CN113283785A - Cooperative scheduling optimization method for multi-task manufacturing resources - Google Patents
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Abstract
The invention discloses a cooperative scheduling optimization method of multi-task manufacturing resources, which comprises the following steps: the method comprises the steps of obtaining manufacturing resource information from a manufacturing resource provider, obtaining manufacturing demand information from a manufacturing resource demander, obtaining a plurality of evaluation indexes of manufacturing resource services based on the manufacturing resource information by combining a Gale-Shapley algorithm, conducting weight matching on the manufacturing demand information and the evaluation indexes of the manufacturing resource services by utilizing a random game algorithm, constructing at least one manufacturing resource service chain, obtaining an optimal matching result according to the manufacturing resource service chain, and sending the optimal matching result, the evaluation indexes of the manufacturing resource services and the manufacturing resource service chain to the manufacturing demander and the manufacturing resource demander which are pre-matched in the optimal matching result. The invention discloses a cooperative scheduling optimization method of multi-task manufacturing resources, which ensures the smooth operation of manufacturing requirements and manufacturing resource cooperative scheduling.
Description
Technical Field
The invention relates to the technical field of multi-task manufacturing resource scheduling distribution, in particular to a method for optimizing the cooperative scheduling of multi-task manufacturing resources.
Background
The cloud manufacturing mode supports multiple resource service modes, mainly comprises a single service-single task, a single service-multiple task, multiple services-single task, multiple services-multiple task and the like, a manufacturing resource provider packages manufacturing resource services in a cloud platform in a virtualization and service-oriented mode through an online registration mode to form resource service nodes, and each manufacturing resource service node forms different manufacturing levels among upstream and downstream resource services according to industrial chain distribution characteristics to form a three-level manufacturing resource service library which is produced by upstream raw materials, processed by a midstream core technology and technically packaged by a downstream customer-oriented technology. And the manufacturing resource demander registers user requirements through the cloud platform, submits manufacturing task requirements to the cloud manufacturing resource service processing center and obtains corresponding services as required.
However, in the face of various manufacturing task requirements in a cloud manufacturing mode, due to the problems of communication cost, information interaction delay and the like, the manufacturing efficiency is low, and the problem that the manufacturing requirements and manufacturing resources cannot be quickly matched occurs. When a plurality of registered users participate in cloud resource cooperative scheduling, an NP problem is formed, and therefore in the multitask resource cooperative scheduling process of a manufacturing resource service processing center of a cloud platform, a matched manufacturing resource service chain needs to be found as fast as possible through a certain decision matching algorithm.
Therefore, in order to ensure smooth operation of manufacturing requirements and manufacturing resource cooperative scheduling and solve the technical problem that the manufacturing requirements and the manufacturing resources cannot be quickly matched due to the multiple manufacturing task requirements in the current cloud manufacturing mode, it is necessary to construct a cooperative scheduling optimization method for multi-task manufacturing resources
Disclosure of Invention
The invention provides a cooperative scheduling optimization method of multi-task manufacturing resources, which solves the technical problem that the manufacturing requirements and the manufacturing resources cannot be quickly matched due to various manufacturing task requirements in a current cloud manufacturing mode.
In a first aspect, the present invention provides a method for optimizing cooperative scheduling of multitask manufacturing resources, including:
obtaining manufacturing resource information from a manufacturing resource provider and manufacturing requirement information from a manufacturing resource demander;
based on the manufacturing resource information, combining with a Gale-Shapley algorithm to obtain a plurality of evaluation indexes of the manufacturing resource service;
carrying out weight matching on the manufacturing demand information and the evaluation index of the manufacturing resource service by using a random game algorithm to construct at least one manufacturing resource service chain;
obtaining an optimal matching result according to the manufacturing resource service chain;
and sending the optimal matching result, the evaluation index of the manufacturing resource service and the manufacturing resource service chain to a manufacturing demand side and a manufacturing resource side which are pre-matched in the optimal matching result.
Optionally, obtaining manufacturing resource information from a manufacturing resource provider includes:
performing functional block type packaging registration on manufacturing resource information provided by a manufacturing resource provider;
and performing networked library packaging on the manufacturing resource information to form manufacturing resource nodes, and placing the manufacturing resource nodes on a cloud platform for global broadcasting.
Optionally, obtaining the manufacturing requirement information from the manufacturing resource demander includes:
carrying out task decoupling on manufacturing demand information provided by a manufacturing resource demand party to form a plurality of manufacturing demand events;
and performing event node encapsulation of a communication layer on the manufacturing demand event.
