CN115081831A - Event recombination optimization method and system for industrial internet manufacturing resource classification - Google Patents
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Abstract
The invention provides an event recombination optimization method and system for industrial internet manufacturing resource classification, which comprises the following steps: uploading a manufacturing event by a manufacturing party, decomposing the manufacturing event into a plurality of sub-events according to a manufacturing process, and constructing an event cloud pool, wherein the manufacturing event and the sub-events comprise a manufacturing entity, a manufacturing process and a manufacturing result; the employer uploads the manufacturing tasks, associates at least one candidate sub-event which can achieve the manufacturing effect of the sub-tasks for each sub-task from the event pool according to the manufacturing process of the manufacturing tasks, and completes the decomposition of the manufacturing tasks; recombining the candidate sub-events of all the sub-tasks to obtain a plurality of candidate manufacturing events for completing the manufacturing tasks, and selecting the candidate manufacturing event with the optimal service quality as the optimal manufacturing event for completing the manufacturing tasks; the service quality comprises at least one of three service quality indexes of termination time, cost and reliability. The method can be effective, the calculation range of subsequent manufacturing resource allocation is reduced, the calculation redundancy is reduced, and the calculation efficiency is improved.
Description
Technical Field
The invention relates to the technical field of manufacturing resource scheduling and distribution, in particular to an event recombination optimization method and system for industrial internet manufacturing resource classification.
Background
Digital services under an industrial internet of things platform driven by big data and internet of things technology have become a development trend of manufacturing services. In order to better perform manufacturing services in an industrial internet platform, a problem to be solved first is to uniformly, accurately and dynamically describe various manufacturing resources in the industrial internet platform. In 2020, a paper entitled "cloud manufacturing resource description based on environment video semantics" is disclosed in the Chinese journal "computer integrated manufacturing system", and the paper provides a hierarchical environment video semantic model for expressing dynamic changes of resources in a shape display manner aiming at the problems that resources in various industries are difficult to be uniformly described, the update of available resources in a resource cloud pool is delayed, the execution strength of a resource matching plan is weak and the like in the cloud manufacturing environment. The model defines the hierarchical structure of environment video semantics and the hierarchical expression of data by facing to three changed domains (entity domain (manufacturing resources) -behavior process domain-result domain), organically combines the environment video semantics with the video content semantics in each level semantic description, supports the associated expression of multi-place environment video data, and lays a foundation for the dynamic real-time, high-efficiency and reliable resource matching in the subsequent cloud manufacturing environment. However, the digital Manufacturing Resource (MR) of video semantic description is accompanied by complication of data structure and rapid increase of data volume, resulting in overload of the industrial internet platform IIP information, and difficulty in supporting efficient matching of manufacturing resource and manufacturing service of manufacturing task.
Manufacturing Resource Classification (MRC) is a prerequisite for manufacturing service applications such as production process control, equipment management services, asset allocation coordination, and the like. According to the white paper of the industrial internet in 2021, the industrial internet has wide application (application scene accounts for 80%) in manufacturing service-related production process control, equipment management service, asset allocation protocol, and the like, but manufacturing and process modules closely related to efficient manufacturing resource classification and digitization of manufacturing resources are less digitized (only 1%). Meanwhile, the existing manufacturing resource classification digital manufacturing resource method mainly classifies slowly updated single manufacturing resources, and has the problems of unreasonable resource classification granularity and redundancy in calculation, so that the method is difficult to be used for accurately and efficiently classifying the digital manufacturing resources facing an industrial internet platform.
As a potential tool for classifying manufacturing resources and digitizing manufacturing resources, Event Description (ED) is widely studied in the fields of multi-stage production systems, social media, manufacturing information systems, and discrete event systems, but it is mostly used to describe processes that occur infrequently and have unfixed event results (e.g., CBM stop page events, Traffic acquisition events, and Equipment failure events). The frequency of the manufacturing process is high, the result is determined (for example, tooth profile machining with the precision of 6-10 grades and machining with the precision of 7-9 grades), and compared with the existing events, the manufacturing events have the characteristics of large number of events, complex event structure and the like. The problem of information overload of an industrial internet platform is aggravated by the large number of events and the complex event structure, so that the existing event description is difficult to be directly applied to the description of the whole manufacturing process. How to combine the advantages of high description accuracy and strong information aggregation of event description to solve the problems of reasonable division of manufacturing resource granularity and information overload, and is a problem to be solved urgently by applying the event description to accurate and efficient classification of digital manufacturing resources of an industrial internet platform.
Disclosure of Invention
The invention aims to at least solve the technical problems in the prior art and provides an event reorganization optimization method and system for industrial internet manufacturing resource classification.
