CN110264081B - Cloud manufacturing service combination method and device based on E-CARGO model - Google Patents

Cloud manufacturing service combination method and device based on E-CARGO model Download PDF

Info

Publication number
CN110264081B
CN110264081B CN201910540345.9A CN201910540345A CN110264081B CN 110264081 B CN110264081 B CN 110264081B CN 201910540345 A CN201910540345 A CN 201910540345A CN 110264081 B CN110264081 B CN 110264081B
Authority
CN
China
Prior art keywords
service
task
type
candidate
manufacturing
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN201910540345.9A
Other languages
Chinese (zh)
Other versions
CN110264081A (en
Inventor
张以文
钱晨
吴其林
张峻玮
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Anhui University
Chaohu University
Original Assignee
Anhui University
Chaohu University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Anhui University, Chaohu University filed Critical Anhui University
Priority to CN201910540345.9A priority Critical patent/CN110264081B/en
Publication of CN110264081A publication Critical patent/CN110264081A/en
Application granted granted Critical
Publication of CN110264081B publication Critical patent/CN110264081B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06313—Resource planning in a project environment
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/04—Manufacturing
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/30—Computing systems specially adapted for manufacturing

Landscapes

  • Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Physics & Mathematics (AREA)
  • Human Resources & Organizations (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Mathematical Physics (AREA)
  • Economics (AREA)
  • Strategic Management (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Mathematical Optimization (AREA)
  • Marketing (AREA)
  • Mathematical Analysis (AREA)
  • Computational Mathematics (AREA)
  • Tourism & Hospitality (AREA)
  • Pure & Applied Mathematics (AREA)
  • General Business, Economics & Management (AREA)
  • Data Mining & Analysis (AREA)
  • Game Theory and Decision Science (AREA)
  • Quality & Reliability (AREA)
  • Computing Systems (AREA)
  • Operations Research (AREA)
  • Educational Administration (AREA)
  • Development Economics (AREA)
  • Biodiversity & Conservation Biology (AREA)
  • Algebra (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Databases & Information Systems (AREA)
  • Software Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Manufacturing & Machinery (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • General Factory Administration (AREA)

Abstract

The invention relates to a cloud manufacturing service combination method based on an E-CARGO model, S1, nine tuples are obtained, and in a cloud manufacturing service environment, the nine tuples are simplified according to elements concerned by needs; s2, acquiring the competence degree of the service to any type of task; s3, obtaining a target function of the E-CARGO model; s4, solving an objective function to obtain a distribution matrix T; taking the task allocation matrix T as an optimal service combination scheme; the scheme also discloses a cloud manufacturing service combination device based on the E-CARGO model. The method is based on the E-CARGO model and combined with a cloud manufacturing scene, and an optimal service combination scheme is obtained by calculating the task allocation matrix T, so that the satisfaction degree of a user is improved, and the resource waste is reduced.

