CN115933569A - Workshop collaborative intelligent manufacturing system based on Internet of things - Google Patents
Workshop collaborative intelligent manufacturing system based on Internet of things Download PDFInfo
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- CN115933569A CN115933569A CN202211690045.7A CN202211690045A CN115933569A CN 115933569 A CN115933569 A CN 115933569A CN 202211690045 A CN202211690045 A CN 202211690045A CN 115933569 A CN115933569 A CN 115933569A
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
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Abstract
The invention discloses a workshop collaborative intelligent manufacturing system based on the Internet of things, which relates to the technical field of intelligent manufacturing and solves the technical problem of purchasing raw materials when the raw materials in a workshop do not meet the production requirements; acquiring an order form through a data acquisition module, and acquiring the order quantity of the required raw materials according to the order form; the data statistics module compares the order quantity of the required raw materials with the stock quantity of the required raw materials; when the stock of the corresponding raw materials does not meet the production requirement, acquiring the to-be-purchased amount of the raw materials which do not meet the production requirement according to a calculation formula; the raw material purchasing module screens and acquires target raw material supply plants in the raw material supply plants according to preset rules, acquires target raw material supply plant coefficients through a calculation formula, acquires target raw material supply plant sequences, and performs raw material purchasing according to the target raw material supply plant sequences; the problem that raw materials in a workshop cannot meet production requirements is solved, and the production cost is reduced.
Description
Technical Field
The invention belongs to the field of Internet of things, relates to an intelligent manufacturing technology, and particularly relates to a workshop collaborative intelligent manufacturing system based on the Internet of things.
Background
The intelligent manufacturing is based on intelligent equipment, and in the face of the contradiction between high human cost and the huge demand of the traditional manufacturing industry on labor force, an unmanned or less-humanized intelligent factory is constructed, the dependence on people is reduced, and the intelligent manufacturing method is a feasible technical route for solving the current contradiction.
The existing Chinese patent (CN 202011437263.0) proposes an intelligent manufacturing system cooperative control method, system and computer equipment; the specific working steps comprise the following steps: the system receives a part processing requirement and receives a part processing program; the warehouse terminal receives a part processing demand sent by the server, calls a server processing product raw material comparison table, performs raw material preparation, and sends a signal of completing the preparation to the processing arrangement terminal; the server arranges a processing plan of the processing part according to the processing requirement and the processing arrangement condition of the processing equipment and stores the processing plan; the processing arrangement terminal receives the processing plan discharged by the server, confirms that the warehouse terminal sends a raw material receiving application, and arranges production according to the processing plan; and confirming the machining completion condition every day, transmitting the confirmation signal condition to the server, and adjusting the machining plan by the server according to the confirmation signal. Input the production demand through production demand terminal, arrange the terminal to assign to workshop terminal and processing through the server, processing is arranged the terminal and can directly carries out the raw materials according to the data of demand and the inside storage of server and is prepared, processing is arranged the terminal simultaneously and can be controlled the processing equipment and can be strictly according to the processing demand, and then guarantee that the processing plan goes on smoothly, can observe the processing condition through production demand terminal simultaneously, and then can real-time understanding processing plan execution conditions, make the cooperation more smooth and easy between each production department, avoid prior art people for there being the problem of careless omission in carrying out the work handing-over process, make whole production process go on smoothly.
However, this patent does not teach how to efficiently procure raw materials when the raw materials in the plant do not meet the production requirements. Therefore, the workshop collaborative intelligent manufacturing system based on the Internet of things is provided.
Disclosure of Invention
The present invention is directed to solving at least one of the problems of the prior art. Therefore, the invention provides the workshop collaborative intelligent manufacturing system based on the Internet of things, which solves the problem of effectively purchasing raw materials when the raw materials in a workshop do not meet the production requirements.
