CN111915137A - Construction method of manufacturing industry data model - Google Patents
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- 239000011265 semifinished product Substances 0.000 claims abstract description 50
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Abstract
The invention relates to the technical field of data processing, in particular to a construction method of a manufacturing industry data model. A construction method of a statistical data model of manufacturing industry is characterized in that important data entities of enterprises, raw materials, semi-finished products, production lines, products, sales and the like are determined based on the data service requirements of the manufacturing industry, the relationship among the data entities is established, further, the name, the relationship, the attribute, the main key and the external key of each entity are determined, and a fact table and a dimension table meeting the data service requirements are determined according to the specific conditions of the manufacturing industry. On the basis, after data are acquired from various existing management databases, real-time online monitoring data of a production line and direct connection field sensors, standardized processing conforming to the data model is carried out. The invention provides a method for establishing a manufacturing data model aiming at the characteristics of a typical manufacturing process flow, and the collected industrial data is standardized according to the established data model so as to serve various manufacturing data businesses by using the data.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a construction method of a manufacturing industry data model.
Background
At present, many manufacturing industries are mainly industrial clusters of small and medium-sized enterprises, and the common problems of the traditional manufacturing industries are that the product design and manufacture are mainly in a mode of style, variety and large batch, and the development trend of individual customization is difficult to adapt; the production process has various process types, and information digitization and intelligent decision are lacked; the raw materials are various in types, quantity and sources, and purchasing and inventory are difficult to effectively control. In order to effectively help traditional manufacturing enterprises to realize intelligent production and management, help enterprises to realize innovation of production modes and business modes, improve production efficiency of manufacturing industries, reduce production cost and improve product quality, data is required to be used for understanding and solving visible problems; analyzing and predicting invisible problems with the data; new knowledge is mined from the data and reused to redefine the problem so that visible or invisible problems can be avoided in the manufacturing system.
The integration and real-time dynamic tracking of data generated in the enterprise manufacturing process are the basis of industrial internet innovation application. The method comprises the steps of collecting industrial field production data, extracting existing management data, carrying out standardized processing on the obtained data, and constructing a database/data warehouse as a data base.
In order to correctly represent all the data objects required in a database/data warehouse, it is necessary to use a data model that helps to design the database conceptually, logically, and physically; the method is beneficial to defining the data entity relation table, the primary key, the foreign key and the storage process; the data model provides clear appearance of basic data, and a database developer can create a physical database by using the data model; missing and redundant data is found by the aid of the data model; data modeling also facilitates visually representing data and enforcing business rules, regulations, compliance, and government policies on the data; the data model can ensure consistency in naming convention, default values, semantics and safety, and can also ensure data quality.
Aiming at the current situation of the manufacturing industry, the invention provides a method for establishing a data model of the manufacturing industry.
Disclosure of Invention
The invention provides a data modeling method based on manufacturing industry characteristics, which is used for meeting all-around requirements of all manufacturing enterprises in the industry on raw material purchasing, production processing, semi-finished product and product control, sales data and the like.
In order to achieve the purpose, the technical scheme of the invention comprises two parts: establishing a data model, and acquiring and processing data.
The establishment of the data model comprises the following steps:
step 1: analyzing the data service requirements of specific manufacturing industry, and determining entity names corresponding to the data service requirements, including enterprises, production lines, raw materials, semi-finished products, sales and the like, and the relationships among the entities. Wherein, the enterprise refers to all enterprises in the industry within the management range; the production line is various production lines reflecting the whole process of the production process in enterprises in the industry, for example: production lines of individual members, partial assembly and production lines, overall assembly production lines, packaging production lines, and the like; the entities related to warehousing are raw materials, semi-finished products, and products (or finished products); also, the entity sales include attributes indicating the number sold, the amount sold, the sales channel, etc.
