CN117273238B - Wooden furniture service combination method and system based on QoS constraint - Google Patents
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Abstract
The invention relates to the technical field of data processing, in particular to a wooden furniture service combination method and system based on QoS constraint, wherein the method comprises the following steps: receiving service requirements about wooden furniture input by a user; generating a service type according to the service requirement through a semantic analyzer; determining a plurality of feasible solutions meeting the QoS constraint according to the service type and designer data stored in a designer database; generating a plurality of Pareto suboptimal solutions from the feasible solutions; and combining the decision attributes of the Pareto suboptimal solutions, and selecting a final scheme from the Pareto suboptimal solutions. The invention can ensure the timely and accurate distribution of orders, and avoid the problems of asynchronous information, data loss and the like caused by operations such as repeated transfer, assignment and the like. Meanwhile, the order system of the furniture enterprise can be more efficient and intelligent, so that the production efficiency is improved, the cost is reduced, and the customer satisfaction is improved.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a wooden furniture service combination method and system based on QoS constraint.
Background
Market adjustment in the wood furniture industry results in lower and lower market share of standard products, steady improvement of the duty ratio of customized products, diversity of customized products, and uniqueness, and results in products that can enter production after being designed by designers, and the design task matching of the designers is a critical problem to be solved. With the proliferation of internet usage, it has become commonplace to use web services to handle the order selection of designers in the wood furniture industry.
The dynamic combination method can automatically select and bind related Web services in the Web service library only by inputting a function request by a combination requester, and simultaneously gives an optimal service combination scheme. Currently, dynamic Web service combinations are mainly divided into the following methods:
(1) A method based on AI (Artificial Intelligence) planning: automatic planning and artificial intelligence techniques are conventional approaches to solving the problem of Web service composition. Such methods generate an effective composition plan by mapping Web services to activities in the planning domain.
However, the AI-planning-based method is relatively high in computational complexity, and at the same time it neither generates an optimal combining scheme (minimizing the number of activities) nor ensures that the multi-dimensional quality of service of the combining scheme is optimal.
(2) Graph-based search method: compared with AI planning, the graph search-based method does not need excessive formal representation and complicated logic reasoning, and is therefore widely applied to solving the Web service combination problem. Most of the current work is based on semantic information of the service interface parameters (i.e. input, output parameters). Such methods typically utilize semantic ontology similarities of input and output parameters in Web services to construct a service ontology dependent call graph, and then use graph traversal algorithms to find a combined path that meets the user's needs.
However, the graph search-based method, when semantic matching is ambiguous, the combination scheme obtained by matching inputs and outputs between services does not accurately satisfy the functional needs of the user.
(3) Keyword query-based method: setting function keywords for Web services is an effective way to refine the user's function needs. The main idea of these methods is to describe Web services using an ontology-based semantic Web service description language and a design logic-based service retrieval inference algorithm.
However, based on the keyword query method, the user must give keywords representing the first and last tasks in each query, otherwise it is difficult to determine the end-to-end tasks in the finally generated SBS; since this method connects different Web services according to the collaboration history of the services instead of the semantic relationships of inputs and outputs, they ignore the flow structure-and-or structure that is common in SBS.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a wooden furniture service combination method and system based on QoS constraint:
in a first aspect of the present invention,
the invention provides a wooden furniture service combination method based on QoS constraint, which comprises the following steps:
s101: receiving service requirements about wooden furniture input by a user;
s102: generating a service type according to the service requirement through a semantic analyzer;
s103: determining a plurality of feasible solutions meeting the QoS constraint according to the service type and designer data stored in a designer database;
s104: generating a plurality of Pareto suboptimal solutions from the feasible solutions;
s105: and selecting a final scheme from the Pareto sub-optimal solutions by combining the decision attributes of the Pareto sub-optimal solutions.
In a second aspect of the present invention,
the invention provides a wooden furniture service combination system based on QoS constraint, which can execute the wooden furniture service combination method based on QoS constraint in the first aspect.
