CN112950390A - Equipment system investment portfolio selection method, electronic equipment and storage medium - Google Patents

Equipment system investment portfolio selection method, electronic equipment and storage medium Download PDF

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CN112950390A
CN112950390A CN202110361474.9A CN202110361474A CN112950390A CN 112950390 A CN112950390 A CN 112950390A CN 202110361474 A CN202110361474 A CN 202110361474A CN 112950390 A CN112950390 A CN 112950390A
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attribute data
equipment system
hesitation
value
equipment
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豆亚杰
李卓倩
杨克巍
姜江
李孟军
葛冰峰
徐向前
向南
刘泽水
贾青扬
马玉凤
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National University of Defense Technology
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Abstract

The embodiment of the disclosure provides an equipment system investment portfolio selection method, electronic equipment and a storage medium, which comprehensively consider the combination action relationship among equipment systems to determine the optimal investment portfolio proportion of a plurality of equipment systems, and avoid the repeated construction and resource waste of a large amount of equipment. The method comprises the following steps: acquiring attribute data of multiple indexes of a plurality of equipment systems in a hesitation fuzzy number form, and carrying out standardized processing to obtain compatible attribute data; calculating the hesitation degree corresponding to each index of the equipment system according to the compatible attribute data, and determining the integration hesitation degree of the equipment system according to the hesitation degree; performing grey correlation analysis on the equipment system according to the compatible attribute data and the hesitation degree to determine a value parameter of the equipment system; and constructing a combined decision function according to the value parameters and the integration hesitation degrees of the equipment systems, and solving the combined decision function to determine the optimal investment portfolio proportion of the equipment systems.

Description

Equipment system investment portfolio selection method, electronic equipment and storage medium
Technical Field
One or more embodiments of the present disclosure relate to the field of resource orchestration and distribution technologies, and in particular, to an equipment system investment portfolio selection method, an electronic device, and a storage medium.
Background
The reasonable development planning of the weapon equipment system plays a crucial role in improving the fighting capacity, various complex decision selection problems are inevitably faced in the implementation of the equipment development planning, and the selection of various equipment systems is in a dilemma: on the one hand, it is not possible to develop indiscriminate multiple weaponry systems under conditions of budget tightening; on the other hand, appropriate equipment systems must be selected for investment development according to diversified requirements in future application scenarios. The combination selection of the equipment systems should be actually positioned at the research and development stage of the equipment systems, the traditional equipment system investment selection is decided based on the accurate index attribute data of the equipment systems obtained after the equipment is put into production, and the decision mode usually ignores the combination action relationship among a plurality of equipment systems, so that the serious problems of 'exceeding budget, dragging progress and reducing performance' are caused, and a large amount of equipment is repeatedly constructed and resources are wasted.
Disclosure of Invention
In view of the above, an object of the present disclosure is to provide an equipment system portfolio selection method, an electronic device and a storage medium.
In view of the above object, a first aspect of the present disclosure provides an equipment system portfolio selection method, including: acquiring attribute data of multiple indexes of a plurality of equipment systems, and carrying out standardization processing on the attribute data to obtain compatible attribute data, wherein the attribute data is expressed in a form of a hesitation fuzzy number; calculating the hesitation degree corresponding to each index of the equipment system according to the compatible attribute data, and determining the integration hesitation degree of the equipment system according to the hesitation degree; performing grey correlation analysis on the equipment system according to the compatible attribute data and the hesitation degree to determine a value parameter of the equipment system; and constructing a combined decision function according to the value parameters and the integration hesitation degrees of the equipment systems, and solving the combined decision function to determine the optimal investment portfolio proportion of the equipment systems.
In a second aspect of the disclosure, an electronic device is provided, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method according to the first aspect when executing the program.
In a third aspect of the disclosure, a non-transitory computer-readable storage medium is provided, which stores computer instructions for causing the computer to perform the method of the first aspect.
As can be seen from the above, the equipment system investment portfolio selection method, the electronic device and the storage medium provided by the present disclosure use the hesitation fuzzy number to effectively describe the index attribute information of the equipment system that is not accurate yet in the equipment development phase, and on this basis, calculate and measure the risk caused by the uncertainty of the equipment system value parameter and data, wherein the risk degree is represented by the integration hesitation degree of the equipment system, and a combined decision function is constructed by combining the equipment system value parameter and the integration hesitation degree, the combined decision function is substantially an objective optimization problem, and the optimal investment portfolio proportions of a plurality of equipment systems can be determined in the equipment development phase by solving the combined decision function.
Drawings
In order to more clearly illustrate one or more embodiments or prior art solutions of the present specification, the drawings that are needed in the description of the embodiments or prior art will be briefly described below, and it is obvious that the drawings in the following description are only one or more embodiments of the present specification, and that other drawings may be obtained by those skilled in the art without inventive effort from these drawings.
FIG. 1 is a schematic diagram of a method for equipment system portfolio selection as provided in one or more embodiments of the present disclosure;
FIG. 2 is a schematic diagram of a gray correlation analysis method in an equipment system portfolio selection method according to one or more embodiments of the present disclosure;
fig. 3 is a schematic diagram of total hesitation and total value when values of different risk tolerance parameters θ are obtained by an equipment system portfolio selection method according to one or more embodiments of the present disclosure;
fig. 4 is a schematic diagram of an investment proportion of each equipment system when a different risk tolerance parameter θ is taken in an equipment system portfolio selection method provided in one or more embodiments of the present disclosure;
FIG. 5 is a schematic diagram illustrating comparison analysis of a value parameter and an integration hesitation degree in an equipment system portfolio selection method according to one or more embodiments of the present disclosure;
fig. 6 is a schematic diagram of an equipment system portfolio selection electronics provided in one or more embodiments of the present disclosure.
Detailed Description
For the purpose of promoting a better understanding of the objects, aspects and advantages of the present disclosure, reference is made to the following detailed description taken in conjunction with the accompanying drawings.
It is to be noted that unless otherwise defined, technical or scientific terms used in one or more embodiments of the present specification should have the ordinary meaning as understood by those of ordinary skill in the art to which this disclosure belongs. The use of "first," "second," and similar terms in one or more embodiments of the specification is not intended to indicate any order, quantity, or importance, but rather is used to distinguish one element from another. The word "comprising" or "comprises", and the like, means that the element or item listed before the word covers the element or item listed after the word and its equivalents, but does not exclude other elements or items. The terms "connected" or "coupled" and the like are not restricted to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "upper", "lower", "left", "right", and the like are used merely to indicate relative positional relationships, and when the absolute position of the object being described is changed, the relative positional relationships may also be changed accordingly.