In order to achieve the above object, an embodiment according to a first aspect of the present invention provides an internet-of-things-based workshop collaborative intelligent manufacturing system, including a data acquisition module, a data statistics module, a raw material purchasing module, and a database; information interaction is carried out among all modules based on digital signals;
the database is used for recording the stock of raw materials of a workshop and the stock of raw materials of a raw material supply plant;
the data acquisition module is used for acquiring an order form and acquiring the order quantity of the required raw materials according to the order form;
the data statistics module is used for receiving the order quantity of the required raw materials and acquiring the inventory quantity of the corresponding required raw materials in the workshop;
comparing the order quantity of the required raw materials with the stock quantity of the required raw materials;
when the order quantity is larger than the stock quantity, the stock quantity of the corresponding raw material does not meet the production requirement, and the corresponding raw material obtains a label which does not meet the production requirement;
obtaining the quantity to be purchased of the raw materials which do not meet the production requirements according to a calculation formula;
the raw material purchasing module is used for receiving the amount of raw materials to be purchased which do not meet the production requirement;
screening and obtaining a target raw material supply plant in the raw material supply plant according to a preset rule;
acquiring the unit price of the raw material in the target raw material supply plant and the distance between the target raw material supply plant and the workshop,
obtaining a target raw material supply plant coefficient through a calculation formula;
arranging the target raw material supply plant coefficients in an ascending manner to obtain a target raw material supply plant sequence;
and (5) according to the target raw material supply plant sequence, carrying out raw material purchase until the corresponding amount to be purchased is purchased, and finishing the purchase.
Preferably, the ordered amount of the desired material includes an expected loss of material to produce.
Preferably, when the order quantity is less than or equal to the inventory quantity, the inventory quantity of the corresponding raw materials meets the production requirement, and the corresponding raw materials obtain a label meeting the production requirement.
Preferably, the method for acquiring the quantity to be purchased of the raw materials which do not meet the production requirement according to the calculation formula comprises the following steps:
the data statistics module acquires the order quantity and the stock quantity of the raw materials which do not meet the production requirements;
the data statistical module marks the order quantity of the raw materials which do not meet the production requirement as A n ；
The data statistics module marks the stock of the raw materials which do not meet the production requirements as A Library n ；
Wherein N is the number of the raw material, and the value of N is 1,2,3, 8230, 8230and N;
obtaining the quantity to be purchased of the raw materials which do not meet the production requirement according to the order quantity and the inventory quantity of the raw materials which do not meet the production requirement, and marking the quantity to be purchased as delta A n ；
The calculation formula of the amount to be purchased is as follows: delta A n ＝A n -A Library n 。
Preferably, the preset rule is that the stock quantity of the raw materials in the raw material supply plant is at least half of the quantity to be purchased of the corresponding raw materials.
Preferably, the obtaining of the target raw material supply plant coefficient by the calculation formula comprises the following steps:
the raw material purchasing module numbers the target raw material supply plant; the number of the target raw material supply plant is represented by I, and the value of I is 1,2,3, 8230; I;
the unit price of the raw material in the target raw material supply plant and the distance from the target raw material supply plant to the workshop are respectively marked as O in And L in ；
Obtaining a target raw material supply plant coefficient through a calculation formula, and marking the target raw material supply plant coefficient as P in 。
Preferably, the target raw material supply plant coefficient is calculated by the following formula: p is in ＝αO in +βL in ；
In the formula, α and β are correction coefficients of unit price and distance, respectively.
Preferably, the data acquisition module is in communication and/or electrical connection with the data statistics module;
the data statistics module is in communication and/or electrical connection with the database;
the data statistics module is in communication and/or electrical connection with the raw material purchasing module;
the raw material procurement module is in communication and/or electrical connection with the database.
Compared with the prior art, the invention has the beneficial effects that:
the method comprises the steps of obtaining an order form through a data acquisition module, and obtaining the order quantity of required raw materials according to the order form; the data statistics module compares the order quantity of the required raw materials with the stock quantity of the required raw materials; when the stock of the corresponding raw materials does not meet the production requirement, acquiring the to-be-purchased amount of the raw materials which do not meet the production requirement according to a calculation formula; the raw material purchasing module screens and acquires target raw material supply plants in the raw material supply plants according to preset rules, acquires target raw material supply plant coefficients through a calculation formula, acquires target raw material supply plant sequences, and performs raw material purchasing according to the target raw material supply plant sequences; the storage in the workshop is combined with the storage in the raw material supply plant, the optimal raw material supply plant is selected for purchase by integrating the unit price of the raw materials and the distance factors between the plants, the problem that the raw materials in the workshop cannot meet the production requirement is solved, and the production cost is reduced.
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FIG. 1 is a schematic diagram of the present invention;
FIG. 2 is a flow chart of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood 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.
As shown in fig. 1-2, a workshop collaborative intelligent manufacturing system based on the internet of things includes a data acquisition module, a data statistics module, a raw material purchasing module and a database; information interaction is carried out among all modules based on digital signals;
the database is used for recording the stock of raw materials of a workshop and the stock of raw materials of a raw material supply plant; it should be further noted that the stock of raw materials is the stock of each raw material in a plant or a raw material supply plant.