Optionally, obtaining, based on the manufacturing resource information and in combination with a Gale-sharley algorithm, evaluation indexes of a plurality of manufacturing resource services includes:
simulating the manufacturing demand information and the manufacturing resource information by a simulation technique;
and calculating the manufacturing resource information by using a Gale-Shapley algorithm to obtain a plurality of evaluation indexes of the manufacturing resource service.
Optionally, sending the optimal matching result, the evaluation index of the manufacturing resource service, and the manufacturing resource service chain to the manufacturing demander and the manufacturing resource demander that are pre-matched in the optimal matching result, includes:
combining the matching result, the evaluation indexes of the various manufacturing resource services and the manufacturing resource service chain to form an information set, and providing the information set to a manufacturing demander and a manufacturing resource provider which are pre-matched in the optimal matching result; the set of information is used to facilitate the pre-matched manufacturing demander to achieve an objective of collaboration with a manufacturing resource provider.
In a second aspect, the present invention provides an apparatus for collaborative scheduling optimization of multitask manufacturing resources, including:
the acquisition module is used for acquiring manufacturing resource information from a manufacturing resource provider and acquiring manufacturing requirement information from a manufacturing resource demander;
the computing module is used for obtaining evaluation indexes of a plurality of manufacturing resource services by combining a Gale-Shapley algorithm based on the manufacturing resource information;
the construction module is used for carrying out weight matching on the manufacturing demand information and the evaluation index of the manufacturing resource service by utilizing a random game algorithm to construct at least one manufacturing resource service chain;
the matching module is used for obtaining an optimal matching result according to the manufacturing resource service chain;
and the sending module is used for sending the optimal matching result, the evaluation index of the manufacturing resource service and the manufacturing resource service chain to the manufacturing demand side and the manufacturing resource side which are pre-matched in the optimal matching result.
Optionally, the obtaining module includes:
the registration submodule is used for carrying out functional block type encapsulation registration on the manufacturing resource information provided by the manufacturing resource provider;
and the broadcasting submodule is used for carrying out networked library packaging on the manufacturing resource information to form manufacturing resource nodes and placing the manufacturing resource nodes on a cloud platform for global broadcasting.
Optionally, the obtaining module further includes:
the decoupling submodule is used for carrying out task decoupling on the manufacturing requirement information provided by the manufacturing resource demander to form a plurality of manufacturing requirement events;
and the packaging submodule is used for packaging the event node of the communication layer of the manufacturing demand event.
Optionally, the calculation module comprises:
the simulation submodule is used for simulating the manufacturing requirement information and the manufacturing resource information through a simulation technology;
and the calculating submodule is used for calculating the manufacturing resource information by utilizing a Gale-Shapley algorithm to obtain the evaluation indexes of a plurality of manufacturing resource services.
Optionally, the sending module includes:
the set submodule is used for combining the matching result, the evaluation indexes of the manufacturing resource services and the manufacturing resource service chain to form an information set;
the sending submodule is used for providing the information set to a manufacturing demand side and a manufacturing resource provider side which are matched in advance in the optimal matching result; the set of information is used to facilitate the pre-matched manufacturing demander to achieve an objective of collaboration with a manufacturing resource provider.
According to the technical scheme, the invention has the following advantages: the invention provides a cooperative scheduling optimization method of multitask manufacturing resources, which is characterized by acquiring manufacturing resource information from a manufacturing resource provider and manufacturing demand information from a manufacturing resource demand party, acquiring evaluation indexes of a plurality of manufacturing resource services by combining a Gale-sharey algorithm based on the manufacturing resource information, performing weight matching on the manufacturing demand information and the evaluation indexes of the manufacturing resource services by using a random game algorithm to construct at least one manufacturing resource service chain, acquiring an optimal matching result according to the manufacturing resource service chain, sending the optimal matching result, the evaluation indexes of the manufacturing resource services and the manufacturing resource service chain to a manufacturing demand party and a manufacturing resource party which are pre-matched in the optimal matching result, and solving the technical problem that the manufacturing demand and the manufacturing resources cannot be quickly matched due to the requirements of a plurality of manufacturing tasks in a cloud manufacturing mode at present, the smooth operation of the collaborative scheduling of the manufacturing requirements and the manufacturing resources is ensured, so that the communication cost among enterprises can be better reduced, the manufacturing efficiency is improved, and the cost waste of idle manufacturing resources is saved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, 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, and for those skilled in the art, other drawings can be obtained according to these drawings without inventive exercise.