The reasonable development planning of the weapon equipment system plays a crucial role in improving the fighting capacity, various complex decision selection problems are inevitably faced in the implementation of the equipment development planning, and the selection of various equipment systems is in a dilemma: on the one hand, it is not possible to develop indiscriminate multiple weaponry systems under conditions of budget tightening; on the other hand, appropriate equipment systems must be selected for investment development according to diversified requirements in future application scenarios. The combination selection of the equipment systems should be actually positioned at the research and development stage of the equipment systems, the traditional equipment system investment selection is decided based on the accurate index attribute data of the equipment systems obtained after the equipment is put into production, and the decision mode usually ignores the combination action relationship among a plurality of equipment systems, so that the serious problems of 'exceeding budget, dragging progress and reducing performance' are caused, and a large amount of equipment is repeatedly constructed and resources are wasted. How to reasonably and efficiently select and invest a plurality of equipment systems in the research and development stage before the equipment systems are put into production is an urgent problem to be solved. And in the equipment research and development stage, various index attribute information of the equipment system is unknown, so that great difficulty is caused in the investment of multiple equipment and the same selection in the research and development stage.
In view of the above, the present disclosure employs a hesitation ambiguity number to describe inaccurate indicator attribute information of an equipment system. On the basis, a hesitation fuzzy grey correlation analysis method is used for evaluating the value of the equipment system, wherein the influence of the hesitation degree of the hesitation fuzzy number on the grey correlation degree can be considered by adopting an improved hesitation fuzzy number distance measure. Further, the combined investment proportions of the plurality of equipment systems are measured and determined on the basis of the value and hesitation of the equipment systems.
Based on the inventive thought, the present disclosure provides an equipment system investment portfolio selection method.
As shown in fig. 1, some alternative embodiments of the present disclosure provide an equipment system portfolio selection method, including:
s1: acquiring attribute data of multiple indexes of a plurality of equipment systems, and carrying out standardization processing on the attribute data to obtain compatible attribute data, wherein the attribute data is expressed in a form of a hesitation fuzzy number;
in the development stage of the equipment system or other possible situations, the attribute data of each index of the equipment system cannot be expressed by accurate quantitative data, more than one possible value may exist for the attribute data of a certain index, and the attribute data of the index can be expressed by a hesitation fuzzy number. Specifically, the hesitation ambiguity number may be used to express the probability that each possible value of the attribute data corresponds to. According to the method, the equipment system index attribute information can be effectively described by using the hesitation fuzzy number.
A plurality of items of index attribute data of the equipment system are used for evaluating the equipment system, the evaluation dimensions and the measurement degrees of the attribute data of different indexes are usually different, some index attribute data can bring positive influence on the evaluation of the equipment system, and other index attribute data can possibly cause negative influence on the evaluation of the equipment system. Based on this, the attribute data of the multinomial index is normalized to be compatible attribute data in the present disclosure.
S2: calculating the hesitation degree corresponding to each index of the equipment system according to the compatible attribute data, and determining the integration hesitation degree of the equipment system according to the hesitation degree;
the method is characterized in that each item of index attribute data of the equipment system is expressed by using the hesitation fuzzy number, and the attribute data has uncertainty, so that the uncertainty of the attribute data can be represented by calculating the hesitation degree of the corresponding hesitation fuzzy number in the method. For the equipment system, the uncertainty of the whole equipment system is comprehensively determined through the uncertainty of the multiple items of index attribute data contained in the equipment system, so that the integrated hesitation of the whole equipment system can be further calculated and determined according to the hesitation of the multiple items of attribute data;
s3: performing grey correlation analysis on the equipment system according to the compatible attribute data and the hesitation degree to determine a value parameter of the equipment system;
in the disclosure, a hesitation fuzzy grey correlation analysis method can be used for determining a value parameter of the equipment system so as to evaluate the value of the equipment system;
s4: and constructing a combined decision function according to the value parameters and the integration hesitation degrees of the equipment systems, and solving the combined decision function to determine the optimal investment portfolio proportion of the equipment systems.
In the method, the value parameters can be used for representing the value of the equipment system, the uncertainty risk of the equipment system can be measured by using the integration hesitation degree, and a combined decision function is further constructed and solved according to the value parameters and the integration hesitation degree of the equipment systems.
As can be seen from the above, the equipment system investment portfolio selection method provided by the present disclosure uses the hesitation fuzzy number to effectively describe the equipment system index attribute information that is not accurate yet in the equipment development stage, and calculates and measures the risk caused by the uncertainty of the equipment system value parameters and data on the basis of the equipment system index attribute information, wherein the risk degree is represented by the integration hesitation degree of the equipment system, a combined decision function is constructed by combining the equipment system value parameters and the integration hesitation degree, the combined decision function is substantially an objective optimization problem, and the optimal investment portfolio proportions of a plurality of equipment systems can be determined in the equipment development stage by solving the combined decision function.