The data acquisition module is used for acquiring an order form and acquiring the order quantity of the required raw materials according to the order form; it is further noted that the ordered amount of the required raw material includes the expected loss amount of the raw material; wherein the required raw materials at least comprise one raw material;
and sending the order quantity of the required raw materials to the data statistics module.
Specifically, required raw materials and corresponding quantities are recorded on an order table of the workshop, an order table of products is obtained, and the order quantity of the required raw materials is obtained according to the order table.
The data statistics module is used for receiving the order quantity of the required raw materials and acquiring the inventory quantity of the corresponding required raw materials in the workshop;
comparing the order quantity of the required raw materials with the stock quantity of the required raw materials;
when the order quantity is less than or equal to the inventory quantity, the inventory quantity of the corresponding raw materials meets the production requirement, and the corresponding raw materials obtain a label meeting the production requirement;
when the order quantity is larger than the stock quantity, the stock quantity of the corresponding raw material does not meet the production requirement, and the corresponding raw material obtains a label which does not meet the production requirement;
and acquiring the quantity to be purchased of the raw materials which do not meet the production requirements according to a calculation formula.
The method for acquiring the quantity to be purchased of the raw materials which do not meet the production requirements according to the calculation formula comprises the following steps:
the data statistics module acquires order quantity and inventory quantity of raw materials which do not meet production requirements;
the data statistical module marks the order quantity of the raw materials which do not meet the production requirement as A n ；
The data statistics module marks the stock of the raw materials which do not meet the production requirements as A Library n ；
Wherein N is the number of the raw material, the value of N is 1,2,3 \8230, 8230, N, N is the total number of the raw material types, and N is an integer more than 0;
obtaining the quantity to be purchased of the raw materials which do not meet the production requirement according to the order quantity and the inventory quantity of the raw materials which do not meet the production requirement, and marking the quantity to be purchased as delta A n ；
The calculation formula of the amount to be purchased is as follows: delta A n ＝A n -A Library n 。
For example, the following steps are carried out:
the order information contains 100 parts of orders of A-type raw materials, 60 parts of orders of B-type raw materials and 120 parts of orders of C-type raw materials;
the stock of the A-type raw materials in the workshop is 120 parts, the stock of the C-type raw materials in the workshop is 80 parts, and the workshop does not contain the B-type raw materials;
according to the order quantity of the raw materials and the stock quantity of the raw materials, the stock quantity of the A-type raw materials in the workshop can meet the production requirement, and the stock quantity of the B-type raw materials and the stock quantity of the C-type raw materials can not meet the production requirement;
60 portions of the B-type raw materials and 40 portions of the C-type raw materials are purchased.
The raw material purchasing module is used for receiving the amount of raw materials to be purchased which do not meet the production requirement;
screening and obtaining a target raw material supply plant in the raw material supply plant according to a preset rule; it is further noted that the target raw material supply plant is a raw material supply plant capable of providing raw materials which do not meet production requirements for the workshop;
specifically, the preset rule is that the stock quantity of the raw materials in the raw material supply plant is at least half of the quantity to be purchased of the corresponding raw materials;
the raw material purchasing module numbers the target raw material supply plant; the number of the target raw material supply plant is represented by I, wherein the value of I is 1,2,3 \8230: \8230I, and I is the total number of the target raw material supply plants;
obtaining unit price and purpose of raw material in target raw material supply plantDistance between the standard raw material supply factory and the workshop, and marking the unit price and the distance as O in And L in ；
Obtaining a target raw material supply plant coefficient through a calculation formula, and marking the target raw material supply plant coefficient as P in ；
The calculation formula of the target raw material supply plant coefficient is as follows: p in ＝αO in +βL in ；
In the formula, alpha and beta are correction coefficients of unit price and distance respectively;
arranging the target raw material supply plant coefficients in an ascending manner to obtain a target raw material supply plant sequence;
and purchasing raw materials according to the target raw material supply plant sequence until the corresponding amount to be purchased is purchased, and finishing purchasing.
For example, the following steps are carried out:
60 portions of the B-type raw material are purchased;
arranging the coefficients of the raw material supply plants in an ascending manner, wherein the stock quantity of the B-type raw materials in the first raw material supply plant is 70 parts, namely 60 parts of the B-type raw materials are directly ordered in the first raw material supply plant, and purchasing is finished;
and arranging the raw material supply plant coefficients in an ascending manner, wherein the stock quantity of the B-type raw materials in the first raw material supply plant is 40 parts, the stock quantity of the B-type raw materials in the second raw material supply plant is 30 parts, 40 parts of the B-type raw materials are ordered in the first raw material supply plant, 20 parts of the B-type raw materials are ordered in the second raw material supply plant, and purchasing is finished.
