CN115759510B - Matching method of cloud manufacturing task and machining manufacturing service - Google Patents
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Abstract
The invention discloses a matching method of cloud manufacturing tasks and machining manufacturing services, which comprises the following steps: constructing a preliminary matching rule of multi-type manufacturing tasks and manufacturing services, and screening out service resources meeting the minimum requirements of sub-manufacturing tasks; constructing a double-layer evaluation index system of the service resource, calculating an evaluation value of the service resource under the subjective function index, and calculating an evaluation value of the service resource under the objective performance index; the subjective function index and the objective performance index factors jointly determine the comprehensive evaluation value of the service resource; corresponding weights are set for all factor values in the subjective function indexes, corresponding weights are set for all factor values in the objective performance indexes, a comprehensive matching degree solving model is built, and service resources with high matching degree are selected from the candidate service resource sets to form a service resource set. The invention comprehensively evaluates the candidate service resources by utilizing the subjective function index and the objective performance index, and comprehensively and objectively completes the solution of the matching degree of the manufacturing task and the manufacturing service.
Description
Technical Field
The invention belongs to the field of cloud manufacturing service optimal configuration, and particularly relates to a matching method of cloud manufacturing tasks and machining manufacturing services.
Background
With the rapid development of social productivity, users' demands for equipment products tend to be personalized, dynamic and customized. In this context, rigid line-based manufacturing modes have been difficult to quickly respond to a user's personalized needs; therefore, in the cloud manufacturing environment, the platform end rapidly matches the manufacturing task submitted by the user to a proper and high-quality machining manufacturing service based on the characteristics and requirements of the task.
The Chinese patent application No. CN201710763293.2, named "intelligent matching method for supply and demand in cloud manufacturing environment", wherein the matching method uses keyword matching rules to perform initial matching between user demands and service resources in a service resource information base, and obtains a service resource primary selection set; in the initial selection set, establishing a relation reasoning rule based function information matching of the demand and service resources to obtain a service resource pre-selection set; and in the preselection set, matching the evaluation information of the service demands and the service resources by using a fuzzy comprehensive evaluation method, so as to obtain the optimal service resources. The matching method is used for matching the optimal person from the mass service resources for the service demand party by analyzing the basic information, the functional information and the evaluation information of the service resources. However, the matching method has the problem of poor accuracy of supply and demand matching due to incomplete analysis of evaluation information and functional information of service resources.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide a matching method of cloud manufacturing tasks and machining manufacturing services, so as to solve the problem of poor matching precision between mass cloud manufacturing resources and manufacturing requirements in the prior art.
In order to achieve the above purpose, the invention adopts the following technical scheme:
the invention relates to a matching method of cloud manufacturing tasks and machining manufacturing services, which comprises the following steps:
1) Constructing a preliminary matching rule of multi-type manufacturing tasks and manufacturing services, and screening out service resources meeting the minimum requirements of sub-manufacturing tasks by comparing the manufacturing tasks with manufacturing service information items one by one to form a candidate service resource set;
2) Constructing a double-layer evaluation index system of the service resource, calculating an evaluation value of the service resource under subjective function indexes through the sub-manufacturing tasks and the function index association degree of the service resource, and calculating the evaluation value of the service resource under objective performance indexes by using a fuzzy comprehensive evaluation method; the subjective function index and the objective performance index factors jointly determine the comprehensive evaluation value of the service resource;
3) And setting corresponding weights for the factor values in the subjective function indexes by adopting an analytic hierarchy process, setting corresponding weights for the factor values in the objective performance indexes by adopting an entropy value process, constructing a comprehensive matching degree solving model by adopting a linear weighting sum process, and selecting service resources with high matching degree from the candidate service resource sets to form a high-quality and ordered service resource set.
Further, the information items of the manufacturing task include: task granularity, task object, required material, expected price, tact requirement, machining size requirement, machining precision requirement and surface roughness requirement.
Further, the information item of the manufacturing service includes: service granularity, service object, available material, service price, production cycle, achievable machining size, achievable machining precision, and achievable surface roughness.