In an equipment system portfolio selection method provided in some alternative embodiments of the disclosure, a plurality of the equipment systems may be represented as { S }1，S2，S3，…，SnA plurality of said indexes related to a plurality of equipment systems can be expressed as { C }1，C2，C3，…，CmAcquiring attribute data of a plurality of indexes of a plurality of equipment systems, further comprising:
acquiring a value set of the attribute data and the corresponding possible degree of each possible value in the value set;
the possible degree corresponding to the possible value is called a hesitation fuzzy number, a hesitation fuzzy number set is formed according to all possible degrees corresponding to all possible values in the value set, and the attribute data is represented by the hesitation fuzzy number set;
wherein the ith (i ∈ {1, 2, 3, … n }) equipment system SiJ (j. epsilon. {1, 2, 3, …, m }) of index CjThe value set of the attribute data is XijValue set XijComprising at least one possible value xijPossibly taking the value xijThe corresponding likelihood is h (x)ij)∈[0，1]；
And value set XijCorresponding hesitation fuzzy number set is hij＝{h(xij)|xij∈Xij}；
Using the set of hesitation fuzzy numbers hijIndicating the ith equipment system SiJ (th) index CjThe attribute data of (2);
the normalizing the attribute data into compatible attribute data further comprises:
for the ith equipment system SiJ (th) index CjAttribute data h ofijCorresponding said compatibility attributeThe data are as follows:
Figure BDA0003005729210000051
wherein, h'ijThe compatibility attribute data is represented by a table of values,
Figure BDA0003005729210000052
representing the set of hesitation ambiguities hijThe complement of (1);
Figure BDA0003005729210000061
in some alternative embodiments of the disclosure, the method for selecting an equipment system portfolio includes:
determining the hesitation degree of the corresponding index according to the variance of all elements of the compatible attribute data:
wherein the ith equipment system SiJ (th) index CjOf compatible attribute data h'ijCan be expressed as:
h′ij＝{h′1(xij)，h′2(xij)，…h′l(xij)}
wherein l represents the number of elements in the compatible attribute data;
the compatible attribute data h'ijThe mean value of (A) is:
Figure BDA0003005729210000062
the compatible attribute data h'ijCorresponding index CjThe corresponding hesitation degrees are:
Figure BDA0003005729210000063
determining the integration hesitation degree of the equipment system according to the hesitation degree, and the method comprises the following steps:
determining the integration hesitation degree of the equipment system according to the mean value of the plurality of hesitation degrees corresponding to the plurality of indexes of the equipment system:
wherein the ith equipment system SiSaid integration hesitation degree of
Figure BDA0003005729210000065
Comprises the following steps:
Figure BDA0003005729210000064
as shown in fig. 2, in an equipment system portfolio selection method provided in some alternative embodiments of the disclosure, the performing a gray correlation analysis on the equipment system according to the compatible attribute data and the hesitation degree to determine a value parameter of the equipment system further includes:
s201: setting positive and negative reference sequences according to the attribute data, and expanding the positive and negative reference sequences and the compatible attribute data of the equipment system to enable the hesitation fuzzy numbers of the expanded positive and negative reference sequences and the compatible attribute data to have the same element number;
when the gray correlation analysis is performed on the equipment system to determine the value parameter thereof, the reference sequence needs to be determined first, and in some optional embodiments, the maximum value in the attribute data of each index can be selected to form a positive reference sequence H+Respectively selecting minimum values in attribute data of each index to form a negative reference sequence H-Each reference number in the positive and negative reference sequences is also a hesitation fuzzy number;
it is understood that there may be other ways to set the positive and negative reference sequences, and the positive and negative reference sequences may be set according to practical problems during the implementation and application of the method of the present disclosure.
It can be understood that the number of possible values of different index attribute data of different equipment systems is different, and therefore the number of elements of the hesitation ambiguity number of the compatible attribute data is also different. Before the gray correlation degree analysis of the next step, a plurality of the compatible attribute data and the hesitation fuzzy numbers of the positive and negative reference sequences are required to be expanded, so that the expanded hesitation fuzzy numbers of the positive and negative reference sequences and the compatible attribute data have the same element number, thereby facilitating the gray correlation degree analysis.
In some optional embodiments, the extending the positive and negative reference sequences and the compatible attribute data of the equipment system further comprises:
determining decision risk attitudes, wherein the decision risk attitudes comprise a risk aversion type, a risk preference type and a risk neutral type;
according to the decision risk attitude, expanding the positive and negative reference sequences and the hesitation fuzzy number with less elements in the compatible attribute data of the equipment system;
the expanding of the hesitation fuzzy data with few elements in the positive and negative reference sequences and the compatible attribute data of the equipment system can adopt different expanding modes according to different decision risk attitudes, wherein:
in response to that the decision risk attitude is risk aversion type, in which case the result is expected to be generally pessimistic, selecting the minimum value of the hesitation fuzzy numbers with fewer elements to be added to the hesitation fuzzy numbers for expansion;
in response to that the decision risk attitude is of a risk preference type, in which case the result is expected to be generally optimistic, a maximum value of the hesitation fuzzy numbers with fewer elements can be selected and added to the hesitation fuzzy numbers for expansion;
and responding to the decision risk attitude being a risk neutral type, determining the average value of all elements in the hesitation fuzzy number with less elements, and adding the average value into the hesitation fuzzy number for expansion.
S202: calculating the distance measure of the corresponding indexes of the equipment system and the extended positive and negative reference sequences according to the extended positive and negative reference sequences and the extended compatible attribute data;
the basic idea of grey correlation analysis is to judge whether the relation between different sequences is tight according to a sequence curve, and measure the similarity of the variation trend of system factors according to the distance measure between corresponding points of the sequences. In the embodiment of the present disclosure, when performing gray correlation analysis on the equipment system according to the compatible attribute data and the hesitation degree, it is necessary to calculate a distance measure of an index corresponding to the equipment system and the extended positive and negative reference sequences according to the extended positive and negative reference sequences and the extended compatible attribute data, so as to determine similarity between the attribute data and the reference sequences.
It should be noted that, for the distance measure, there are a hamming distance, a euclidean distance, and a hadov distance, which are more commonly used, and unlike the conventional hamming distance measure and the conventional euclidean distance measure, the present disclosure uses the improved hamming distance measure and the improved euclidean distance measure to fully consider the influence of the hesitation ambiguity number on the gray correlation.
In some optional embodiments, the calculating, according to the extended positive and negative reference sequences and the extended compatible attribute data, a distance measure of an index corresponding to the equipment system and the extended positive and negative reference sequences further includes:
with the ith equipment system SiJ (th) index CjCorresponding extended compatible attribute data is recorded as h'ij kExtended positive reference sequence H+ kMiddle and j index CjThe corresponding reference number is marked as H+j kExtended negative reference sequence H- kMiddle and j index CjThe corresponding reference number is marked as H-j k；
Respectively calculating extended compatible attribute data h 'by adopting an improved Hamming distance formula'ij kWith reference number H+j kReference number H-j kMeasure of distance between dI(h′ij k，H+j k)、dI(h′ij k，H-j k)：
Figure BDA0003005729210000081
Figure BDA0003005729210000082
Wherein d isIH(h′ij k，H+j k) Representing extended compatibility attribute data h'ij kWith reference number H+j kMeasure of improved hamming distance between, dIH(h′ij k，H-j k) Representing extended compatibility attribute data h'ij kWith reference number H-j kImproved hamming distance measure therebetween;
l represents extended compatibility attribute data h'ij kReference number H+j kAnd reference number H-j kNumber of elements (d), h'ij kλ、H+j kλ、H-j kλRespectively representing extended compatibility attribute data hij kReference number H+j kAnd reference number H-j kThe lambda-th element is formed by arranging the medium elements from small to large;
Figure BDA0003005729210000083
respectively represent extended compatibility attribute data h'ij kReference number H+j kAnd reference number H-j kHesitation degree of;
and the jth index CjIs correspondingly marked as H+ kAnd the jth index CjThe corresponding extended negative reference sequence is denoted as H- k；
Or respectively calculating the extended compatible attribute data h 'by adopting an improved Euclidean distance formula'ij kWith reference number H+j kReference number H-j kMeasure of distance between dI(h′ij k，H+j k)、dI(h′ij k，H-j k)：
Figure BDA0003005729210000091
Figure BDA0003005729210000092
Wherein d isIE(h′ij k，H+j k) Representing extended compatibility attribute data h'ij kWith reference number H+j kImproved Euclidean distance measure between, dIE(h′ij k，H-j k) Representing extended compatibility attribute data h'ij kWith reference number H-j kAn improved euclidean distance measure between them.