FIG. 1 is a flowchart illustrating a first embodiment of a method for collaborative scheduling optimization of multi-tasking manufacturing resources according to the present invention;
FIG. 2 is a flowchart illustrating steps of a second method for optimizing co-scheduling of multi-tasking resources according to an embodiment of the present invention;
fig. 3 is a structural diagram of a cloud platform registration system according to an embodiment of the present invention;
fig. 4 is a structural diagram of a cloud platform information interaction system according to an embodiment of the present invention;
FIG. 5 is a block diagram illustrating an embodiment of a device for collaborative scheduling optimization of multitask manufacturing resources according to the present invention.
Detailed Description
The embodiment of the invention provides a cooperative scheduling optimization method for multi-task manufacturing resources, which is used for solving the technical problems that the manufacturing requirements cannot be quickly matched and the cooperative scheduling of the manufacturing resources cannot be realized due to various manufacturing task requirements in the current cloud manufacturing mode.
In order to make the objects, features and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the embodiments described below are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In a first embodiment, referring to fig. 1, fig. 1 is a flowchart illustrating a first step of a method for optimizing a coordinated scheduling of a multitask manufacturing resource according to a first embodiment of the present invention, including:
step S101, acquiring manufacturing resource information from a manufacturing resource provider and acquiring manufacturing requirement information from a manufacturing resource demander;
step S102, obtaining a plurality of evaluation indexes of the manufacturing resource service based on the manufacturing resource information by combining a Gale-Shapley algorithm;
step S103, carrying out weight matching on the manufacturing demand information and the evaluation index of the manufacturing resource service by using a random game algorithm to construct at least one manufacturing resource service chain;
step S104, obtaining an optimal matching result according to the manufacturing resource service chain;
and step S105, sending the optimal matching result, the evaluation index of the manufacturing resource service and the manufacturing resource service chain to a manufacturing demand side and a manufacturing resource side which are pre-matched in the optimal matching result.
In the embodiment of the invention, manufacturing resource information is obtained from a manufacturing resource provider and manufacturing requirement information is obtained from a manufacturing resource demander through a multitask manufacturing resource coordinated scheduling optimization method, evaluation indexes of a plurality of manufacturing resource services are obtained by combining a Gale-Shapley algorithm based on the manufacturing resource information, weight matching is carried out on the manufacturing requirement information and the evaluation indexes of the manufacturing resource services by utilizing a random game algorithm to construct at least one manufacturing resource service chain, an optimal matching result is obtained according to the manufacturing resource service chain, the optimal matching result, the evaluation indexes of the manufacturing resource services and the manufacturing resource service chain are sent to a manufacturing demander and a manufacturing resource demander which are pre-matched in the optimal matching result, and the technical problem that the manufacturing requirements and the manufacturing resources cannot be quickly matched due to the requirements of a plurality of manufacturing tasks in a cloud manufacturing mode at present is solved, therefore, communication cost among enterprises can be reduced better, manufacturing efficiency is improved, and cost waste of idle manufacturing resources is saved.
In a second embodiment, referring to fig. 2, fig. 2 is a flowchart illustrating a second step of a method for optimizing a collaborative scheduling of a multitask manufacturing resource according to a second embodiment of the present invention, including:
step S201, performing function block type encapsulation registration on manufacturing resource information provided by a manufacturing resource provider;
referring to fig. 3, fig. 3 is a structural diagram of a cloud platform registration system according to an embodiment of the present invention, where a is a manufacturing requirement user, B is a manufacturing requirement registry, C is a manufacturing resource providing user, D is a manufacturing resource registry, 301 is information transmission, 302 is information reception, 303 is information reception, 304 is information transmission, 305 is requirement issue, 306 is requirement feedback, 307 is resource feedback, and 308 is resource provision. The manufacturing resource provider C performs virtualization and service registration on a user to become a manufacturing resource providing user, transmits the provided resource information to a manufacturing resource registry D through an information transmission 304, the manufacturing resource registry D performs matching according to the received provided resource information and the required resource information received by the manufacturing requirement registry B to obtain a matching result, the manufacturing resource registry D performs resource providing 308 on the manufacturing requirement registry B according to the matching result, and the requirement registry B performs resource feedback 309 on the manufacturing resource registry D according to the matching result; the manufacturing demand user A sends required resource information to a manufacturing demand registry B through an information sending 301, the manufacturing demand registry B matches the received required resource information with the provided resource information received by the manufacturing resource registry D to obtain a matching result, the demand registry B issues a demand to the manufacturing resource registry D according to the matching result 305, and the manufacturing resource registry D performs a demand feedback 306 to the manufacturing demand registry B according to the matching result.
