquiring various operation indexes to be evaluated in a ship system; forming an evaluation index set according to various operation indexes; constructing a corresponding evaluation grade set according to the evaluation index set, wherein the evaluation grade set is used for representing the evaluation result grade 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 grade 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 a result. The cloud model is used for carrying out classification evaluation on various operation indexes in the ship system so as to effectively judge the integrity of the ship 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 combat readiness integrity assessment is to establish a mapping relationship between accurate test quantitative indicators and fuzzy qualitative concepts, which is an uncertainty assessment problem. As an important method in the fuzzy mathematics category, the fuzzy comprehensive evaluation method quantitatively represents the membership degree and the membership relation between an evaluation object and a qualitative concept through the membership degree, and can well express the uncertainty through quantitative numerical values to obtain a scientific evaluation result. However, the membership function of the warship readiness integrity evaluation index has no unified specification, needs expert experience to determine, introduces large subjective influence, and causes the generalization to be weak and the evaluation result to be inaccurate. In conclusion, how to provide an efficient and accurate fuzzy comprehensive evaluation method is an urgent problem to be solved.
Disclosure of Invention
In view of this, a fuzzy comprehensive assessment method and device based on a normal cloud model are needed to solve the problem that the warship readiness integrity assessment process in the prior art is not standardized.
The invention provides a fuzzy comprehensive evaluation method based on a normal cloud model, which comprises the following steps:
acquiring various operation indexes to be evaluated in a ship system;
forming an evaluation index set according to the plurality of operation indexes;
constructing a corresponding evaluation grade set according to the evaluation index set, wherein the evaluation grade set is used for representing the evaluation result grade 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 grade 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 to judge a result, wherein the weight set is determined according to the calculation weights corresponding to the various operation indexes.
Further, the forming an evaluation index set according to the plurality of operation indexes includes:
layering the multiple operation indexes, and determining the index number of each layer;
dividing the operation indexes of each layer into a plurality of index subsets according to the attribution relation;
determining the set of assessment metrics from a plurality of the subsets of metrics.
Further, the constructing a corresponding evaluation grade set according to the evaluation index set includes:
determining an evaluation result grade 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 the determining the corresponding first cloud model parameter includes:
inputting the operation index input 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 inputting the first reverse expected value, the first reverse variance value and the first reverse super-entropy input into a forward cloud generator respectively to generate N corresponding first forward cloud droplets, wherein the N first forward cloud droplets 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;
randomly generating a plurality of groups of index data corresponding to the operation indexes aiming at different state intervals;
inputting the multiple groups of index data into the reverse cloud generator to generate corresponding second reverse expected values, second reverse variance values and second reverse super-entropy values;
and inputting the second reverse expected value, the second reverse variance value and the second reverse super-entropy value into the forward cloud generator to generate N corresponding second forward cloud droplets, wherein the second N forward cloud droplets form the second cloud model corresponding to different state intervals.
Further, the plurality of state intervals include a normal state interval, an attention state interval, an abnormal state interval, and a serious state interval, wherein different state intervals are divided according to a range of the operation index.
Further, the determining the corresponding cloud similarity according to the first cloud model and the second cloud model includes:
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;
determining corresponding membership degrees according to the relative distances;
and determining the cloud similarity according to the membership degree.