It is further noted that the improved distance measure employed by the present disclosure satisfies the following properties of distance:
(1) nonnegativity and identity: dIH(h1，h2)≥0，dIE(h1，h2) Is not less than 0, and dIH(h1，h2) 0 and only h1＝h2，dIE(h1，h2) 0 and only h1＝h2；
(2) Symmetry: dIH(h1，h2)＝dIH(h2，h1)，dIE(h1，h2)＝dIE(h2，h1)；
(3) The direct transmission property: dIH(h1，h2)+dIH(h2，h3)≥dIH(h1，h3)，dIE(h1，h2)+dIE(h2，h3)≥dIE(h1，h3)。
S203: respectively calculating gray correlation coefficients between the equipment system and the expanded positive and negative reference sequences according to the distance measures, and respectively calculating and determining gray correlation degrees between the equipment system and the expanded positive and negative reference sequences according to the gray correlation coefficients;
in some optional embodiments, the calculating the gray correlation coefficient between the equipment system and the extended positive and negative reference sequences according to the distance measure further comprises:
ith equipment system SiThe extended post-compatibility attribute data h 'of (1)'ij kAnd the reference number H+j kThe reference number H-i kThe grey correlation coefficients between the two are respectively:
Figure BDA0003005729210000101
Figure BDA0003005729210000102
wherein, ξ (h'ij k，H+j k)、ξ(h′ij k，H-j k) Respectively represent extended compatibility attribute data h'ij kAnd the reference number H+j kThe reference number H-j kThe gray correlation coefficient between rho is in [0, 1 ]]Is a resolution factor;
the step of respectively calculating and determining the gray correlation degrees between the equipment system and the expanded positive and negative reference sequences according to the gray correlation coefficients further comprises the following steps:
ith equipment system SiAnd the extended positive reference sequence H+ kExtended negative reference sequence H- kDegree of gray correlation between
Figure BDA0003005729210000103
Respectively as follows:
Figure BDA0003005729210000104
Figure BDA0003005729210000105
s204: and calculating and determining the value parameters of the equipment system according to the grey correlation degrees between the equipment system and the expanded positive and negative reference sequences.
In some optional embodiments, calculating the value parameter of the equipment system according to the gray correlation further comprises:
ith equipment system SiThe value parameters are as follows:
Figure BDA0003005729210000106
in some alternative embodiments of the disclosure, the method for selecting an equipment system portfolio, wherein the constructing a combined decision function according to the value parameters and the integration hesitation degrees of a plurality of the equipment systems, further comprises:
determining a decision strategy type, and constructing the combined decision function according to the decision strategy type, wherein the decision strategy type comprises the following steps:
and responding to the decision strategy type being neutral, constructing a combined decision function of the double targets according to the value parameters and the integration hesitation degree:
Figure BDA0003005729210000111
Figure BDA0003005729210000112
Figure BDA0003005729210000113
wherein S (P) represents the total score of the combination P, wiIndicating the ith equipment system SiInvestment proportion of viIndicating the ith equipment system SiThe value parameter, the combination P, is in accordance with the investment ratio wiA portfolio of selective investments is made for a plurality of equipment systems, H (P) representing the total risk of portfolio P,
Figure BDA0003005729210000114
indicating the ith equipment system SiSaid integration hesitation, pi、oiRespectively represent the investment proportions wiUpper and lower threshold values of (c);
and the neutral decision strategy keeps neutral on the value and the risk of the equipment system, and the combined decision function simultaneously comprises a Max objective function Max S (P) and a Min objective function Min H (P). One point to be considered is also the upper and lower threshold values of the investment proportions of the different equipment systems. It should be noted that the optimal combination under the neutral decision strategy is a pareto optimal solution under two target situations of maximum value and minimum hesitation degree, which is also called as a non-dominated solution, and generally there are a plurality of solutions, and the solution result of the combined decision function under this situation has a small meaning.
Therefore, further, the combined decision function may be adjusted according to a non-neutral decision policy, and specifically, in response to that the decision policy type is a value preference type, a single-target combined decision function targeting a high value is constructed according to the value parameter and the integration hesitation degree:
Figure BDA0003005729210000115
Figure BDA0003005729210000116
wherein:
α＝θH(P)Ma+(1-θ)H(P)Mi
H(P)Mi＝minH(P)
Figure BDA0003005729210000121
H(P)Ma＝maxH(P)
Figure BDA0003005729210000122
wherein α represents a risk threshold and θ represents a risk tolerance parameter;
in a value-biased decision strategy, the decision is made to obtain the largest-valued investment portfolio under acceptable risk conditions, and the acceptable risk can be regulated by a risk threshold.
Or, in response to the decision strategy type being a risk aversion type, constructing a single-target combined decision function with low risk as a target according to the value parameters and the integrated hesitation degree:
Figure BDA0003005729210000123
Figure BDA0003005729210000124
wherein:
β＝ψS(P)Ma+(1-ψ)S(P)Min
S(P)Min＝min S(P)
Figure BDA0003005729210000125
S(P)Max＝max S(P)
Figure BDA0003005729210000131
where β represents a value threshold and ψ represents a value acceptance parameter.
In a risk aversion type decision strategy, the decision is aimed at determining the least risky portfolio under combinable value acceptable conditions, which can be adjusted by a value threshold.
It should be noted that the method of one or more embodiments of the present disclosure may be performed by a single device, such as a computer or server. The method of the embodiment can also be applied to a distributed scene and completed by the mutual cooperation of a plurality of devices. In such a distributed scenario, one of the devices may perform only one or more steps of the method of one or more embodiments of the present disclosure, and the devices may interact with each other to complete the method.