It should be noted that, in the cloud platform system, since the subtasks formed by splitting the manufacturing requirements are constrained by the order of the business process and the availability of resources, the communication cost of the networked service matching information is very high. High dynamic and real-time performance can be achieved in the manufacturing resource scheduling process, and the requirement of computing resources for large data high concurrency and information interaction characteristics between tasks is high at the same time. Aiming at the problem, the manufacturing resource registration form can be effectively used for solving the problem, and the manufacturing resources are subjected to virtualization packaging and service information registration synchronously, so that the manufacturing resources are stored in a cloud platform in a nodularization and servitization mode, and the problem of manufacturing resource heterogeneity is directly solved.
In the embodiment of the invention, the manufacturing resources are provided and registered, the manufacturing resource provider is required to upload the manufacturing resources, and after the manufacturing resources become the manufacturing resource service of the cloud platform, the manufacturing resource provider can more conveniently use the cloud platform resources for cooperative scheduling, so that the communication cost among enterprises can be better reduced.
In a specific implementation, the manufacturing resources are registered, and function blocks of the manufacturing resources are encapsulated in a form that an event interface triggers internal data processing, so that the manufacturing resources become a plurality of function blocks which independently complete a specific manufacturing task, and according to different manufacturing demand event triggers, certain specific manufacturing service indexes, such as delivery time, delivery quality, delivery cost and the like, are preferentially weighted and strengthened.
Through an information registration system, a certain manufacturing resource is decomposed into the following information points in detail: beacon: manufacturing resources are regularly broadcasted in a cloud platform manufacturing demand library in real time, and information interaction cost is reduced; and (4) popularization: through the description of the manufacturing capacity, the manufacturing resources are classified in an industry chain level manner in detail, and the concurrent access amount of the computing resources is reduced; and (3) response: when the manufacturing service is captured by the cloud platform system, platform questions and answers are fed back, the latest available resource capacity is reported, and the problem of uncontrollable cost caused by misoperation and information delay is reduced; and (3) exiting: when the manufacturing resources are violated for many times or the manufacturing capability is insufficient, an exit mechanism is set reasonably, and the dynamic update of the manufacturing resource library is guaranteed.
Step S202, performing networked library packaging on the manufacturing resource information to form manufacturing resource nodes and placing the manufacturing resource nodes on a cloud platform for global broadcasting;
in the embodiment of the invention, the manufacturing resources provided by the manufacturing resource provider are subjected to networked library packaging to form manufacturing resource nodes, and the manufacturing resource nodes are placed on a cloud platform for global broadcasting; and the manufacturing requirement users of the service requirement library in the cloud platform see that the broadcast can provide own requirements for the cloud platform, and the cloud platform receives the requirements and can be directly matched with the manufacturing resource provider.
Step S203, performing task decoupling on the manufacturing demand information provided by the manufacturing resource demand party to form a plurality of manufacturing demand events;
in the embodiment of the invention, the manufacturing demand information of a manufacturing resource demand party is acquired, the manufacturing demand information is classified to form a manufacturing demand node, and the manufacturing demand node is task decoupled to form a plurality of manufacturing demand events.
Step S204, the event node encapsulation of the communication layer is carried out on the manufacturing demand event;
in the embodiment of the invention, the manufacturing demand event is packaged by event nodes at a communication layer, and all manufacturing service demands and manufacturing resources are uniformly packaged again to form resource nodes of a whole industrial chain and a method for forming a cloud-based service resource library by centralizing all service resources, so that a cloud platform can more conveniently, more quickly and more directly match all resource information with demand information; all manufacturing requirements are collected to form a cloud-based manufacturing requirement library, information is exchanged between the two libraries through two bidirectional channels, and a resource chain is matched.
Step S205, simulating the manufacturing requirement information and the manufacturing resource information by a simulation technology;
it is to be understood that a simulation is a virtual of a real thing or process. The simulation is to exhibit key characteristics of a selected physical system or abstract system. Key issues of simulation include the acquisition of valid information, the selection of key features and expressions, approximate simplifications and hypothetical applications, and the reproducibility and validity of the simulation. The simulation effect is shown as follows: the system with highly complex internal interaction can be researched and tested; various different schemes can be envisaged, observing their impact on the structure and behaviour of the system; the method can reflect the interrelationship among the variables, and shows which variables are more important and how to influence other variables and the whole system; the dynamic relation among different periods can be researched, and the change condition of the system behavior along with the change of time can be reflected; the hypothesis of the model can be checked, and the structure of the model can be improved.
In the embodiment of the invention, the manufacturing demand information and the manufacturing resource information are simulated through a simulation technology, the manufacturing resource information and the manufacturing demand information are simulated and matched in a virtual scene according to the optimal matching result and the simulation technology, and the problems encountered by the two parties in the matching process and the parameters for measuring the target are obtained through a method for establishing a model.