Description

Cloud manufacturing service combination method and device based on E-CARGO model
Technical Field
The invention relates to the technical field of cloud manufacturing service combination, in particular to a cloud manufacturing service combination method and device based on an E-CARGO model.
Background
With the rapid development of information technology, a cloud manufacturing concept is proposed to solve the problem of uneven distribution of manufacturing resources and improve the production efficiency of the manufacturing industry. The cloud manufacturing is a new networked manufacturing mode which organizes online manufacturing resources according to the requirements of users by utilizing a network and a cloud manufacturing service platform and provides various on-demand manufacturing services for the users. By establishing a cloud manufacturing service platform, idle resources of enterprises or individuals are virtualized to the cloud platform, sharing of the idle resources is achieved, the cloud platform has many resources and cannot be utilized by other users at the first time or a combination scheme is not optimal, resource waste is caused, and in order to improve satisfaction of users and reduce resource waste, appropriate resources need to be matched with service requests.
In the existing cloud manufacturing service platform, too many resources are available, and many resources cannot be allocated at the first time, or allocation is wrong, which causes that many resources cannot be utilized in time, so that resource waste is serious in the cloud manufacturing service platform, which is a problem to be solved urgently.
Disclosure of Invention
The invention aims to provide a cloud manufacturing service combination method and device based on an E-CARGO model, so as to solve the problem of serious resource waste of a cloud manufacturing service platform in the prior art.
In order to solve the above problems, the present invention provides the following technical solutions:
a cloud manufacturing service combination method based on an E-CARGO model comprises the following steps:
s1, acquiring a simplified nine-tuple in a cloud manufacturing service environment;
s2, acquiring the competence degree of the candidate service to any type of task;
s3, obtaining a target function of the E-CARGO model;
s4, solving an objective function to obtain a distribution matrix T; and taking the task allocation matrix T as an optimal service combination scheme.
As a further scheme of the invention: the step S1 includes:
the E-CARGO model is represented as a nine-tuple as follows:
∑::＝<C、O、A、S、R、E、G、s0、H>，
where C represents a class in the manufacturing service collaboration system, O represents an object in the manufacturing service collaboration system, A is an agent in the manufacturing service collaboration system, S is a message in the manufacturing service collaboration system, R is a role in the manufacturing service collaboration system, E is an environment in the manufacturing service collaboration system, G is a group in the manufacturing service collaboration system, S0Is the initial state of the manufacturing service collaboration system, H is the limited set of users in the manufacturing service collaboration system;
in a cloud manufacturing environment, acquiring E, C, O, R, A and G elements needing attention from a nine-tuple, and obtaining a simplified nine-tuple:
∑::＝<C′、O′、A′、R′、E′、G′>，
wherein E ' represents a set of plans and recommendations for a cloud manufacturing service, C ' represents a set of classes defined by abstract concepts associated with E ', O ' represents a set of concrete objects associated with C ', R ' represents a set of task types in cloud manufacturing, and A ' represents a set of services in cloud manufacturing.
As a further scheme of the invention: the step S2 includes:
by means of the formula (I) and (II),
Figure GDA0003071646650000021
three numerical features of the cloud model are computed, wherein,
ex representation set
Figure GDA0003071646650000022
σ is the standard deviation of Ex, S2Is the sample variance of Ex, N represents the set
Figure GDA0003071646650000035
The number of middle elements; lambda [ alpha ]i0 αRepresenting candidate services si0For QoS aggregation of alpha type tasks, λi0 α＝{qi0，1 α，qi0，2 α…qi0，p αP denotes candidate service si0The number of sub-tasks of the alpha type completed,
Figure GDA0003071646650000036
representing candidate services si0QoS evaluation for the g-th subtask belonging to the α type, g ═ 1,2,3, …, p;
characterizing a set of three numerical features using the formula cm ═ Ex, En, He };
reuse of formula si0＝{λi0 1，λi0 2…λi0 βCalculating candidate services si0And calculating the three numerical feature calculation formulas with cm ═ Ex, ESubstitution of n, He into candidate service si0In (1), obtaining:
Figure GDA0003071646650000031
wherein s isi0Represents the i0 th service in the service candidate set, i0 is 1, 2.. m, m is a positive integer;
si0＝{λi0 1，λi0 2...λi0 βdenotes candidate service si0The historical QoS records are combined according to task types;
using the formula RSS ═ s1、s2、...smCalculating a candidate service set RSS; will be provided with
Figure GDA0003071646650000032
si0＝{λi0 1，λi0 2...λi0 βSubstituting RSS into s1、s2、...smIn (b), the following was obtained:
Figure GDA0003071646650000033
wherein, cmm，βRepresenting three sets of digital features in a beta-type task in the mth service;
in the RSS candidate service set(s),
using formulas
Figure GDA0003071646650000034
Acquiring three digital characteristics under the optimal condition;
by means of the formula (I) and (II),
Figure GDA0003071646650000041
acquiring three digital characteristics under the worst condition;
wherein, cm+Representing the characteristics of three digits in the most ideal caseSet, max { Exi0 αDenotes taking the largest EX, min { En } in the candidate service set RSSi0 αRepresents taking the smallest En, min { He } in the candidate service set RSSi0 αExpressing the minimum He in the RSS of the candidate service set;
cm-represents the worst case set of three numerical features, min { Ex }i0 αDenotes taking the smallest EX, max En in the candidate service set RSSi0 αDenotes taking the largest En, max { He in the candidate service set RSSi0 αThe maximum He in the RSS of the candidate service set is taken;
using Euclidean distance
Figure GDA0003071646650000042
Computing candidate services si0For cm in alpha type task+And cm-The degree of similarity of (a) to (b),
wherein X represents a set of elements, Y represents a set of elements, and Z represents a positive integer; x is the number ofioRepresents any one element of the set X, yi0Represents any one element in the set Y;
by means of the formula (I) and (II),
Figure GDA0003071646650000043
computing candidate services si0The degree of competence for the alpha-type task, wherein,
Figure GDA0003071646650000044
value of [0, 1]]In the above-mentioned manner,
Figure GDA0003071646650000045
as candidate service si0Obtaining candidate service s for the competence degree of alpha type taski0The competence for any type of task.
As a further scheme of the invention: the step S3 includes:
using formulas
Figure GDA0003071646650000046
Obtaining an objective function of the E-CARGO model,
the constraints of the objective function include:
T[i][j]∈{0,1}(0≤i＜m)；
Figure GDA0003071646650000051
Figure GDA0003071646650000052
C[i1，i2]×(T[i1，j]+T[i2，j])≤1，(0≤i1，i2＜m，i1≠i2，)；
C[i1，i2]×(T[i1，j1]+T[i2，j2])≤1，(0≤i1，i2＜m，i1≠i2，0≤j1,j2＜n)；
wherein j represents a task type, which is called task type j for short, the number of the task types j is more than or equal to zero and less than n, and n is a positive integer; j is a function of1、j2Representing two different positive numbers of n, i representing a service, abbreviated as service i, i1And i2Two different services in the representative service i, which are respectively called the first service i for short1A second service i2；
Task lower bound vector L: l (j) represents the number of services that must be allocated to task type j, L [ j ] ═ 1 represents that one service i is allocated to task type j, L [ j ] ═ 0 represents that no service i is allocated to task type j, and L [ j ] > 1 represents that multiple services i are allocated to task type j, that is, the task type j includes multiple subtasks;
qualification matrix Q: q is an m multiplied by n matrix, m represents the number of the service i, Q [ i, j ] represents the competence degree of the service i to the task type j, 0 is the lowest value, and 1 is the highest value; in an embodiment, Q [ i, j ] is an eligibility assessment value, and the larger the value of Q [ i, j ], the more qualified the service i is for the task type j;
task allocation matrix T: t is an m × n matrix, m represents the number of services, T [ i, j ] represents whether a service i is allocated to a task type j, and when T [ i, j ] is 1, it represents that the service i is allocated to the task type j, and the service i at this time is an allocated service; t [ i, j ] ═ 0, meaning that service i is not assigned to task type j;
a collision matrix C: the dimension size is m × m, m representing the number of services i, Cj1][i2]Representing a first service i1And a second service i2Presence or absence of conflict, Ci1][i2]Belongs to {0, 1}, Cj1][i2]1 denotes a first service i1And a second service i2Conflict, tasks cannot be completed in the same group; c [ i ]1][i2]0 denotes a first service i1And a second service i2May cooperate within the same group;
preference vector P: p [ i ] represents preference degree of service i corresponding to any task type, and belongs to [ -0.5,0.5] (i is more than or equal to 0 and less than m); p [ i ] < 0 indicates that any type of task has no preference for service i, P [ i ] < 0 indicates that any type of task has a negative preference for service i, and P [ i ] > 0 indicates that any type of task has a positive preference for service i.
As a further scheme of the invention: the step S4 includes:
each type of task alpha can be used as Role in the E-CARGO model, and the candidate service set RSS is { s }1,s2,…,smThe Agents are agents in an E-CARGO model, each Agent has a qualification evaluation value for each Role, and the qualification evaluation values Q [ i ] corresponding to the service set and the task type in the cloud manufacturing service][j]Will be
Figure GDA0003071646650000061
Value as Q [ i][j]A value of (d); first calculate Q [ i][j]*(1+P[i]) By the value of QT [ i][j]To characterize Q [ i][j]*(1+P[i]) Namely: QT [ i][j]＝Q[i][j]*(1+P[i])；P[i]Representing the preference degree of the task to the service, and representing multiplication; QT [ i][j]Substituting the target function, and solving a task allocation matrix T by using a calculation tool, wherein the task allocation matrix T is the optimal service combination scheme.
A combination device based on the E-CARGO model-based cloud manufacturing service combination method comprises the following steps:
the nine-tuple module is used for acquiring a simplified nine-tuple in a cloud manufacturing service environment;
the acquisition module is used for acquiring the submitted task, the candidate service set and the candidate service of the service requester and calculating the competence degree of the candidate service to any type of task;
the target function module is used for acquiring a target function of the E-CARGO model;