Further, determining a comprehensive evaluation vector according to the cloud similarity, the weight set and a preset fuzzy operator to perform result judgment, wherein the determining of the weight set according to the calculation weights corresponding to the multiple operation indexes comprises:
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 comprises:
for each operation index, performing cooperative game calculation on the operation index according to an analytic hierarchy process, an entropy weight method and a gray correlation degree method to determine a corresponding weight;
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, forming a corresponding evaluation index set, so as to facilitate the subsequent data processing between sets; 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 various operation indexes in the evaluation index set, and feeding back data characteristics of the data to be evaluated; further, a second cloud model serving as a reference is formed according to the evaluation grade set, and standard data characteristics of different state intervals are fed back; finally, the difference between the two cloud models is fed back through the cloud similarity of the two cloud models, so that the subsequent grade classification can be conveniently carried out through comparison; and finally, carrying out fuzzy operation according to the fuzzy operator through the cloud similarity of each operation index and the weight set corresponding to the operation index, thereby realizing efficient fuzzy comprehensive evaluation. In summary, the cloud model is used as a conversion model between qualitative and quantitative, so that the inherent relevance of randomness and fuzziness is fully reflected, the division of an evaluation set is soft, the evaluation set can be directly used for single evaluation of indexes, the problem that a membership function is difficult to determine can be reasonably avoided by replacing the membership degree with the cloud similarity, and the cloud model is used for carrying out classification evaluation on various operation indexes in a ship system so as to effectively judge the integrity of the readiness of the ship.
Drawings
FIG. 1 is a schematic flow chart of fuzzy comprehensive evaluation based on a normal cloud model according to the present invention;
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 first diagram of a cloud model provided by the present invention;
FIG. 6 is a second cloud model diagram provided by 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 parameter diagram of a second cloud model provided by 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
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate preferred embodiments of the invention and together with the description, serve to explain the principles of the invention and not 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 with reference to fig. 1, fig. 1 is a schematic flow chart of the fuzzy comprehensive evaluation based on the normal cloud model provided by the invention, and the method includes steps S1 to S7, wherein:
in step S1, obtaining a plurality of operation indexes to be evaluated in the ship system, where the plurality of operation indexes may be indexes such as radar indexes and diesel engine operation indexes that feed back the ship operation state;
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, a two-layer fuzzy comprehensive evaluation model is taken as an example, and a first layer is assumed to have m operation indexes, which are denoted as U ═ in total1，u2，L，umDividing m operation indexes into k subsets according to the attribution relationship, and recording as U-U ═ U1，U2，L，Uk}；
In step S3, an evaluation grade set is constructed according to the evaluation index set, where the evaluation grade set is used to represent the evaluation result grades of multiple operation indexes in the evaluation index set, and in a specific embodiment of the present invention, if there are L evaluation result grades corresponding to the operation indexes, the evaluation grade set is represented as V ═ V1，v2，L，vl}；
In step S4, constructing a first cloud model of the evaluation index set, and determining corresponding first cloud model parameters, wherein expectation (Ex), entropy (En), and hyper-entropy (He) are extracted as the first cloud model parameters after modeling;
in step S5, constructing a second cloud model of the evaluation level set, and determining corresponding second cloud model parameters, wherein after modeling, the expectation (Ex), the entropy (En), and the hyper-entropy (He) of the extracted cloud are taken as the second cloud model parameters;
in step S6, determining a corresponding cloud similarity according to the first cloud model and the second cloud model, wherein the membership of the two cloud models is determined by calculating the cloud similarities of the two cloud models;
in step S7, determining a comprehensive evaluation vector according to the cloud similarity, the weight set, and a preset fuzzy operator, so as to determine a result, where the weight set is determined according to the calculation weights corresponding to the multiple operation indexes, and where 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, a plurality of operation indexes are effectively obtained; then, aiming at the operation indexes, forming a corresponding evaluation index set, so as to facilitate the subsequent data processing between sets; 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 various operation indexes in the evaluation index set, and feeding back data characteristics of the data to be evaluated; further, a second cloud model serving as a reference is formed according to the evaluation grade set, and standard data characteristics of different state intervals are fed back; finally, the difference between the two cloud models is fed back through the cloud similarity of the two cloud models, so that the subsequent grade classification can be conveniently carried out through comparison; and finally, carrying out fuzzy operation according to the fuzzy operator through the cloud similarity of each operation index and the 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 characteristic things in the fuzzy mathematics category, and is widely applied to processing uncertainty problems with prominent subjective factors. The ship system has a plurality of index data information such as radar detection distance and the like which are not in accordance with the random distribution characteristics, and the fuzzy data are difficult to describe and process by a probability method. In the fuzzy theory, the fuzzy characteristics of uncertain information are described through a fuzzy set, the quantitative conversion of a fuzzy qualitative concept is realized through a membership function, and the clearness of the fuzzy information is realized by substituting uncertain data into an evaluation model for calculation.