Further, the preliminary matching rule of the multi-type manufacturing task and the manufacturing service is specifically as follows:
the task granularity is consistent with the service granularity; the task object needs to be contained in the service object; the materials required for the task need to be contained in the usable materials; the expected price interval of the manufacturing task and the service price interval of the manufacturing resource need to have an overlapping part; the process size requirements of a manufacturing task need to be contained in the reachable process size intervals of the manufacturing resources; the machining precision requirement of the manufacturing task needs to be contained in an accessible machining precision interval of the manufacturing resource; manufacturing tasks require that the component surface roughness requirements be contained in the range of achievable surface roughness for the manufacturing resources.
Further, the tact requirement of the manufacturing task requires indirect calculation; the production takt is a target time, reflects the time expected by the service resource to produce a single part or component task, and the value of the target time depends on the number of components in the sub-manufacturing task and the delivery time, and service man-hour and state information in the service resource, and the calculation formula is as follows:
wherein PTakt represents a tact time in minutes per piece (min/PC); SRTime represents the service man-hour of the service resource in minutes per day (min/d); starTime represents service resource start processing time, delitime represents time of product delivery; PNums represents the number of products in units of pieces.
Further, the number of components in the sub-manufacturing task is a set of the number of components in the meta-task, and the delivery time in the sub-manufacturing task is a set of the delivery time of the meta-task; the production tact of the manufacturing task is described in the form of a fuzzy section, and the calculation method is as follows:
wherein STPNums is the number of components; deliTimes is the lead time of the sub-manufacturing task; sum (STPNums) represents the sum of the component numbers of all meta-tasks; min (DeliTimes) the minimum delivery time in the sub-manufacturing task and max (DeliTimes) the maximum delivery time in the sub-manufacturing task; the range of tact intervals for the manufacturing task is known as the minimum and maximum tacts.
Further, the expected price in the sub-manufacturing task is a set of expected prices of the meta-task, the expected price of the meta-task is an interval value, and the value range is the minimum and maximum expected prices; the matching principle of the expected price and the service price is as follows: the intersection of the service price and the expected price of any one of the sub-manufacturing tasks is not empty; the expected price calculation method for the sub-manufacturing task is as follows:
ExpectPrice＝[min(STExPrices left )，min(STExPrices right )] (3)
where STExPrices is the desired price for the sub-manufacturing task; STExPrices left With STExPrices right Respectively representing a minimum value set and a maximum value set of expected prices of all meta-tasks in the sub-manufacturing tasks; thus, min (STExPries left ) Is the minimum value in the minimum value set of expected prices for all meta-tasks, min (STExPrices right ) Is the minimum in the set of expected price maxima for all meta-tasks.
Further, the construction of the double-layer evaluation index system of the service resource specifically comprises the following steps:
setting the evaluation index set as U= { U 1 ，U 2 U, where 1 The first layer of the evaluation index system, namely the functional index, reflects the service capability of the service resource, namely the service information quintuple in the service resource; the service capability is set by the resource provider, and belongs to subjective function indexes; u (U) 2 The second layer of the evaluation index system, namely the performance index, namely the service quality information in the service resource, belongs to the objective performance evaluation index; the subjective function index and the objective performance index together form a double-layer evaluation index system of the service resource.
Further, the service information quintuple is a service object, a usable material, a production period, a service price and a service capability, respectively.
Further, the function index is U 1 = { SMSi, SMP, SRa }, including process size range SMSi, highest achievable process accuracy SMP, and minimum surface roughness SRa; u (U) 1 The index item of (2) corresponds to the processing size requirement, the processing precision requirement and the surface roughness requirement of the sub-manufacturing task; the method for evaluating the functional index of the service resource comprises the following steps:
taking the processing requirement of the sub-manufacturing task as a reference target;
comparing the functional index of each candidate service resource with the processing requirement of the corresponding sub-manufacturing task, wherein the closer the values of the corresponding items are, the more the service resource is adapted to the sub-manufacturing task on the index item;
the gray correlation analysis is adopted to calculate the correlation epsilon between the functional index item of the service resource and the processing demand item of the manufacturing task, the epsilon reflects the index correlation between the service resource and the task processing demand on the functional index, the index correlation is used as the evaluation value of the functional index of the service resource, and the calculation formula is as follows:
wherein:
the association degree of the kth function index of the service resource j in the candidate service resource set of the sub-manufacturing task i is represented; />
A value representing a machining requirement information item k of the sub-manufacturing task i; />
A value representing a kth function indicator of a service resource j in the candidate set of service resources; />
Representing the minimum value of the difference values of all the sub-manufacturing tasks and the candidate service resources on the function index corresponding items; />
Representing the maximum value of the difference values of all the sub-manufacturing tasks and the candidate service resources on the function index corresponding items; ρ represents the resolution coefficient, ρ ε (0, 1).
