CN113191540A - Construction method and device of industrial link manufacturing resources - Google Patents
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Abstract
本发明公开了一种产业链路制造资源的构建方法及装置，其中，构建方法包括：构建制造资源及历史数据的知识图谱模型；根据制造任务对现有数据进行相似度匹配，利用所述知识图谱模型筛选出符合条件的制造资源；确定制造资源个体评估规则；根据所述个体评估规则对筛选出的制造资源进行个体分析评估，获得最优匹配对象；该构建方法通过对制造资源和历史制造任务信息构建知识图谱模型，随着数据量的增加，模型规模不断扩大，对制造资源匹配的结果可靠性不断增强，形成一个良性循环的匹配系统；另外，根据现有数据对制造任务进行相似度匹配，获得更加接近实际生产环境的匹配结果。
The invention discloses a method and device for constructing manufacturing resources of industrial links, wherein the constructing method includes: constructing a knowledge graph model of manufacturing resources and historical data; performing similarity matching on existing data according to manufacturing tasks, and utilizing the knowledge The graph model screens out qualified manufacturing resources; determines individual evaluation rules for manufacturing resources; performs individual analysis and evaluation on the selected manufacturing resources according to the individual evaluation rules to obtain optimal matching objects; the construction method Task information builds a knowledge graph model. As the amount of data increases, the scale of the model continues to expand, and the reliability of the results of matching manufacturing resources continues to increase, forming a virtuous cycle matching system; in addition, based on the existing data, the similarity of manufacturing tasks is calculated match to obtain matching results that are closer to the actual production environment.
Description
Technical Field
The invention relates to the field of manufacturing resource rapid reconstruction, in particular to a method for optimizing the scheduling degree of discrete manufacturing resources, and specifically relates to a method and a device for constructing manufacturing resources of an industrial chain.
Background
With the rapid development of the traditional manufacturing industry, the manufacturing market demand continuously presents the trend of individuation and diversification, which brings great challenges to the mass production mode of the traditional assembly line. In order to quickly respond to market changes and meet individualized and diversified manufacturing requirements, improving the production flexibility of manufacturing equipment and production lines has become a common endeavor target of the whole manufacturing industry, and one of the keys is to effectively and quickly reconstruct the existing manufacturing resources, and the quick and accurate matching of the manufacturing tasks and the manufacturing resources is also the key point of quick reconstruction of the manufacturing resources.
To achieve rapid reconfiguration of manufacturing resources, it is necessary to collect and collate existing manufacturing resource information and match existing manufacturing tasks on the premise of being based on a large amount of historical manufacturing data. Therefore, an ontology base and a knowledge base need to be constructed for manufacturing resources to complete the integration of the manufacturing resources, so as to achieve the purpose of quickly and accurately matching the manufacturing tasks.
The Chinese patent application with publication number CN111582561A and publication number 2020.08.25 discloses a small-batch multi-variety-oriented reconfigurable production line scheduling optimization method, which constructs a manufacturing resource body and an inference mechanism, realizes knowledge expression of manufacturing resources and optimizes resource allocation in a single-batch secondary production process from a decision level. The disadvantages of this method are: manufacturing resources are not systematically integrated, and only an optimization algorithm is used for customized scheduling in the resource configuration process, so that the reasonability and feasibility of results are difficult to ensure.
Disclosure of Invention
In order to solve the above problems, the present invention provides a method and an apparatus for constructing an industrial link manufacturing resource, which can perform similarity matching on an existing manufacturing task according to historical manufacturing data, thereby performing manufacturing resource allocation and obtaining a matching result more suitable for an actual manufacturing process.
In order to achieve the above object, an aspect of the present invention provides a method for constructing an industrial link manufacturing resource, including:
constructing a knowledge map model of manufacturing resources and historical data;
carrying out similarity matching on the existing data according to the manufacturing task, and screening out manufacturing resources meeting the conditions by using the knowledge graph model;
determining manufacturing resource individual evaluation rules;
and carrying out individual analysis and evaluation on the screened manufacturing resources according to the individual evaluation rule to obtain an optimal matching object.
As a preferred technical solution, the constructing a knowledge graph model of manufacturing resources and historical data further includes:
constructing an ontology model of manufacturing resources and manufacturing tasks; the ontology model comprises a knowledge graph entity type and a knowledge graph attribute type;
building a knowledge base of manufacturing resources and manufacturing tasks;
the knowledge base is updated based on the real-time status of the manufacturing resource.
Further, the types of knowledge-graph entities include: equipment type, cutter type, processing material and manufacturing precision; the types of knowledge-graph attributes include: equipment number, equipment location, operating state, machining type, machining characteristics, and manufacturing cycle.
As a preferred technical solution, the constructing a knowledge base of manufacturing resources and manufacturing tasks further includes: and acquiring manufacturing resources and historical manufacturing task information by using an NLP technology to obtain structured data, corresponding to the constructed entities and attributes, and storing the structured data in a graph database.
Preferably, the performing similarity matching on the existing data according to the manufacturing task further includes:
determining a similarity matching rule of the manufacturing tasks;
carrying out similarity matching on the existing manufacturing task and the historical manufacturing task;
and adding the manufacturing resources corresponding to the matching result into the to-be-selected set.
As a preferred technical solution, the determining a matching rule of similarity of manufacturing tasks further includes:
determining a similarity algorithm according to each entity and attribute of the manufacturing task;
comparing the similarity percentage p of the entities or attributes corresponding to the two manufacturing tasks according to the similarity algorithmiAnd setting a weight q for each entity and attributei。
Further, the similarity matching between the existing manufacturing task and the historical manufacturing task further includes:
calculating a weighted sum of all n entity and attribute similarities, i.e.
Regarding historical manufacturing tasks with results greater than a set threshold as similar tasks
Preferably, the evaluation rule is determined according to the operating state of the manufacturing resource, the load rate and the manufacturing cost.
In another aspect, the present invention further provides an apparatus for constructing an industrial link manufacturing resource, including:
the building module is used for building a knowledge graph model of manufacturing resources and historical data;
the screening module is used for carrying out similarity matching on the existing data according to the manufacturing task and screening out manufacturing resources meeting the conditions by utilizing the knowledge graph model;
the determining module is used for determining manufacturing resource individual evaluation rules;
and the individual analysis and evaluation module is used for carrying out individual analysis and evaluation on the screened manufacturing resources according to the individual evaluation rule to obtain an optimal matching object.
Compared with the prior art, the invention has the beneficial effects that: a knowledge graph model is constructed for manufacturing resources and historical manufacturing task information, the scale of the model is continuously enlarged along with the increase of data volume, and the reliability of the result of matching the manufacturing resources is continuously enhanced, so that a virtuous circle matching system is formed;
in addition, similarity matching is carried out on the manufacturing tasks according to the existing data, and a matching result which is closer to the actual production environment is obtained;
in addition, the manufacturing resources are analyzed and evaluated individually, and the purposes of maximizing equipment utilization rate, balancing load and minimizing cost are achieved.
Drawings
FIG. 1 is a flow chart of a method for building an industrial link manufacturing resource according to the present invention;
FIG. 2 is a diagram of an ontological model of manufacturing resources and manufacturing tasks in accordance with an embodiment of the invention;
FIG. 3 is a schematic illustration of a manufacturing resource and manufacturing task knowledge graph in accordance with an embodiment of the present invention;
fig. 4 is a block diagram of an apparatus for constructing an industrial link manufacturing resource according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, the embodiment provides a method for constructing an industrial link manufacturing resource, which specifically includes the following steps:
s10: constructing a knowledge map model of manufacturing resources and historical data;
specifically, an ontology model of the manufacturing resource and the manufacturing task needs to be constructed first, and in this embodiment, referring to fig. 2, 4 types of knowledge graph entities and 6 types of knowledge graph attributes are constructed to complete the design of the knowledge graph entity relationship between the manufacturing resource and the manufacturing task. The knowledge-graph entity types include: equipment type, tool type, machining material, manufacturing accuracy. The types of knowledge-graph attributes include: equipment number, equipment location, operating state, machining type, machining characteristics, and manufacturing cycle. The device type, the tool type, the device number, the device position and the operation state are used for constructing the manufacturing resource body, and the processing material, the manufacturing precision, the processing type, the processing characteristic and the manufacturing period are used for constructing the manufacturing task body.
Then, a knowledge base of manufacturing resources and manufacturing tasks is constructed, specifically, manufacturing resources and historical manufacturing task information are collected by using NLP technology, structured data are obtained and stored in a database corresponding to the constructed entities and attributes, in this embodiment, attribute information of existing manufacturing resources and manufacturing tasks is collected, structured data are obtained by using NLP technology, and the data are stored in a Neo4j graph database, so that a data structure as shown in fig. 3 is obtained.
And finally, updating the knowledge base according to the real-time state of the manufacturing resource, specifically, monitoring the state of the existing manufacturing resource in real time, and uploading the updated data to a graph database after the operation state changes so as to realize that the data in the database is consistent with the actual data.
S20: carrying out similarity matching on the existing data according to the manufacturing task, and screening out manufacturing resources meeting the conditions by using the knowledge graph model;
specifically, firstly, a manufacturing task similarity matching rule needs to be formulated; in this embodiment, a specific similarity algorithm is formulated for the attributes of the manufacturing task currently required to be matched, and a similarity weight is set for each attributeqiAnd a threshold value determined as a similar task is set. Specifically, for one manufacturing task, its key attributes such as machining accuracy, machining type, etc. are given greater weight, while for a manufacturing cycle, secondary attributes such as machining material, etc. may be given lower similarity weights. For example, for machining accuracy, different accuracy intervals may be preset, and each interval may be assigned with a similarity value, and the interval closer to the original accuracy has a larger similarity value. The size of the decision threshold can be determined by the popularity of the manufacturing task, and the decision threshold can be increased for conventional manufacturing tasks with larger matching quantities, and conversely the threshold can be decreased.
Then, carrying out similarity matching on the existing manufacturing task and the historical manufacturing task; specifically, the similarity p of the current manufacturing task and the attributes of the historical manufacturing tasks T1, T2, T3 and T4 in the database is calculated respectivelyiAnd weighted summing, i.e. computing
And regarding the historical manufacturing tasks with the results larger than the set threshold value as similar tasks.
Finally, adding the manufacturing resources corresponding to the matching result into a to-be-selected set; specifically, the manufacturing resources corresponding to the screened similar tasks are searched for and serve as available resources meeting the processing conditions, and the available resources are used for evaluating and screening resource individuals in the next step.
S30: determining manufacturing resource individual evaluation rules;
specifically, the attributes of each manufacturing resource are quantitatively evaluated in consideration of the operating state, load rate, manufacturing cost and other factors of the manufacturing resource, and the evaluation rule is designed for the evaluation of one manufacturing resource with the purposes of maximizing equipment utilization rate, balancing load and minimizing cost.
S40: performing individual analysis and evaluation on the screened manufacturing resources according to the individual evaluation rule to obtain an optimal matching object; specifically, different attributes may be given different weights for different manufacturing tasks, a higher manufacturing cost may be given to a task having a cost priority, and a higher load factor may be given to a task having an efficiency priority.
Compared with the traditional manufacturing resource scheduling optimization method, the method applies knowledge graph technology to integrate the manufacturing resources, constructs a huge knowledge system, and can perform similarity matching on the existing manufacturing tasks according to historical manufacturing data so as to perform manufacturing resource allocation and obtain a matching result which is more in line with the actual manufacturing process.
In another embodiment, the present invention further provides a building apparatus of industrial link manufacturing resources, and referring to fig. 4, the building apparatus specifically includes:
a construction module 100 for constructing a knowledge graph model of manufacturing resources and historical data; it should be noted that, since the specific construction method and process are already described in detail in step S10 of the construction method of the industrial link manufacturing resource, they are not described herein again.
The screening module 200 is used for performing similarity matching on the existing data according to the manufacturing task and screening out manufacturing resources meeting the conditions by using the knowledge graph model; it should be noted that, since the specific screening method and process are already described in detail in step S20 of the method for constructing the industrial link manufacturing resource, they are not described herein again.
A determining module 300, configured to determine a manufacturing resource individual evaluation rule; it should be noted that, since the specific determination method and process are already described in detail in step S30 of the method for constructing the industrial link manufacturing resource, they are not described herein again.
The individual analysis and evaluation module 400 is used for performing individual analysis and evaluation on the screened manufacturing resources according to the individual evaluation rule to obtain an optimal matching object; it should be noted that, since the specific individual analysis and evaluation manner and process are already described in detail in step S40 of the method for constructing the industrial link manufacturing resource, they are not described herein again.
In addition, an embodiment of the present invention further provides a computer-readable storage medium, where the computer-readable storage medium may store a program, and the program includes, when executed, some or all of the steps of the method for constructing any industrial link manufacturing resource described in the above method embodiments.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable memory. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a memory and includes several instructions for causing a computer device (which may be a personal computer, a server, a network device, or the like) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned memory comprises: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by associated hardware instructed by a program, which may be stored in a computer-readable memory, which may include: flash Memory disks, Read-Only memories (ROMs), Random Access Memories (RAMs), magnetic or optical disks, and the like.
An exemplary flow chart of a method for implementing a service chain according to an embodiment of the present invention is described above with reference to the accompanying drawings. It should be noted that the numerous details included in the above description are merely exemplary of the invention and are not limiting of the invention. In other embodiments of the invention, the method may have more, fewer, or different steps, and the order, inclusion, function, etc. of the steps may be different from that described and illustrated.
Claims (10)
1. A method for constructing an industrial link manufacturing resource, comprising:
constructing a knowledge map model of manufacturing resources and historical data;
carrying out similarity matching on the existing data according to the manufacturing task, and screening out manufacturing resources meeting the conditions by using the knowledge graph model;
determining manufacturing resource individual evaluation rules;
and carrying out individual analysis and evaluation on the screened manufacturing resources according to the individual evaluation rule to obtain an optimal matching object.
2. The method of constructing as claimed in claim 1, wherein said constructing a knowledge graph model of manufacturing resources and historical data further comprises:
constructing an ontology model of manufacturing resources and manufacturing tasks; the ontology model comprises a knowledge graph entity type and a knowledge graph attribute type;
building a knowledge base of manufacturing resources and manufacturing tasks;
the knowledge base is updated based on the real-time status of the manufacturing resource.
3. The building method of claim 2, wherein the knowledge-graph entity types comprise: equipment type, cutter type, processing material and manufacturing precision; the types of knowledge-graph attributes include: equipment number, equipment location, operating state, machining type, machining characteristics, and manufacturing cycle.
4. The build method of claim 2, wherein building a knowledge base of manufacturing resources and manufacturing tasks further comprises: and acquiring manufacturing resources and historical manufacturing task information by using an NLP technology to obtain structured data, corresponding to the constructed entities and attributes, and storing the structured data in a graph database.
5. The build method of claim 1 wherein the similarity matching existing data according to a manufacturing task further comprises:
determining a similarity matching rule of the manufacturing tasks;
carrying out similarity matching on the existing manufacturing task and the historical manufacturing task;
and adding the manufacturing resources corresponding to the matching result into the to-be-selected set.
6. The build method of claim 5 wherein the determining a manufacturing task similarity matching rule further comprises:
determining a similarity algorithm according to each entity and attribute of the manufacturing task;
comparing the similarity percentage p of the entities or attributes corresponding to the two manufacturing tasks according to the similarity algorithmiAnd setting a weight q for each entity and attributei。
7. The build method of claim 6, wherein the similarity matching of existing manufacturing tasks to historical manufacturing tasks further comprises:
calculating a weighted sum of all n entity and attribute similarities, i.e.
Historical manufacturing tasks with results greater than a set threshold are considered similar tasks.
8. The construction method according to claim 1, characterized in that: the evaluation rule is determined according to the operation state of the manufacturing resource, the load rate and the manufacturing cost.
9. An apparatus for building an industrial link manufacturing resource, comprising:
the building module is used for building a knowledge graph model of manufacturing resources and historical data;
the screening module is used for carrying out similarity matching on the existing data according to the manufacturing task and screening out manufacturing resources meeting the conditions by utilizing the knowledge graph model;
the determining module is used for determining manufacturing resource individual evaluation rules;
and the individual analysis and evaluation module is used for carrying out individual analysis and evaluation on the screened manufacturing resources according to the individual evaluation rule to obtain an optimal matching object.
10. A computer-readable storage medium storing a computer program, wherein the computer program when executed by a processor implements the steps of a method of building an industrial link manufacturing resource as claimed in any one of claims 1 to 8.
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| CN112434169A (en) * | 2020-11-13 | 2021-03-02 | 北京创业光荣信息科技有限责任公司 | Knowledge graph construction method and system and computer equipment |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116090559A (en) * | 2023-02-03 | 2023-05-09 | 深圳智现未来工业软件有限公司 | A Method for Generating Knowledge Points Based on Wafer Map Inspection Data |
| CN118626882A (en) * | 2024-08-15 | 2024-09-10 | 四川亚度家具有限公司 | Internet-based furniture production task automatic matching method, system and terminal |
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