Step S206, calculating the manufacturing resource information by using a Gale-Shapley algorithm to obtain evaluation indexes of a plurality of manufacturing resource services;
in the embodiment of the invention, according to the optimal matching result, the manufacturing resource information and the manufacturing demand information are combined, simulation is carried out in a virtual scene, the parameters of the problems encountered by the two parties in the matching process and the parameters of the quantity reaching the target are obtained by a method of establishing a model, and the manufacturing resource information is calculated by utilizing a Gale-sharley algorithm to obtain the evaluation indexes of a plurality of manufacturing resource services.
In a specific reality, for a firm and stable cooperative relationship formed between the sub chains of the manufacturing resource services in the cloud platform manufacturing resources, the stability decision of each stage of manufacturing resources can be matched by a Gale-sharey algorithm, which is expressed as follows:
fij＝fij(tag,st,CSD)
wherein f isijA decision function representing a jth process manufacturing unit of an ith level process node; tag ═ tag1，tag2，L，tagnDenotes the label of the selected process manufacturing unit, st ═ st1，st2，L，stjDenotes the first process manufacturing unit task, CSD denotesA candidate service index for a manufacturing unit in a manufacturing resource. Each manufacturing resource attribute should be assigned a weight.
The method provides two evaluation systems of subjective and objective for manufacturing service resources of each level of process in the production process:
the subjective aspect is that an upper-level contractor of each level of manufacturing service resources grades all contractors of directly contacted lower-level manufacturing service resources one by one through historical cooperative evaluation into a positive grade (+1), a negative grade (1) and a neutral grade (0).
The objective aspect is that each level of manufacturing service resources is used as the evaluation of the contractor on the performance capability of the superior contracting party, and all contractors of inferior manufacturing service resources relative to the superior service contracting party are graded one by one into a positive grade (+1), a negative grade (1) and a neutral grade (0).
Grading each process manufacturing unit through an evaluation system to generate a CSD index, wherein the main evaluation parameters comprise:
manufacturing service provider a ═ a1，a2，L，am}；
Candidate service set a '═ a'1，a′2，L，a′m}；
Manufacturing service contractor B ═ { B ═ B1，b2，L，bn}；
Subjective evaluation index C ═ { CijD, objective evaluation index D ═ Dij}；
Then, when the evaluation index CSD is maximum, C + D is selected for the first rank.
Subjective negative evaluation and subjective positive evaluation.
Objective negative evaluation and objective positive evaluation. When all the indexes are calculated, a CSD index is statistically summarized for evaluation of the current manufacturing resources.
When the superior contracting party determines to change the inferior manufacturing service resource contractor due to a certain factor, the alternative manufacturing service resources are sequentially replaced from high to low in the candidate service pool according to the CSD index, and therefore the purpose of the flexible reconfigurable manufacturing resource service chain is achieved.
Step S207, carrying out weight matching on the manufacturing demand information and the evaluation index of the manufacturing resource service by using a random game algorithm, and constructing at least one manufacturing resource service chain;
the weight matching is performed by ranking according to the importance degree of the evaluation index of the manufacturing resource service, matching the evaluation index of the manufacturing resource service according to the relative importance of the evaluation index of the manufacturing resource service, and having a priority matching weight over the evaluation index of the manufacturing resource service with the highest weight.
In the embodiment of the invention, the important degree of the evaluation index of the manufacturing resource service is utilized to construct the matching results under different demand weights, so that at least one manufacturing resource service chain is obtained;
in the concrete implementation, after a manufacturing task or a manufacturing requirement is registered on a cloud platform, firstly, a manufacturing resource service processing center performs task combination decoupling, a specific manufacturing requirement is divided into a plurality of subtasks for division processing, for a hybrid system with a plurality of subtasks and a plurality of decision states according to different manufacturing resources, the subtasks can be regarded as an intelligent agent to perform random game solution of a plurality of intelligent agents by using a random game, the maximum return expected sum of all subtasks under a balance strategy is found, all optimal resources form a group of complete manufacturing resource service chains, and the random game can be expressed as a tuple (n, S, A) and can be expressed as a random game1,L,An,T,γ,R1,L,Rn) Specifically, the following modeling is performed for the manufacturing task:
n is the number of subtasks; m: a number of manufacturing resource services registered in the cloud platform; s is a subtask that isAn agent state; a. thet(t ═ 1,2,3) is the set of behaviors for each subtask; t is a state transfer function; gamma is a discount factor Rt(t ═ 1,2,3) is a reward function, which represents the reward obtained in state after the subtask takes joint action in state S; i is a fully cooperative manufacturing resource among the registered manufacturing resources; j is a mixed manufacturing resource in the registered manufacturing resources; k is a fully competitive manufacturing resource among registered manufacturing resources;
for n subtasks, each subtask performs policy pi selection in a packaged manufacturing resource service library, and performs random selection before marking a CSD index label, so that the policy number of the subtask in one decision is n. According to the strategy pi, all the subtasks will perform one action, i.e. n manufacturing subtasks grab the manufacturing resource, and the number of actions is also n. The n actions correspond to a reward of (R)1,L,Rn) The state is (S)1,L,Sn) And T represents the probability distribution of the next state in the current state of the given subtask and when the joint action is taken.