the solving module is used for solving the objective function to obtain a distribution matrix T; and taking the task allocation matrix T as an optimal service combination scheme.
As a further scheme of the invention: the nine-tuple module further comprises: the E-CARGO model is represented as a nine-tuple as follows:
∑::＝<C、O、A、S、R、E、G、s0、H>，
where C represents a class in the manufacturing service collaboration system, O represents an object in the manufacturing service collaboration system, A is an agent in the manufacturing service collaboration system, S is a message in the manufacturing service collaboration system, R is a role in the manufacturing service collaboration system, E is an environment in the manufacturing service collaboration system, G is a group in the manufacturing service collaboration system, S0Is the initial state of the manufacturing service collaboration system, H is the limited set of users in the manufacturing service collaboration system;
in a cloud manufacturing environment, acquiring E, C, O, R, A and G elements needing attention from a nine-tuple, and obtaining a simplified nine-tuple:
∑::＝<C′、O′、A′、R′、E′、G′>，
wherein E ' represents a set of plans and recommendations for a cloud manufacturing service, C ' represents a set of classes defined by abstract concepts associated with E ', O ' represents a set of concrete objects associated with C ', R ' represents a set of task types in cloud manufacturing, and A ' represents a set of services in cloud manufacturing.
As a further scheme of the invention: the acquisition module further comprises:
by means of the formula (I) and (II),
Figure GDA0003071646650000071
three numerical features of the cloud model are computed, wherein,
ex representation set
Figure GDA0003071646650000072
σ is the standard deviation of Ex, S2Is the sample variance of Ex, N represents the set
Figure GDA0003071646650000081
The number of middle elements;
characterizing a set of three numerical features using the formula cm ═ Ex, En, He };
using the formula si0＝{λi0 1，λi0 2...λi0 βCalculating candidate services si0And substituting the three numerical characteristic calculation formulas and cm ═ Ex, En, He into the candidate service si0In (1), obtaining:
Figure GDA0003071646650000082
wherein s isi0Represents the i0 th service in the service candidate set, i0 is 1,2 … m, and m is a positive integer;
si0＝{λi0 1，λi0 2...λi0 βdenotes candidate service si0The historical QoS records of (a) are combined by task type, lambdai0 αRepresenting candidate services si0For QoS set of α -type task, α ═ 1,2,3, …, β; beta represents a positive integer; lambda [ alpha ]i0 α＝{qi0，1 α，qi0，2 α…qi0，p αP denotes candidate service si0The number of sub-tasks of the alpha type completed,
Figure GDA0003071646650000083
representing candidate services si0QoS evaluation for the g-th subtask belonging to the α type, g ═ 1,2,3, …, p;
using the formula RSS ═ s1、s2、...smCalculating a candidate service set RSS; will be provided with
Figure GDA0003071646650000084
si0＝{λi0 1，λi0 2...λi0 βSubstituting RSS into s1、s2、...smIn (b), the following was obtained:
Figure GDA0003071646650000085
wherein, cmm，βRepresenting three sets of digital features in a beta-type task in the mth service;
in the candidate service set RSS:
using formulas
Figure GDA0003071646650000091
Acquiring three digital characteristics under the optimal condition;
by means of the formula (I) and (II),
Figure GDA0003071646650000092
acquiring three digital characteristics under the worst condition;
wherein, cm+Representing the set of three numerical features, cm, in the most ideal case-Represents a worst case set of three numerical features; exi0 βRepresents the i0 th expectation in the beta type task,
Figure GDA0003071646650000093
represents the i0 th entropy, He, in the beta type taski0 βRepresents the i0 th super entropy in the beta type task;
using Euclidean distance
Figure GDA0003071646650000094
Computing candidate services si0For cm in alpha type task+And cm-The similarity of (2);
wherein X represents a set of elements, Y represents a set of elements, and Z represents a positive integer; x is the number ofioRepresents any one element of the set X, yi0Represents any one element in the set Y;
by means of the formula (I) and (II),
Figure GDA0003071646650000095
computing candidate services si0The degree of competence for the alpha-type task, wherein,
Figure GDA0003071646650000096
value of [0, 1]]In the above-mentioned manner,
Figure GDA0003071646650000097
as candidate service si0Obtaining candidate service s for the competence degree of alpha type taski0The competence for any type of task.
As a further scheme of the invention: a target function module 303, for obtaining a target function of the E-CARGO model;
the objective function module 303 further includes:
using formulas
Figure GDA0003071646650000098
Obtaining an objective function of the E-CARGO model,
the constraints of the objective function include:
T[i][j]∈{0,1}(0≤i＜m)；
Figure GDA0003071646650000101
Figure GDA0003071646650000102
C[i1，i2]×(T[i1，j]+T[i2，j])≤1，(0≤i1，i2＜m，i1≠i2，)；
C[i1，i2]×(T[i1，j1]+T[i2，j2])≤1，(0≤i1，i2＜m，i1≠i2，0≤j1,j2＜n)；
wherein j represents a task type, which is called task type j for short, the number of the task types j is more than or equal to zero and less than n, and n is a positive integer; j is a function of1、j2Representing two different positive numbers of n, i representing a service, abbreviated as service i, i1And i2Two different services in the representative service i, which are respectively called the first service i for short1A second service i2；
Task lower bound vector L: l (j) represents the number of services that must be allocated to task type j, L [ j ] ═ 1 represents that one service i is allocated to task type j, L [ j ] ═ 0 represents that no service i is allocated to task type j, and L [ j ] > 1 represents that multiple services i are allocated to task type j, that is, the task type j includes multiple subtasks;
qualification matrix Q: q is an m multiplied by n matrix, m represents the number of the service i, Q [ i, j ] represents the competence degree of the service i to the task type j, 0 is the lowest value, and 1 is the highest value; in an embodiment, Q [ i, j ] is an eligibility assessment value, and the larger the value of Q [ i, j ], the more qualified the service i is for the task type j;
task allocation matrix T: t is an m × n matrix, m represents the number of services, T [ i, j ] represents whether a service i is allocated to a task type j, and when T [ i, j ] is 1, it represents that the service i is allocated to the task type j, and the service i at this time is an allocated service; t [ i, j ] ═ 0, meaning that service i is not assigned to task type j;
a collision matrix C: the dimension size is m × m, m representing the number of services i, Cj1][i2]Representing a first service i1And a second service i2Presence or absence of conflict, Ci1][i2]Belongs to {0, 1}, Cj1][i2]1 denotes a first service i1And a second service i2Conflict, tasks cannot be completed in the same group; c [ i ]1][i2]0 denotes a first service i1And a second service i2May cooperate within the same group;
preference vector P: p [ i ] represents preference degree of service i corresponding to any task type, and belongs to [ -0.5,0.5] (i is more than or equal to 0 and less than m); p [ i ] < 0 indicates that any type of task has no preference for service i, P [ i ] < 0 indicates that any type of task has a negative preference for service i, and P [ i ] > 0 indicates that any type of task has a positive preference for service i.
As a further scheme of the invention: the solving module further comprises:
each type of task alpha can be used as Role in an E-CARGO model, namely corresponding to a task type set in cloud manufacturing; candidate service set RSS ═ s1,s2,…,smThe E-CARGO model is an Agent in the E-CARGO model and corresponds to a service set in the cloud manufacturing service; each Agent has an qualification value for each Role, and the qualification values Q [ i ] of the service set and the task type in the corresponding cloud manufacturing service][j]Will be
Figure GDA0003071646650000111
Value as Q [ i][j]A value of (d); first calculate Q [ i][j]*(1+P[i]) By the value of QT [ i][j]To characterize Q [ i][j]*(1+P[i]) Namely: QT [ i][j]＝Q[i][j]*(1+P[i])；P[i]Representing the preference degree of the task to the service, and representing multiplication; QT [ i][j]Substituting the target function, and solving a task allocation matrix T by using a calculation tool, wherein the task allocation matrix T is the optimal service combination scheme.
Compared with the prior art, the invention has the beneficial effects that:
1. the method is based on the E-CARGO model and combines a cloud manufacturing scene, the task allocation matrix T is obtained through calculation, the optimal service combination scheme is obtained, the satisfaction degree of a user is improved, and the resource waste is reduced.
2. According to the invention, idle resources of enterprises or individuals are virtualized to the cloud platform by establishing the cloud manufacturing service platform, so that the sharing of the idle resources is realized, and the E-CARGO model is applied to a cloud manufacturing scene for the first time.
3. Aiming at the characteristic that task types in cloud manufacturing present diversity, the QoS cloud model is adopted to convert QoS of services for various types of tasks into three cloud digital characteristics, and the similarity between the services is measured by using Euclidean distance.
4. The invention adopts CPLEX to solve, and the solver can obtain the optimal solution in a limited data range aiming at the NP-hard problem. We therefore rely on QoS history data and the service combinations derived from it are the optimal solution.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention.
Fig. 1 is a schematic flow chart of a cloud manufacturing service combination method based on an E-carmo model according to embodiment 1 of the present invention.
Fig. 2 is a schematic structural diagram of a cloud manufacturing service combination device based on an E-carmo model according to embodiment 1 of the present invention.
Detailed Description
In order to make the technical problems, technical solutions and advantageous effects to be solved by the present invention more clearly apparent, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Example 1
In the embodiment of the present invention, fig. 1 is a schematic flow chart of a cloud manufacturing service combination method based on an E-carmo model according to the embodiment of the present invention, and as shown in fig. 1, a cloud manufacturing service combination method based on an E-carmo model includes the following steps:
s1, obtaining a nine-tuple, and simplifying the nine-tuple according to the elements concerned by the needs in the cloud manufacturing service environment;
the E-CARGO model is represented as a nine-tuple as follows:
∑::＝<C、O、A、S、R、E、G、s0、H>，
wherein C represents Class (Class) in the manufacturing service collaboration system, O represents Object (Object) in the manufacturing service collaboration system, A is Agent (Agent) in the manufacturing service collaboration system, S is Message (Message) in the manufacturing service collaboration system, R is Role (Role) in the manufacturing service collaboration system, E is Environment (Environment) in the manufacturing service collaboration system, G is Group (Group) in the manufacturing service collaboration system, S is Object in the manufacturing service collaboration system, A is Agent (Agent) in the manufacturing service collaboration system, S is Object in the manufacturing service collaboration system, S is Object in the manufacturing service collaboration system, S is Object in the manufacturing service collaboration system, and the manufacturing service in the manufacturing service0Is the initial state of the manufacturing service collaboration system, H is the limited set of users in the manufacturing service collaboration system;