It should be noted that the above description describes certain embodiments of the present disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
The following describes an implementation flow of the equipment system portfolio selection method provided by the present disclosure with reference to specific embodiments.
Table 18 4 index attribute data of equipment systems
c1 c2 c3 c4
S1 {0.45} {0.35，0.55，0.60} {0.70，0.75，0.8} {0.60，0.70}
S2 {0.35，0.65，0.75} {0.20，0.35} {0.55，0.7} {0.45，0.50}
S3 {0.75，0.45} {0.34，0.45} {0.62，0.75} {0.30，0.45，0.60，}
S4 {0.55，0.70} {0.30，0.56} {0.3，0.70} {0.4}
S5 {0.35，0.67} {0.50，0.60} {0.73} {0.42，0.50}
S6 {0.30，0.46，0.63} {0.45} {0.35，0.90} {0.35，0.54}
S7 {0.40，0.45} {0.22，0.38，0.50} {0.70，0.80，0.85} {0.70，0.75}
S8 {0.50，0.70} {0.30，0.55} {0.55，0.67} {0.55}
Decision making this requires 8 equipment systems to be developed S1，S2，S3，S4，S5，S6，S7，S8Invested capital to support the development of each item of equipment, and 4 key indexes (C) in total are set1，C2，C3，C4The attribute is used for equipment system evaluation, where c4Is a cost-type index, and the rest are benefit-type indexes. Table 1 above shows 4 pieces of index attribute data of the 8 equipment systems. Consider wholeSetting the lower limit threshold o of the investment proportion of each equipment system according to the development requirement of each equipment systemi0.05, 0.05, 0.1, 0.05, 0.1, 0.05, 0.15, 0.05, and an upper threshold value pi＝{0.2，0.2，0.3，0.1，0.2，0.1，0.45，0.1}。
In this embodiment, the positive and negative reference sequences may be determined as the maximum and minimum values, H, respectively, of each index at maximum+＝{{0.75}，{0.6}，{0.9}，{0.75}}，H-After that, the index value c in the equipment system and the positive and negative reference sequences is set to {0.3}, {0.2}, {0.3}, and {0.25} }4And carrying out normalization processing, and converting the data into benefit type attribute data. To equip the system S1For example, c thereof4Conversion of index value into
Figure BDA0003005729210000145
Figure BDA0003005729210000146
Next, the system S is mainly equipped1The description is given for the sake of example.
Computing equipment system S1The hesitation degree of each item of index attribute data:
Figure BDA0003005729210000141
calculated by the same formula
Figure BDA0003005729210000142
Computing equipment system S1Integration hesitation degree of (c):
Figure BDA0003005729210000143
when gray correlation analysis is performed, the positive and negative reference sequences and the compatible attribute data of the equipment system need to be expanded, and in this embodiment, a risk-neutral corresponding expanding party can be adoptedFormula (h)11The extension is {0.45, 0.45, 0.45}, h14The extension is 0.40, 0.30, 0.40.
And after the expansion, calculating the distance measure of the corresponding indexes of the attribute data of each equipment system and the positive and negative reference sequences. In this embodiment, an improved hamming distance formula is selected for calculation.
Figure BDA0003005729210000144
D is calculated by the same formulaIH(h′12 k，H+2 k)＝0.208，dIH(h′13 k，H+3 k)＝0.191，dIH(h′14 k，H+4 k)＝0.383
Figure BDA0003005729210000151
D is calculated by the same formula1H(h′12 k，H-2 k)＝0.408，dIH(h′13 k，H-3 k)＝0.491，dIH(h′14 k，H-4 k)＝0.167.
The grey correlation coefficients between the respective equipment solutions and the positive and negative reference sequences are then calculated.
For equipment system S1In conjunction with the positive reference sequence, the sequence,
Figure BDA0003005729210000152
Figure BDA0003005729210000153
the grey correlation coefficient is then:
Figure BDA0003005729210000154
likewise, ξ (h'12 k，H+2 k)≈0.737，ξ(h′13 k，H+3 k)≈0.765，ξ(h′14 k，H+4 k)≈0.538
For equipment system S1Like the negative reference sequence, ξ (h'11 k，H-1 k)≈0.815，ξ(h′12 k，H-2 k)≈0.536，ξ(h′13 k，H-3 k)≈0.483，ξ(h′14 k，H-4 k)≈0.798
Further, the grey correlation degree between the positive and negative reference sequences is calculated
Figure BDA0003005729210000155
Figure BDA0003005729210000156
Figure BDA0003005729210000157
Then according to grey correlation degree
Figure BDA0003005729210000158
Computing equipment system S1Value parameter of
Figure BDA0003005729210000159
Figure BDA00030057292100001510
And calculating the grey correlation degree, the value parameters and the integration hesitation degree between each equipment system and the positive and negative reference sequences according to the mode.
Table 28 Grey correlation, value parameters and integration hesitation between the equipment system and the positive and negative reference sequences
Figure BDA00030057292100001511
Figure BDA0003005729210000161
Table 2 above shows the grey correlation, value parameters and integration hesitation between these 8 equipment systems and the positive and negative reference sequences.
Then, a combined decision function is constructed according to the value parameters and the integration hesitation degree, and in this embodiment, a single-target combined decision function with high value as a target is constructed:
Figure BDA0003005729210000162
Figure BDA0003005729210000163
wherein H (P) is obtained by calculationMax＝0.0939，H(P)MinWhen the value is 0.0682, α is θ × 0.0939+ (1- θ) × 0.0682.
Different values of the risk tolerance parameter theta may be set for solving. The final optimal portfolio ratio results for each equipment system are shown in table 3 below.
TABLE 3 proportion of investment portfolio of each equipment system solved under different theta values
Figure BDA0003005729210000164
Figure BDA0003005729210000171
Further, the impact of the risk tolerance parameter θ on the investment results can be analyzed from the data in table 3 above.
Through analysis, the value of the risk bearing parameter theta influences the total value of the equipment system combination. As shown in fig. 3, a schematic diagram of total hesitation and total value of corresponding equipment system combinations is taken for values of different risk tolerance parameters θ. When theta is not more than 6/9, the total hesitation degree and the score of the equipment system combination are increased along with the increase of the value of theta, when theta is 6/9, the score of the equipment system combination reaches the maximum of 0.5569, and at the moment, the hesitation degree of the equipment system combination is 0.0851. When theta is larger than 6/9, the hesitation degree and the score are not changed along with the change of the theta value. The maximum score obtained when θ is 0 is the smallest, 0.5277, and the hesitation is 0.0682. It can be seen from the results that the greater the risk that a decision maker can afford in general, the higher the total value of the resulting equipment system combination.