Further, the performance index U 2 Comprising the following steps: product qualification rate, delivery time, cost performance, service response speed and customer evaluation; u (U) 2 The values of the index items dynamically change along with time, and when the service resource completes a certain manufacturing task, the service resource can be producedNew group of U 2 A value; in the preliminary matching stage, screening service resources by comparing the expected price with the service price; the service price is a predicted interval value, and the cost performance is used as an evaluation index for measuring whether the service price is low or not; calculating an evaluation value of the service resource under the objective performance index by adopting a fuzzy comprehensive evaluation method, which specifically comprises the following steps:
21 Making evaluation grade division rule of index set, U 2 The evaluation grade classification rule of each index is as follows: the evaluation grade degree of the index from left to right is gradually reduced; u (U) 2 The qualification rate, delivery time and service response speed of the medium products are evaluated through historical service data of manufacturing resources;
22 Computing service resources at U 2 The calculation formula for defining the membership degree r in each evaluation level of each index is as follows:
wherein:
representing the membership degree of the jth service resource in the candidate service resource set under the q-th evaluation level of the index k; n (N) (j) The number of times of task completion of the jth service resource is represented; />
Representing the task times of the jth service resource to obtain the qth evaluation under the index k;
23 Building a membership matrix, obtaining membership of each index according to a formula (5), and combining the membership matrix into a membership matrix R shown as follows:
24 Normalized index value, i.e. solving for the ensemble of service resources under a certain indexThe combined evaluation value is calculated by using normalized vector (1,0.85,0.75,0.6) T And respectively representing scoring values of evaluation grades I, II, III and IV, wherein the comprehensive evaluation vector A of the service resource under each index is as follows:
from this, it can be seen that A is a 5×1 column vector, and each vector element corresponds to a performance index evaluation set U 2 The evaluation value of each performance index.
Further, the step 3) specifically includes:
correlation of indexes
Evaluation value->
Decision vector x of the composition model, while U is to be obtained by analytic hierarchy process 1 Weight vector w of (2) k U obtained by entropy method 2 Weight vector w 'of (2)' k A weight coefficient vector w as a model;
the functional index weight value solving system based on the analytic hierarchy process is divided into a target layer, an index layer and a scheme layer; the target layer refers to the optimal service resources finally required; the index layer refers to the processing size, the processing precision and the surface roughness; the scheme layer corresponds to candidate service resources screened by the matching rule; (because the geographic position of the service resource relates to logistics cost, the matching result of the service resource and the manufacturing task is influenced) introducing a geographic position coefficient Lo of the service resource to optimize the comprehensive matching degree solving model; the comprehensive matching degree solving model of the manufacturing task and the service resource is as follows:
wherein e (i，j) Representation sonComprehensive matching degree of manufacturing task i and service resource j and Lo (i，j) Representing a geographic location coefficient between manufacturing task i and service resource j; taking the geographical position of the service resource nearest to the task publisher as a reference, wherein the Lo of the service resource is 1, and the Lo of other service resources is equal to the ratio of the position distance of the service resource to the reference position distance;
traversing all the sub-manufacturing tasks, calculating the comprehensive matching degree between the sub-manufacturing tasks and the candidate service resources, and sequencing the sub-manufacturing tasks according to the matching degree to obtain the ordered optimal service resources of each sub-task.
The invention has the beneficial effects that:
1. the invention realizes the preliminary elimination and selection of mass manufacturing services by using the matching rules based on the information items of the manufacturing tasks, effectively improves the optimal matching speed of the service resources, and can be applied to solving the problem of mass platform manufacturing service matching.
2. The invention comprehensively evaluates the candidate service resources by utilizing the subjective function index and the objective performance index, and comprehensively and objectively completes the solution of the matching degree of the manufacturing task and the manufacturing service.
Drawings
FIG. 1 is a schematic diagram of the method of the present invention.
Detailed Description
The invention will be further described with reference to examples and drawings, to which reference is made, but which are not intended to limit the scope of the invention.
Referring to fig. 1, a matching method of cloud manufacturing task and machining manufacturing service according to the present invention comprises the following steps:
constructing a preliminary matching rule of multi-type manufacturing tasks and manufacturing services, and screening out service resources meeting the minimum requirements of sub-manufacturing tasks by comparing the manufacturing tasks with manufacturing service information items one by one to form a candidate service resource set;
wherein the information items of the manufacturing task include: task granularity, task object, required material, expected price, tact requirement, machining size requirement, machining precision requirement and surface roughness requirement.
Wherein the information items of the manufacturing service include: service granularity, service object, available material, service price, production cycle, achievable machining size, achievable machining precision, and achievable surface roughness.
The preliminary matching rule of the multi-type manufacturing task and the manufacturing service is specifically as follows:
the task granularity is consistent with the service granularity; the task object needs to be contained in the service object; the materials required for the task need to be contained in the usable materials; the expected price interval of the manufacturing task and the service price interval of the manufacturing resource need to have an overlapping part; the process size requirements of a manufacturing task need to be contained in the reachable process size intervals of the manufacturing resources; the machining precision requirement of the manufacturing task needs to be contained in an accessible machining precision interval of the manufacturing resource; manufacturing tasks require that the component surface roughness requirements be contained in the range of achievable surface roughness for the manufacturing resources.
Wherein, the production takt requirement of the manufacturing task needs to be indirectly calculated; the production takt is a target time, reflects the time expected by the service resource to produce a single part or component task, and the value of the target time depends on the number of components in the sub-manufacturing task and the delivery time, and service man-hour and state information in the service resource, and the calculation formula is as follows:
wherein PTakt represents a tact time in minutes per piece (min/PC); SRTime represents the service man-hour of the service resource in minutes per day (min/d); starTime represents service resource start processing time, delitime represents time of product delivery; PNums represents the number of products in units of pieces.
Wherein, the number of components in the sub-manufacturing task is the set of the number of components of the meta-task, and the delivery time in the sub-manufacturing task is the set of the delivery time of the meta-task; the production tact of the manufacturing task is described in the form of a fuzzy section, and the calculation method is as follows:
wherein STPNums is the number of components; deliTimes is the lead time of the sub-manufacturing task; sum (STPNums) represents the sum of the component numbers of all meta-tasks; min (DeliTimes) the minimum delivery time in the sub-manufacturing task and max (DeliTimes) the maximum delivery time in the sub-manufacturing task; the range of tact intervals for the manufacturing task is known as the minimum and maximum tacts.
The expected price in the sub-manufacturing task is a set of expected prices of the meta-task, the expected price of the meta-task is an interval value, and the value range is the minimum and maximum expected prices; the matching principle of the expected price and the service price is as follows: the intersection of the service price and the expected price of any one of the sub-manufacturing tasks is not empty; the expected price calculation method for the sub-manufacturing task is as follows:
ExpectPrice＝[min(STExPrices left )，min(STExPrices right )] (3)
where STExPrices is the desired price for the sub-manufacturing task; STExPrices left With STExPrices right Respectively representing a minimum value set and a maximum value set of expected prices of all meta-tasks in the sub-manufacturing tasks; thus, min (STExPries left ) Is the minimum value in the minimum value set of expected prices for all meta-tasks, min (STExPrices right ) Is the minimum in the set of expected price maxima for all meta-tasks.
Constructing a double-layer evaluation index system of the service resource, calculating an evaluation value of the service resource under subjective function indexes through the sub-manufacturing tasks and the function index association degree of the service resource, and calculating the evaluation value of the service resource under objective performance indexes by using a fuzzy comprehensive evaluation method; the subjective function index and the objective performance index factors jointly determine the comprehensive evaluation value of the service resource;
the double-layer evaluation index system for constructing the service resource specifically comprises the following steps:
setting the evaluation index set as U= { U 1 ，U 2 U, where 1 The first layer of the evaluation index system, namely the functional index, reflects the service capability of the service resource, namely the service information quintuple in the service resource; the service capability is set by the resource provider, and belongs to subjective function indexes; u (U) 2 The second layer of the evaluation index system, namely the performance index, namely the service quality information in the service resource, belongs to the objective performance evaluation index; the subjective function index and the objective performance index together form a double-layer evaluation index system of the service resource.
Wherein the service information quintuple is a service object, a usable material, a production period, a service price and a service capability respectively.