in the cloud manufacturing environment, the elements we are primarily concerned with are E, C, O, R, a, G; therefore, in the nine-tuple, the elements needing attention are reserved, the elements not needing attention are removed, and the nine-tuple is converted into:
∑::＝<C′、O′、A′、R′、E′、G′>，
also, in a manufacturing environment, E ' represents a set of plans and recommendations for a cloud manufacturing service, and C ' represents a set of classes defined by abstract concepts associated with E '. O 'represents a specific group of objects related to C', R 'represents a task type set in cloud manufacturing, and A' represents a service set in cloud manufacturing;
to obtain the optimal set we need to assign the appropriate service to the appropriate task;
s2, in the cloud manufacturing service combination system, the tasks submitted by the users comprise different subtasks and different task types, and the efficiency and the cost for completing different types of tasks by different services are different; in order to accurately measure the QoS (Quality of Service), a QoS cloud model theory is introduced; the cloud has two generators, namely a forward cloud generator and a reverse cloud generator, wherein the reverse cloud generator is adopted; the cloud has three numerical features: expectation (Ex), entropy (En), super entropy (He).
Using formulas
Figure GDA0003071646650000141
Calculating three digital characteristics of the cloud model;
wherein Ex is the value that QoS has the most representatives, En represents the granularity scale of QoS, and He describes the uncertainty of QoS granularity; ex representation set
Figure GDA0003071646650000142
σ is the standard deviation of Ex, S2Is the sample variance of Ex, N represents the set
Figure GDA0003071646650000143
The number of the middle elements and the size of the Ex value measure the candidate service si0For the competence degree of the alpha type task, the larger the value is, the better the value is; the values of En and He measure the set
Figure GDA0003071646650000144
The fluctuation of the median, smaller values representing candidate services si0The more stable the QoS for the alpha type task; lambda [ alpha ]i0 αRepresenting candidate services si0For QoS aggregation of alpha type tasks, λi0 α＝{qi0，1 α，qi0，2 α...qi0，p αP denotes candidate service si0The number of sub-tasks of the alpha type completed,
Figure GDA0003071646650000145
representing candidate services si0QoS evaluation for the g-th subtask belonging to the α type, g ═ 1,2,3, …, p;
characterizing a set of three digital features with the formula cm ═ { Ex, En, He };
for example
Figure GDA0003071646650000146
The corresponding three numerical features are cmi0,α＝{0.29,0.23,0.15}；
Then, using the formula si0＝{λi0 1，λi0 2...λi0 βCalculating candidate services si0And substituting the three numerical characteristic calculation formulas and cm ═ Ex, En, He into the candidate service si0In (1), obtaining:
Figure GDA0003071646650000147
wherein s isi0Represents the i0 th service in the service candidate set, i0 is 1, 2.. m, m is a positive integer; si0＝{λi0 1，λi0 2...λi0 βDenotes candidate service si0The historical QoS records of (a) are combined by task type, lambdai0 αRepresenting candidate services si0For QoS set of α -type task, α ═ 1,2,3, …, β; beta represents a positive integer;
using the formula RSS ═ s1、s2、...smCalculating a candidate service set RSS;
cause si0As an element in the candidate service set RSS, i0 ═ 1, 2.. m; so will
Figure GDA0003071646650000151
si0＝{λi0 1，λi0 2...λi0 βSubstituting RSS into s1、s2、...smIn (b), the following was obtained:
Figure GDA0003071646650000152
wherein, cmm，βRepresenting three sets of numerical features in the class beta task in the mth service,
if the candidate service si0If the sub-task of alpha type is not completed, the set lambda isi0 αQoS in (1) is 0, i.e. cmm,α＝{0,0,0}；
In the candidate service set RSS:
the most ideal case is to take the maximum expected Ex, minimum entropy En and minimum hyper-entropy He, the most desirable within the RSS represented by the three digital featuresIdeally three digital features cm+= Ex, En, He may be defined as:
Figure GDA0003071646650000153
wherein, cm+Representing the set of three numerical characteristics in the most ideal case, max { Ex }i0 αDenotes taking the largest EX, min { En } in the candidate service set RSSi0 αRepresents taking the smallest En, min { He } in the candidate service set RSSi0 αExpressing the minimum He in the RSS of the candidate service set;
the worst case is to take the minimum expected Ex, the maximum entropy En and the maximum super entropy He within the RSS represented by the three digital characteristics; the worst case three numerical characteristics are defined as:
Figure GDA0003071646650000161
wherein, cm-Represents the worst case set of three numerical features, min { Ex }i0 αDenotes taking the smallest EX, max En in the candidate service set RSSi0 αDenotes taking the largest En, max { He in the candidate service set RSSi0 αThe maximum He in the RSS of the candidate service set is taken;
in order to select proper service for the task, it is crucial to identify the difference between the service QoS by calculating the similarity between QoS cloud models, and the Euclidean distance formula is adopted
Figure GDA0003071646650000162
To calculate the similarity; wherein X represents a set of elements, Y represents a set of elements, and Z represents a positive integer; x is the number ofioRepresents any one element of the set X, yi0Represents any one element in the set Y;
using formulas
Figure GDA0003071646650000163
Computing candidate services si0Competence for alpha type tasks;
Figure GDA0003071646650000164
is a [0, 1]]In the above-mentioned manner,
Figure GDA0003071646650000165
the larger the value of (A), the service s is representedi0Obtaining candidate service s when the integrating degree of alpha type task is higheri0The competence for any type of task.
S3, obtaining a target function of the E-CARGO model;
by means of the formula (I) and (II),
Figure GDA0003071646650000166
obtaining an objective function of the E-CARGO model,
the constraints of the objective function include:
T[i][j]∈{0,1}(0≤i＜m)；
Figure GDA0003071646650000171
Figure GDA0003071646650000172
C[i1，i2]×(T[i1，j]+T[i2，j])≤1，(0≤i1，i2＜m，i1≠i2，)；
C[i1，i2]×(T[i1，j1]+T[i2，j2])≤1，(0≤i1，i2＜m，i1≠i2，0≤j1,j2＜n)；
wherein j represents a task type, which is called task type j for short, the number of the task types j is more than or equal to zero and less than n, and n is a positive integer; j is a function of1、j2Represents two different positive numbers of n,i stands for service, abbreviated as service i, i1And i2Two different services in the representative service i, which are respectively called the first service i for short1A second service i2；
Task lower bound vector L: l (j) represents the number of services that must be allocated to task type j, L [ j ] ═ 1 represents that one service i is allocated to task type j, L [ j ] ═ 0 represents that no service i is allocated to task type j, and L [ j ] > 1 represents that multiple services i are allocated to task type j, that is, the task type j includes multiple subtasks;
qualification matrix Q: q is an m multiplied by n matrix, m represents the number of the service i, Q [ i, j ] represents the competence degree of the service i to the task type j, 0 is the lowest value, and 1 is the highest value; in an embodiment, Q [ i, j ] is an eligibility assessment value, and the larger the value of Q [ i, j ], the more qualified the service i is for the task type j;
task allocation matrix T: t is an m × n matrix, m represents the number of services, T [ i, j ] represents whether a service i is allocated to a task type j, and when T [ i, j ] is 1, it represents that the service i is allocated to the task type j, and the service i at this time is an allocated service; t [ i, j ] ═ 0, meaning that service i is not assigned to task type j;
a collision matrix C: the dimension size is m × m, m representing the number of services i, Cj1][i2]Representing a first service i1And a second service i2Presence or absence of conflict, Ci1][i2]Belongs to {0, 1}, Cj1][i2]1 denotes a first service i1And a second service i2Conflict, tasks cannot be completed in the same group; c [ i ]1][i2]0 denotes a first service i1And a second service i2May cooperate within the same group;
preference vector P: p [ i ] represents preference degree of service i corresponding to any task type, and belongs to [ -0.5,0.5] (i is more than or equal to 0 and less than m); p [ i ] < 0 indicates that any type of task has no preference for the service i, P [ i ] < 0 indicates that any type of task has negative preference for the service i, and P [ i ] > 0 indicates that any type of task has positive preference for the service i;
s4, taking the task allocation matrix T as an optimal service combination scheme;
in the tasks submitted by the user, each type of task alpha can be used as a role in an E-CARGO model, namely corresponding to a task type set in cloud manufacturing; candidate service set RSS ═ s1,s2,…,smThe cloud manufacturing service is an agent in an E-CARGO model and corresponds to a service set in the cloud manufacturing service; each agent has an qualification value for each role, and the qualification values Q [ i ] of the service set and the task type in the corresponding cloud manufacturing service][j]Will be
Figure GDA0003071646650000181
Value as Q [ i][j]A value of (d);
calculating the value of Q [ i ] [ j ] (1+ P [ i ]), and expressing Q [ i ] [ j ] (1+ P [ i ]) by QT [ i ] [ j ], namely:
QT[i][j]＝Q[i][j]*(1+P[i])；
wherein, P [ i ] represents the preference degree of the task to the service, and represents multiplication; through QT [ i ] [ j ], the simplification of a target function can be realized, and the calculation is more convenient;
and substituting QT [ i ] [ j ] into the objective function, substituting into the constraint condition of the objective function, and solving the numerical value of max [ rho ] and a task allocation matrix T by using a calculation tool, wherein the task allocation matrix T is the optimal service combination scheme.
The calculation tool adopted in the embodiment is IBM ILOG CPLEX, which is a high-performance mathematical programming problem solver of IBM company, and the solver can obtain an optimal solution in a limited data range aiming at NP-hard problems, and can quickly and stably solve a series of programming problems such as linear programming, mixed integer programming, quadratic programming and the like; IBM ILOG CPLEX Optimization Studio has fast execution speed, its own language is simple and easy to understand, and is compatible with many optimized software and languages (interfaces with C + +, JAVA, EXCEL, Matlab, etc.).
Fig. 2 is a schematic structural diagram of a cloud manufacturing service combination device based on an E-carmo model according to an embodiment of the present invention, and as shown in fig. 2, a combination device of a cloud manufacturing service combination method based on an E-carmo model includes:
a nine-tuple module 301, configured to obtain a simplified nine-tuple in a cloud manufacturing service environment;
the nine-tuple module 301 further comprises: the E-CARGO model is represented as a nine-tuple as follows:
∑::＝<C、O、A、S、R、E、G、s0、H>，
where C represents a class in the manufacturing service collaboration system, O represents an object in the manufacturing service collaboration system, A is an agent in the manufacturing service collaboration system, S is a message in the manufacturing service collaboration system, R is a role in the manufacturing service collaboration system, E is an environment in the manufacturing service collaboration system, G is a group in the manufacturing service collaboration system, S0Is the initial state of the manufacturing service collaboration system, H is the limited set of users in the manufacturing service collaboration system;
in a cloud manufacturing environment, acquiring E, C, O, R, A and G elements needing attention from a nine-tuple, and obtaining a simplified nine-tuple:
∑::＝<C′、O′、A′、R′、E′、G′>，
wherein E ' represents a set of plans and recommendations for a cloud manufacturing service, C ' represents a set of classes defined by abstract concepts associated with E ', O ' represents a set of concrete objects associated with C ', R ' represents a set of task types in cloud manufacturing, and A ' represents a set of services in cloud manufacturing.
The obtaining module 302 is used for obtaining the submitted task, the candidate service set and the candidate service of the service requester and calculating the competence degree of the candidate service to any type of task;
the obtaining module 302 further includes:
by means of the formula (I) and (II),
Figure GDA0003071646650000201
three numerical features of the cloud model are computed, wherein,
ex representation set
Figure GDA0003071646650000202
σ is the standard deviation of Ex, S2Is a sample side of ExDifference, N represents set
Figure GDA0003071646650000203
The number of middle elements; lambda [ alpha ]i0 αRepresenting candidate services si0For QoS aggregation of alpha type tasks, λi0 α＝{qi0，1 α，qi0，2 α...qi0，p αP denotes candidate service si0The number of sub-tasks of the alpha type completed,
Figure GDA0003071646650000204
representing candidate services si0QoS evaluation for the g-th subtask belonging to the α type, g ═ 1,2,3, …, p;
characterizing a set of three numerical features using the formula cm ═ Ex, En, He };
reuse formula
Figure GDA0003071646650000205
Computing candidate services si0And substituting the three numerical characteristic calculation formulas and cm ═ Ex, En, He into the candidate service si0In (1), obtaining:
Figure GDA0003071646650000206
wherein s isi0Represents the i0 th service in the service candidate set, i0 is 1, 2.. m, m is a positive integer;
si0＝{λi0 1，λi0 2...λi0 βdenotes candidate service si0The historical QoS records are combined according to task types;
using the formula RSS ═ s1、s2、...smCalculating a candidate service set RSS; will be provided with
Figure GDA0003071646650000207
si0＝{λi0 1，λi0 2...λi0 βSubstituting RSS into s1、s2、...smIn (b), the following was obtained:
Figure GDA0003071646650000211
wherein, cmm，βRepresenting three sets of digital features in a beta-type task in the mth service;
in the RSS candidate service set(s),
using formulas
Figure GDA0003071646650000212
Acquiring three digital characteristics under the optimal condition;
by means of the formula (I) and (II),
Figure GDA0003071646650000213
acquiring three digital characteristics under the worst condition;
wherein, cm+Representing the set of three numerical characteristics in the most ideal case, max { Ex }i0 αDenotes taking the largest EX, min { En } in the candidate service set RSSi0 αRepresents taking the smallest En, min { He } in the candidate service set RSSi0 αExpressing the minimum He in the RSS of the candidate service set;
cm-represents the worst case set of three numerical features, min { Ex }i0 αDenotes taking the smallest EX, max En in the candidate service set RSSi0 αDenotes taking the largest En, max { He in the candidate service set RSSi0 αThe maximum He in the RSS of the candidate service set is taken;
using Euclidean distance
Figure GDA0003071646650000214
Computing candidate services si0For cm in alpha type task+And cm-The degree of similarity of (a) to (b),
wherein X represents a set of elements, Y represents a set of elements, and Z represents a positive integer;xiorepresents any one element of the set X, yi0Represents any one element in the set Y;
by means of the formula (I) and (II),
Figure GDA0003071646650000215
computing candidate services si0The degree of competence for the alpha-type task, wherein,
Figure GDA0003071646650000221
value of [0, 1]]In the above-mentioned manner,
Figure GDA0003071646650000222
as candidate service si0Obtaining candidate service s for the competence degree of alpha type taski0The competence for any type of task.
A target function module 303, for obtaining a target function of the E-CARGO model;
the objective function module 303 further includes:
by means of the formula (I) and (II),
Figure GDA0003071646650000223
obtaining an objective function of the E-CARGO model,
the constraints of the objective function include:
T[i][j]∈{0,1}(0≤i＜m)；
Figure GDA0003071646650000224
Figure GDA0003071646650000225
C[i1，i2]×(T[i1，j]+T[i2，j])≤1，(0≤i1，i2＜m，i1≠i2，)；
C[i1，i2]×(T[i1，j1]+T[i2，j2])≤1，(0≤i1，i2＜m，i1≠i2，0≤j1,j2＜n)；
wherein j represents a task type, which is called task type j for short, the number of the task types j is more than or equal to zero and less than n, and n is a positive integer; j is a function of1、j2Representing two different positive numbers of n, i representing a service, abbreviated as service i, i1And i2Two different services in the representative service i, which are respectively called the first service i for short1A second service i2；
Task lower bound vector L: l (j) represents the number of services that must be allocated to task type j, L [ j ] ═ 1 represents that one service i is allocated to task type j, L [ j ] ═ 0 represents that no service i is allocated to task type j, and L [ j ] > 1 represents that multiple services i are allocated to task type j, that is, the task type j includes multiple subtasks;
qualification matrix Q: q is an m multiplied by n matrix, m represents the number of the service i, Q [ i, j ] represents the competence degree of the service i to the task type j, 0 is the lowest value, and 1 is the highest value; in an embodiment, Q [ i, j ] is an eligibility assessment value, and the larger the value of Q [ i, j ], the more qualified the service i is for the task type j;
task allocation matrix T: t is an m × n matrix, m represents the number of services, T [ i, j ] represents whether a service i is allocated to a task type j, and when T [ i, j ] is 1, it represents that the service i is allocated to the task type j, and the service i at this time is an allocated service; t [ i, j ] ═ 0, meaning that service i is not assigned to task type j;
a collision matrix C: the dimension size is m × m, m representing the number of services i, Cj1][i2]Representing a first service i1And a second service i2Presence or absence of conflict, Ci1][i2]Belongs to {0, 1}, Cj1][i2]1 denotes a first service i1And a second service i2Conflict, tasks cannot be completed in the same group; c [ i ]1][i2]0 denotes a first service i1And a second service i2May cooperate within the same group;
preference vector P: p [ i ] represents preference degree of service i corresponding to any task type, and belongs to [ -0.5,0.5] (i is more than or equal to 0 and less than m); p [ i ] < 0 indicates that any type of task has no preference for the service i, P [ i ] < 0 indicates that any type of task has negative preference for the service i, and P [ i ] > 0 indicates that any type of task has positive preference for the service i;
a solving module 304, which comprises solving an objective function to obtain a distribution matrix T; taking the task allocation matrix T as an optimal service combination scheme;
the solving module 304 further comprises:
set Γ ═ T1,T2,…,TnEach element in the cloud manufacturing system is used as a role in an E-CARGO model, namely corresponding to a task type set in cloud manufacturing; candidate service set RSS ═ s1,s2,…,smThe E-CARGO model is an agent in the E-CARGO model, namely a service set in the corresponding cloud manufacturing; each agent has an eligibility rating Q [ i ] for each role][j]Will obtain
Figure GDA0003071646650000241
Value as Q [ i][j]According to Q [ i ]][j]The eligibility evaluation matrix Q may be obtained.
Calculating the value of Q [ i ] [ j ] (1+ pi ]), and setting QT [ i ] [ j ] ═ Q [ i ] [ j ] (1+ pi ]); wherein P [ i ] represents the preference degree of the task to the service, and represents multiplication;
and substituting QT [ i ] [ j ] into the objective function, and calculating the objective function by using a calculation tool to obtain a distribution matrix T and obtain an optimal service combination scheme.
Example 2
In machine manufacturing, a large number of common parts are used, which can be divided into many types, where we choose four types: shafts, discs, gears and boxes. The shaft comprises a hollow shaft, an optical shaft and a stepped shaft, the disc comprises a flange plate, a bearing cover and a brake disc, the gear comprises a cylindrical gear, a straight gear and an external gear, and the box comprises a main shaft box and a reduction box.
In a cloud manufacturing service system, if a certain enterprise needs a batch of parts, a task is submitted: the shaft part comprises: one hollow shaft; disc type parts: one brake disc, gear parts: column gear and external gear each, box class part: one reduction box.
According to the tasks submitted by the users, dividing the tasks into a plurality of subtasks, wherein the number of the candidate services and the number of the task types do not need to be equal, and the cloud manufacturing service platform selects a candidate service set as follows:
RSS＝{s1,s2,s3,s4,s5,s6}; respectively representing a set of first to sixth services;
any one of the candidate services si0The historical QoS record of can be expressed as s according to the task type combinationi0＝{λi0 1，λi0 2...λi0 6Obtaining the task of each type of each candidate service by adopting a reverse cloud generator
Figure GDA0003071646650000242
A value;
solving an objective function, wherein conflicts may exist among services in the cloud manufacturing resources, and a conflict matrix is constructed according to the actual situation, wherein the conflict matrix C is as follows:
Figure GDA0003071646650000251
task preference vector P [6] ═ 0,0.2,0,0, -0.2, -0.4;
the lower task bound vector L [5] ═ 1,1,2, 1;
will obtain
Figure GDA0003071646650000252
Value as Q [ i][j]To obtain a Q matrix:
Figure GDA0003071646650000253
QT [ i ] [ j ] ═ Q [ i ] [ j ] (1+ pi ]), resulting in a QT matrix:
Figure GDA0003071646650000254
calculating an objective function by using a calculation tool, wherein max ρ is 1.47, and a task allocation matrix T is as follows:
Figure GDA0003071646650000261
therefore, the best service combination scheme is S1，S5，{S2，S6}，S4And the tasks are respectively allocated to shaft tasks, disc tasks, gear tasks and box tasks.
In the description of the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.