In addition, the value of the risk tolerance parameter θ also affects the investment proportion of each equipment system in the equipment system combination. As shown in fig. 4, the schematic diagram of the investment proportion of each equipment system when the risk tolerance parameter θ is taken as a value. When theta is larger than theta, the system S is equipped1、S8The investment ratio of (A) is relatively reduced, and S3、S4The investment proportion of (2) is relatively increased; but no matter how the risk tolerance parameter changes, S5All at 0.25 at a preset upper investment limit, S2，S6The investment ratio of the two equipment systems is 0.05 and is at a preset lower investment limit value. The decision maker will put more resources into the equipment system S when the acceptable risk is smaller1、S5、S8In the development of (2), investment equipment systems S are favored when the acceptable risk is greater3、S4、S5。
In addition, the value parameters of each equipment system can be compared with the integration hesitation degree for analysis. Fig. 5 is a schematic diagram showing comparison analysis of the value parameters and the integration hesitation of the 8-item equipment system. The value parameters of the 8 equipment system are ranked as
Figure BDA0003005729210000173
The integration hesitation degree is ordered into
Figure BDA0003005729210000172
Equipment system S5Has the highest value parameter of 0.5925 and the integration hesitation degree of only 0.0625, so S is preferred under any hesitation degree5Is an equipment worth investing. Equipment system S4Although the value parameter is 0.5712, the integration hesitation degree is 0.1013, so that the investment ratio of the equipment is low when the value of theta is small, and the equipment system S3As well as so. If it is
Figure BDA0003005729210000182
And is
Figure BDA0003005729210000181
Then the equipment system S is considerediAbsolute advantage over equipment system Sj. Equipment system S2、S6And S7In an eight item equipment system, it is not absolutely preferable to any one item of equipment system, except that the equipment system S is prepared when θ is 07And a larger investment ratio is obtained, and the investment ratios of the three pieces of equipment under the other theta values are all in the preset investment lower limit values.
The risk tolerance parameter theta can reflect the hesitation preference of a decision maker on the whole equipment combination, the value of the risk tolerance parameter theta influences the total value and the investment ratio of the equipment combination, and the influence of the risk tolerance parameter theta on the investment ratio depends on the balance of the value parameter of an equipment system and the integration hesitation degree.
Based on the same inventive idea as the method, the present disclosure also provides an electronic device. The electronic device includes a memory, a processor, and a computer program stored on the memory and executable on the processor that, when executed by the processor, implements the equipment system portfolio selection method.
Fig. 6 is a schematic diagram illustrating a more specific hardware structure of an electronic device according to this embodiment, where the electronic device may include: a processor 1010, a memory 1020, an input/output interface 1030, a communication interface 1040, and a bus 1050. Wherein the processor 1010, memory 1020, input/output interface 1030, and communication interface 1040 are communicatively coupled to each other within the device via bus 1050.
The processor 1010 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an Application Specific Integrated Circuit (ASIC), or one or more Integrated circuits, and is configured to execute a relevant program to implement the technical solution of the equipment system investment portfolio selection method provided in the embodiments of the present specification.
The Memory 1020 may be implemented in the form of a ROM (Read Only Memory), a RAM (Random Access Memory), a static storage device, a dynamic storage device, or the like. The memory 1020 may store an operating system and other application programs, and when the technical solution of the equipment system portfolio selection method provided in the embodiments of the present specification is implemented by software or firmware, the relevant program codes are stored in the memory 1020 and called and executed by the processor 1010.
The input/output interface 1030 is used for connecting an input/output module to input and output information. The i/o module may be configured as a component in a device (not shown) or may be external to the device to provide a corresponding function. The input devices may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output devices may include a display, a speaker, a vibrator, an indicator light, etc.
The communication interface 1040 is used for connecting a communication module (not shown in the drawings) to implement communication interaction between the present apparatus and other apparatuses. The communication module can realize communication in a wired mode (such as USB, network cable and the like) and also can realize communication in a wireless mode (such as mobile network, WIFI, Bluetooth and the like).
Bus 1050 includes a path that transfers information between various components of the device, such as processor 1010, memory 1020, input/output interface 1030, and communication interface 1040.
It should be noted that although the above-mentioned device only shows the processor 1010, the memory 1020, the input/output interface 1030, the communication interface 1040 and the bus 1050, in a specific implementation, the device may also include other components necessary for normal operation. In addition, those skilled in the art will appreciate that the above-described apparatus may also include only the components necessary to implement the embodiments of the present disclosure, and not necessarily all of the components shown in the drawings.
The electronic device of the foregoing embodiment is used to implement the corresponding method in the foregoing embodiment, and has the beneficial effects of the corresponding method embodiment, which are not described herein again.
Based on the same inventive concept as the above method, the present disclosure also provides a non-transitory computer-readable storage medium. The non-transitory computer-readable storage medium stores computer instructions for causing the computer to execute the equipment system portfolio selection method.
Computer-readable media of the present embodiments, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device.
Those of ordinary skill in the art will understand that: the discussion of any embodiment above is meant to be exemplary only, and is not intended to intimate that the scope of the disclosure, including the claims, is limited to these examples; within the spirit of the present disclosure, features from the above embodiments or from different embodiments may also be combined, steps may be implemented in any order, and there are many other variations of different aspects of one or more embodiments of the present description as described above, which are not provided in detail for the sake of brevity.
In addition, well-known power/ground connections to Integrated Circuit (IC) chips and other components may or may not be shown in the provided figures, for simplicity of illustration and discussion, and so as not to obscure one or more embodiments of the disclosure. Furthermore, devices may be shown in block diagram form in order to avoid obscuring the understanding of one or more embodiments of the present description, and this also takes into account the fact that specifics with respect to implementation of such block diagram devices are highly dependent upon the platform within which the one or more embodiments of the present description are to be implemented (i.e., specifics should be well within purview of one skilled in the art). Where specific details (e.g., circuits) are set forth in order to describe example embodiments of the disclosure, it should be apparent to one skilled in the art that one or more embodiments of the disclosure can be practiced without, or with variation of, these specific details. Accordingly, the description is to be regarded as illustrative instead of restrictive.