The invention has the beneficial effects that according to the service requirement input by the user on the wooden furniture, the allocation of wooden furniture design orders can be automatically carried out by combining each designer based on QoS constraint, and a plurality of feasible allocation and combination schemes are determined. Further, a plurality of Pareto suboptimal solutions are generated from the feasible solutions, and the final scheme is selected from the Pareto suboptimal solutions by combining the decision attribute of each Pareto suboptimal solution, so that the accuracy and satisfaction of the combined scheme can be improved. The invention can ensure the timely and accurate distribution of orders, and avoid the problems of asynchronous information, data loss and the like caused by operations such as repeated transfer, assignment and the like. Meanwhile, the order system of the furniture enterprise can be more efficient and intelligent, so that the production efficiency is improved, the cost is reduced, and the customer satisfaction is improved. Compared with the method based on AI planning, the method can output the optimal combination scheme, and can ensure that the quality of service of the combination scheme in multidimensional QoS is optimal. The combination scheme satisfying the customer service requirement can be accurately output with respect to the graph search-based method. Compared with a method based on keyword query, the method has the advantages that keywords representing the first task and the last task are not required to be given with time and effort, and the accuracy of the generated combination scheme is improved.
Drawings
Fig. 1 is a schematic flow chart of a QoS constraint-based wooden furniture service combination method provided by the invention.
Fig. 2 is a schematic structural diagram of a QoS constraint-based wooden furniture service combining method according to the present invention.
Fig. 3 is a schematic structural diagram of a Web service body provided by the present invention.
Fig. 4 is a schematic flow chart of a Pareto suboptimal solution generating method provided by the invention.
Fig. 5 is a flow chart of a method for converting a dependency graph into a hierarchical path generation graph according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In the case of example 1,
referring to fig. 1, a flow diagram of a method of wood furniture service composition based on QoS constraints is shown.
Referring to fig. 2, a schematic structural diagram of a QoS constraint based wooden furniture service combining method is shown.
The invention provides a wooden furniture service combination method based on QoS constraint, which comprises the following steps:
s101: service requirements for wooden furniture entered by a user are received.
Wherein the service requirements include: furniture type, furniture style and customer requirements.
Specifically, the service requirements filled in the paper order by the user can be received, and the service requirements filled in the electronic order of the application program or the webpage interface by the user can also be received. The invention is not limited to a specific way of obtaining the service requirements about wooden furniture entered by the user.
S102: and generating the service type according to the service requirement through a semantic analyzer.
The semantic analyzer is a computer program or system that is intended to interpret and understand the meaning of natural language text and convert it into a structured form that can be processed by a computer. Semantic analyzers are key technologies in the field of Natural Language Processing (NLP) that enable computing mechanisms to solve the meaning and context of human language, thereby enabling higher level semantic understanding and reasoning.
In the invention, the service type generated by the semantic analyzer can more accurately capture the actual requirements of the order task, such as furniture type, furniture style, customer requirements and the like, in the wooden furniture order task distribution process. The automatic distribution accuracy of wooden furniture order tasks is improved, the design quality of wooden furniture is improved, and further the customer satisfaction is improved.
In one possible implementation, S102 specifically includes substeps S1021 to S1024:
s1021: and resolving the service requirements.
S1022: and mapping the parsed information into a service ontology to construct a service demand description.
Referring to fig. 3, a schematic diagram of a Web service body provided by the present invention is shown.
The Service ontology includes a Service Profile (Service Profile), a Service Model (Service Model), and Service details (Service Grounding).
Wherein the service profile is used for service description, introducing what the service does.
Wherein the service model is used for service interaction to introduce how the service operates.
Wherein the service details are used for specifying service access details and describing how to access the service.
It should be noted that constructing a service demand description may enable a subsequently generated service type to more accurately capture the actual requirements of an order task.
S1023: and carrying out semantic matching on the service requirement description and the service type.
It should be noted that the essence of semantic matching is semantic matching of interface parameters between services. The invention realizes Web service combination according to semantic matching among input and output parameters of different Web services. In short, if the output of service a matches the input of service B to a high degree in terms of semantic relationship, service a and service B may be connected together.
Furthermore, semantic matching considers the semantic relation of input and output parameters, so that applicable services can be combined more accurately, and the quality and matching degree of service combination are improved.
S1024: and generating the service type according to the semantic matching level.
Wherein, the semantic matching level includes: exact match (Exact), insert match (Plug In), contain match (Subsume), and match Fail (Fail).
For Exact matches (Exact), if the concept of the request and the concept of the response are the same concept located at the same location in the ontology classification tree, then both are Exact matches. In addition, if the concept of the request is a direct subclass of the concept of the answer, then there is also an exact match between the two.
For Plug In, if the concept of the request is a subclass of the answer concept but not a direct subclass, then a Plug In match is between the two.
For a contain match (Subsume), if the concept of the request contains the concept of the answer, then there is a contain match between the two. In this case, the provider may not fully meet the requirements, and the requester may achieve its goal with the provider, but the requester is likely to modify the plan or perform other requests to complete its task.