Claims (8)

1. A cloud manufacturing service combination method based on an E-CARGO model is characterized by comprising the following steps:
s1, obtaining a nine-tuple, and simplifying the nine-tuple according to the elements concerned by the needs in the cloud manufacturing service environment;
s2, acquiring the competence degree of the candidate service to any type of task;
by means of the formula (I) and (II),
Figure FDA0003071646640000011
three numerical features of the cloud model are computed, wherein,
ex representation set
Figure FDA0003071646640000012
σ is the standard deviation of Ex, S2Is the sample variance of Ex, N represents the set
Figure FDA0003071646640000013
The number of middle elements; lambda [ alpha ]i0 αRepresenting candidate services si0For QoS aggregation of alpha type tasks, λi0 α＝{qi0，1 α，qi0，2 α...qi0，p αP denotes candidate service si0The number of sub-tasks of the alpha type completed,
Figure FDA0003071646640000014
representing candidate services si0QoS evaluation for the g-th subtask belonging to the α type, g ═ 1,2,3, …, p;
characterizing a set of three numerical features using the formula cm ═ Ex, En, He };
reuse of formula si0＝{λi0 1，λi0 2...λi0 βCalculating candidate services si0And substituting the three numerical characteristic calculation formulas and cm ═ Ex, En, He into the candidate service si0In (1), obtaining:
Figure FDA0003071646640000015
wherein s isi0Represents the i0 th service in the service candidate set, i0 is 1, 2.. m, m is a positive integer;
si0＝{λi0 1，λi0 2...λi0 βdenotes candidate service si0The historical QoS records are combined according to task types;
using the formula RSS ═ s1、s2、…smCalculating a candidate service set RSS; s1 、 s2 、… smRepresents the 1 st candidate service, the 2 nd candidate service … the m < th > candidate service
Figure FDA0003071646640000021
si0＝{λi0 1，λi0 2...λi0 βSubstituting RSS into s1、s2、...smIn (b), the following was obtained:
Figure FDA0003071646640000022
wherein, cmm，βRepresenting three sets of digital features in a beta-type task in the mth service;
in the RSS candidate service set(s),
using formulas
Figure FDA0003071646640000023
Acquiring three digital characteristics under the optimal condition;
by means of the formula (I) and (II),
Figure FDA0003071646640000024
acquiring three digital characteristics under the worst condition;
wherein, cm+Representing the set of three numerical characteristics in the most ideal case, max { Ex }i0 αDenotes taking the largest EX, min { En } in the candidate service set RSSi0 αRepresents taking the smallest En, min { He } in the candidate service set RSSi0 αExpressing the minimum He in the RSS of the candidate service set;
cm-represents the worst case set of three numerical features, min { Ex }i0 αDenotes taking the smallest EX, max En in the candidate service set RSSi0 αDenotes taking the largest En, max { He in the candidate service set RSSi0 αThe maximum He in the RSS of the candidate service set is taken;
using Euclidean distance
Figure FDA0003071646640000031
Computing candidate services si0For cm in alpha type task+And cm-The degree of similarity of (a) to (b),
wherein X represents cloud digital feature cm, and Y represents cloud digital feature cm under ideal conditions+And cm-Z represents a positive integer; x is the number ofioRepresents any one element of the set X, yi0Represents any one element in the set Y;
by means of the formula (I) and (II),
Figure FDA0003071646640000032
computing candidate services si0The degree of competence for the alpha-type task, wherein,
Figure FDA0003071646640000033
value of [0, 1]]In the above-mentioned manner,
Figure FDA0003071646640000034
as candidate service si0Obtaining candidate service s for the competence degree of alpha type taski0The competency level for any type of task;
s3, obtaining a target function of the E-CARGO model;
s4, solving an objective function to obtain a task allocation matrix T; and taking the task allocation matrix T as an optimal service combination scheme.
2. The cloud manufacturing service composition method based on the E-CARGO model according to claim 1, wherein the step S1 comprises:
the E-CARGO model is represented as a nine-tuple as follows:
∑::＝<C、O、A、S、R、E、G、s0、H>，
where C represents a class in the manufacturing service collaboration system, O represents an object in the manufacturing service collaboration system, A is an agent in the manufacturing service collaboration system, S is a message in the manufacturing service collaboration system, R is a role in the manufacturing service collaboration system, E is an environment in the manufacturing service collaboration system, G is a group in the manufacturing service collaboration system, S0Is the initial state of the manufacturing service collaboration system, H is the limited set of users in the manufacturing service collaboration system;
in a cloud manufacturing environment, acquiring E, C, O, R, A and G elements needing attention from a nine-tuple, and simplifying to obtain:
∑::＝<C′、O′、A′、R′、E′、G′>，
wherein E ' represents a set of plans and recommendations for a cloud manufacturing service, C ' represents a set of classes defined by abstract concepts associated with E ', O ' represents a set of concrete objects associated with C ', R ' represents a set of task types in cloud manufacturing, and A ' represents a set of services in cloud manufacturing.
3. The cloud manufacturing service composition method based on the E-CARGO model according to claim 1, wherein the step S3 comprises:
by means of the formula (I) and (II),
Figure FDA0003071646640000041
obtaining an objective function of the E-CARGO model,
the constraints of the objective function include:
T[i][j]∈{0,1}，0≤i＜m；
Figure FDA0003071646640000042
Figure FDA0003071646640000043
C[i1，i2]×(T[i1，j]+T[i2，j])≤1，0≤i1，i2＜m，i1≠i2；
C[i1，i2]×(T[i1，j1]+T[i2，j2])≤1，0≤i1，i2＜m，i1≠i2，0≤j1,j2＜n；
wherein j represents a task type, which is called task type j for short, the number of the task types j is more than or equal to zero and less than n, and n is a positive integer; j is a function of1、j2Representing two different positive numbers of n, i representing a service, abbreviated as service i, i1And i2Two different services in the representative service i, which are respectively called the first service i for short1A second service i2；
Task lower bound vector L: l (j) represents the number of services that must be allocated to task type j, L [ j ] ═ 1 represents that one service i is allocated to task type j, L [ j ] ═ 0 represents that no service i is allocated to task type j, and L [ j ] > 1 represents that multiple services i are allocated to task type j, that is, the task type j includes multiple subtasks;
qualification matrix Q: q is an m multiplied by n matrix, m represents the number of the service i, Q [ i, j ] represents the competence degree of the service i to the task type j, 0 is the lowest value, and 1 is the highest value; in an embodiment, Q [ i, j ] is an eligibility assessment value, and the larger the value of Q [ i, j ], the more qualified the service i is for the task type j;
task allocation matrix T: t is an m × n matrix, m represents the number of services, T [ i, j ] represents whether a service i is allocated to a task type j, and when T [ i, j ] is 1, it represents that the service i is allocated to the task type j, and the service i at this time is an allocated service; t [ i, j ] ═ 0, meaning that service i is not assigned to task type j;
a collision matrix C: the dimension size is m × m, m representing the number of services i, Cj1][i2]Representing a first service i1And a second service i2Presence or absence of conflict, Ci1][i2]Belongs to {0, 1}, Cj1][i2]1 denotes a first service i1And a second service i2Conflict, cannot be in the same placeThe group completes the task; c [ i ]1][i2]0 denotes a first service i1And a second service i2May cooperate within the same group;
preference vector P: p [ i ] represents the preference degree of the service i corresponding to any task type, wherein P [ i ] belongs to [ -0.5,0.5], and i is more than or equal to 0 and less than m; p [ i ] < 0 indicates that any type of task has no preference for service i, P [ i ] < 0 indicates that any type of task has a negative preference for service i, and P [ i ] > 0 indicates that any type of task has a positive preference for service i.
4. The cloud manufacturing service composition method based on the E-CARGO model according to claim 1, wherein the step S4 comprises:
each type of task alpha is a role in the E-CARGO model, and the candidate service set RSS is { s }1,s2,…,smThe cloud manufacturing service system is characterized in that the agents in the E-CARGO model are provided with qualification evaluation values for each role, and the qualification evaluation values Q [ i ] corresponding to service sets and task types in the cloud manufacturing service][j]Will be
Figure FDA0003071646640000051
Value as Q [ i][j]The value of (a) is,
Figure FDA0003071646640000061
as candidate service si0Competence for alpha type tasks; first calculate Q [ i][j]*(1+P[i]) By the value of QT [ i][j]To characterize Q [ i][j]*(1+P[i]) Namely: QT [ i][j]＝Q[i][j]*(1+P[i])；P[i]Representing the preference degree of the task to the service, and representing multiplication; QT [ i][j]Substituting the target function, and solving a task allocation matrix T by using a calculation tool, wherein the task allocation matrix T is the optimal service combination scheme.
5. A combination device based on the E-CARGO model-based cloud manufacturing service combination method of any one of claims 1-4, comprising:
the nine-tuple module (301) is used for acquiring nine tuples and simplifying the nine tuples according to the elements which need to be concerned in the cloud manufacturing service environment;