While the present disclosure has been described in conjunction with specific embodiments thereof, many alternatives, modifications, and variations of these embodiments will be apparent to those of ordinary skill in the art in light of the foregoing description. For example, other memory architectures (e.g., dynamic ram (dram)) may use the discussed embodiments.
It is intended that the one or more embodiments of the present specification embrace all such alternatives, modifications and variations as fall within the broad scope of the appended claims. Therefore, any omissions, modifications, substitutions, improvements, and the like that may be made without departing from the spirit and principles of one or more embodiments of the present disclosure are intended to be included within the scope of the present disclosure.

Claims (10)

1. An equipment system portfolio selection method, the method comprising:
acquiring attribute data of multiple indexes of a plurality of equipment systems, and carrying out standardization processing on the attribute data to obtain compatible attribute data, wherein the attribute data is expressed in a form of a hesitation fuzzy number;
calculating the hesitation degree corresponding to each index of the equipment system according to the compatible attribute data, and determining the integration hesitation degree of the equipment system according to the hesitation degree;
performing grey correlation analysis on the equipment system according to the compatible attribute data and the hesitation degree to determine a value parameter of the equipment system;
and constructing a combined decision function according to the value parameters and the integration hesitation degrees of the equipment systems, and solving the combined decision function to determine the optimal investment portfolio proportion of the equipment systems.
2. The method of claim 1, wherein a plurality of the equipment systems are represented as { S }1，S2，S3，…，SnA plurality of said indices denoted as { C }1，C2，C3，…，CmAcquiring attribute data of a plurality of indexes of a plurality of equipment systems, further comprising:
acquiring a value set of the attribute data and the corresponding possible degree of each possible value in the value set;
the possible degree corresponding to the possible value is called a hesitation fuzzy number, a hesitation fuzzy number set is formed according to all possible degrees corresponding to all possible values in the value set, and the attribute data is represented by the hesitation fuzzy number set;
wherein the ith (i ∈ {1, 2, 3, … n }) equipment system SiJ (j. epsilon. {1, 2, 3, …, m }) of index CjThe value set of the attribute data is XijValue set XijComprising at least one possible value xijPossibly taking the value xijThe corresponding likelihood is h (x)ij)∈[0，1]；
And value set XijCorresponding hesitation fuzzy number set as
Figure FDA0003005729200000011
Using the set of hesitation fuzzy numbers hijIndicating the ith equipment system SiJ (th) index CjThe attribute data of (2);
the normalizing the attribute data into compatible attribute data further comprises:
for the ith equipment system SiJ (th) index CjAttribute data h ofijThe corresponding compatibility attribute data is as follows:
Figure FDA0003005729200000012
wherein, h'ijThe compatibility attribute data is represented by a table of values,
Figure FDA0003005729200000021
representing the set of hesitation ambiguities hijThe complement of (1);
Figure FDA0003005729200000022
3. the method of claim 1, wherein calculating hesitations corresponding to various indicators of the equipment system based on the compatibility attribute data comprises:
determining the hesitation degree of the corresponding index according to the variance of all elements of the compatible attribute data:
wherein the ith equipment system SiJ (th) index CjOf compatible attribute data h'ijCan be expressed as:
h′ij＝{h′1(xij)，h′2(xij)，…h′l(xij)}
wherein l represents the number of elements in the compatible attribute data;
the compatible attribute data h'ijHas a mean value of：
Figure FDA0003005729200000023
The compatible attribute data h'ijCorresponding index CjThe corresponding hesitation degrees are:
Figure FDA0003005729200000024
determining the integration hesitation degree of the equipment system according to the hesitation degree, and the method comprises the following steps:
determining the integration hesitation degree of the equipment system according to the mean value of the plurality of hesitation degrees corresponding to the plurality of indexes of the equipment system:
wherein the ith equipment system SiSaid integration hesitation degree of
Figure FDA0003005729200000026
Comprises the following steps:
Figure FDA0003005729200000025
4. the method of claim 2, wherein the gray correlation analysis of the equipment system based on the compatibility attribute data and the hesitation to determine the value parameter of the equipment system further comprises:
setting positive and negative reference sequences according to the attribute data, and expanding the positive and negative reference sequences and the compatible attribute data of the equipment system to enable the hesitation fuzzy numbers of the expanded positive and negative reference sequences and the compatible attribute data to have the same element number;
calculating the distance measure of the corresponding indexes of the equipment system and the extended positive and negative reference sequences according to the extended positive and negative reference sequences and the extended compatible attribute data;
respectively calculating gray correlation coefficients between the equipment system and the expanded positive and negative reference sequences according to the distance measures, and respectively calculating and determining gray correlation degrees between the equipment system and the expanded positive and negative reference sequences according to the gray correlation coefficients;
and calculating and determining the value parameters of the equipment system according to the grey correlation degrees between the equipment system and the expanded positive and negative reference sequences.
5. The method of claim 4, wherein said setting positive and negative reference sequences according to said attribute data further comprises:
respectively selecting the maximum value in the attribute data of each index to form a positive reference sequence H+Respectively selecting minimum values in attribute data of each index to form a negative reference sequence H-Each reference number in the positive and negative reference sequences is also a hesitation fuzzy number;
said extending said positive and negative reference sequences and said compatibility attribute data of said equipment system further comprises:
determining decision risk attitudes, wherein the decision risk attitudes comprise a risk aversion type, a risk preference type and a risk neutral type;
according to the decision risk attitude, expanding the positive and negative reference sequences and the hesitation fuzzy number with less elements in the compatible attribute data of the equipment system;
wherein the expanding the positive and negative reference sequences and the hesitation ambiguity data with fewer elements in the compatible attribute data of the equipment system further comprises:
in response to the decision risk attitude being of a risk aversion type, selecting a minimum value of the hesitation fuzzy numbers with less elements to be added into the hesitation fuzzy numbers for expansion;
in response to the decision risk attitude being a risk preference type, selecting a maximum value of the hesitation fuzzy numbers with less elements to be added into the hesitation fuzzy numbers for expansion;
and responding to the decision risk attitude being a risk neutral type, determining the average value of all elements in the hesitation fuzzy number with less elements, and adding the average value into the hesitation fuzzy number for expansion.