For a failed match (Fail), a match fails when there is no inclusive, inclusive relationship between the two concepts of the request and the reply.
Further, the above matching levels are classified according to the degree of dispersion among concepts. Undoubtedly, exact match (Exact) is better than the other three match levels; plug in is a matching level next to Exact match (Exact) because the result of its reply is likely to be used to replace the requestor's expectations; inclusion matching (Subsume) is the third matching level following Plug In matching, since the demand of the requester can only be partially satisfied; the match failure (Fail) is the worst match level, and the result indicates that there is no link between the two concepts. Through the four matching rules, the input and output parameters between the Web services can be subjected to semantic matching, which is the basis for combining the Web services together.
It should be noted that, multiple service types will be generated according to the semantic matching level, including precision matching, insert matching, inclusion matching, and the like. This variety of service types allows the system to provide a variety of options based on meeting the needs, increasing flexibility and adaptability.
Further, by matching the class classification, the system can generate the service type more accurately, thereby being beneficial to optimizing the resource allocation and improving the task completion efficiency.
S103: a plurality of feasible solutions meeting the QoS constraints are determined based on the service type and designer data stored in the designer database.
Among other things, qoS (Quality of Service ) is widely used in the concept of information technology and communication for measuring and managing performance and availability levels of computer networks, systems or services.
In one possible implementation, S103 specifically includes substeps S1031 to S1034:
s1031: the QoS constraint value for each service is described in terms of tuples.
Wherein the QoS constraint values of the respective services are described in the form of tuples as:where RT represents response time, T represents throughput, and R represents reliability.
Among these, response time, throughput and reliability are typical QoS parameters. Further, throughput and reliability are positive QoS parameters, with higher throughput and reliability meaning better performance. While the response time is a negative QoS parameter, smaller response time means better performance.
In the invention, typical QoS parameters such as response time, throughput, reliability and the like are used, so that the comprehensive consideration of the requirements on different performance is ensured, and the method is beneficial to generating a feasible solution meeting the multi-dimensional performance requirements.
S1032: and generating a plurality of groups of service combination schemes meeting the service types according to the QoS constraint value of each service.
Specifically, the service type is used as query input, each service is activated, and a plurality of groups of service combination schemes meeting the service type are generated.
In the invention, a plurality of groups of service combination schemes meeting the service types are generated according to the QoS constraint value of each service, so that the generated combination schemes are matched with the user demands and the service types, the generation of combination schemes irrelevant to the demands can be avoided, and the efficiency is improved.
S1033: the QoS constraint values of the service combining scheme are calculated according to the QoS constraint values of the respective services included in the service combining scheme.
In one possible implementation, S1033 is specifically:
when the service combination scheme includes the first service and the second service in series, a QoS constraint value of the service combination scheme is calculated by the following formula：
Wherein,QoS constraint value representing service combination scheme, RT represents response time of service combination scheme, T represents throughput of service combination scheme, R represents reliability of service combination scheme,/-for service combination scheme>Constraint value, RT, representing a first service 1 Representing response time of the first service, T 1 Representing throughput of the first service, R 1 Representing reliability of the first service, +.>Constraint value, RT, representing a second service 2 Representing response time of the second service, T 2 Representing throughput of the second service, R 2 Indicating the reliability of the second service.
When the service combination scheme includes the third service and the fourth service in parallel, a QoS constraint value of the service combination scheme is calculated by the following formula：
Wherein,constraint value, RT, representing third service 3 Representing response time of third service, T 3 Representing throughput of third service, R 3 Indicating the reliability of the third service, +.>Constraint value, RT, representing fourth service 4 Representing the response time of the fourth service, T 4 Representing throughput of fourth service, R 4 Indicating the reliability of the fourth service.
When the service combination scheme includes both serial and parallel service combinations, the QoS constraint values of the service combination scheme are calculated in a serial-first-parallel-last manner.
In the invention, by adopting different calculation formulas for different types of service combination schemes, the QoS constraint value of each combination scheme can be estimated more accurately, which is helpful for ensuring that the generated combination scheme meets the preset performance requirement. By providing a more accurate QoS assessment, the efficiency and quality of wood furniture design order allocation is improved.
S1034: and judging whether the QoS constraint value of the service combination scheme meets the QoS constraint, and if so, reserving as a feasible solution.
In the invention, after the QoS constraint value is calculated, the combination schemes are screened, only those schemes meeting the QoS constraint are reserved, so that the scheme with insufficient performance can be eliminated, and the reserved feasible solutions are ensured to have the required performance level.
Wherein the QoS constraints include global QoS constraints and local QoS constraints.
In one possible implementation, the local QoS constraints include: the reliability of the services participating in the service composition scheme is greater than 70%.
In one possible implementation, the global QoS constraints include: the reliability of the service combining scheme is greater than 60% and the response time of the service combining scheme is less than 2.5s.
According to the invention, by comprehensively considering QoS constraint and service type and effectively calculating and screening feasible solutions, a wooden furniture design scheme which is efficient and meets performance requirements can be provided, service allocation is optimized, customer satisfaction is improved, and meanwhile, performance and quality are ensured to reach required levels.
S104: a plurality of Pareto sub-optimal solutions are generated from the feasible solutions.
Referring to fig. 4, a flow chart of a Pareto suboptimal solution generating method provided by the invention is shown.
It should be noted that in a multi-objective problem, there are often multiple objective functions, which may be contradictory, i.e. improving one objective may lead to a deterioration of another objective. Pareto suboptimal solutions are a set of solutions, none of which can defeat other solutions on all targets. In other words, none of the Pareto sub-optimal solutions dominate the other solutions.
In one possible implementation, S104 specifically includes sub-steps S1041 to S1044:
s1041: non-dominant schemes in the feasible solution are determined, and Pareto array lines are formed.
Wherein Pareto lineup refers to a non-dominant solution set.
It should be noted that in a multi-objective combination problem, we may not find a single solution that is optimal in all aspects, but rather we may find a Pareto line that consists of a set of non-dominant solutions, i.e. the suboptimal solution in our non-dominant solution.
S1042: a dependency graph between the services is constructed.
The dependency graph is a graph representation tool used for displaying dependency relationships and interrelationships among different elements. These elements may be tasks, operations, events, resources, objects, or any other entity, and the dependency graph is used to clearly show the interactions and dependencies between them.
It should be noted thatThe dependency graph d= (V, E) is a directed graph, where V is the node set and E is the edge set. Each node v i E V corresponds to a service W eventually activated by query input i E W, each directed edge (v i , v j ) E represents a direct dependency between two services, that is, node v i The output generated by the corresponding service is node v j Input of the corresponding service. Each edge is annotated by the input-output of the service.
In the invention, constructing the dependency graph among services helps to better understand the interaction and dependency among different services, can help decision makers consider problems more comprehensively, and better evaluate the feasibility and applicability of each solution.
S1043: and converting the dependency graph into a hierarchical path generation graph.
Wherein the hierarchical path generation graph (Hierarchical Path Generation Graph) is a graphical representation tool for modeling and analyzing problem space in multi-objective optimization, decision analysis, and graph algorithms. It is very useful in dealing with complex dependencies and multi-level decision problems. The hierarchical path generation graph divides the problem space into multiple hierarchies or levels. Each level represents a set of related decision variables or solutions, typically organized according to some logical or dependency relationship. This hierarchy helps organize and clarify the complex problems.
In the present invention, converting the dependency graph into a hierarchical path generation graph helps to identify dependencies and associations between solutions, helps to better understand interrelationships between solutions, and helps decision makers better understand how to select the final solution.
Referring to fig. 5, a flow diagram of a method for converting a dependency graph into a hierarchical path generation graph according to the present invention is shown.
In one possible implementation, the substep S1043 specifically includes grandchild steps S10431 to S10435:
s10431: the first node is removed from the queue on a first-in first-out basis.
It should be noted that by processing nodes from the queue one by one on a first-in first-out basis, this approach ensures that non-dominant solutions in Pareto lines are preserved, helping to preserve a set of optimal solutions in a multi-objective optimization problem, rather than just a single optimal solution.
S10432: a precursor node set of the first node is constructed.
S10433: for each precursor node of the first node, a temporary Pareto optimal front up to the precursor node is constructed or modified and at the same time local QoS constraints are verified, if any are violated, the tuples from Pareto lines are removed.
S10434: for Pareto optimal front-to-precursor nodes, the global QoS constraints are verified, and if any local QoS constraints are violated, then tuples from Pareto lines are removed.
In the invention, the temporary pareto optimal front edge of each precursor node is verified to ensure that the local QoS constraint and the global QoS constraint are met. This helps to screen out those solutions that do not meet performance and reliability requirements to ensure that the resulting solution meets certain QoS criteria.
S10435: if the same precursor node is not contained in the queue, each precursor node is inserted into the queue to form a hierarchical path generation diagram.
In the invention, the hierarchical path generation diagram is constructed by combining the reservation of the Pareto optimal solution and the verification of QoS constraint, which is helpful for solving the multi-objective combination problem, providing effectiveness and high efficiency when processing complex problems, being helpful for finding the optimal solution meeting a plurality of targets and constraints, and providing more comprehensive problem representation and visualization by constructing the hierarchical path generation diagram.
S1044: and determining a Pareto suboptimal solution in the Pareto array line according to the hierarchical path generation diagram.
In the present invention, by determining Pareto suboptimal solutions in a Pareto lineup, helping a decision-maker find the best trade-off among multiple objectives, these solutions represent different trade-offs and diversity in the problem space, allowing the decision-maker to choose the most appropriate solution according to its priority and needs.
S105: and combining the decision attributes of the Pareto suboptimal solutions, and selecting a final scheme from the Pareto suboptimal solutions.
Wherein, the decision attribute comprises furniture timber, style, color and the like.
In one possible embodiment, S105 specifically includes substeps S1051 to S1059:
s1051: determining attribute values of each Pareto suboptimal solution to each attribute, and determining an ith Pareto suboptimal solution A i For the j-th attribute C j The attribute values of (2) are expressed as an intuitive fuzzy number:，/>representing the i-th Pareto suboptimal solution A i For the j-th attribute C j Lower limit value of (2),>representing the i-th Pareto suboptimal solution A i For the j-th attribute C j Is a fuzzy upper limit value of (2).
The uncertainty of the attribute value is represented using an intuitive fuzzy number. This helps to take into account ambiguity in the attribute values, especially when the attribute values are not exact values. Uncertainty of the attribute value often exists in actual decisions, so that actual conditions can be reflected better.
S1052: constructing a direct fuzzy decision matrix of Pareto suboptimal solution:
wherein X represents a direct fuzzy decision matrix,representing the i-th Pareto suboptimal solution A i For the j-th attribute C j Lower limit value of (2),>representing the i-th Pareto suboptimal solution A i For the j-th attribute C j Is used for the fuzzy upper limit value of (c),m represents the total number of Pareto suboptimal solutions,/->N represents the total number of attributes.
S1053: calculating direct fuzzy entropy of each attribute:
wherein,represents the jth attribute C j Direct fuzzy entropy of>Representing the i-th Pareto suboptimal solution A i For the j-th attribute C j Is the core of the intuitive fuzzy number, +.>Representing the i-th Pareto suboptimal solution A i For the j-th attribute C j Is a degree of hesitation of the intuitive fuzzy number.
The intuitionistic fuzzy entropy is a quantitative description of the uncertainty of the intuitionistic fuzzy set and is used for measuring the fuzzy degree of the intuitionistic fuzzy set. The greater the intuitionistic fuzzy entropy, the higher the intuitionistic fuzzy set uncertainty.
S1054: calculating direct fuzzy similarity between the various attributes:
wherein,represents the alpha-th attribute->And property beta->The degree of direct fuzzy similarity between the two,。
s1055: according to the direct fuzzy similarity among the attributes, calculating the direct fuzzy similarity of the attributes:
wherein,represents the jth attribute C j Similarity of->N represents the total number of attributes.
The intuitionistic fuzzy similarity is a quantitative description of the similarity of two intuitionistic fuzzy sets and is used for measuring the similarity degree of the intuitionistic fuzzy sets. The higher the intuitionistic blur similarity, the more similar the different intuitionistic blur sets are. The comparison finds that the existing intuitive fuzzy similarity measurement method can generate an opposite result to intuition, and a novel intuitive fuzzy similarity measurement method which accords with the intuitionistic judgment of people is provided by defining upper and lower bounds of membership and non-membership and combining hesitation.
S1056: combining subjective weight, direct fuzzy entropy and direct fuzzy similarity, and calculating the comprehensive weight of each attribute:
wherein,represents the jth attribute C j Is/are comprehensive weight of->Represents the jth attribute C j Is included in the set of parameters.
In the invention, the comprehensive weight of each attribute needs to consider two parts, namely the weight subjectively determined by a decision maker and the weight objectively determined by the attribute itself. In general, subjective weights are given directly based on decision maker experience or environmental conditions, while objective weights are determined by the specifics of decision information. In the context of an intuitive fuzzy number, objective weights of attributes can be comprehensively considered through both aspects of an intuitive fuzzy entropy and an intuitive fuzzy similarity.
S1057: according to the direct fuzzy decision matrix, determining fuzzy evaluation grades of the Pareto suboptimal solutions:
wherein,representing the i-th Pareto suboptimal solution A i For the j-th attribute C j Is a fuzzy evaluation grade of (2).
S1058: and (3) calculating the comprehensive evaluation result of each Pareto suboptimal solution by combining the comprehensive weight of each attribute:
wherein,representing the i-th Pareto suboptimal solution A i Is a comprehensive evaluation result of (a).
S1059: and according to the comprehensive evaluation result, sequencing the Pareto suboptimal solutions according to the sequence from high to low, selecting a preset number of Pareto suboptimal solutions with the front sequencing as a final scheme, and outputting the final scheme.
Wherein, the person skilled in the art can set the preset number of sizes according to the actual situation, and the invention is not limited.
Alternatively, the preset number is 3.
In the invention, the preset number of Pareto suboptimal solutions with the earlier sequencing are output as the final solution, some emergency situations can be considered, when the solution with the earlier sequencing cannot be executed, alternative solutions are executed instead, the robustness and the flexibility of decision making can be increased, and the executable solutions can be ensured to be selected under different conditions.
The invention has the beneficial effects that according to the service requirement input by the user on the wooden furniture, the allocation of wooden furniture design orders can be automatically carried out by combining each designer based on QoS constraint, and a plurality of feasible allocation and combination schemes are determined. Further, a plurality of Pareto suboptimal solutions are generated from the feasible solutions, and the final scheme is selected from the Pareto suboptimal solutions by combining the decision attribute of each Pareto suboptimal solution, so that the accuracy and satisfaction of the combined scheme can be improved. The invention can ensure the timely and accurate distribution of orders, and avoid the problems of asynchronous information, data loss and the like caused by operations such as repeated transfer, assignment and the like. Meanwhile, the order system of the furniture enterprise can be more efficient and intelligent, so that the production efficiency is improved, the cost is reduced, and the customer satisfaction is improved. Compared with the method based on AI planning, the method can output the optimal combination scheme, and can ensure that the quality of service of the combination scheme in multidimensional QoS is optimal. The combination scheme satisfying the customer service requirement can be accurately output with respect to the graph search-based method. Compared with a method based on keyword query, the method has the advantages that keywords representing the first task and the last task are not required to be given with time and effort, and the accuracy of the generated combination scheme is improved.
In the case of example 2,
the wooden furniture service combination system based on QoS constraint provided by the embodiment of the invention can execute the wooden furniture service combination method based on QoS constraint in the embodiment 1.
The wooden furniture service combination system based on the QoS constraint provided by the embodiment of the invention can realize the steps and effects of the wooden furniture service combination method based on the QoS constraint in the embodiment 1, and the invention is not repeated for avoiding repetition.
The invention has the beneficial effects that according to the service requirement input by the user on the wooden furniture, the allocation of wooden furniture design orders can be automatically carried out by combining each designer based on QoS constraint, and a plurality of feasible allocation and combination schemes are determined. Further, a plurality of Pareto suboptimal solutions are generated from the feasible solutions, and the final scheme is selected from the Pareto suboptimal solutions by combining the decision attribute of each Pareto suboptimal solution, so that the accuracy and satisfaction of the combined scheme can be improved. The invention can ensure the timely and accurate distribution of orders, and avoid the problems of asynchronous information, data loss and the like caused by operations such as repeated transfer, assignment and the like. Meanwhile, the order system of the furniture enterprise can be more efficient and intelligent, so that the production efficiency is improved, the cost is reduced, and the customer satisfaction is improved. Compared with the method based on AI planning, the method can output the optimal combination scheme, and can ensure that the quality of service of the combination scheme in multidimensional QoS is optimal. The combination scheme satisfying the customer service requirement can be accurately output with respect to the graph search-based method. Compared with a method based on keyword query, the method has the advantages that keywords representing the first task and the last task are not required to be given with time and effort, and the accuracy of the generated combination scheme is improved.
In describing embodiments of the present invention, it should be understood that the terms "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "center", "top", "bottom", "inner", "outer", "inside", "outside", etc. indicate orientations or positional relationships based on the drawings are merely for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the present invention. Wherein "inside" refers to an interior or enclosed area or space. "peripheral" refers to the area surrounding a particular component or region.