Step 2: determining a logical model for each entity in step 1, wherein:
the enterprise includes: enterprise name (main key), time ID (external key), p raw material IDs (external keys), m semi-finished product IDs (external keys), k production line IDs, product ID (external key) and sales ID (external key), and related attributes such as the area where the enterprise is located and the type of the enterprise;
the p raw material entities each comprise: raw material ID, time ID, business name, and related raw material attributes, such as raw material name, category, quantity, etc.;
the m semi-finished entities respectively comprise: a semi-finished product ID, a time ID, an enterprise name, a production line ID (for producing the semi-finished product), and related attributes such as a raw material name, a category, a quantity and the like;
the k production line entities respectively comprise: production line ID, time ID, company name, raw material
ID. The system comprises a semi-finished product ID, n equipment IDs (n is an integer greater than or equal to 1), and related production line types, equipment descriptions, equipment states and production line states, wherein the equipment states and the production line states represent normal or abnormal operation;
the product comprises the following components: product ID, business name, date, line ID (of the final product, e.g., packaging), and product attributes related to product description, product classification, category description, unit price, quantity, etc.;
the selling comprises the following steps: sales ID, business name, product ID, date, and related attributes such as sales volume, sales amount, and sales channel, wherein the sales channel can be online, offline, or mixed;
and step 3: establishing a corresponding physical model for the logic model in the step 2, wherein an enterprise is taken as a center to serve as a fact table, and other entities are taken as different dimensions for observing the enterprise to establish a dimension table, wherein:
the enterprise fact table includes: a business name (string), a date ID (integer), p raw material IDs (integer), m semi-finished product IDs (integer), k production line IDs (integer), product IDs (integer), sales IDs (integer);
the raw material dimension table comprises: raw material ID (integer), business name (string), date ID (integer), raw material name (string), category (string), quantity (integer); the semi-finished product dimension table comprises: semi-finished product ID (integer), business name (string), production line ID (integer), date ID (integer), raw material ID (integer), semi-finished product name (string), and number of varieties (integer);
the production line dimension table includes: production line ID (integer), date ID (integer), raw material ID (integer), semi-finished product ID (integer), production line type (string), equipment ID, equipment description (string), equipment status (boolean) production line status (boolean);
the product warehouse dimension table comprises: product ID (integer), business name (string), date ID (integer), production line ID (integer), product description (string), product category (integer), category description (string), quantity (integer), unit price (floating point);
the sales dimension table comprises: sales ID (integer), business name (string), product ID (integer), date ID (integer), sales volume (integer), sales amount (floating point), and sales channel (integer).
In one embodiment of the invention, if the point of interest is a particular element within the industry, such as a selling entity, the same method can be used to create a fact table for the sale and dimension tables for other entities to observe the impact of other data entity dimensions on the sale.
The data acquisition and processing comprises:
step 1: collecting production line equipment data and management data of different enterprises in the industry, wherein the production line equipment data comprises online data transmitted in real time on an industrial control network and direct data directly connected to a field sensor, and the management data mainly comes from an ERP system, an MES system, a WMS system and an OA system (by means of ETL, Extract-Transform-Load extraction, API, Application Program Interface synchronization and the like);
step 2: and standardizing the acquired data according to a preset standard, filtering, cleaning and fusing the data, and standardizing by taking a physical model in the data model as a reference.
A construction method of a statistical data model of manufacturing industry is characterized in that important data entities of enterprises, raw materials, semi-finished products, production lines, products, sales and the like are determined based on the data service requirements of the manufacturing industry, the relationship among the data entities is established, further, the name, the relationship, the attribute, the main key and the external key of each entity are determined, and a fact table and a dimension table meeting the data service requirements are determined according to the specific conditions of the manufacturing industry. On the basis, after data are acquired from various existing management databases, real-time online monitoring data of a production line and direct connection field sensors, standardized processing conforming to the data model is carried out.
The invention has the beneficial effects that: the invention provides a method for establishing a manufacturing data model aiming at the characteristics of a typical manufacturing process flow, and the collected industrial data is standardized according to the established data model so as to serve various manufacturing data businesses by using the data.
Drawings
FIG. 1 is a schematic diagram of one embodiment of a process flow plan for a manufacturing facility
FIG. 2 is a relational diagram of data entities of the present invention
FIG. 3 is a logical model of a data entity of the present invention
FIG. 4 is a data physics model of the present invention
FIG. 5 is a block diagram of a data acquisition and processing embodiment of the present invention
Reference numerals:
Detailed Description
In the following detailed description of the preferred embodiments of the invention, reference is made to the accompanying drawings that form a part hereof, and in which is shown by way of illustration, specific features of the invention, such that the advantages and features of the invention may be more readily understood and appreciated. The following description is an embodiment of the claimed invention, and other embodiments related to the claims not specifically described also fall within the scope of the claims.
The invention is further described with reference to the following figures and detailed description of embodiments.
FIG. 1 is a schematic diagram of an embodiment of a process planning of a manufacturing enterprise, where specific devices, processes, raw materials, semi-finished products, components, etc. are not specifically described herein for the purpose of simplification, and generally, a manufacturing enterprise includes a plurality of production lines (lines 1-4 in the figure) to process raw materials into semi-finished products and components through different devices and processes, and the semi-finished products and the components have similar properties, so that the semi-finished products and the components can be divided into different entities or unified as semi-finished product entities; the semi-finished products and/or components are stored in a semi-finished product warehouse and then supplied to one or more component assembly and processing lines (line 5 in the figure) for assembly and then transferred to a packaging line (line 6 in the figure) which produces the final products and delivers them to a product warehouse for storage and distribution after they are sold, together with necessary accessories such as tools and spare parts. Fig. 1 illustrates the relationship between raw materials, production lines and equipment, semi-finished products, and the overall process and framework are similar, although different manufacturing industries have different equipment and production lines.
Fig. 2 is a data entity relationship diagram of an embodiment of the present invention, in which the enterprise in fig. 2 is a central entity concerned, and is linked with other entities involved in data services, first, a time entity is a reference for observing the enterprise, and the knowledge of an enterprise situation includes all-round knowledge of entities such as raw material supply, production line and equipment status, inventory of semi-finished products (including components), inventory of product stock, sales situation, and the like, and these data entities provide a service concept within an organization range in a specific manufacturing industry, are data frames that users expect to see, and in future data applications, a basic data basis is laid for realizing intelligent production and management, realizing innovation of production modes and business modes, improving production efficiency of manufacturing industry, reducing production cost, and improving product quality.
FIG. 3 is a logical model of data entities according to one embodiment of the present invention, which adds more information to the data entities of FIG. 3, defines the structure of the data elements, sets the relationships between them, and also the attributes of the data entities.
In one embodiment of the invention, an enterprise data entity comprises: the business name is a primary key, in different embodiments, a business unique code may also be used as the primary key, and other data entities related to the business data entity are used as foreign keys, in this embodiment, a date (foreign key), p raw material IDs (foreign keys), m semi-finished product IDs (foreign keys), k production line IDs (foreign keys), a product ID (foreign key), and a sales ID (foreign key) are associated with the business data entity, and the business has some attributes, such as a location area (geographic information), a business type (national camp, collective, civil camp), and the like.
The time data entity includes a date and other time-related attributes including: date description, month description, year, week description, date being independent of other data entities, so no foreign key exists;
raw material data entities include raw material ID, business name, date, and raw material attributes (name, category, quantity, etc.);
the semi-finished product data entity comprises a semi-finished product ID, a business name, a date, a production line ID (related to the semi-finished product ID), a raw material ID (related to the semi-finished product ID), and attributes (name, number and the like) of the semi-finished product;
the production line data entity includes: production line ID, date, raw material ID (related to the production line), semi-finished product ID, n equipment IDs (n is an integer greater than or equal to 1), production line description, operation state and other attributes;
the product data entity includes: product ID, business name, date, line ID (related to the final product, e.g., packaging), and product attributes related to product description, product classification, class description, unit price, quantity, etc.;
the sales data entities include: business name, product ID, date, and related attributes (offline, online, mixed) such as sales volume, and sales channel;
the time data entity includes: date, and attributes of the time entity (date description, month description, year, week description).
FIG. 4 is a physical model corresponding to the logical model of the data entities in FIG. 3, i.e., how the model is built in a database, the physical model expressing the structure of all tables, converting entities into tables on the basis of the logical model of the data entities, foreign keys determining the relationships between tables, attributes converting into columns, and defining the data types of the columns.
FIG. 5 is a schematic diagram of a system for data acquisition according to an embodiment of the present invention, wherein a data acquisition site is deployed in a manufacturing enterprise, the data acquisition site is connected to an enterprise management network to obtain management data of ERP, MES, WMS and OA systems, and the data acquisition site is connected to a control network of the production line of the enterprise to obtain real-time online monitoring data and directly obtain field data from a field sensor to obtain the operation status of the production line and equipment of the enterprise.
In one embodiment of the invention, in order to improve efficiency, the data acquisition point can clean, process and standardize the acquired data according to the definition of the physical model of the data entity, and then transmit the data to a data warehouse of a data center through the internet for storage, and finally apply to various data applications.
The above description is only an embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes performed by the present specification and drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.
Claims (9)
1. A construction method of a manufacturing industry statistical data model is characterized by comprising the following steps: the method comprises the following steps:
1) determining related data entity names including enterprises, raw materials, semi-finished products, production lines, products, sales, time and relations among the data entities according to data service requirements of the manufacturing industry;
2) constructing a logic model of each data entity, wherein the logic model comprises entity names, relations, attributes, primary keys and foreign keys;
3) constructing a physical model corresponding to the logic model based on the actual conditions of a specific manufacturing enterprise, wherein the physical model comprises a table name, a column name and a column data type of a main key and an external key corresponding to each data entity logic model; the foreign key determines the relationship between tables, the attribute is converted into a column, and the data type of the column is defined;
4) collecting production line equipment data and management data of different enterprises in the industry; the collected data is standardized based on a physical model of the data entity.