Preferably, referring to fig. 2, fig. 2 is a schematic flow chart of forming the evaluation index set according to the present invention, and the step S2 includes steps S21 to S23, where:
in step S21, the multiple operation indexes are layered, and the index number of each layer is determined;
in step S22, dividing the operation index of each layer into a plurality of index subsets according to the attribution relationship;
in step S23, an evaluation index set is determined from the plurality of index subsets.
As a specific embodiment, the embodiment of the invention stratifies a plurality of operation indexes to effectively form an evaluation index set. In one embodiment of the present invention, two are usedFor example, assuming that the first layer has m operation indexes, which are denoted as U ═ U1，u2，L，umDividing m operation indexes into k subsets according to the attribution relationship, and recording as U-U ═ U1，U2，L，Uk}. It is to be understood that the plurality of operation indexes may be indexes such as a radar index, a diesel engine operation index, and the like that feed back the operation state of the ship.
Preferably, referring to fig. 3, fig. 3 is a schematic flow chart of forming an evaluation level set according to the present invention, and the step S3 includes steps S31 to S32, where:
in step S31, determining an evaluation result level corresponding to the operation index according to the evaluation index set;
in step S32, an evaluation grade set is formed based on the evaluation result grade.
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 index, the evaluation level set is represented as V ═ { V ═ V1，v2，L，vl}。
Preferably, referring to fig. 4, fig. 4 is a schematic flowchart of the process of forming the first cloud model provided by the present invention, and the step S4 includes steps S41 to S42, where:
in step S41, inputting the operation index input in the evaluation index set into the reverse cloud generator, respectively, to generate a corresponding first reverse expected value, a first reverse variance value, and a first reverse super-entropy value;
in step S42, the first inverse expected value, the first inverse variance value, and the first inverse super-entropy input are respectively input to the forward cloud generator to generate N corresponding first forward cloud droplets, where the N first forward cloud droplets form a first cloud model.
As a specific embodiment, the embodiment of the present invention extracts data features of an operation index according to a reverse cloud generator, and then generates a corresponding first cloud model according to a forward cloud generator.
It should be noted that the cloud model is the core of the cloud theory, and is a model that considers ambiguity and randomness and realizes the uncertainty conversion between the qualitative concept and the quantitative representation of the natural language representation. The cloud model image is different from the membership curve of a probability density function and a fuzzy theory and is composed of countless points which are randomly distributed in space, have no fixed rule and do not determine a boundary, and the cloud model image is similar to the cloud in nature, so the theory is named by 'cloud'. The cloud takes expectation (Ex), entropy (En), and super entropy (He) as its numerical characteristics. The expected value Ex represents the gravity center position of the cloud, the expected value Ex represents the highest point of the cloud in the cloud image, the corresponding certainty factor is 1, and the expected value is shown to belong to the qualitative concept reflected by the cloud. Entropy En is derived from concepts in thermodynamics, characterizing the uncertainty of qualitative concepts. The greater the entropy, the greater the degree of dispersion that appears as cloud droplets in the cloud image, and the greater the cloud span. The super-entropy He represents the uncertainty of the entropy En and reflects the degree of agglomeration of cloud droplets in the domain space. The larger the super entropy, the larger the thickness of the cloud represented in the cloud image, and the smaller the degree of stabilization.