In conclusion, how to find nash balance in random games is the key point for solving the problem. Nash equilibrium in random gaming can be described as n policy tuples
For all t ═ 1,2, L, n, satisfy:
wherein, pit∈Πt，ΠtA set of policies for all tasks;
is the sum of the return of the subtasks under the S all subtask balancing strategy in the given current state.
Firstly, after task decoupling, n subtasks trigger the requirement of a manufacturing resource service processing center in an event form, and the processing centerQuickly traverse through m manufacturing resources, and estimate the expected return on subtasks t (t is 1,2, …, n) according to the strategy set
To express the state-value function under the Nash equilibrium strategy
Defining behavior-value functions also in random games
Given the current state of subtask t and the current joint behavior of all subtasks, the discount for subtask t returns an aggregate of expectations (the farther the state is from the MDP value function the less impact it has on expectations), followed by a Nash equilibrium policy. Can obtain the product
According to the above formula, Nash equilibrium can be rewritten as
When nash balance is achieved, that is, all manufacturing subtasks obtain the optimal manufacturing service resources, the capturing of the manufacturing resource services from the manufacturing resource library for the complex manufacturing tasks in the manufacturing requirement library after decoupling can be completed.
Step S208, obtaining an optimal matching result according to the manufacturing resource service chain;
in the embodiment of the invention, an optimal matching result is obtained according to the manufacturing resource service chain, and the optimal matching result comprises a manufacturing demand party and a manufacturing resource party which are pre-matched and manufacturing resource information and manufacturing demand information which are pre-matched.
In a concrete reality, all users registered in the cloud platform are divided into a manufacturing demander and a manufacturing resource provider according to different demand modes, and each user can have the two characteristics at the same time, so that the interaction of a whole industry chain is formed; each user respectively carries out virtualization registration in the two resource libraries, and respectively interacts with information in the two resource libraries through the packaged information interaction interface, all resource information butt joints are only butted with the cloud platform, and the cloud platform is internally responsible for information forwarding and matching, so that the communication cost is further reduced, the communication efficiency is improved, and the interaction calculation cost is also transferred to the cloud platform.
Step S209, combining the matching result, the evaluation indexes of the manufacturing resource services and the matching industry chain to form an information set, and providing the information set to a manufacturing demand side and a manufacturing resource provider side which are pre-matched in the optimal matching result; the information set is used for promoting the pre-matched manufacturing demander to achieve the cooperation intention with the manufacturing resource provider;
referring to fig. 4, fig. 4 is a structural diagram of a cloud platform information interaction system according to an embodiment of the present invention, where E is a manufacturing requirement library, F is a manufacturing resource library, G is an OPCua server, H is an OPCua client, and 401, 402, and 403 are all OPCua communication protocols. The manufacturing requirement library E and the manufacturing resource library F respectively send the OPCua communication protocol to the OPCua server G, and the OPCua server C forwards the OPCua communication protocol to the OPCua client H.
It should be noted that resource scheduling is one of the key technologies of the cloud platform, and because the problem of resource scheduling in cloud manufacturing has a certain similarity to the problem of resource scheduling in the traditional distributed environment, and the problem of cloud service scheduling is essentially an NP complete problem; therefore, under the condition of limited computing resources, how to quickly approach the existing optimal solution is the key point concerned by the scheduling problem; the resource combination problem is inevitably solved by taking economic benefit as a main contradiction, and is subdivided into time cost, manufacturing cost, labor cost and the like, so that the cooperative technology adopts a game theory as a strategy for solving flexible real-time dynamic scheduling. Each manufacturing resource in the game model is used as a participant of the game, and a payment function is formed by using the load index and the utilization rate. In response to the same service requirement, manufacturing resource nodes with the same function compete with each other to obtain the opportunity of being captured by the cloud platform, an advanced internet of things technology is utilized in the cloud platform management system, for example, a working condition access system of a network registration system is constructed through an OPCua communication protocol, and the running state and related data of the manufacturing resources can be obtained in real time.