the acquisition module (302) is used for acquiring the submitted task, the candidate service set and the candidate service of the service requester and calculating the competence degree of the candidate service to any type of task; further comprising:
by means of the formula (I) and (II),
Figure FDA0003071646640000062
three numerical features of the cloud model are computed, wherein,
ex representation set
Figure FDA0003071646640000063
σ is the standard deviation of Ex, S2Is the sample variance of Ex, N represents the set
Figure FDA0003071646640000064
The number of middle elements; lambda [ alpha ]i0 αRepresenting candidate services si0For QoS aggregation of alpha type tasks, λi0 α＝{qi0，1 α，qi0，2 α…qi0，p αP denotes candidate service si0The number of sub-tasks of the alpha type completed,
Figure FDA0003071646640000065
representing candidate services si0QoS evaluation for the g-th subtask belonging to the α type, g ═ 1,2,3, …, p;
characterizing a set of three numerical features using the formula cm ═ Ex, En, He };
reuse of formula si0＝{λi0 1，λi0 2…λi0 βCalculating candidate services si0And substituting the three numerical characteristic calculation formulas and cm ═ Ex, En, He into the candidate service si0In (1), obtaining:
Figure FDA0003071646640000071
wherein s isi0Represents the i0 th service in the service candidate set, i0 is 1, 2.. m, m is a positive integer;
si0＝{λi0 1，λi0 2...λi0 βdenotes candidate service si0The historical QoS records are combined according to task types;
using the formula RSS ═ s1、s2、...smCalculating a candidate service set RSS; s1 、 s2 、… smRepresents the 1 st candidate service, the 2 nd candidate service … the m < th > candidate service
Figure FDA0003071646640000072
si0＝{λi0 1，λi0 2...λi0 βSubstituting RSS into s1、s2、...smIn (b), the following was obtained:
Figure FDA0003071646640000073
wherein, cmm，βRepresenting three sets of digital features in a beta-type task in the mth service;
in the RSS candidate service set(s),
using formulas
Figure FDA0003071646640000074
Acquiring three digital characteristics under the optimal condition;
by means of the formula (I) and (II),
Figure FDA0003071646640000075
acquiring three digital characteristics under the worst condition;
wherein, cm+Representing the set of three numerical characteristics in the most ideal case, max { Ex }i0 αDenotes taking the largest EX, min { En } in the candidate service set RSSi0 αRepresents taking the smallest En, min { He } in the candidate service set RSSi0 αExpressing the minimum He in the RSS of the candidate service set;
cm-represents the worst case set of three numerical features, min { Ex }i0 αDenotes taking the smallest EX, max En in the candidate service set RSSi0 αDenotes taking the largest En, max { He in the candidate service set RSSi0 αThe maximum He in the RSS of the candidate service set is taken;
using Euclidean distance
Figure FDA0003071646640000081
Computing candidate services si0For cm in alpha type task+And cm-The degree of similarity of (a) to (b),
wherein X represents cloud digital feature cm, and Y represents cloud digital feature cm under ideal conditions+And cm-Z represents a positive integer; x is the number ofioRepresents any one element of the set X, yi0Represents any one element in the set Y;
by means of the formula (I) and (II),
Figure FDA0003071646640000082
computing candidate services si0The degree of competence for the alpha-type task, wherein,
Figure FDA0003071646640000083
value of [0, 1]]In the above-mentioned manner,
Figure FDA0003071646640000084
as candidate service si0Obtaining candidate service s for the competence degree of alpha type taski0The competency level for any type of task;
a target function module (303) for acquiring a target function of the E-CARGO model;
a solving module (304) for solving the objective function to obtain a task distribution matrix T; and taking the task allocation matrix T as an optimal service combination scheme.
6. The E-CARGO model-based cloud manufacturing service assembly of claim 5, wherein the nine-tuple module (301) further comprises:
the E-CARGO model is represented as a nine-tuple as follows:
∑::＝<C、O、A、S、R、E、G、s0、H>，
where C represents a class in the manufacturing service collaboration system, O represents an object in the manufacturing service collaboration system, A is an agent in the manufacturing service collaboration system, S is a message in the manufacturing service collaboration system, R is a role in the manufacturing service collaboration system, E is an environment in the manufacturing service collaboration system, G is a group in the manufacturing service collaboration system, S0Is the initial state of the manufacturing service collaboration system, H is the limited set of users in the manufacturing service collaboration system;
in a cloud manufacturing environment, acquiring E, C, O, R, A and G elements needing attention from a nine-tuple, and obtaining a simplified nine-tuple:
∑::＝<C′、O′、A′、R′、E′、G′>，
wherein E ' represents a set of plans and recommendations for a cloud manufacturing service, C ' represents a set of classes defined by abstract concepts associated with E ', O ' represents a set of concrete objects associated with C ', R ' represents a set of task types in cloud manufacturing, and A ' represents a set of services in cloud manufacturing.
7. The E-CARGO model-based cloud manufacturing service assembly device according to claim 5, wherein the objective function module (303) further comprises:
using formulas
Figure FDA0003071646640000091
Obtaining an objective function of the E-CARGO model,
the constraints of the objective function include:
T[i][j]∈{0,1}，0≤i＜m；
Figure FDA0003071646640000092
Figure FDA0003071646640000093
C[i1，i2]×(T[i1，j]+T[i2，j])≤1，0≤i1，i2＜m，i1≠i2；
C[i1，i2]×(T[i1，j1]+T[i2，j2])≤1，0≤i1，i2＜m，i1≠i2，0≤j1,j2＜n；
wherein j represents a task type, which is called task type j for short, the number of the task types j is more than or equal to zero and less than n, and n is a positive integer; j is a function of1、j2Representing two different positive numbers of n, i representing a service, abbreviated as service i, i1And i2Two different services in the representative service i, which are respectively called the first service i for short1A second service i2；
Task lower bound vector L: l (j) represents the number of services that must be allocated to task type j, L [ j ] ═ 1 represents that one service i is allocated to task type j, L [ j ] ═ 0 represents that no service i is allocated to task type j, and L [ j ] > 1 represents that multiple services i are allocated to task type j, that is, the task type j includes multiple subtasks;
qualification matrix Q: q is an m multiplied by n matrix, m represents the number of the service i, Q [ i, j ] represents the competence degree of the service i to the task type j, 0 is the lowest value, and 1 is the highest value; in an embodiment, Q [ i, j ] is an eligibility assessment value, and the larger the value of Q [ i, j ], the more qualified the service i is for the task type j;
task allocation matrix T: t is an m × n matrix, m represents the number of services, T [ i, j ] represents whether a service i is allocated to a task type j, and when T [ i, j ] is 1, it represents that the service i is allocated to the task type j, and the service i at this time is an allocated service; t [ i, j ] ═ 0, meaning that service i is not assigned to task type j;
a collision matrix C: the dimension size is m × m, m representing the number of services i, Cj1][i2]Representing a first service i1And a second service i2Presence or absence of conflict, Ci1][i2]Belongs to {0, 1}, Cj1][i2]1 denotes a first service i1And a second service i2Conflict, tasks cannot be completed in the same group; c [ i ]1][i2]0 denotes a first service i1And a second service i2May cooperate within the same group;
preference vector P: p [ i ] represents the preference degree of the service i corresponding to any task type, wherein P [ i ] belongs to [ -0.5,0.5], and i is more than or equal to 0 and less than m; p [ i ] < 0 indicates that any type of task has no preference for service i, P [ i ] < 0 indicates that any type of task has a negative preference for service i, and P [ i ] > 0 indicates that any type of task has a positive preference for service i.
8. The E-CARGO model-based cloud manufacturing service assembly of claim 5, wherein the solving module (304) further comprises:
each type of task alpha is a role in the E-CARGO model, and the candidate service set RSS is { s }1,s2,…,smThe cloud manufacturing service system is characterized in that the agents in the E-CARGO model are provided with qualification evaluation values for each role, and the qualification evaluation values Q [ i ] corresponding to service sets and task types in the cloud manufacturing service][j]Will be
Figure FDA0003071646640000111
Value as Q [ i][j]The value of (a) is,
Figure FDA0003071646640000112
as candidate service siCompetence for alpha type tasks; first calculate Q [ i][j]*(1+P[i]) By the value of QT [ i][j]To characterize Q [ i][j]*(1+P[i]) Namely: QT [ i][j]＝Q[i][j]*(1+P[i])；P[i]Representing the preference degree of the task to the serviceMultiplication by a table; QT [ i][j]Substituting the target function, and solving a task allocation matrix T by using a calculation tool, wherein the task allocation matrix T is the optimal service combination scheme.
CN201910540345.9A 2019-06-21 2019-06-21 Cloud manufacturing service combination method and device based on E-CARGO model Active CN110264081B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201910540345.9A CN110264081B (en) 2019-06-21 2019-06-21 Cloud manufacturing service combination method and device based on E-CARGO model