2. The method of constructing a manufacturing data model of claim 1, wherein: the entity definition of the data service in the step 1) comprises enterprises, raw materials, production lines, semi-finished products, sales and time, and the enterprise entity is directly related to the raw materials, the production lines, the semi-finished products, the sales and the time as a center. Further, production line is directly related to equipment and time, semi-finished product is directly related to production line, raw material, time, product is directly related to raw material, semi-finished product, production line and time, and sales is directly related to product and time.
3. The method of constructing a manufacturing data model of claim 1, wherein: a logical model of the data service is constructed in step 3), wherein,
an enterprise: a business name (primary key), a date (foreign key), p raw material IDs (foreign keys) (p foreign keys, p being an integer of 1 or more), m semi-finished product IDs (m foreign keys, m being an integer of 1 or more), k production line IDs (k foreign keys, k being an integer of 1 or more), product IDs (foreign keys), sales IDs (foreign keys), and one or more business attributes;
raw materials: a raw material ID, a business name, a date, and one or more raw material attributes;
semi-finished product: a semi-finished product ID, a business name, a date, a line ID (associated with the semi-finished product ID), a raw material ID (associated with the semi-finished product ID), and one or more semi-finished product attributes;
the production line includes: a line ID, a date, a raw material ID (associated with the line), a semi-finished product ID, n device IDs (n is an integer greater than or equal to 1), and one or more line attributes;
the product comprises the following components: product ID, business name, date, production line ID (end product related, e.g., packaging), and one or more product attributes;
the selling comprises the following steps: a business name, a product ID, a date, and one or more sales attributes;
time: date, date description, month description, year, week description.
4. The method of constructing a manufacturing data model of claim 1, wherein: constructing a physical model corresponding to the logic model in the step 3), wherein:
the enterprise fact table includes: business name (string), date ID (integer), p raw material IDs (integer), m semi-finished product IDs (integer), k production line IDs (integer), product IDs (integer), sales ID (integer), and location area (string), business type (integer);
the raw material dimension table comprises: raw material ID (integer), business name (string), date ID (integer), raw material name (string), category (string), quantity (integer);
the semi-finished product dimension table comprises: a semi-finished product ID (integer), a business name (string), a line ID (integer) associated with the semi-finished product ID, a date ID (integer), a raw material ID associated with the semi-finished product ID, and a semi-finished product name (string) and number (integer);
the production line dimension table includes: production line ID (integer), date ID (integer), raw material ID (integer), semi-finished product ID (integer), production line type (string), n pieces of equipment ID (integer), equipment description (string), equipment status (boolean), production line status (boolean);
the product dimension table comprises: product ID (integer), business name (string) date ID (integer), production line ID (integer), product description (string), product category (integer), category description (string), unit price (floating point), quantity (integer);
the sales dimension table comprises: sales ID (integer), business name, product ID (integer), date ID (integer), sales volume (floating point), and sales channel (integer)
5. The method of constructing a manufacturing data model of claim 4, wherein: and constructing a physical model corresponding to the logic model, or constructing fact tables for raw materials, semi-finished products, production lines, products and sales in the entities according to the requirements of data services, and constructing dimension tables for the rest entities.
6. The method of constructing a manufacturing data model of claim 1, wherein: the method comprises the steps of collecting production line equipment data of different enterprises in the industry, wherein the production line equipment data comprises online control monitoring data obtained from a production line control system network and field data directly collected from production field sensors, and further, the field data is processed and analyzed to obtain state data of each equipment and a production line.
7. The method for building production line equipment data according to claim 6, wherein: wherein the data of the device comprises: a device ID, a device description, and device data, further the device data comprising: the manufacturer, the model, the unique device identification number, the device state 1, the device state 2, the device state …, and the device state j, j are integers greater than or equal to 1.
8. The method of constructing a manufacturing data model of claim 1, wherein: the management data of different enterprises in the industry is acquired directly from enterprise databases, and comprises an Enterprise Resource Planning (ERP) system, a Manufacturing Execution System (MES), a Warehouse Management System (WMS) system and an Office Automation (OA) system.
9. The method for constructing a manufacturing data model according to claim 1 and the physical model corresponding to the logical model of the data entity according to claim 4, wherein the collected data is standardized.
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| CN112818069A (en) * | 2021-01-27 | 2021-05-18 | 中科雨辰科技有限公司 | Dynamic model-based cataloguing data acquisition system |
| CN113254544A (en) * | 2021-04-29 | 2021-08-13 | 西安交通大学 | Data processing device and method based on dimension modeling |
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