In order to achieve the above object, according to a first aspect of the present invention, there is provided an event reorganization optimization method for industrial internet manufacturing resource classification, comprising: uploading a manufacturing event by a manufacturing party, decomposing the manufacturing event into a plurality of sub-events according to a manufacturing process, and constructing an event cloud pool by using the plurality of sub-events and the manufacturing event, wherein the manufacturing event and the sub-event comprise a manufacturing entity, a manufacturing process and a manufacturing result; an employer uploads a manufacturing task, associates at least one candidate sub-event which can achieve the manufacturing effect of the sub-task for each sub-task from an event cloud pool according to the manufacturing process of the manufacturing task, and completes the decomposition of the manufacturing task; recombining the candidate sub-events of all the sub-tasks to obtain a plurality of candidate manufacturing events for completing the manufacturing task, calculating the service quality of the candidate manufacturing events, and selecting the candidate manufacturing event with the optimal service quality as the optimal manufacturing event for completing the manufacturing task; the service quality comprises at least one of three service quality indexes of termination time, cost and reliability.
The technical scheme is as follows: decomposing the manufacturing event into a plurality of manufacturing sub-events according to the manufacturing process based on the manufacturing event; the manufacturing sub-event is associated with the sub-task through a manufacturing result, and then the decomposition from the manufacturing task to the sub-task is completed; matching a candidate sub-event set for each sub-task to complete the task from the event cloud pool. The manufacturing event and the manufacturing task are uniformly decomposed, effective manufacturing information can be integrated, and the calculation range in subsequent recombination optimization is reduced. The method has the advantages that the candidate sub-events are recombined and optimized based on the service quality of the manufacturing event, the effective data sparsity of the digital manufacturing resources is relieved through service quality calculation, the calculation redundancy is reduced, and the calculation efficiency is improved. The manufacturing event granularity is a sub-event comprising a manufacturing entity, a manufacturing process and a manufacturing result in an actual production process, and the manufacturing event decomposition method using the sub-event as a dynamic granularity unit comprises more manufacturing information and integrates the manufacturing sub-events corresponding to related manufacturing subtasks, so that the method has better performance in terms of accuracy and computational efficiency.
In a preferred embodiment of the present invention, in the event cloud pool, different enterprises have different manufacturing processes for the same manufacturing task, and the different manufacturing processes correspond to different manufacturing events and different sub-events.
The technical scheme is as follows: the manufacturing event and the sub-event are represented by three elements including a manufacturing entity, a manufacturing process and a manufacturing result, and any element is different and represents different manufacturing events or sub-events, so that manufacturing services provided by different enterprises can be contained in the event cloud pool.
In a preferred embodiment of the present invention, when the service quality includes at least two of the three service quality indicators of the termination time, the cost and the reliability, the calculation process of the service quality of the mth candidate manufacturing event is: by using
A quality of service indicator x representing the mth candidate manufacturing event, m being a positive integer, x being t, c, rel,
indicating the end time of the mth candidate manufacturing event,
representing the cost of the mth candidate manufacturing event,
representing the reliability of the mth candidate manufacturing event; to pair
And (3) carrying out normalization treatment:
The technical scheme is as follows: when the number of the service quality indexes is more than or equal to two, the service quality indexes are fused in the mode, firstly, the numerical dimensions of all the service quality indexes are unified through normalization processing of each service quality index, the normalized service quality indexes are weighted, the service quality of a manufacturing event is obtained, the weight of each index can be adjusted according to manufacturing practice, and the service quality is more accurate.
In a preferred embodiment of the present invention, the quality of service of all candidate manufacturing events is ranked, and the candidate manufacturing event with the largest quality of service number value is selected as the optimal manufacturing event for completing the manufacturing task.
The technical scheme is as follows: the maximum number of quality of service values for the optimal manufacturing event to achieve the manufacturing task allows for a more efficient, less costly, and more reliable manufacturing process for the manufacturing task.
In a preferred embodiment of the present invention, when the quality of service includes only the end time, the end times of all candidate manufacturing events are ranked, and the candidate manufacturing event with the smallest end time is selected as the optimal manufacturing event for completing the manufacturing task; or when the service quality only comprises the cost, sorting the cost of all candidate manufacturing events, and selecting the candidate manufacturing event with the minimum cost as the optimal manufacturing event for completing the manufacturing task; alternatively, when the quality of service includes only reliability, the reliability of all candidate manufacturing events is ranked, and the candidate manufacturing event with the highest reliability is selected as the optimal manufacturing event for completing the manufacturing task.
According to the technical scheme, the single quality service index is adopted, and the selection speed is improved.
In a preferred embodiment of the present invention, after obtaining the optimal manufacturing event for completing the manufacturing task, when there is a historical optimal manufacturing event with the same or similar manufacturing result as the manufacturing task in the event cloud pool, if the service quality of the current optimal manufacturing event is not worse than that of the historical optimal manufacturing event, the current optimal manufacturing event is considered to be valid.