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201910540345.9A CN110264081B (en) 2019-06-21 2019-06-21 Cloud manufacturing service combination method and device based on E-CARGO model

Publications (2)

Publication Number Publication Date
CN110264081A CN110264081A (en) 2019-09-20
CN110264081B true CN110264081B (en) 2021-07-23

Family

ID=67920086

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201910540345.9A Active CN110264081B (en) 2019-06-21 2019-06-21 Cloud manufacturing service combination method and device based on E-CARGO model

Country Status (1)

Country Link
CN (1) CN110264081B (en)

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113947252B (en) * 2021-10-25 2024-10-01 中国人民解放军军事科学院战争研究院 System task decomposition and distribution method and device
CN116341015A (en) * 2023-05-30 2023-06-27 安徽省模式识别信息技术有限公司 Data privacy protection enhancement method and system in edge computing environment

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106209978A (en) * 2016-06-22 2016-12-07 安徽大学 Alliance relation service combination selection system and method
CN109615188A (en) * 2018-11-20 2019-04-12 南京理工大学 A kind of predistribution combines the multi-robot Task Allocation of Hungary Algorithm

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106209978A (en) * 2016-06-22 2016-12-07 安徽大学 Alliance relation service combination selection system and method
CN109615188A (en) * 2018-11-20 2019-04-12 南京理工大学 A kind of predistribution combines the multi-robot Task Allocation of Hungary Algorithm

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
基于E-CARGO模型的装备维修保障资源调度研究;付启剑;《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》;20181115;第C032-2页 *

Also Published As

Publication number Publication date
CN110264081A (en) 2019-09-20

Similar Documents

Publication Publication Date Title
CN107291545B (en) Task scheduling method and device for multiple users in computing cluster
CN110264081B (en) Cloud manufacturing service combination method and device based on E-CARGO model
CN106681835B (en) The method and resource manager of resource allocation
US20070211280A1 (en) Method and apparatus for assigning candidate processing nodes in a stream-oriented computer system
CN106650993B (en) Dynamic resource optimization method based on Markov decision process
CN114902273A (en) System and method for optimizing resource allocation using GPU
CN106534302A (en) Multi-task demand service combination method and system
Alharbi et al. Algorithms for investment project distribution on regions
Lin et al. Multi-centric management and optimized allocation of manufacturing resource and capability in cloud manufacturing system
Jain et al. Machine repair problem with an unreliable server and controlled arrival of failed machines
CN112418341A (en) Model fusion method, prediction method, device, equipment and storage medium
CN114154962A (en) Batch processing monitoring method, device and equipment
Tébar-Rubio et al. Conducting action research to improve operational efficiency in manufacturing: the case of a first-tier automotive supplier
Lin et al. Manufacturing capability service modeling, management and evaluation for matching supply and demand in cloud manufacturing
CN109190798A (en) A kind of cloud manufacturing service preferred method of combination
CN101071489A (en) Work flow management system and method for realizing work process automation
Lvovich et al. Modeling and optimization of resources in information systems
US8510152B1 (en) System for capability assessment and development
CN114581220A (en) Data processing method and device and distributed computing system
CN114037326A (en) Cloud ERP modeling and evolution method and system based on E-CARGO model
Dean Contract award and bidding strategies
Brzostowski et al. Using an analytic hierarchy process to develop a scoring system for a set of continuous feasible alternatives in negotiation
CN116187646A (en) Production scheduling method, device and equipment
CN117252403B (en) Industrial resource allocation management method and system based on asset information data
CN114581160B (en) Resource allocation method, distributed computing system and equipment

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant