CN115081831B - Event reorganization optimization method and system for industrial Internet manufacturing resource classification - Google Patents

Event reorganization optimization method and system for industrial Internet manufacturing resource classification Download PDF

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CN115081831B
CN115081831B CN202210635724.8A CN202210635724A CN115081831B CN 115081831 B CN115081831 B CN 115081831B CN 202210635724 A CN202210635724 A CN 202210635724A CN 115081831 B CN115081831 B CN 115081831B
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CN115081831A (en
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吴映波
张哲�
王旭
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Chongqing University
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Abstract

The invention provides an event reorganization optimization method and system for industrial Internet manufacturing resource classification, comprising the following steps: 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 built, and the manufacturing event and the sub-events comprise a manufacturing entity, a manufacturing process and a manufacturing result; uploading a manufacturing task by an employer, associating at least one candidate sub-event capable of achieving the manufacturing effect of the sub-task for each sub-task from an event pool according to the manufacturing process of the manufacturing task, and completing the manufacturing task decomposition; recombining the candidate sub-events of all the sub-tasks to obtain a plurality of candidate manufacturing events for completing the manufacturing task, and selecting the candidate manufacturing event with the optimal service quality as the optimal manufacturing event for completing the manufacturing task; the quality of service includes at least one of three quality of service indicators of expiration time, cost and reliability. The method can effectively reduce the calculation range of the subsequent manufacturing resource allocation, reduce the calculation redundancy and improve the calculation efficiency.

Description

Event reorganization optimization method and system for industrial Internet manufacturing resource classification
Technical Field
The invention relates to the technical field of manufacturing resource scheduling and distribution, in particular to an event reorganization optimization method and system for industrial Internet manufacturing resource classification.
Background
Digital services under industrial internet of things platform driven by big data and internet of things technologies have become a development trend of manufacturing services. In order to better perform manufacturing services in an industrial internet platform, a first problem to be solved is to uniformly, accurately and dynamically describe various manufacturing resources in the industrial internet platform. In 2020, a paper named 'cloud manufacturing resource description based on environment video semantics' is disclosed in Chinese journal 'computer integrated manufacturing system', and the paper provides a hierarchical environment video semantic model for displaying dynamic changes of resources, aiming at the problems that resources of various industries are difficult to describe uniformly in cloud manufacturing environment, available resources in a resource cloud pool are updated with lag, execution strength of a resource matching plan is weak and the like. The model defines the hierarchical structure of the environment video semantics and the hierarchical expression of data through three fields (entity fields (manufacturing resources) -behavior process fields-result fields) oriented to changes, organically combines the environment video semantics with the video content semantics in each hierarchical semantic description, supports the associated representation of multi-environment video data, and lays a foundation for the dynamic real-time, efficient and reliable resource matching in the subsequent cloud manufacturing environment. However, the digital Manufacturing Resources (MR) of the video semantic description are necessarily accompanied by a complication of the data structure and a rapid increase of the data volume, resulting in overload of the IIP information of the industrial internet platform, and difficulty in supporting efficient matching of manufacturing resources and manufacturing services of the manufacturing task.
Manufacturing Resource Classification (MRC) is a precondition for manufacturing service applications such as production process control, equipment management services, asset allocation agreements, and the like. According to the 2021 industrial internet white paper, industrial internet is widely applied (the application scene accounts for 80%) in terms of production process control, equipment management service and asset allocation protocols related to manufacturing services, but the manufacturing and process modules closely related to efficient manufacturing resource classification and digitization of manufacturing resources are less (only 1%). Meanwhile, the existing method for classifying the digital manufacturing resources by manufacturing resources mainly classifies single manufacturing resources which are slowly updated, and has the problems of unreasonable granularity of resource classification and computational redundancy, 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 to digitize manufacturing resources, event Descriptions (EDs) are widely studied in the fields of multi-stage production systems, social media, manufacturing information systems, discrete event systems, etc., but are mostly used to describe processes that occur less frequently and event results are not fixed (e.g., CBM stop PAGE EVENTS, TRAFFIC ACCIDENT EVENTS, and Equipment failure events). The manufacturing process has a large frequency of occurrence and a predetermined result (for example, tooth form machining with a precision of 6 to 10 stages and machining with a precision of 7 to 9 stages, etc.), and compared with the existing event, the manufacturing event has the characteristics of a large number of events, a complex event structure, etc. The problem of industrial internet platform information overload is exacerbated by the large number of events and the complex structure of events, so that existing event descriptions are difficult to apply directly to descriptions of the entire manufacturing process. The problems of reasonable division of manufacturing resource granularity and information overload are solved by combining the advantages of high description accuracy and strong information aggregation of the event description, and the problem that the accurate and efficient classification of the digital manufacturing resource for applying the event description to an industrial Internet platform is needed to be solved is urgent.
Disclosure of Invention
The invention aims at least solving the technical problems existing in the prior art and provides an event reorganization optimization method and system for industrial Internet manufacturing resource classification.
To achieve the above object of the present invention, according to a first aspect of the present invention, there is provided an event reorganization optimization method for industrial internet manufacturing resource classification, including: the manufacturing party uploads a manufacturing event, the manufacturing event is decomposed into a plurality of sub-events according to the manufacturing process, an event cloud pool is built by utilizing 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; uploading a manufacturing task by an employer, associating at least one candidate sub-event capable of achieving the manufacturing effect of the sub-task for each sub-task from an event cloud pool according to the manufacturing task manufacturing process, and completing manufacturing task decomposition; recombining the candidate sub-events of all 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 quality of service includes at least one of three quality of service indicators of expiration 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 the manufacturing result, so that the decomposition from the manufacturing task to the sub-task is completed; a set of candidate sub-events that complete the task is matched for each sub-task from the event cloud pool. And the manufacturing events and the manufacturing tasks are uniformly decomposed, so that effective manufacturing information can be integrated, and the calculation range in the follow-up recombination optimization is reduced. The candidate sub-events are recombined and optimized based on the service quality of the manufacturing event, and the effective data sparsity of the digital manufacturing resource is relieved through the service quality calculation, so that the calculation redundancy is reduced, and the calculation efficiency is improved. The granularity of the manufacturing event is the sub-event comprising the manufacturing entity, the manufacturing process and the manufacturing result in the actual production process, the manufacturing event decomposition method taking the sub-event as a dynamic granularity unit comprises more manufacturing information, and the manufacturing sub-event corresponding to the related manufacturing sub-task is integrated, so that the accuracy and the computing efficiency are better represented.
In a preferred embodiment of the present invention, in the event cloud pool, different enterprises have different manufacturing processes for the same manufacturing task, the different manufacturing processes corresponding to different manufacturing events and different sub-events.
The technical scheme is as follows: the manufacturing event and the sub-event comprise three elements of a manufacturing entity, a manufacturing process and a manufacturing result, wherein any element represents different manufacturing events or sub-events differently, so that manufacturing services provided by different enterprises can be contained in an event cloud pool.
In a preferred embodiment of the present invention, when the quality of service includes at least two of three quality of service indicators of expiration time, cost and reliability, the calculation process of the quality of service of the mth candidate manufacturing event is: by usingThe quality of service index x, m representing the mth candidate manufacturing event is a positive integer, x=t, c, rel,/>Representing the expiration time of the mth candidate manufacturing event,/>Representing the cost of the mth candidate manufacturing event,/>Representing reliability of the mth candidate manufacturing event; pair/>Normalization processing:
QoS x,max and QoS x,min represent the maximum and minimum, respectively, of quality of service index x for all candidate manufacturing events; w x denotes the weight of the quality of service index x, Σ xwx =1; the quality of service for the mth candidate manufacturing event is QoS m, the/>
The technical scheme is as follows: when the number of the service quality indexes is greater than or equal to two, the service quality indexes are fused in the mode, the normalization processing is carried out on each service quality index, the unification of the numerical dimensions of all the service quality indexes is ensured, the service quality of a manufacturing event is obtained by the weighted sum of the normalized service quality indexes, and the weight of each index can be regulated according to the manufacturing practice, so that 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 value is selected as the optimal manufacturing event for completing the manufacturing task.
The technical scheme is as follows: the maximum quality of service value for the optimal manufacturing event to achieve the manufacturing task results in a more efficient, cost effective and reliable manufacturing process for the manufacturing task.
In a preferred embodiment of the present invention, when the quality of service includes only a termination time, the termination times of all candidate manufacturing events are ordered, 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 cost, sorting the cost of all the candidate manufacturing events, and selecting the candidate manufacturing event with the minimum cost as the optimal manufacturing event for completing the manufacturing task; or when the service quality only comprises reliability, sequencing the reliability of all the candidate manufacturing events, and selecting the candidate manufacturing event with the highest reliability as the optimal manufacturing event for completing the manufacturing task.
According to the technical scheme, the single quality service index is adopted, so that the selection speed is improved.
In a preferred embodiment of the present invention, after obtaining an optimal manufacturing event for completing the manufacturing task, when there is a historical optimal manufacturing event for the same or similar manufacturing result as the manufacturing task in the event cloud pool, the current optimal manufacturing event is considered valid if the service quality of the current optimal manufacturing event is not worse than the service quality of the historical optimal manufacturing event.
The technical scheme is as follows: the validity of the obtained optimal manufacturing event is verified.
In a preferred embodiment of the present invention, obtaining quality of service for candidate manufacturing events specifically includes: the service quality of all the candidate sub-events is calculated, and the service quality of the candidate manufacturing event is obtained by adding the service quality of the candidate sub-events included in the candidate manufacturing event.
According to the technical scheme, the service quality of the candidate sub-event is firstly obtained, the candidate sub-event is taken as the minimum granularity of service quality calculation, the service quality of the candidate manufacturing event is obtained by adding the service quality of a plurality of candidate sub-events included in the candidate manufacturing event, redundant calculation of the service quality can be reduced, and the calculation efficiency is improved.
In a preferred embodiment of the present invention, the optimal manufacturing event to complete the manufacturing task is stored in an event cloud pool.
The technical scheme is as follows: and the event cloud pool is enriched, so that events in the event cloud pool are continuously optimized, the event cloud pool can be directly called when similar manufacturing tasks exist subsequently, and the matching time is saved.
In a preferred embodiment of the present invention, manufacturing events in the event cloud pool are clustered according to manufacturing results of the manufacturing events, such that manufacturing events having identical and similar manufacturing results are grouped into a class, and an optimal manufacturing event for completing the manufacturing task is obtained from the class of manufacturing events matching 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 summarized, and the manufacturing results are used as the standard of personalized classification to be associated with the manufacturing tasks, so that personalized classification of the manufacturing tasks is realized, the matching range can be reduced, and the optimal event set of the manufacturing tasks of the relevant platform can be directly given.
To achieve the above object of the present invention, according to a second aspect of the present invention, there is provided an event reorganization optimization system for industrial internet manufacturing resource classification, including: the event cloud pool platform is used for uploading a manufacturing event by a manufacturing party, 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 comprise a manufacturing entity, a manufacturing process and a manufacturing result; an employer manufacturing task decomposition unit for uploading the employer to a manufacturing task, and associating at least one candidate sub-event capable of achieving the manufacturing effect of the sub-task for each sub-task from an event cloud pool according to the manufacturing task manufacturing process to complete manufacturing task decomposition; an optimal manufacturing event obtaining unit, which 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 quality of service includes at least one of three quality of service indicators of expiration 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 the manufacturing result, so that the decomposition from the manufacturing task to the sub-task is completed; a set of candidate sub-events that complete the task is matched for each sub-task from the event cloud pool. And the manufacturing events and the manufacturing tasks are uniformly decomposed, so that effective manufacturing information can be integrated, and the calculation range in the follow-up recombination optimization is reduced. The candidate sub-events are recombined and optimized based on the service quality of the manufacturing event, and the effective data sparsity of the digital manufacturing resource is relieved through the service quality calculation, so that the calculation redundancy is reduced, and the calculation efficiency is improved. The granularity of the manufacturing event is the sub-event comprising the manufacturing entity, the manufacturing process and the manufacturing result in the actual production process, the manufacturing event decomposition method taking the sub-event as a dynamic granularity unit comprises more manufacturing information, and the manufacturing sub-event corresponding to the related manufacturing sub-task is integrated, so that the accuracy and the computing efficiency are better represented.
Drawings
FIG. 1 is a schematic flow diagram of an event reorganization optimization method facing classification of industrial Internet manufacturing resources in a preferred embodiment of the present invention;
FIG. 2 is a schematic diagram of a framework in an application scenario of an event reorganization optimization method 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 oriented to industrial Internet manufacturing resource classification in accordance with a preferred embodiment of the present invention.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative only and are not to be construed as limiting the invention.
In the description of the present invention, it should be understood that the terms "longitudinal," "transverse," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like indicate orientations or positional relationships based on the orientation or positional relationships shown in the drawings, merely to facilitate describing the present invention and simplify the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and therefore should not be construed as limiting the present invention.
In the description of the present invention, unless otherwise specified and defined, it should be noted that the terms "mounted," "connected," and "coupled" are to be construed broadly, and may be, for example, mechanical or electrical, or may be in communication with each other between two elements, directly or indirectly through intermediaries, as would be understood by those skilled in the art, in view of the specific meaning of the terms described above.
The invention discloses an event reorganization optimization method for industrial Internet manufacturing resource classification, as shown in fig. 1, in a preferred embodiment, the method comprises the following steps:
Step S1, a manufacturing party uploads a manufacturing event, the manufacturing event is decomposed into a plurality of sub-events according to a manufacturing process, an event cloud pool is built by utilizing 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, carrying out event description on the manufacturing task by utilizing cloud manufacturing resource description based on the environment video semantics, wherein the manufacturing task corresponds to the manufacturing event, and completing the manufacturing task by executing the manufacturing event. The manufacturing event includes a description of the manufacturing entity (device, material, person, environment, etc.), manufacturing process, and manufacturing result, the manufacturing event is broken down into a plurality of sub-events, each sub-event also including the manufacturing entity, manufacturing process, and manufacturing result, which may have a hierarchy. Such as for manufacturing tasks: gear machining, the manufacturing process corresponding to the manufacturing task may be generally described by a manufacturing event, the manufacturing process includes a plurality of sub-processes of cutting, machining, heat treating, drilling, keyway, etc., each sub-process may be described by a sub-event, but each sub-process may also include finer sub-processes, such as machining sub-processes further including machining end faces, machining chamfer, machining outer circles, etc., and these fine sub-processes may be described by fine sub-events. Each process or sub-process may be completed by a different enterprise and may also achieve different manufacturing results, e.g., sub-events for processing the end surfaces may include manufacturing results of different accuracy, and different manufacturing entities, manufacturing processes, combinations of manufacturing results form different manufacturing events or manufacturing sub-events. Different enterprises, different manufacturing results and manufacturing events and sub-events of different manufacturing processes which can complete different types of manufacturing tasks are stored in the event cloud pool.
The granularity dynamic change 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 to dynamic, which more accurately supports the subsequent recombination of the digital manufacturing resources MRs. Compared with the existing task granularity method for dividing the manufacturing task process into the smallest (inseparable) task, the method takes the sub-event describing the manufacturing process and the result as granularity, and can combine all existing related manufacturing processes to achieve the effect of improving the computing efficiency. Each sub-event maps to one or a class of sub-tasks, and therefore, in order to more quickly and accurately match tasks and manufacturing events, the present invention also employs a corresponding decomposition granularity of manufacturing event decomposition in manufacturing task decomposition. To avoid the problem of heterogeneous data structures, the basic structure of a particular subtask is unified into order, parallelism, selectivity, and loops.
Attributes of a manufacturing event or sub-event include service task category, number of manufacturing tasks that can be serviced, unit cost, efficiency coefficient, event expiration time, and reliability.
Step S1, uploading a manufacturing task by an employer, associating at least one candidate sub-event capable of achieving the manufacturing effect of the sub-task from an event cloud pool according to the manufacturing task manufacturing process, and completing the manufacturing task decomposition.
Decomposing a manufacturing task uploaded by an employer into a plurality of subtasks, associating at least one candidate subtask capable of achieving the manufacturing effect of each subtask in an event cloud pool, and finding out all the subtasks capable of achieving the manufacturing effect requirement of the subtasks in the event cloud pool to be matched and associated with the subtasks; the event cloud pool includes a plurality of manufacturing events describing a manufacturing task execution process, and a plurality of sub-events describing a sub-task execution process decomposed by the manufacturing events, each of the manufacturing events and the sub-events including a manufacturing entity, a manufacturing process, and a manufacturing result. All candidate sub-events for all sub-tasks of the manufacturing task constitute a candidate sub-event set. If a certain subtask is gear hobbing, if the manufacturing result of the subtask is that the hobbing precision is 5 levels, gear hobbing processing sub-events with the manufacturing result hobbing precision being more than or equal to 5 levels can be matched with the subtask and used as candidate sub-events of the subtask.
The attributes of the subtasks include ideal event service task category, ideal event service time, and optimal manufacturing event benchmarks (single index optimal manufacturing event) in the storage event cloud pool.
Step S2, 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 quality of service includes at least one of three quality of service indicators of expiration time, cost and reliability.
In an embodiment, in candidate sub-event reorganization, each sub-task can be performed by only 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 implementation, multiple employers may input manufacturing tasks simultaneously, or the same employer may input multiple manufacturing tasks, as shown in fig. 2, which may be event reorganization optimized simultaneously according to the scheme described above.
In a preferred embodiment, different enterprises have different manufacturing processes for the same manufacturing task in an event cloud pool, the different manufacturing processes corresponding to different manufacturing events and different sub-events.
In a preferred embodiment, when the quality of service includes only a termination time, the termination times of all candidate manufacturing events are ordered, and the candidate manufacturing event with the smallest termination 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 smallest cost is selected as the optimal manufacturing event for completing 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 for completing the manufacturing task.
In a preferred embodiment, when the quality of service comprises at least two of three quality of service indicators, such as, for example, termination time and cost, or cost and reliability, or termination time, cost and reliability. The method comprises the steps that M candidate manufacturing events are arranged in a manufacturing task, M is a positive integer, and the calculation process of the service quality of the M candidate manufacturing events is as follows:
Step 1, using The quality of service index x, m representing the mth candidate manufacturing event is a positive integer, x=t, c, rel,/>Representing the expiration time of the mth candidate manufacturing event,/>Representing the cost of the mth candidate manufacturing event,Representing reliability of the mth candidate manufacturing event;
For a pair of Normalization processing:
QoS x,max and QoS x,min represent the maximum and minimum, respectively, of quality of service index x for all candidate manufacturing events; w x denotes the weight of the quality of service index x, Σ xwx =1.
Step 2, the quality of service for the mth candidate manufacturing event is QoS m,
In a preferred embodiment, obtaining quality of service for candidate manufacturing events specifically includes: the service quality of all the candidate sub-events is calculated, and the service quality of the candidate manufacturing event is obtained by adding the service quality of the candidate sub-events included in the candidate manufacturing event.
In the present embodiment, preferably, there are provided M candidate manufacturing events, and the termination time of the mth candidate manufacturing event is
Where m.epsilon.1, M, EWT m represents the effective working time of the mth candidate manufacturing event,The manufacturing task is decomposed into n subtasks, the effective working time of the o subtask is EWT m,o,EWTm,o＝tdm,o/Capm,o,tdm,o, the total target time of the o subtask is represented, td m,o＝tm,o×sm,o×αo,i,tm,o represents the ideal event service time of the o subtask, alpha o,i represents the optimal event set standard of the o subtask, s m,o represents the ideal service unit of the o subtask, cap m,o represents the number of the o subtasks, LT m represents the logistics time of the m candidate manufacturing event,/>Representing the logistic time between the o-th subtask and the o-1 th subtask,/> Representing the flow parameters between the o-th subtask and the o-1 th subtask, when no flow is required between the o-th subtask and the o-1 th subtask,When a stream is required between the o-th subtask and the o-1 th subtask, a stream is fed to the systemUT l represents a unit logistics event between two subtasks, d ii' represents a geographical distance parameter of the manufacturing environment of the o-1 th subtask and the o-1 th subtask, d ii' is a dimensionless parameter, and d ii' is positively related to the geographical distance of the o-1 th subtask and the manufacturing environment of the o-1 th subtask. WT m represents the wait time of the mth candidate manufacturing event,/>WT m,o represents the waiting time of the o-th subtask, WT m,o＝RSTm,o-PSTm,o+WTmm,o,RSTm,o represents the actual service time of the o-th subtask, PST m,o represents the planned service time of the o-th subtask, and WT mm,o represents the maintenance time of the o-th subtask.
In the present embodiment, preferably, there are provided M candidate manufacturing events, and the cost of the mth candidate manufacturing event is Where SC m represents the effective workload of the mth candidate manufacturing event,/>SC m,o represents the effective workload of the nth subtask in the mth candidate manufacturing event, SC m,o＝Qwm,o×cm,o,Qwm,o represents the workload of the subtask corresponding to the nth subtask in the mth candidate manufacturing event, and c m,o represents the unit service cost of the subtask corresponding to the nth subtask in the mth candidate manufacturing event; LC m represents the logistic cost for the mth candidate manufacturing event, Representing the cost of logistics between completion of the o-th subtask and completion of the (o-1) -th subtask,/> Boolean variable representing whether or not there is a logistic cost between the o-th subtask and the (o-1) th subtask, when there is a logistic cost between the o-th subtask and the (o-1) th subtask,/>1, When there is no logistic cost between the o-th subtask and the (o-1) -th subtask/>For 0, UCT l represents the basis weight or logistics cost of a single product, d ii' represents the geographical distance parameter of the manufacturing environment of the o-1 st subtask and the o-1 th subtask, d ii' is a dimensionless parameter, and the size of d ii' is positively correlated to the geographical distance of the o-1 th subtask and the manufacturing environment of the o-1 st subtask.
In the present embodiment, preferably, there are provided M candidate manufacturing events, and the reliability of the mth candidate manufacturing event is ERel m,o denotes the reliability of the sub-event corresponding to the o-th sub-task, o.e. [1, n ].
In a preferred embodiment, the optimal manufacturing event to complete the manufacturing task is stored in an event cloud pool. The optimal manufacturing event is used as potentially valid information to supplement the event cloud data to mitigate valid information scarcity.
In a preferred embodiment, the manufacturing events in the event cloud pool are clustered according to the manufacturing results of the manufacturing events, so that the manufacturing events with the same and similar manufacturing results are grouped into one category, and the optimal manufacturing event for completing the manufacturing task is obtained from the manufacturing event category matched with the manufacturing results of the manufacturing task uploaded by the employer, specifically, the optimal manufacturing event for completing the manufacturing task is obtained from the manufacturing event set (i.e., the manufacturing event category) matched with the manufacturing task 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 join the industrial internet platform, accurate and efficient classification of the digital manufacturing resources uploaded to a cloud platform becomes an urgent need for the industrial internet platform. The optimization method provided by the invention can cluster the digital manufacturing resources according to the manufacturing tasks, further reduce the candidate set for matching the manufacturing tasks with the manufacturing events, realize the efficient searching and matching of the digital manufacturing resources, and is specific:
1) And uniformly decomposing the manufacturing event and the task. Through decomposition, effective manufacturing information is integrated, the subsequent searching and matching range is reduced from the whole digital manufacturing resource cloud base to an integrated candidate sub-event set, and the searching and matching efficiency is greatly improved.
2) A quality of service calculation method for manufacturing time is provided, and for similar manufacturing subtasks, qoS values of each manufacturing sub-event are calculated and compared in a unified way. Under this approach, the QoS values of the sub-event elements remain unchanged unless the associated digital manufacturing resources MR and capability information are updated, which can effectively reduce the amount of computation.
3) The event reorganization optimization method not only realizes the optimization reorganization of manufacturing sub-events, but also provides the optimal event schemes of different numbers of manufacturing tasks. Compared with the method for calculating the matching schemes of all manufacturing tasks respectively, the method reduces the redundancy of calculation and improves the calculation time and efficiency.
In an application scenario of the present invention, a specific frame diagram of an 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 stage 0 mainly realizes conversion from physical digital manufacturing resources to digital manufacturing resources based on cloud manufacturing resource description based on environment video semantics. Stages 1, 2, 3 and 4 are presented here to discuss how to effectively categorize the described events and tasks. The goal and implementation procedures of these four phases are as follows:
The first stage: decomposition of manufacturing events and manufacturing tasks. The first stage aims at decomposing the manufacturing event and the manufacturing task by determining a granularity of the decomposition of the manufacturing event and the manufacturing task. The manufacturing process and the manufacturing result are divided into a plurality of manufacturing sub-events, and then the manufacturing sub-tasks are related to each other according to the characteristics of the manufacturing sub-tasks and the manufacturing result of the manufacturing sub-events, so that the decomposition of the manufacturing tasks is completed. At this stage, through semantic description of the event, the digitized manufacturing resources completing the manufacturing task are clustered together, so that the matching range is reduced from the resource cloud pool to the event cloud set, and the effective reduction of the calculation range is realized.
And a second stage: manufacturing sub-event quality of service QoS calculations. The purpose of the second stage is to calculate quality of service QoS values for candidate sub-events and candidate manufacturing events. The participation index and calculation model of QoS calculation are first determined according to the key factors (time, cost and reliability) that determine the QoS value of quality of service, and then the QoS value of the manufacturing sub-event is calculated. The QoS value of the sub-event is only needed to be calculated in the stage, 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: recombination optimization of sub-events. The third stage aims at reorganizing the sub-events that complete the manufacturing sub-tasks to obtain optimal manufacturing events that complete the corresponding manufacturing tasks. And acquiring the QoS of the candidate manufacturing time according to the QoS value of each manufacturing sub-event, reorganizing and optimizing the optimal event set of the manufacturing task by screening the QoS, and giving the optimal manufacturing event. The calculation result in the 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.
Fourth stage: aggregation of manufacturing events for similar manufacturing tasks for manufacturing results. The fourth stage aims to aggregate 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, manufacturing events that produce specific results are aggregated and associated with the manufacturing tasks with the manufacturing results as a benchmark for personalized classification, thereby achieving personalized classification of the manufacturing tasks.
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 event sets can be combined, the event sets for completing a certain type of task are combined, the matching range can be reduced, and the optimal event set of the manufacturing task of a related platform can be directly given. The manufacturing event and the manufacturing task related in the third stage not only comprise the semantically described manufacturing event and the corresponding manufacturing task, but also comprise the mined potential event and task, and the potential mined event and task can effectively relieve the scarcity of manufacturing data and improve the response speed of similar manufacturing tasks.
The optimization method EQoS 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. EQoS still offers significant advantages over IAASMAC and BIDI in terms of computational range and data reliability, etc. The classification result of EQoS method performs better in narrowing the calculation range than DSCNN. In addition, the method has the advantages of wide applicability, large effective information of a computing main body, high utilization rate of computing results and the like, EQoS can effectively solve the problem of sparse effective data in 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 optimizing system facing to 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 manufacturing party, 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 comprise a manufacturing entity, a manufacturing process and a manufacturing result; an employer manufacturing task decomposition unit for uploading the employer to the manufacturing task, and associating at least one candidate sub-event capable of achieving the manufacturing effect of the sub-task for each sub-task from the event pool according to the manufacturing task manufacturing process to complete the manufacturing task decomposition; an optimal manufacturing event acquisition unit, which 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 quality of service includes at least one of three quality of service indicators of expiration time, cost and reliability.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means 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 present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. 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: many changes, modifications, substitutions and variations may be made to the embodiments without departing from the spirit and principles of the invention, the scope of which is defined by the claims and their equivalents.

Claims (7)

1. The event reorganization optimization method for industrial Internet manufacturing resource classification is characterized by comprising the following steps of:
The manufacturing party uploads a manufacturing event, the manufacturing event is decomposed into a plurality of sub-events according to the manufacturing process, an event cloud pool is built by utilizing 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;
Uploading a manufacturing task by an employer, associating at least one candidate sub-event capable of achieving the manufacturing effect of the sub-task for each sub-task from an event cloud pool according to the manufacturing task manufacturing process, and completing manufacturing task decomposition;
Recombining the candidate sub-events of all 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, and when the service quality comprises at least two of the three service quality indexes of the termination time, the cost and the reliability, the service quality calculation process of the m candidate manufacturing event is as follows:
By using The quality of service index x, m representing the mth candidate manufacturing event is a positive integer, x=t, c, rel,/>Representing the expiration time of the mth candidate manufacturing event,/>Representing the cost of the mth candidate manufacturing event,/>Representing reliability of the mth candidate manufacturing event; pair/>Normalization processing:
QoS x,max and QoS x,min represent the maximum and minimum, respectively, of quality of service index x for all candidate manufacturing events; w x denotes the weight of the quality of service index x, Σ xwx＝1;QoSx denotes Normalizing the initial value before processing;
The quality of service for the mth candidate manufacturing event is QoS m, which is
Sequencing the service quality of all the candidate manufacturing events, and selecting the candidate manufacturing event with the largest service quality value as the optimal manufacturing event for completing the manufacturing task;
When the service quality only comprises the termination time, sequencing the termination time of all the candidate manufacturing events, and selecting the candidate manufacturing event with the minimum termination time as the optimal manufacturing event for completing the manufacturing task;
Or when the service quality only comprises cost, sorting the cost of all the candidate manufacturing events, and selecting the candidate manufacturing event with the minimum cost as the optimal manufacturing event for completing the manufacturing task;
Or when the service quality only comprises reliability, sequencing the reliability of all the candidate manufacturing events, and selecting the candidate manufacturing event with the highest reliability as the optimal manufacturing event for completing the manufacturing task.
2. The method of claim 1, wherein in the event cloud pool, for the same manufacturing task, different enterprises have different manufacturing processes, and different manufacturing processes correspond to different manufacturing events and different sub-events.
3. The method for event reorganization optimization of industrial internet-oriented manufacturing resource classification of claim 1, wherein the optimal manufacturing event to complete the manufacturing task is stored in an event cloud pool.
4. The method of claim 3, wherein after obtaining the optimal manufacturing event for completing the manufacturing task, when there is a historical optimal manufacturing event for the same or similar manufacturing result as the manufacturing task in the event cloud pool, the current optimal manufacturing event is considered valid if the service quality of the current optimal manufacturing event is not worse than the service quality of the historical optimal manufacturing event.
5. The method of event reorganization optimization for industrial internet-oriented manufacturing resource classification as recited in any one of claims 1-4, wherein obtaining quality of service for candidate manufacturing events comprises:
The service quality of all the candidate sub-events is calculated, and the service quality of the candidate manufacturing event is obtained by adding the service quality of the candidate sub-events included in the candidate manufacturing event.
6. The method of claim 5, wherein the manufacturing events in the event cloud pool are clustered according to manufacturing results of the manufacturing events such that manufacturing events having identical and similar manufacturing results are grouped into a class, and wherein an optimal manufacturing event for completing the manufacturing task is obtained from a class of manufacturing events matching the manufacturing results of the manufacturing task entered by an employer.
7. An event reorganization optimization system for industrial internet manufacturing resource classification, configured to implement the event reorganization optimization method for industrial internet manufacturing resource classification according to one of claims 1 to 6, and comprising:
The event cloud pool platform is used for uploading a manufacturing event by a manufacturing party, 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 comprise a manufacturing entity, a manufacturing process and a manufacturing result;
An employer manufacturing task decomposition unit for uploading the employer to a manufacturing task, and associating at least one candidate sub-event capable of achieving the manufacturing effect of the sub-task for each sub-task from an event cloud pool according to the manufacturing task manufacturing process to complete manufacturing task decomposition;
An optimal manufacturing event obtaining unit, which 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 quality of service includes at least one of three quality of service indicators of expiration time, cost and reliability.
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