Wherein the function index is U 1 = { SMSi, SMP, SRa }, including process size range SMSi, highest achievable process accuracy SMP, and minimum surface roughness SRa; u (U) 1 The index item of (2) corresponds to the processing size requirement, the processing precision requirement and the surface roughness requirement of the sub-manufacturing task; the method for evaluating the functional index of the service resource comprises the following steps:
taking the processing requirement of the sub-manufacturing task as a reference target;
comparing the functional index of each candidate service resource with the processing requirement of the corresponding sub-manufacturing task, wherein the closer the values of the corresponding items are, the more the service resource is adapted to the sub-manufacturing task on the index item;
the gray correlation analysis is adopted to calculate the correlation epsilon between the functional index item of the service resource and the processing demand item of the manufacturing task, the epsilon reflects the index correlation between the service resource and the task processing demand on the functional index, the index correlation is used as the evaluation value of the functional index of the service resource, and the calculation formula is as follows:
wherein:
the association degree of the kth function index of the service resource j in the candidate service resource set of the sub-manufacturing task i is represented; />
A value representing a machining requirement information item k of the sub-manufacturing task i; />
A value representing a kth function indicator of a service resource j in the candidate set of service resources; />
Representing the minimum value of the difference values of all the sub-manufacturing tasks and the candidate service resources on the function index corresponding items; />
Representing the maximum value of the difference values of all the sub-manufacturing tasks and the candidate service resources on the function index corresponding items; ρ represents the resolution coefficient, ρ ε (0, 1).
Wherein the performance index U 2 Comprising the following steps: product qualification rate, delivery time, cost performance, service response speed and customer evaluation; u (U) 2 The values of the index items dynamically change along with time, and when the service resource completes a certain manufacturing task, a new set of U is generated 2 A value; in the preliminary matching stage, screening service resources by comparing the expected price with the service price; the service price is a predicted interval value, and the cost performance is used as an evaluation index for measuring whether the service price is low or not; calculating an evaluation value of the service resource under the objective performance index by adopting a fuzzy comprehensive evaluation method, which specifically comprises the following steps:
21 Making evaluation grade division rule of index set, U 2 The evaluation grade classification rule of each index is as follows: the evaluation grade degree of the index from left to right is gradually reduced; u (U) 2 The qualification rate, delivery time and service response speed of the medium products are evaluated through historical service data of manufacturing resources;
wherein, the evaluation of the cost performance and the customer evaluation is a scoring system; specifically, the product yield according to service resources is classified into four classes: 90%, 85%, 75% and 60%. The service resources are classified into four classes according to delivery time: advanced for 1 day, on time, delay for 7 days and delay for 15 days. The response time of the service resource is divided into four grades: day response, 1 day post response, 2 days post response, 3 days post response. U (U) 2 The evaluation ranking rule of each index in (c) is shown in table 1 below,
TABLE 1
22 Computing service resources at U 2 The calculation formula for defining the membership degree r in each evaluation level of each index is as follows:
wherein:
representing the membership degree of the jth service resource in the candidate service resource set under the q-th evaluation level of the index k; n (N) (j) The number of times of task completion of the jth service resource is represented; />
Representing the task times of the jth service resource to obtain the qth evaluation under the index k;
23 Building a membership matrix, obtaining membership of each index according to a formula (5), and combining the membership matrix into a membership matrix R shown as follows:
24 Normalized index value, i.e. solving for the ensemble of service resources under a certain indexThe combined evaluation value is calculated by using normalized vector (1,0.85,0.75,0.6) T And respectively representing scoring values of evaluation grades I, II, III and IV, wherein the comprehensive evaluation vector A of the service resource under each index is as follows:
from this, it can be seen that A is a 5×1 column vector, and each vector element corresponds to a performance index evaluation set U 2 The evaluation value of each performance index.