In the description of embodiments of the present invention, the terms "first," "second," "third," "fourth" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first", "a second", "a third" and a fourth "may explicitly or implicitly include one or more such feature. In the description of the present invention, unless otherwise indicated, the meaning of "a plurality" is two or more.
In describing embodiments of the present invention, it should be noted that the terms "mounted," "connected," and "assembled" are to be construed broadly, as they may be fixedly connected, detachably connected, or integrally connected, unless otherwise specifically indicated and defined; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art.
In the description of embodiments of the invention, a particular feature, structure, material, or characteristic may be combined in any suitable manner in one or more embodiments or examples.
In describing embodiments of the present invention, it will be understood that the terms "-" and "-" are intended to be inclusive of the two numerical ranges, and that the ranges include the endpoints. For example: "A-B" means a range greater than or equal to A and less than or equal to B. "A-B" means a range of greater than or equal to A and less than or equal to B.
In the description of embodiments of the present invention, the term "and/or" is merely an association relationship describing an association object, meaning that three relationships may exist, e.g., a and/or B, may represent: a exists alone, A and B exist together, and B exists alone. In addition, the character "/" herein generally indicates that the front and rear associated objects are an "or" relationship.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (8)
1. A method for combining wooden furniture services based on QoS constraints, comprising:
s101: receiving service requirements about wooden furniture input by a user;
s102: generating a service type according to the service requirement through a semantic analyzer;
s103: determining a plurality of feasible solutions meeting the QoS constraint according to the service type and designer data stored in a designer database;
s104: generating a plurality of Pareto suboptimal solutions from the feasible solutions;
s105: combining the decision attributes of the Pareto sub-optimal solutions, and selecting a final scheme from the Pareto sub-optimal solutions;
wherein S103 specifically includes:
s1031: describing the QoS constraint value of each service in the form of a tuple;
s1032: generating a plurality of groups of service combination schemes meeting the service types according to the QoS constraint value of each service;
s1033: calculating QoS constraint values of the service combination schemes according to QoS constraint values of all services included in the service combination schemes;
s1034: judging whether the QoS constraint value of the service combination scheme meets QoS constraint or not, if so, reserving the QoS constraint value as the feasible solution;
wherein the QoS constraint values of the respective services are described in the form of tuples as:wherein, the method comprises the steps of, wherein,RTthe response time is indicated as being indicative of the time of the response,Tthe throughput is indicated by the term "throughput",Rrepresenting reliability; the step S1033 specifically includes:
when the service combination scheme includes the serial first service and the second service, a QoS constraint value of the service combination scheme is calculated by the following formula：
；
Wherein,a QoS constraint value representing a service combining scheme,RT* Indicating the response time of the service composition scheme,T* Representing the throughput of the service combination scheme,R* Representing the reliability of the service composition scheme, +.>A constraint value representing a first service is presented,RT 1 indicating the response time of the first service,T 1 representing the throughput of the first service,R 1 representing reliability of the first service, +.>A constraint value representing a second service is presented,RT 2 indicating the response time of the second service,T 2 representing the throughput of the second service,R 2 representing reliability of the second service;
when the service combination scheme includes the third service and the fourth service in parallel, a QoS constraint value of the service combination scheme is calculated by the following formula：
；
Wherein,a constraint value representing a third service is presented,RT 3 indicating the response time of the third service,T 3 indicating the throughput of the third service,R 3 indicating the reliability of the third service, +.>A constraint value representing a fourth service is presented,RT 4 indicating the response time of the fourth service,T 4 indicating the throughput of the fourth service,R 4 representing reliability of the fourth service;
when the service combination scheme comprises both serial service combination and parallel service combination, the QoS constraint value of the service combination scheme is calculated in a serial-first-parallel-last mode.
2. The QoS constraint-based wooden furniture service combining method according to claim 1, wherein S102 specifically comprises:
s1021: analyzing the service requirement;
s1022: mapping the parsed information into a service ontology to construct service demand description;
s1023: carrying out semantic matching on the service demand description and the service type;
s1024: and generating the service type according to the semantic matching level.
3. The QoS constraint based wood furniture service combining method of claim 2, wherein the service requirements include: furniture type, furniture style and customer's demand, the service ontology includes service configuration file, service model and service details, the semantic matching level includes: precision matching, insert matching, include matching and match failure.
4. The QoS constraint-based wood furniture service combining method of claim 1, wherein the QoS constraint comprises a global QoS constraint and a local QoS constraint;
the local QoS constraints include: the reliability of the services participating in the service combination scheme is greater than 70%;
the global QoS constraints include: the reliability of the service composition scheme is greater than 60% and the response time of the service composition scheme is less than 2.5s.
5. The QoS constraint-based wooden furniture service combining method according to claim 1, wherein S104 specifically comprises:
s1041: determining non-dominant schemes in the feasible solutions, and forming Pareto array lines;
s1042: constructing a dependency graph among the services;
s1043: converting the dependency graph into a hierarchical path generation graph;
s1044: and determining a Pareto suboptimal solution in the Pareto array line according to the hierarchical path generation diagram.
6. The QoS constraint based wooden furniture service combining method according to claim 5, wherein S1043 specifically comprises:
s10431: removing the first node from the queue according to the first-in first-out principle;
s10432: constructing a precursor node set of the first node;
s10433: for each precursor node of the first node, constructing or modifying a temporary Pareto optimal front edge up to the precursor node, and simultaneously verifying local QoS constraints, if any local QoS constraints are violated, removing tuples from the Pareto lineup;
s10434: verifying global QoS constraints for Pareto optimal front-to-precursor nodes, removing tuples from the Pareto lineup if any local QoS constraints are violated;
s10435: if the same precursor nodes are not contained in the queue, each precursor node is inserted into the queue to form the hierarchical path generation graph.
7. The QoS constraint-based wooden furniture service combining method according to claim 1, wherein S105 specifically comprises:
s1051: determining attribute values of each Pareto suboptimal solution to each attribute, the firstiPareto sub-optimal solutionsA i For the firstjPersonal attributesC j The attribute values of (2) are expressed as an intuitive fuzzy number:，/>represent the firstiPareto sub-optimal solutionsA i For the firstjPersonal attributesC j Lower limit value of (2),>represent the firstiPareto sub-optimal solutionsA i For the firstjPersonal attributesC j Is a fuzzy upper limit value;
s1052: constructing a direct fuzzy decision matrix of Pareto suboptimal solution:
；
wherein,Xthe direct fuzzy decision matrix is represented and,represent the firstiPareto sub-optimal solutionsA i For the firstjPersonal attributesC j Lower limit value of (2),>represent the firstiPareto sub-optimal solutionsA i For the firstjPersonal attributesC j Is the fuzzy upper limit value,/->，mRepresenting the total number of Pareto suboptimal solutions, < >>，nRepresenting the total number of attributes;
s1053: calculating direct fuzzy entropy of each attribute:
；
wherein,represent the firstjPersonal attributesC j Direct fuzzy entropy of>Represent the firstiPareto sub-optimal solutionsA i For the firstjPersonal attributesC j Is the core of the intuitive fuzzy number, +.>Represent the firstiPareto sub-optimal solutionsA i For the firstjPersonal attributesC j Is the hesitation of the intuitive fuzzy number;
s1054: calculating direct fuzzy similarity between the various attributes:
；
wherein,represent the firstαPersonal attribute->And the firstβPersonal attribute->Direct fuzzy similarity between +_>；
S1055: according to the direct fuzzy similarity among the attributes, calculating the direct fuzzy similarity of the attributes:
；
wherein,represent the firstjPersonal attributesC j Similarity of->，nRepresenting the total number of attributes;
s1056: combining subjective weight, direct fuzzy entropy and direct fuzzy similarity, and calculating the comprehensive weight of each attribute:
；
wherein,represent the firstjPersonal attributesC j Is/are comprehensive weight of->Representing the jth attributeC j Is a subjective weight of (2);
s1057: determining the fuzzy evaluation grade of each Pareto suboptimal solution according to the direct fuzzy decision matrix:
；
wherein,represent the firstiPareto sub-optimal solutionsA i For the firstjPersonal attributesC j Is a fuzzy evaluation grade of (2);
s1058: and (3) calculating the comprehensive evaluation result of each Pareto suboptimal solution by combining the comprehensive weight of each attribute:
；
wherein,represent the firstiPareto sub-optimal solutionsA i Is a comprehensive evaluation result of (1);
s1059: and according to the comprehensive evaluation result, sequencing the Pareto suboptimal solutions according to the sequence from high to low, selecting a preset number of Pareto suboptimal solutions with the front sequencing as a final scheme, and outputting the final scheme.
8. A QoS constraint based wood furniture service composition system, wherein the QoS constraint based wood furniture service composition method of any one of claims 1 to 7 is executable.
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