With reference to fig. 5 and 6, fig. 5 is a first schematic diagram of a cloud model provided by the present invention, fig. 6 is a second schematic diagram of a cloud model provided by the present invention, where U is a quantitative discourse domain represented by an accurate numerical value, C is a qualitative concept on U, and x is an element randomly generated from the qualitative concept represented by C, if: x to N (Ex, En'2) Wherein En' to N (En, He)2) Then, the degree μ i of determination for any x pair C is represented by the following formula:
as can be seen from the above formula, the boundary of the normal cloud model is an irregular, discrete point, but the mathematical expectation curve of the normal cloud model is a smooth curve as shown in the following formula:
fig. 5 and 6 show two normal cloud models, 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 cloud model of fig. 5 is centered at x-15 and the cloud model of fig. 6 is centered at x-20, illustrating that the distribution position of the cloud is determined by the expected Ex; the cloud model of fig. 5 has a lower entropy than the cloud model of fig. 6, the cloud droplets are closer to the middle, and the cloud model of fig. 6 is more dispersed than the cloud model of fig. 5, which illustrates that the degree of dispersion of the cloud is determined by the entropy En; the cloud model of fig. 5 has a lower degree of super-entropy than the cloud model of fig. 6, and is thinner than the cloud model, indicating that the degree of cloud droplet coalescence is determined by the degree of super-entropy He.
Preferably, referring to fig. 7, fig. 7 is a schematic flowchart of the process of forming the second cloud model provided by the present invention, and the step S5 includes steps S51 to S54, where:
in step S51, dividing the operation index into a plurality of state sections according to the standard limit value corresponding to the operation index;
in step S52, for different state intervals, multiple sets of index data corresponding to the operation indexes are randomly generated;
in step S53, inputting multiple sets of index data into a reverse cloud generator to generate a corresponding second reverse expected value, a second reverse variance value, and a second reverse 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 to generate N corresponding second forward cloud droplets, where the second N forward cloud droplets form a second cloud model corresponding to different state intervals.
As a specific embodiment, in the embodiment of the present invention, data features of random operation indexes in different state intervals are extracted according to a reverse cloud generator, and then a corresponding second cloud model is generated according to a forward cloud generator as a reference.
It should be noted that the forward cloud generator is mainly used to implement qualitative to quantitative mapping function, and its basic principle is to generate cloud droplets in precise numerical domain space according to cloud digital features (Ex, En, He), and mainly involves the following five basic steps:
in a first step, a obedience expectation is En and a variance is He2A normally distributed random number of (a);
second, a obedience expectation is generated as Ex, and the variance is generated as En2A normally distributed random number of (a);
thirdly, calculating the membership degree according to the following formula:
the fourth step, (x)i，μi(x) Is a cloud droplet, representing a random realization of the qualitative concept on the precision domain U;
and fifthly, repeating the first step to the fourth step to generate N cloud droplets.
It should be noted that the inverse cloud generator is mainly used to implement a mapping function from quantitative to qualitative, and the basic principle is to calculate and determine the cloud model digital features (Ex, En, He) according to a certain amount of accurate data, and mainly involves the following two basic steps:
firstly, according to the sample point xi, calculating the mean value, the first-order sample absolute central moment and the sample variance of the sample as shown in the following formula:
secondly, calculating the digital characteristic values Ex, En and He according to the following formula:
preferably, the plurality of state intervals include a normal state interval, an attention state interval, an abnormal state interval, and a serious state interval, wherein different state intervals are divided according to a range of the operation index. As a specific embodiment, the embodiment of the present invention sets a plurality of different state intervals, and reflects data characteristics of the different state intervals.
In a specific embodiment of the present invention, the first cloud model is generated as follows:
the first step is as follows: inputting a certain state quantity index to-be-evaluated test data group into a reverse cloud generator to respectively obtain digital characteristic values Ex, En and He of a cloud model of the to-be-evaluated data;
the second step is that: and inputting the digital characteristic value of the cloud model of the data to be evaluated into the forward cloud generator to form the cloud model of the data to be evaluated.
In a specific embodiment of the present invention, with reference to fig. 8 and 9, 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 the second cloud model provided by the present invention, and the generating steps of the second cloud model are as follows:
the first step is as follows: randomly generating 5000 groups of data in a normal interval of the index parameters;
the second step is that: inputting 5000 groups of randomly generated data into a reverse cloud generator to obtain cloud digital characteristic values Ex, En and He in a normal interval;
the third step: and inputting the cloud digital characteristic value into a forward cloud generator to obtain a 'normal' evaluation grade cloud model of the index.
The fourth step: and repeating the first step to the third step, calculating the cloud digital characteristic values of the rest evaluation grades, and drawing all the evaluation grade cloud models in the same domain space to obtain the 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 warfare system and the comprehensive power system index are respectively determined, wherein j is an evaluation level, j is 1,2,3, and 4 respectively correspond to an evaluation level "normal state interval", "attention state interval", "abnormal state interval", and "severe state interval", and the digital characteristic values of different state intervals are referred to fig. 9.
Preferably, referring to fig. 10, fig. 10 is a schematic flowchart of the process of determining the cloud similarity provided by the present invention, where the 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, determining a corresponding degree of membership based on the relative distance;
in step S63, the cloud similarity is determined according to the membership degree.
As a specific embodiment, the embodiment of the invention feeds back the difference between the two cloud models through the cloud similarity of the two cloud models, so that the subsequent grade classification of different operation indexes is facilitated through comparison.
Preferably, referring to fig. 11, fig. 11 is a schematic flow chart illustrating the process of determining the comprehensive evaluation vector provided by the present invention, where 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 fuzzy operator, and the primary evaluation vector are multiplied in sequence to determine a corresponding comprehensive evaluation vector.
As a specific embodiment, the fuzzy operation is carried out according to the fuzzy operator through the cloud similarity of each operation index and the weight set corresponding to the operation index, so that efficient fuzzy comprehensive evaluation is realized.
In a specific embodiment of the present invention, it is assumed that the first layer of the evaluation index set has m indexes, and is denoted as U ═1，u2，L，umDividing m operation indexes into k subsets according to the attribution relationship, and recording as U-U ═ U1，U2，L，UkV ═ V denotes the set of assessment ratings1，v2，L，vlSuppose the weight set of the operation index of the first layer is W ═ W1，W2，L，Wk}k×m TMultiplying the weight set, the fuzzy operator and the cloud similarity in sequence, and determining a corresponding primary evaluation vector B through fuzzy operation as follows:
wherein o is a fuzzy operator, and R is a cloud similarity matrix;
assume that the index weight of the second layer is w ═ w1，w2，L，wk}1×kThe final calculated integrated estimate vector is represented by the following equation:
and judging the comprehensive evaluation vector, and determining a grade result of an evaluation object (operation index).
Preferably, the weight set is determined by performing cooperative game calculation on the operation indexes according to an analytic hierarchy process, an entropy weight method and a gray correlation degree method. As a specific embodiment, the embodiment of the invention obtains a more accurate weight set by using a plurality of 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, wherein firstly, a plurality of operation indexes are effectively obtained; then, aiming at the operation indexes, forming a corresponding evaluation index set, so as to facilitate the subsequent data processing between sets; 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 various operation indexes in the evaluation index set, and feeding back data characteristics of the data to be evaluated; further, a second cloud model serving as a reference is formed according to the evaluation grade set, and standard data characteristics of different state intervals are fed back; finally, the difference between the two cloud models is fed back through the cloud similarity of the two cloud models, so that the subsequent grade classification can be conveniently carried out through comparison; and finally, carrying out fuzzy operation according to the fuzzy operator through the cloud similarity of each operation index and the 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 conversion model between qualitative and quantitative, so that the inherent relevance of randomness and fuzziness is fully reflected, the division of an evaluation set is soft, the evaluation set can be directly used for single evaluation of indexes, the problem that a membership function is difficult to determine can be reasonably avoided by replacing the membership degree with the cloud similarity, and the cloud model is used for carrying out classification evaluation on various operation indexes in a ship system so as to effectively judge the readiness integrity of the ship.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention.
Claims (10)
1. A fuzzy comprehensive evaluation method based on a normal cloud model is characterized by comprising the following steps:
acquiring various operation indexes to be evaluated in a ship system;
forming an evaluation index set according to the plurality of operation indexes;
constructing a corresponding evaluation grade set according to the evaluation index set, wherein the evaluation grade set is used for representing the evaluation result grade 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 grade 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, the 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 calculation weights corresponding to various running indexes.
2. The fuzzy comprehensive evaluation method based on the normal cloud model according to claim 1, wherein the forming an evaluation index set according to the plurality of operation indexes comprises:
layering the multiple operation indexes, and determining the index number of each layer;
dividing the operation indexes of each layer into a plurality of index subsets according to the attribution relation;
determining the set of assessment metrics from a plurality of the subsets of metrics.
3. The fuzzy comprehensive evaluation method based on the normal cloud model according to claim 1, wherein the constructing a corresponding evaluation grade set according to the evaluation index set comprises:
determining an evaluation result grade 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 comprehensive evaluation method based on the normal cloud model according to claim 1, wherein the constructing the first cloud model of the evaluation index set and the determining the corresponding first cloud model parameters comprise:
inputting the operation index input 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 inputting the first reverse expected value, the first reverse variance value and the first reverse super-entropy input into a forward cloud generator respectively to generate N corresponding first forward cloud droplets, wherein the N first forward cloud droplets form the first cloud model.
5. The method according to claim 4, wherein the constructing the second cloud model of the evaluation level set and the determining the corresponding second cloud model parameters comprise:
dividing the operation index into a plurality of state intervals according to the standard limit value corresponding to the operation index;
randomly generating a plurality of groups of index data corresponding to the operation indexes aiming at different state intervals;
inputting the multiple groups of index data into the reverse cloud generator to generate corresponding second reverse expected values, second reverse variance values and second reverse super-entropy values;
and inputting the second reverse expected value, the second reverse variance value and the second reverse super-entropy value into the forward cloud generator to generate N corresponding second forward cloud droplets, wherein the second N forward cloud droplets form the second cloud model corresponding to different state intervals.
6. The fuzzy comprehensive evaluation method based on the normal cloud model according to claim 5, wherein the plurality of state intervals include a normal state interval, an attention state interval, an abnormal state interval, and a severe state interval, and wherein different state intervals are divided according to a range of the operation index.
7. The fuzzy comprehensive evaluation method based on the normal cloud model according to claim 5, wherein the determining the corresponding cloud similarity according to the first cloud model and the second cloud model comprises:
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;
determining corresponding membership degrees according to the relative distances;
and determining the cloud similarity according to the membership degree.
8. The fuzzy comprehensive evaluation method based on the normal cloud model according to claim 7, wherein the determining a comprehensive evaluation vector according to the cloud similarity, the weight set and a preset fuzzy operator to perform result judgment, wherein the determining of the weight set according to the calculation weights corresponding to the plurality of operation indexes comprises:
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.
9. The fuzzy comprehensive evaluation method based on the normal cloud model according to any one of claims 1 to 8, wherein the determination process of the weight set comprises:
for each operation index, performing cooperative game calculation on the operation index according to an analytic hierarchy process, an entropy weight method and a gray correlation degree method to determine a corresponding weight;
each of the weights constitutes the set of weights.
10. A fuzzy comprehensive assessment device based on a normal cloud model, which comprises a processor and a memory, wherein the memory stores a computer program, and when the computer program is executed by the processor, the fuzzy comprehensive assessment device based on the normal cloud model according to any one of claims 1 to 9 is realized.
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