All manufacturing resources can be classified as fully cooperative, fully competitive, and hybrid depending on the type of manufacturing service demand task. In a fully cooperative random game, a global industry chain standard is quickly found out to obtain the maximum common benefit standard for all industry chain participants, and when each registered user obtains the maximum benefit, the common benefit is naturally met, so that the maximum common benefit is also the best. I.e. platform all registered users report back R1、R2…RnMaximizing, the goal of cooperative scheduling is to maximize the collective return. If n is 2, there is only one manufacture resource supplier and manufacture resource provider, if R1＝-R2In the case of the reverse goal, the random game is completely competitive, and the method of completely competitive can firstly filter out resource providing nodes which do not meet the requirements of users for manufacturing requirements, and is also a way to accelerate calculation. Furthermore, there are users that are neither fully competitive nor fully cooperative, referred to as hybrid users, which may be candidates for manufacturing resource providers. According to the game characteristic, the resource selection needs to meet the requirements of stability and adaptability. Stability refers to the stability, i.e., reliability, of the provider of the manufacturing resource. Adaptability ensures that the performance of a manufacturing resource provider is not degraded by other manufacturing resource providers temporarily changing supply capacity, i.e., the production chain of the build meets a certain robustness. Therefore, the resource scheduling is converted into the information of the limited strategy and the completely known static game, and the Nash balance of the game is obtained by using the line marking method. The game theory can realize that the service demand task is distributed to the optimal manufacturing resource, thereby reducing the resource waste and improving the production efficiency.
In the cloud manufacturing platform, the information required by some supply and demand parties can be updated in real time in a dynamic mode through the registration information of all users. Through an OPCua communication protocol, a server for information acquisition can be constructed in the cloud platform. After the manufacturing resource provider and the manufacturing demand party achieve matching handshake, the cloud platform automatically uploads the registration information of the two parties to the information acquisition server, the registration information is dynamically updated in real time, the two parties of supply and demand access the information acquisition server in a client-side mode to acquire the current manufacturing information, the cloud platform uniformly coordinates and uniformly schedules, and communication cost is greatly reduced. All supply and demand information is acquired in real time, so that the whole multi-task manufacturing collaborative industry chain has the characteristics of flexibility and dynamics.
In the embodiment of the invention, the matching result, the evaluation indexes of the various manufacturing resource services and the matching industry chain are combined to form an information set, and the information set is provided for a manufacturing demand side and a manufacturing resource provider side which are pre-matched in the optimal matching result; the set of information is used to facilitate the pre-matched manufacturing demander to achieve an objective of collaboration with a manufacturing resource provider.
In a specific reality, the matching result, the evaluation indexes of the various manufacturing resource services and the manufacturing resource service chain are sent to a manufacturing demander and a manufacturing resource provider which are pre-matched, the two parties jointly determine whether to achieve the intention of cooperation, the manufacturing demander and the manufacturing resource provider realize interaction in a cloud platform in the role of a user, the intention of cooperation is achieved through the result of cloud platform resource collaborative scheduling, and a handshake chain is formed.
In the embodiment of the invention, manufacturing resource information is obtained from a manufacturing resource provider and manufacturing requirement information is obtained from a manufacturing resource demander through a multitask manufacturing resource coordinated scheduling optimization method, evaluation indexes of a plurality of manufacturing resource services are obtained by combining a Gale-Shapley algorithm based on the manufacturing resource information, weight matching is carried out on the manufacturing requirement information and the evaluation indexes of the manufacturing resource services by utilizing a random game algorithm to construct at least one manufacturing resource service chain, an optimal matching result is obtained according to the manufacturing resource service chain, the optimal matching result, the evaluation indexes of the manufacturing resource services and the manufacturing resource service chain are sent to a manufacturing demander and a manufacturing resource demander which are pre-matched in the optimal matching result, and the technical problem that the manufacturing requirements and the manufacturing resources cannot be quickly matched due to the requirements of a plurality of manufacturing tasks in a cloud manufacturing mode at present is solved, therefore, communication cost among enterprises can be reduced better, manufacturing efficiency is improved, and cost waste of idle manufacturing resources is saved.