The technical scheme is as follows: and verifying the validity of the obtained optimal manufacturing event.
In a preferred embodiment of the present invention, the obtaining the quality of service of the candidate manufacturing event specifically includes: and calculating the service quality of all the candidate sub-events, and obtaining the service quality of the candidate manufacturing events by adding the service quality of the candidate sub-events included in the candidate manufacturing events.
According to the technical scheme, the service quality of the candidate sub-events is firstly obtained, the candidate sub-events are used as the minimum granularity of service quality calculation, the service quality of the candidate manufacturing events is obtained by adding the service qualities of the candidate sub-events included in the candidate manufacturing events, redundant calculation of the service quality can be reduced, and the calculation efficiency is improved.
In a preferred embodiment of the invention, the optimal manufacturing events to complete the manufacturing task are stored in an event cloud pool.
The technical scheme is as follows: the event cloud pool is enriched, the events in the event cloud pool are continuously optimized, the event cloud pool can be conveniently and directly called when similar manufacturing tasks exist in the follow-up process, and the matching time is saved.
In a preferred embodiment of the present invention, the manufacturing events in the event cloud pool are clustered according to their manufacturing results, the manufacturing events having the same and similar manufacturing results are grouped into a class, and the optimal manufacturing event to complete the manufacturing task is obtained from the class of manufacturing events that match the manufacturing results of the manufacturing task uploaded by the employer.
The technical scheme is as follows: the manufacturing events of the same and similar manufacturing tasks are clustered, the manufacturing events generating specific results are gathered, and the manufacturing results are used as the reference of personalized classification to be associated with the manufacturing tasks, so that the personalized classification of the manufacturing tasks is realized, the matching range can be reduced, and the optimal event set of the manufacturing tasks of related platforms can be directly provided.
In order to achieve the above object, according to a second aspect of the present invention, there is provided an event reorganization optimizing system for industrial internet manufacturing resource classification, comprising: the event cloud pool platform is used for uploading a manufacturing event by a manufacturer, decomposing the manufacturing event into a plurality of sub-events according to the manufacturing process, and constructing an event cloud pool by utilizing the plurality of sub-events and the manufacturing event, wherein the manufacturing event and the sub-event respectively comprise a manufacturing entity, a manufacturing process and a manufacturing result; the employer manufacturing task decomposition unit uploads the employer to a manufacturing task, associates at least one candidate sub-event which can achieve the manufacturing effect of the sub-task for each sub-task from an event cloud pool according to the manufacturing process of the manufacturing task, and completes the manufacturing task decomposition; the optimal manufacturing event acquisition unit is used for recombining the candidate sub-events of all the sub-tasks to obtain a plurality of candidate manufacturing events for completing the manufacturing task, calculating the service quality of the candidate manufacturing events, and selecting the candidate manufacturing event with the optimal service quality as the optimal manufacturing event for completing the manufacturing task; the service quality comprises at least one of three service quality indexes of termination time, cost and reliability.
The technical scheme is as follows: decomposing the manufacturing event into a plurality of manufacturing sub-events according to the manufacturing process based on the manufacturing event; the manufacturing sub-event is associated with the sub-task through a manufacturing result, and then the decomposition from the manufacturing task to the sub-task is completed; matching a candidate sub-event set for each sub-task to complete the task from the event cloud pool. The manufacturing event and the manufacturing task are uniformly decomposed, effective manufacturing information can be integrated, and the calculation range in subsequent recombination optimization is reduced. The method has the advantages that the candidate sub-events are recombined and optimized based on the service quality of the manufacturing event, the effective data sparsity of the digital manufacturing resources is relieved through service quality calculation, the calculation redundancy is reduced, and the calculation efficiency is improved. The manufacturing event granularity is a sub-event comprising a manufacturing entity, a manufacturing process and a manufacturing result in an actual production process, and the manufacturing event decomposition method using the sub-event as a dynamic granularity unit comprises more manufacturing information and integrates the manufacturing sub-events corresponding to related manufacturing subtasks, so that the method has better performance in terms of accuracy and computational efficiency.
Drawings
FIG. 1 is a schematic flow chart of an event reorganization optimization method for classifying manufacturing resources of an industrial Internet according to a preferred embodiment of the present invention;
FIG. 2 is a schematic diagram of a framework in an application scenario of the method for event reorganization optimization for industrial Internet manufacturing resource classification in accordance with a preferred embodiment of the present invention;
FIG. 3 is a block diagram of an event reorganization optimization system for classifying manufacturing resources of an industrial Internet according to a preferred embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
In the description of the present invention, it is to be understood that the terms "longitudinal", "lateral", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", and the like, indicate orientations or positional relationships based on those shown in the drawings, and are used merely for convenience of description and for simplicity of description, and do not indicate or imply that the referenced devices or elements must have a particular orientation, be constructed in a particular orientation, and be operated, and thus, are not to be construed as limiting the present invention.