6. The method of claim 4, wherein the calculating a distance measure of the corresponding indicator of the equipment system and the extended positive and negative reference sequences according to the extended positive and negative reference sequences and the extended compatibility attribute data further comprises:
with the ith equipment system SiJ (th) index CjCorresponding extended compatibility attribute data is noted as
Figure FDA0003005729200000031
Extended positive reference sequence H+ kMiddle and j index CjThe corresponding reference number is marked as H+j kExtended negative reference sequence H- kMiddle and j index CjThe corresponding reference number is marked as H-j k；
Respectively calculating extended compatible attribute data by adopting improved Hamming distance formula
Figure FDA0003005729200000041
With reference number H+j kReference number H_j kMeasure of distance between
Figure FDA0003005729200000042
Figure FDA0003005729200000043
Figure FDA0003005729200000044
Wherein the content of the first and second substances,
Figure FDA0003005729200000045
representing extended compatibility attribute data
Figure FDA0003005729200000046
With reference number H+j kAn improved measure of the hamming distance between them,
Figure FDA0003005729200000047
representing extended compatibility attribute data
Figure FDA0003005729200000048
With reference number H-j kImproved hamming distance measure therebetween;
l represents extended compatibility attribute data
Figure FDA0003005729200000049
Reference number H+j kAnd reference number H-j kThe number of the elements (c) is,
Figure FDA00030057292000000410
H+j kλ、H-j kλrespectively representing extended compatibility attribute data
Figure FDA00030057292000000411
Reference number H+j kAnd reference number H-j kThe lambda-th element is formed by arranging the medium elements from small to large;
Figure FDA00030057292000000412
respectively representing extended compatibility attribute data
Figure FDA00030057292000000413
Reference number H+j kAnd reference number H-j kHesitation degree of;
and the jth index CjIs correspondingly marked as H+ kAnd the jth index CjThe corresponding extended negative reference sequence is denoted as H- k；
Or respectively calculating the extended compatible attribute data by adopting an improved Euclidean distance formula
Figure FDA00030057292000000414
With reference number H+j kReference number H-j kMeasure of distance between
Figure FDA00030057292000000415
Figure FDA00030057292000000416
Figure FDA00030057292000000417
Wherein the content of the first and second substances,
Figure FDA00030057292000000418
representing extended compatibility attribute data
Figure FDA00030057292000000419
With reference number H+j kAn improved euclidean distance measure between them,
Figure FDA00030057292000000420
representing extended compatibility attribute data
Figure FDA00030057292000000421
With reference number H_j kAn improved euclidean distance measure between them.
7. The method of claim 6, wherein calculating gray correlation coefficients between the equipment system and the extended positive and negative reference sequences, respectively, from the distance measures, further comprises:
ith equipment system SiThe extended compatibility attribute data in
Figure FDA0003005729200000051
And the reference number H+j kThe reference number H_j kThe grey correlation coefficients between the two are respectively:
Figure FDA0003005729200000052
Figure FDA0003005729200000053
wherein the content of the first and second substances,
Figure FDA0003005729200000054
respectively representing extended compatibility attribute data
Figure FDA0003005729200000055
And the reference number H+j kThe reference number H-j kThe gray correlation coefficient between rho is in [0, 1 ]]Is a resolution factor;
the step of respectively calculating and determining the gray correlation degrees between the equipment system and the expanded positive and negative reference sequences according to the gray correlation coefficients further comprises the following steps:
ith equipment system SiAnd the extended positive reference sequence H+ kExtended negative reference sequence H- kDegree of gray correlation r betweeni +、ri -Respectively as follows:
Figure FDA0003005729200000056
Figure FDA0003005729200000057
the calculating and determining the value parameters of the equipment system according to the grey correlation degrees between the equipment system and the expanded positive and negative reference sequences further comprises:
ith equipment system SiThe value parameters are as follows:
vi＝ri +/(ri ++ri -)。
8. the method of claim 2, wherein said constructing a combined decision function from said value parameters and said integration hesitations of a plurality of said equipment systems further comprises:
determining a decision strategy type, and constructing the combined decision function according to the decision strategy type, wherein the decision strategy type comprises the following steps:
and responding to the decision strategy type being neutral, constructing a combined decision function of the double targets according to the value parameters and the integration hesitation degree:
Figure FDA0003005729200000061
Figure FDA0003005729200000062
Figure FDA0003005729200000063
wherein S (P) represents the total score of the combination P, wiIndicating the ith equipment system SiInvestment ratio ofExample viIndicating the ith equipment system SiThe value parameter, the combination P, is in accordance with the investment ratio wiA portfolio of selective investments is made for a plurality of equipment systems, H (P) representing the total risk of portfolio P,
Figure FDA0003005729200000067
indicating the ith equipment system SiSaid integration hesitation, pi、oiRespectively represent the investment proportions wiUpper and lower threshold values of (c);
and in response to the decision strategy type being a value preference type, constructing a single-target combined decision function with high value as a target according to the value parameter and the integration hesitation degree:
Figure FDA0003005729200000064
Figure FDA0003005729200000065
wherein:
α＝θH(P)Max+(1-θ)H(P)Min
H(P)Min＝minH(P)
Figure FDA0003005729200000066
H(P)Max＝maxH(P)
Figure FDA0003005729200000071
wherein α represents a risk threshold and θ represents a risk tolerance parameter;
and in response to the decision strategy type being a risk aversion type, constructing a single-target combined decision function taking low risk as a target according to the value parameters and the integrated hesitation degree:
Figure FDA0003005729200000072
Figure FDA0003005729200000073
wherein:
β＝ψS(P)Max+(1-ψ)S(P)Mi
S(P)Min＝min S(P)
Figure FDA0003005729200000074
S(P)Max＝max S(P)
Figure FDA0003005729200000075
where β represents a value threshold and ψ represents a value acceptance parameter.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method according to any of claims 1 to 8 when executing the program.
10. A non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the method of any one of claims 1 to 8.
CN202110361474.9A 2021-04-02 2021-04-02 Equipment system investment portfolio selection method, electronic equipment and storage medium Pending CN112950390A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113469565A (en) * 2021-07-21 2021-10-01 中国人民解放军国防科技大学 Multifunctional equipment scheme selection method under capacity uncompensable mechanism and related equipment

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113469565A (en) * 2021-07-21 2021-10-01 中国人民解放军国防科技大学 Multifunctional equipment scheme selection method under capacity uncompensable mechanism and related equipment
CN113469565B (en) * 2021-07-21 2023-08-22 中国人民解放军国防科技大学 Multifunctional equipment scheme selection method under capability uncompensated mechanism and related equipment

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