Setting corresponding weights for all factor values in subjective function indexes by adopting an analytic hierarchy process, setting corresponding weights for all factor values in objective performance indexes by adopting an entropy method, constructing a comprehensive matching degree solving model by adopting a linear weighting sum method, and selecting service resources with high matching degree from candidate service resource sets to form a high-quality and ordered service resource set;
correlation of indexes
Evaluation value->
Decision vector x of the composition model, while U is to be obtained by analytic hierarchy process 1 Weight vector w of (2) k U obtained by entropy method 2 Weight vector w 'of (2)' k A weight coefficient vector w as a model;
the functional index weight value solving system based on the analytic hierarchy process is divided into a target layer, an index layer and a scheme layer; the target layer refers to the optimal service resources finally required; the index layer refers to the processing size, the processing precision and the surface roughness; the scheme layer corresponds to candidate service resources screened by the matching rule; (because the geographic position of the service resource relates to logistics cost, the matching result of the service resource and the manufacturing task is influenced) introducing a geographic position coefficient Lo of the service resource to optimize the comprehensive matching degree solving model; the comprehensive matching degree solving model of the manufacturing task and the service resource is as follows:
wherein e (i，j) Representing the comprehensive matching degree of sub-manufacturing task i and service resource j, lo (i，j) Representing a geographic location coefficient between manufacturing task i and service resource j; taking the geographical position of the service resource nearest to the task publisher as a reference, wherein the Lo of the service resource is 1, and the Lo of other service resources is equal to the ratio of the position distance of the service resource to the reference position distance;
traversing all the sub-manufacturing tasks, calculating the comprehensive matching degree between the sub-manufacturing tasks and the candidate service resources, and sequencing the sub-manufacturing tasks according to the matching degree to obtain the ordered optimal service resources of each sub-task.
The present invention has been described in terms of the preferred embodiments thereof, and it should be understood by those skilled in the art that various modifications can be made without departing from the principles of the invention, and such modifications should also be considered as being within the scope of the invention.
Claims (1)
1. A method for matching cloud manufacturing tasks with machining manufacturing services, comprising the following steps:
1) Constructing a preliminary matching rule of multi-type manufacturing tasks and manufacturing services, and screening out service resources meeting the minimum requirements of sub-manufacturing tasks by comparing the manufacturing tasks with manufacturing service information items one by one to form a candidate service resource set;
2) Constructing a double-layer evaluation index system of the service resource, calculating an evaluation value of the service resource under subjective function indexes through the sub-manufacturing task and the function index association degree of the service resource, and calculating the evaluation value of the service resource under objective performance indexes; the subjective function index and the objective performance index factors jointly determine the comprehensive evaluation value of the service resource;
3) Setting corresponding weights for all factor values in subjective function indexes by adopting an analytic hierarchy process, setting corresponding weights for all factor values in objective performance indexes by adopting an entropy method, constructing a comprehensive matching degree solving model by adopting a linear weighting sum method, and selecting service resources with high matching degree from candidate service resource sets to form a service resource set;
the information items of the manufacturing task include: task granularity, task objects, required materials, expected price, production takt requirements, machining size requirements, machining precision requirements and surface roughness requirements;
the information items of the manufacturing service include: service granularity, service object, available material, service price, production cycle, achievable machining size, achievable machining precision and achievable surface roughness;
the preliminary matching rules of the multi-type manufacturing tasks and the manufacturing services are specifically as follows:
the task granularity is consistent with the service granularity; the task object needs to be contained in the service object; the materials required for the task need to be contained in the usable materials; the expected price interval of the manufacturing task and the service price interval of the manufacturing resource need to have an overlapping part; the process size requirements of a manufacturing task need to be contained in the reachable process size intervals of the manufacturing resources; the machining precision requirement of the manufacturing task needs to be contained in an accessible machining precision interval of the manufacturing resource; the manufacturing task requirements for the surface roughness of the part need to be contained in the achievable surface roughness window of the manufacturing resource;
the tact requirement of the manufacturing task requires indirect calculation; the production takt is a target time, reflects the time expected by the service resource to produce a single part or component task, and the value of the target time depends on the number of components in the sub-manufacturing task and the delivery time, and service man-hour and state information in the service resource, and the calculation formula is as follows:
wherein PTakt represents a tact; SRTime represents the service man-hour of the service resource; starTime represents service resource start processing time, delitime represents time of product delivery; PNums represents the number of products;