Referring to fig. 5, fig. 5 is a block diagram illustrating a cooperative scheduling optimization apparatus for multitask manufacturing resources according to the present invention, including:
an obtaining module 501, configured to obtain manufacturing resource information from a manufacturing resource provider and obtain manufacturing requirement information from a manufacturing resource demander;
a calculating module 502, configured to obtain evaluation indexes of multiple manufacturing resource services based on the manufacturing resource information in combination with a Gale-sharley algorithm;
a constructing module 503, configured to perform weight matching on the manufacturing demand information and the evaluation index of the manufacturing resource service by using a random game algorithm, and construct at least one manufacturing resource service chain;
a matching module 504, configured to obtain an optimal matching result according to the manufacturing resource service chain;
a sending module 505, configured to send the optimal matching result, the evaluation index of the manufacturing resource service, and the manufacturing resource service chain to a manufacturing demander and a manufacturing resource demander that are pre-matched in the optimal matching result.
In an optional embodiment, the obtaining module 501 includes:
the registration submodule is used for carrying out functional block type encapsulation registration on the manufacturing resource information provided by the manufacturing resource provider;
and the broadcasting submodule is used for carrying out networked library packaging on the manufacturing resource information to form manufacturing resource nodes and placing the manufacturing resource nodes on a cloud platform for global broadcasting.
In an optional embodiment, the obtaining module 501 further includes:
the decoupling submodule is used for carrying out task decoupling on the manufacturing requirement information provided by the manufacturing resource demander to form a plurality of manufacturing requirement events;
and the packaging submodule is used for packaging the event node of the communication layer of the manufacturing demand event.
In an alternative embodiment, the calculation module 502 includes:
the simulation submodule is used for simulating the manufacturing requirement information and the manufacturing resource information through a simulation technology;
and the calculating submodule is used for calculating the manufacturing resource information by utilizing a Gale-Shapley algorithm to obtain the evaluation indexes of a plurality of manufacturing resource services.
In an optional embodiment, the sending module 505 comprises:
the set submodule is used for combining the matching result, the evaluation indexes of the manufacturing resource services and the manufacturing resource service chain to form an information set;
the sending submodule is used for providing the information set to a manufacturing demand side and a manufacturing resource provider side which are matched in advance in the optimal matching result; the set of information is used to facilitate the pre-matched manufacturing demander to achieve an objective of collaboration with a manufacturing resource provider.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the embodiments provided in the present application, it should be understood that the method, and the apparatus disclosed in the present invention can be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (5)
1. A method for collaborative scheduling optimization of multi-tasking manufacturing resources, comprising:
obtaining manufacturing resource information from a manufacturing resource provider and manufacturing requirement information from a manufacturing resource demander;
based on the manufacturing resource information, combining with a Gale-Shapley algorithm to obtain a plurality of evaluation indexes of the manufacturing resource service;
carrying out weight matching on the manufacturing demand information and the evaluation index of the manufacturing resource service by using a random game algorithm to construct at least one manufacturing resource service chain;
obtaining an optimal matching result according to the manufacturing resource service chain;
and sending the optimal matching result, the evaluation index of the manufacturing resource service and the manufacturing resource service chain to a manufacturing demand side and a manufacturing resource side which are pre-matched in the optimal matching result.
2. The method of claim 1, wherein obtaining manufacturing resource information from a manufacturing resource provider comprises:
performing functional block type packaging registration on manufacturing resource information provided by a manufacturing resource provider;
and performing networked library packaging on the manufacturing resource information to form manufacturing resource nodes, and placing the manufacturing resource nodes on a cloud platform for global broadcasting.
3. The method of claim 1, wherein obtaining manufacturing requirement information from a manufacturing resource demander comprises:
carrying out task decoupling on manufacturing demand information provided by a manufacturing resource demand party to form a plurality of manufacturing demand events;
and performing event node encapsulation of a communication layer on the manufacturing demand event.
4. The method of claim 1, wherein obtaining a plurality of evaluation metrics of the manufacturing resource service based on the manufacturing resource information in combination with a Gale-sharley algorithm comprises:
simulating the manufacturing demand information and the manufacturing resource information by a simulation technique;
and calculating the manufacturing resource information by using a Gale-Shapley algorithm to obtain a plurality of evaluation indexes of the manufacturing resource service.
5. The method according to any one of claims 1 to 4, wherein the step of sending the optimal matching result, the evaluation index of the manufacturing resource service and the manufacturing resource service chain to the pre-matched manufacturing demander and manufacturing resource comprises:
combining the matching result, the evaluation indexes of the various manufacturing resource services and the manufacturing resource service chain to form an information set, and providing the information set to a manufacturing demander and a manufacturing resource provider which are pre-matched in the optimal matching result; the set of information is used to facilitate the pre-matched manufacturing demander to achieve an objective of collaboration with a manufacturing resource provider.
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