In the description of the present invention, unless otherwise specified and limited, it is to be noted that the terms "mounted," "connected," and "connected" are to be interpreted broadly, and may be, for example, a mechanical connection or an electrical connection, a communication between two elements, a direct connection, or an indirect connection via an intermediate medium, and specific meanings of the terms may be understood by those skilled in the art according to specific situations.
The invention discloses an event reorganization optimization method for industrial internet manufacturing resource classification, which comprises the following steps of:
and step S1, the manufacturing party uploads the manufacturing event, the manufacturing event is decomposed into a plurality of sub-events according to the manufacturing process, an event cloud pool is constructed by the plurality of sub-events and the manufacturing event, and the manufacturing event and the sub-event comprise a manufacturing entity, a manufacturing process and a manufacturing result.
And constructing an event cloud pool, performing event description on a manufacturing task by using cloud manufacturing resource description based on environment video semantics, wherein the manufacturing task corresponds to the manufacturing event, and the manufacturing task is completed by executing the manufacturing event. A manufacturing event is described as including a manufacturing entity (device, material, person, environment, etc.), a manufacturing process, and a manufacturing result, the manufacturing event being broken down into sub-events, each sub-event also including a manufacturing entity, a manufacturing process, and a manufacturing result, and the sub-events may have a hierarchy. Such as for manufacturing tasks: the gear machining, the manufacturing process corresponding to the manufacturing task can be generally described by a manufacturing event, the manufacturing process comprises a plurality of sub-processes such as cutting, machining, heat treatment, drilling, key slot and the like, each sub-process can be described by one sub-event, but each sub-process can also comprise smaller sub-processes, such as machining the sub-process and also comprises small sub-processes such as machining an end face, machining a chamfer, machining an outer circle and the like, and the small sub-processes can be described by the small sub-events. Each process or sub-process may be performed by a different entity or entity and may achieve different manufacturing results, e.g., a sub-event for a processing end-face may comprise a manufacturing result of a different accuracy, and different manufacturing entities, manufacturing processes, combinations of manufacturing results may form different manufacturing events or sub-events. The event cloud pool stores manufacturing events and sub-events of different enterprises, different manufacturing results and different manufacturing processes which can complete different types of manufacturing tasks.
The dynamic change of the granularity of the manufacturing event or the sub-event is mainly caused by the change of the constituent elements of the manufacturing event or the sub-event, and the granularity decomposition method using the sub-event as the granularity unit can change the granularity unit from static state to dynamic state, which can more accurately support the subsequent reorganization of the digital manufacturing resources MRs. Compared with the existing task granularity method of dividing the manufacturing task process into minimum (inseparable) tasks, the method takes the sub-events describing the manufacturing process and the result as the granularity, and can combine all existing related manufacturing processes to achieve the effect of improving the calculation efficiency. Each sub-event maps to a sub-task or class of sub-tasks, and thus, in order to more quickly and accurately match tasks to manufacturing events, the present invention also employs a corresponding resolution granularity of manufacturing event resolution in manufacturing task resolution. To avoid the problem of heterogeneous data structures, the basic structure of a particular sub-task is consolidated into sequential, parallel, selective, and circular.
Attributes of a manufacturing event or sub-event include a service task category, a number of manufacturing tasks that can be serviced, a unit cost, an efficiency factor, an event expiration time, and reliability.
And step S1, uploading the manufacturing tasks by the employer, associating at least one candidate sub-event which can achieve the manufacturing effect of the sub-tasks for each sub-task from the event cloud pool according to the manufacturing process of the manufacturing tasks, and completing the decomposition of the manufacturing tasks.
Decomposing a manufacturing task uploaded by an employer into a plurality of subtasks, associating at least one candidate subtask which can achieve the manufacturing effect of each subtask from an event cloud pool, and finding out all the subtasks which can achieve the manufacturing effect requirement of the subtask from the event cloud pool and matching and associating the subtasks; the event cloud pool comprises a plurality of manufacturing events describing the execution process of the manufacturing task and a plurality of sub-events describing the execution process of the sub-tasks and decomposed by the manufacturing events, wherein the manufacturing events and the sub-events respectively comprise manufacturing entities, manufacturing processes and manufacturing results. All candidate sub-events for all sub-tasks of the manufacturing task constitute a set of candidate sub-events. If a certain subtask is to process gear hobbing, if the manufacturing result of the subtask is that the hobbing precision is 5 grades, the gear hobbing processing subevents with the hobbing precision of more than or equal to 5 grades in the manufacturing result can be matched with the subtask to be used as candidate subevents of the subtask.
Attributes of the subtasks include an ideal event service task category, an ideal event service time, and an optimal manufacturing event benchmark (single index optimal manufacturing event) in the storage event cloud pool.
Step S2, the candidate sub-events of all the sub-tasks are recombined to obtain a plurality of candidate manufacturing events for completing the manufacturing tasks, the service quality of the candidate manufacturing events is calculated, and the candidate manufacturing event with the optimal service quality is selected as the optimal manufacturing event for completing the manufacturing tasks; the service quality comprises at least one of three service quality indexes of termination time, cost and reliability.
In an embodiment, in the candidate sub-event reorganization, each sub-task can only be executed by one sub-event in one cycle, and once one sub-event is selected, all mapped results, flows and manufacturing environments will be selected.
In this embodiment, multiple employers may enter manufacturing assignments simultaneously, or the same employer may enter multiple manufacturing assignments, as shown in FIG. 2, which may be optimized for event reassembly according to the above-described scheme.
In a preferred embodiment, in the event cloud pool, different enterprises have different manufacturing processes for the same manufacturing task, and the different manufacturing processes correspond to different manufacturing events and different sub-events.
In a preferred embodiment, when the quality of service includes only the end time, the end times of all candidate manufacturing events are ranked, and the candidate manufacturing event with the smallest end time is selected as the optimal manufacturing event for completing the manufacturing task.
In a preferred embodiment, when the quality of service includes only costs, the costs of all candidate manufacturing events are ranked, and the candidate manufacturing event with the lowest cost is selected as the optimal manufacturing event to complete the manufacturing task.
In a preferred embodiment, when the quality of service includes only reliability, the reliability of all candidate manufacturing events is ranked, and the most reliable candidate manufacturing event is selected as the optimal manufacturing event to complete the manufacturing task.
In a preferred embodiment, when the quality of service includes at least two of the three quality of service indicators of time to terminate, cost and reliability, the time to terminate and the cost are included, or the cost and the reliability are included, or the time to terminate, the cost and the reliability are included. The method is characterized in that a manufacturing task has M candidate manufacturing events, M is a positive integer, and the calculating process of the service quality of the mth candidate manufacturing event comprises the following steps:
to pair
And (3) carrying out normalization treatment:
QoS x,max and QoS x,min Respectively representing the maximum value and the minimum value of the service quality index x in all candidate manufacturing events; w is a x Weight, Σ, representing the quality of service index x x w x ＝1。
In a preferred embodiment, obtaining the quality of service of the candidate manufacturing event specifically comprises: and calculating the service quality of all the candidate sub-events, and obtaining the service quality of the candidate manufacturing events by adding the service quality of the candidate sub-events included in the candidate manufacturing events.
In the present embodiment, preferably, M candidate manufacturing events are provided, and the end time of the mth candidate manufacturing event is
Wherein M is ∈ [1, M ∈]，EWT m Representing the effective operating time of the mth candidate manufacturing event,
the manufacturing task is divided into n subtasks, the o subtask has an effective working time EWT m,o ，EWT m,o ＝td m,o /Cap m,o ，td m,o Represents the total target time of the o-th subtask, td m,o ＝t m,o ×s m,o ×α o,i ，t m,o Represents the ideal event service time, α, of the o-th subtask o,i Representing the optimal event set reference, s, for the o-th subtask m,o Representing the ideal service unit, Cap, of the o-th subtask m,o Indicating the number of the o-th subtasks, LT m Representing the logistics time of the mth candidate manufacturing event,
indicating the logistic time between the o-th sub-task and the o-1 st sub-task,
indicating logistics parameters between the o-th sub-task and the o-1 st sub-task, when no logistics is required between the o-th sub-task and the o-1 st sub-task,
when logistics is required between the o-th and o-1-th subtasks,
UT l representing unit logistic events between two subtasks, d ii' A geographical distance parameter, d, representing the manufacturing environment of the o-th sub-task and the o-1 st sub-task ii' Being a dimensionless parameter, d ii' Is positively correlated with the geographic distance of the manufacturing environment of the o-th sub-task and the o-1 st sub-task. WT (WT) m Representing the latency of the mth candidate manufacturing event,
WT m,o indicating the latency of the o-th subtask, WT m,o ＝RST m,o -PST m,o +WT mm,o ，RST m,o Indicating the actual service time, PST, of the o-th subtask m,o Represents the planned service time of the o-th subtask, WT mm,o Indicating the maintenance time of the o-th subtask.
In this embodiment, preferably, M candidate manufacturing events are provided, and the cost of the mth candidate manufacturing event is
Wherein, SC m Representing the effective workload of the mth candidate manufacturing event,
SC m,o represents the effective workload of the o-th subtask in the m-th candidate manufacturing event, SC m,o ＝Qw m,o ×c m,o ，Qw m,o Representing the work load of the sub-event corresponding to the o sub-task in the m-th candidate manufacturing event, c m,o Representing a unit service cost of a sub-event corresponding to the mth sub-task in the mth candidate manufacturing event; LC (liquid Crystal) m Representing the logistics cost of the mth candidate manufacturing event,
represents the logistical costs between completing the o-th sub-task and completing the (o-1) th sub-task,
a Boolean variable indicating whether there is a logistical cost between the o-th sub-task and the (o-1) -th sub-task whenWhen there is a logistics cost between the (o) th sub-task and the (o-1) th sub-task,
1, when there is no logistics cost between the o-th sub-task and the (o-1) -th sub-task
Is 0, UCT l Representing the cost per weight or per product, d ii' A geographical distance parameter, d, representing the manufacturing environment of the o-th sub-task and the o-1 st sub-task ii' Being a dimensionless parameter, d ii' Is positively correlated with the geographic distance of the manufacturing environment of the o-th sub-task and the o-1 st sub-task.
In the present embodiment, preferably, M manufacturing event candidates are provided, and the reliability of the mth manufacturing event candidate is
ERel m,o Represents the reliability of the sub-event corresponding to the o-th sub-task, and belongs to [1, n ]]。
In a preferred embodiment, the optimal manufacturing events to complete the manufacturing task are stored in an event cloud pool. The optimal manufacturing events are used as potentially valid information to supplement event cloud data to mitigate the scarcity of valid information.
In a preferred embodiment, the manufacturing events in the event cloud pool are clustered according to their manufacturing results, the manufacturing events having the same and similar manufacturing results are grouped into a class, and the optimal manufacturing event to complete the manufacturing task is obtained from the class of manufacturing events that match the manufacturing results of the manufacturing tasks uploaded by the employer, and in particular, from the set of manufacturing events (i.e., the class of manufacturing events) that match the manufacturing tasks uploaded by the employer.
At present, an industrial internet platform is dedicated to real-time dynamic update of digital manufacturing resources, and as more and more manufacturing enterprises add the industrial internet platform, accurate and efficient classification of the digital manufacturing resources uploaded to a cloud platform is an urgent need of the industrial internet platform. The optimization method provided by the invention can cluster the digital manufacturing resources according to the manufacturing task, further reduce the candidate set for matching the manufacturing task and the manufacturing event, and realize the high-efficiency search and matching of the digital manufacturing resources, specifically:
1) the manufacturing events and tasks are uniformly decomposed. Through decomposition, effective manufacturing information is integrated, the subsequent searching and matching range is narrowed from the whole digital manufacturing resource cloud library to the integrated candidate sub-event set, and the searching and matching efficiency is greatly improved.
2) The method for calculating the service quality of the manufacturing time is provided, and for similar manufacturing subtasks, the service quality QoS value of each manufacturing subevent is uniformly calculated and compared. Under the method, unless the related digital manufacturing resource MR and the capability information are updated, the QoS value of the sub-event unit is kept unchanged, and the calculation amount can be effectively reduced.
3) The event recombination optimization method not only realizes the optimized recombination of manufacturing sub-events, but also provides the optimal event scheme with different quantities of manufacturing tasks. Compared with the method for respectively calculating the matching schemes of the manufacturing tasks, the method reduces the redundancy of calculation and improves the calculation time and efficiency.
In an application scenario of the present invention, a specific framework diagram of the event reorganization optimization method for industrial internet manufacturing resource classification is shown in fig. 2. The event reorganization optimization framework mainly comprises four stages, wherein the 0 th stage is mainly used for realizing conversion from physical digital manufacturing resources to digital manufacturing resources based on cloud manufacturing resource description of environment video semantics. Stages 1, 2, 3 and 4 are presented here to explore how to effectively classify the described events and tasks. The goal and implementation of these four phases are as follows:
the first stage is as follows: decomposition of manufacturing events and manufacturing tasks. The first stage is directed to decomposing the manufacturing events and manufacturing tasks by determining a granularity of the manufacturing event and manufacturing task decomposition. The manufacturing event is firstly decomposed into a plurality of manufacturing sub-events according to each manufacturing process and manufacturing results, and then the manufacturing sub-tasks are correlated according to the characteristic that the manufacturing sub-tasks and the manufacturing results of the manufacturing sub-events are correlated, so that the decomposition of the manufacturing task is completed. At this stage, the digital manufacturing resources completing the manufacturing task are gathered together through semantic description of the event, so that the matching range is narrowed from the resource cloud pool to the event cloud set, and the effective narrowing of the computing range is realized.
And a second stage: sub-event quality of service QoS calculations are made. The purpose of the second stage is to calculate the quality of service QoS values for the candidate sub-events and the candidate manufacturing events. The participation index and the calculation model of the QoS calculation are first determined according to the key factors (time, cost and reliability) that determine the QoS value of the quality of service, and then the QoS value of the manufacturing sub-event is calculated. In the stage, only the QoS value of the sub-event needs to be calculated, so that repeated calculation of some candidate sub-events is effectively avoided, and the effective reduction of calculation redundancy is realized.
And a third stage: and (4) recombination optimization of the sub-events. The third stage is directed to reorganizing the sub-events that complete the manufacturing sub-tasks to obtain an optimal manufacturing event that completes the corresponding manufacturing task. And acquiring the QoS of the candidate manufacturing time according to the QoS value of each manufacturing sub-event, recombining and optimizing the optimal event set of the manufacturing task by screening the QoS, and providing the optimal manufacturing event. The calculation result at this stage can be repeatedly used for similar tasks, so that the calculation of the optimal manufacturing event of the similar tasks is avoided, and the effective reduction of the calculation efficiency is realized.
A fourth stage: aggregation of manufacturing events for similar manufacturing tasks oriented to the manufacturing results. The fourth stage is directed to aggregating related manufacturing events in terms of manufacturing results to obtain a set of candidate manufacturing events for a certain class of tasks. And searching out a manufacturing event set meeting the manufacturing requirements according to the manufacturing result of each manufacturing event. At this stage, the manufacturing events that produce a particular outcome are aggregated and associated with the manufacturing task using the manufacturing outcome as a benchmark for personalized classification, thereby achieving personalized classification of the manufacturing task.
Under the given requirements of digital manufacturing resources and manufacturing tasks, the optimized and recombined optimal events are not only suitable for historical manufacturing tasks, but also suitable for similar tasks of the historical manufacturing tasks, so that the optimal event matching time of the similar tasks of the historical tasks can be greatly reduced. The QoS value of the sub-event set for completing a certain manufacturing task is calculated, related events can be gathered, and events for completing a certain type of tasks can be gathered, so that the matching range can be reduced, and the optimal event set of the manufacturing task of a related platform can be directly provided. The manufacturing events and the manufacturing tasks related to the third stage not only comprise the semantic description manufacturing events and the corresponding manufacturing tasks, but also comprise the potential events and the tasks which are mined, and the potential events and the tasks which are mined can effectively relieve the scarcity of manufacturing data and improve the response speed of the similar manufacturing tasks.
The EQoS of the optimization method provided by the invention is compared with other methods, and the comparison result is shown in table 3. Compared with UNFC-2009, EQoS has the advantages of small calculation range, higher data reliability and the like. Compared with IAASMAC and BIDI, EQoS still have significant advantages in terms of calculation range and data reliability. Compared with DSCNN, the classification result of the EQoS method is better in reducing the calculation range. In addition, the EQoS has the advantages of wide applicability, large effective information of a calculation main body, high utilization rate of calculation results and the like, so that the EQoS can effectively solve the problem of rare effective data of the manufacturing industry and can greatly shorten the response time of a platform to similar tasks.
TABLE 3 EQoS comparison with other methods
The invention also discloses an event reorganization and optimization system for industrial internet manufacturing resource classification, as shown in fig. 3, comprising: the event cloud pool platform is used for uploading a manufacturing event by a manufacturer, decomposing the manufacturing event into a plurality of sub-events according to the manufacturing process, and constructing an event cloud pool by utilizing the plurality of sub-events and the manufacturing event, wherein the manufacturing event and the sub-event respectively comprise a manufacturing entity, a manufacturing process and a manufacturing result; the employer manufacturing task decomposition unit uploads the manufacturing tasks to the employer, associates at least one candidate sub-event which can achieve the manufacturing effect of the sub-tasks for each sub-task from the event pool according to the manufacturing process of the manufacturing tasks, and completes the manufacturing task decomposition; the optimal manufacturing event acquisition unit is used for recombining the candidate sub-events of all the sub-tasks to obtain a plurality of candidate manufacturing events for completing the manufacturing tasks, calculating the service quality of the candidate manufacturing events, and selecting the candidate manufacturing event with the optimal service quality as the optimal manufacturing event for completing the manufacturing tasks; the service quality comprises at least one of three service quality indexes of termination time, cost and reliability.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.
Claims (10)
1. An event recombination optimization method for industrial internet manufacturing resource classification is characterized by comprising the following steps:
uploading a manufacturing event by a manufacturing party, decomposing the manufacturing event into a plurality of sub-events according to a manufacturing process, and constructing an event cloud pool by using the plurality of sub-events and the manufacturing event, wherein the manufacturing event and the sub-event comprise a manufacturing entity, a manufacturing process and a manufacturing result;
an employer uploads a manufacturing task, associates at least one candidate sub-event which can achieve the manufacturing effect of the sub-task for each sub-task from an event cloud pool according to the manufacturing process of the manufacturing task, and completes the decomposition of the manufacturing task;
recombining the candidate sub-events of all the sub-tasks to obtain a plurality of candidate manufacturing events for completing the manufacturing task, calculating the service quality of the candidate manufacturing events, and selecting the candidate manufacturing event with the optimal service quality as the optimal manufacturing event for completing the manufacturing task;
the service quality comprises at least one of three service quality indexes of termination time, cost and reliability.