the number of components in the sub-manufacturing task is a set of the number of components of the meta-task, and the delivery time in the sub-manufacturing task is a set of the delivery time of the meta-task; the production tact of the manufacturing task is described in the form of a fuzzy section, and the calculation method is as follows:
wherein STPNums is the number of components; deliTimes is the lead time of the sub-manufacturing task; sum (STPNums) represents the sum of the component numbers of all meta-tasks; min (DeliTimes) the minimum delivery time in the sub-manufacturing task and max (DeliTimes) the maximum delivery time in the sub-manufacturing task;
the expected price in the sub-manufacturing task is a set of expected prices of the meta-task, the expected price of the meta-task is an interval value, and the value range is the minimum and maximum expected prices; the matching principle of the expected price and the service price is as follows: the intersection of the service price and the expected price of any one of the sub-manufacturing tasks is not empty; the expected price calculation method for the sub-manufacturing task is as follows:
ExpectPrice＝[min(STExPrices left ),min(STExPrices right )] (3)
where STExPrices is the desired price for the sub-manufacturing task; STExPrices left With STexPries right Respectively representing a minimum value set and a maximum value set of expected prices of all meta-tasks in the sub-manufacturing tasks; thus, min (STExPries left ) Is the minimum value in the minimum value set of expected prices for all meta-tasks, min (STExPrices right ) Is the minimum value in the maximum value set of expected prices of all meta-tasks;
the double-layer evaluation index system for constructing the service resource specifically comprises the following steps:
setting the evaluation index set as U= { U 1 ,U 2 U, where 1 Representing the first layer of the evaluation index system, namely the functional index,reflecting the service capability of the service resource, namely service information quintuple in the service resource; the service capability is set by the resource provider, and belongs to subjective function indexes; u (U) 2 The second layer of the evaluation index system, namely the performance index, namely the service quality information in the service resource, belongs to the objective performance evaluation index; the subjective function index and the objective performance index together form a double-layer evaluation index system of the service resource;
the function index is U 1 = { SMSi, SMP, SRa }, including process size range SMSi, highest achievable process accuracy SMP, and minimum surface roughness SRa; u (U) 1 The index item of (2) corresponds to the processing size requirement, the processing precision requirement and the surface roughness requirement of the sub-manufacturing task; the method for evaluating the functional index of the service resource comprises the following steps:
taking the processing requirement of the sub-manufacturing task as a reference target;
comparing the functional index of each candidate service resource with the processing requirement of the corresponding sub-manufacturing task, wherein the closer the values of the corresponding items are, the more the service resource is adapted to the sub-manufacturing task on the index item;
the gray correlation analysis is adopted to calculate the correlation epsilon between the functional index item of the service resource and the processing demand item of the manufacturing task, the epsilon reflects the index correlation between the service resource and the task processing demand on the functional index, the index correlation is used as the evaluation value of the functional index of the service resource, and the calculation formula is as follows:
wherein:
the association degree of the kth function index of the service resource j in the candidate service resource set of the sub-manufacturing task i is represented; />
Processing demand letter indicating sub-manufacturing task iThe value of the rest k; />
A value representing a kth function indicator of a service resource j in the candidate set of service resources; />
Representing the minimum value of the difference values of all the sub-manufacturing tasks and the candidate service resources on the function index corresponding items; />
Representing the maximum value of the difference values of all the sub-manufacturing tasks and the candidate service resources on the function index corresponding items; ρ represents the resolution coefficient, ρ ε (0, 1);
the step 3) specifically comprises the following steps:
correlation of indexes
Evaluation value->
Decision vector x of the composition model, while U is to be obtained by analytic hierarchy process 1 Weight vector w of (2) k U obtained by entropy method 2 Weight vector w 'of (2)' k A weight coefficient vector w as a model;
the functional index weight value solving system based on the analytic hierarchy process is divided into a target layer, an index layer and a scheme layer; the target layer refers to the optimal service resources finally required; the index layer refers to the processing size, the processing precision and the surface roughness; the scheme layer corresponds to candidate service resources screened by the matching rule; introducing a geographic position coefficient Lo of a service resource to optimize the comprehensive matching degree solving model; the comprehensive matching degree solving model of the manufacturing task and the service resource is as follows:
wherein e (i,j) Representing the comprehensive matching degree of sub-manufacturing task i and service resource j, lo (i,j) Representing a geographic location coefficient between manufacturing task i and service resource j; taking the geographical position of the service resource nearest to the task publisher as a reference, wherein the Lo of the service resource is 1, and the Lo of other service resources is equal to the ratio of the position distance of the service resource to the reference position distance;
traversing all the sub-manufacturing tasks, calculating the comprehensive matching degree between the sub-manufacturing tasks and the candidate service resources, and sequencing the sub-manufacturing tasks according to the matching degree to obtain the ordered optimal service resources of each sub-task.
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