2. The method for event reorganization optimization for industrial internet manufacturing resource classification as claimed in claim 1, wherein in the event cloud pool, different enterprises have different manufacturing processes for the same manufacturing task, and the different manufacturing processes correspond to different manufacturing events and different sub-events.
3. The method for event reorganization optimization for industrial internet manufacturing resource classification-oriented according to claim 1, wherein when the quality of service includes at least two of the three quality of service indicators of end time, cost and reliability, the quality of service of the mth candidate manufacturing event is calculated by:
by using
A quality of service indicator x representing the mth candidate manufacturing event, m being a positive integer, x being t, c, rel,
indicating the end time of the mth candidate manufacturing event,
representing the cost of the mth candidate manufacturing event,
representing the reliability of the mth candidate manufacturing event; to pair
And (3) carrying out normalization treatment:
QoS x,max and QoS x,min Respectively representing the maximum value and the minimum value of the service quality index x in all candidate manufacturing events; w is a x Weight, Σ, representing the quality of service index x x w x ＝1；
Quality of service for mth candidate manufacturing event is QoS m Said
4. The method for event reorganization optimization for industrial internet manufacturing resource classification as claimed in claim 3, wherein the service quality of all candidate manufacturing events is ranked, and the candidate manufacturing event with the largest service quality number value is selected as the optimal manufacturing event for completing the manufacturing task.
5. The method for event reorganization optimization for industrial internet manufacturing resource classification-oriented according to claim 1, wherein when the quality of service includes only the termination time, the termination times of all candidate manufacturing events are ranked, and the candidate manufacturing event with the smallest termination time is selected as the optimal manufacturing event for completing the manufacturing task;
or when the service quality only comprises the cost, sorting the cost of all candidate manufacturing events, and selecting the candidate manufacturing event with the lowest cost as the optimal manufacturing event for completing the manufacturing task;
alternatively, when the quality of service includes only reliability, the reliability of all candidate manufacturing events is ranked, and the candidate manufacturing event with the highest reliability is selected as the optimal manufacturing event for completing the manufacturing task.
6. The method for event reorganization optimization for industrial internet manufacturing resource classification-oriented according to claim 1, wherein optimal manufacturing events for completing the manufacturing task are stored in an event cloud pool.
7. The industrial internet manufacturing resource classification-oriented event reorganization optimization method according to claim 6, wherein after obtaining the optimal manufacturing event for completing the manufacturing task, when there is a historical optimal manufacturing event of the same or similar manufacturing result as the manufacturing task in the event cloud pool, if the quality of service of the current optimal manufacturing event is not worse than that of the historical optimal manufacturing event, the current optimal manufacturing event is considered to be valid.
8. The method for event reorganization optimization for industrial internet manufacturing resource classification-oriented according to any one of claims 1 to 7, wherein obtaining the quality of service of the candidate manufacturing events specifically comprises:
and calculating the service quality of all the candidate sub-events, and obtaining the service quality of the candidate manufacturing events by adding the service quality of the candidate sub-events included in the candidate manufacturing events.
9. The method for event reorganization optimization for industrial internet manufacturing resource classification as claimed in claim 8, wherein the manufacturing events in the event cloud pool are clustered according to the manufacturing results of the manufacturing events, the manufacturing events having the same and similar manufacturing results are grouped into a class, and the optimal manufacturing event for completing the manufacturing task is obtained from the manufacturing event class matching the manufacturing results of the manufacturing task input by the employer.
10. An event reorganization optimization system for industrial internet manufacturing resource classification, comprising:
the event cloud pool platform is used for uploading a manufacturing event by a manufacturer, decomposing the manufacturing event into a plurality of sub-events according to the manufacturing process, and constructing an event cloud pool by utilizing the plurality of sub-events and the manufacturing event, wherein the manufacturing event and the sub-event respectively comprise a manufacturing entity, a manufacturing process and a manufacturing result;
the employer manufacturing task decomposition unit uploads the employer to a manufacturing task, associates at least one candidate sub-event which can achieve the manufacturing effect of the sub-task for each sub-task from an event cloud pool according to the manufacturing process of the manufacturing task, and completes the manufacturing task decomposition;
the optimal manufacturing event acquisition unit is used for recombining the candidate sub-events of all the sub-tasks to obtain a plurality of candidate manufacturing events for completing the manufacturing task, calculating the service quality of the candidate manufacturing events, and selecting the candidate manufacturing event with the optimal service quality as the optimal manufacturing event for completing the manufacturing task; the service quality comprises at least one of three service quality indexes of termination time, cost and reliability.
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