In this embodiment, the data acquisition module is in communication and/or electrical connection with the data statistics module;
the data statistics module is in communication and/or electrical connection with the database;
the data statistics module is in communication and/or electrical connection with the raw material purchasing module;
the raw material procurement module is in communication and/or electrical connection with the database.
The above formulas are all calculated by removing dimensions and taking numerical values thereof, the formula is a formula which is obtained by acquiring a large amount of data and performing software simulation to obtain the closest real situation, and the preset parameters and the preset threshold value in the formula are set by the technical personnel in the field according to the actual situation or obtained by simulating a large amount of data.
The working principle of the invention is as follows:
the data acquisition module acquires an order form and acquires the order quantity of the required raw materials according to the order form; and sending the order amount of the required raw materials to the data statistics module.
The data statistics module receives the order quantity of the required raw materials and acquires the inventory quantity of the corresponding required raw materials in the workshop; comparing the order quantity of the required raw materials with the stock quantity of the required raw materials;
when the order quantity is less than or equal to the stock quantity, the stock quantity of the corresponding raw material meets the production requirement, and the corresponding raw material obtains a label meeting the production requirement; when the order quantity is larger than the stock quantity, the stock quantity of the corresponding raw materials does not meet the production requirement, and the corresponding raw materials obtain labels which do not meet the production requirement;
the data statistics module acquires the order quantity and the stock quantity of the raw materials which do not meet the production requirements; acquiring the quantity to be purchased of the raw materials which do not meet the production requirement according to the order quantity and the inventory quantity of the raw materials which do not meet the production requirement;
the raw material purchasing module receives the amount of raw materials to be purchased which do not meet the production requirement; screening and obtaining a target raw material supply plant in the raw material supply plant according to a preset rule; acquiring unit prices of raw materials in a target raw material supply plant and a distance from the target raw material supply plant to a workshop; acquiring target raw material supply plant coefficients through a calculation formula, and arranging the target raw material supply plant coefficients in an ascending manner to acquire a target raw material supply plant sequence; and (5) purchasing raw materials according to the sequence of the target raw material supply plant until the corresponding amount to be purchased is purchased, and finishing purchasing.
Although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof without departing from the spirit and scope of the present invention.
Claims (8)
1. The workshop collaborative intelligent manufacturing system based on the Internet of things is characterized by comprising a data acquisition module, a data statistics module, a raw material purchasing module and a database; information interaction is carried out among all modules based on digital signals;
the database is used for recording the stock of raw materials of a workshop and the stock of raw materials of a raw material supply plant;
the data acquisition module is used for acquiring an order form and acquiring the order quantity of the required raw materials according to the order form;
the data statistics module is used for receiving the order quantity of the required raw materials and acquiring the inventory quantity of the corresponding required raw materials in the workshop;
comparing the order quantity of the required raw materials with the stock quantity of the required raw materials;
when the order quantity is larger than the stock quantity, the stock quantity of the corresponding raw material does not meet the production requirement, and the corresponding raw material obtains a label which does not meet the production requirement;
obtaining the quantity to be purchased of the raw materials which do not meet the production requirements according to a calculation formula;
the raw material purchasing module is used for receiving the amount of raw materials to be purchased which do not meet the production requirements;
screening and obtaining a target raw material supply plant in the raw material supply plant according to a preset rule;
acquiring the unit price of the raw material in the target raw material supply plant and the distance between the target raw material supply plant and the workshop,
acquiring a target raw material supply plant coefficient through a calculation formula;
arranging the target raw material supply plant coefficients in an ascending manner to obtain a target raw material supply plant sequence;
and purchasing raw materials according to the target raw material supply plant sequence until the corresponding amount to be purchased is purchased, and finishing purchasing.
2. The Internet of things-based workshop collaborative intelligent manufacturing system according to claim 1, wherein the order quantity of required raw materials includes a raw material loss quantity of expected production.
3. The Internet of things-based workshop collaborative intelligent manufacturing system according to claim 1, wherein when the order quantity is less than or equal to the inventory quantity, the inventory quantity of the corresponding raw materials meets production requirements, and the corresponding raw materials acquire labels meeting the production requirements.
4. The workshop collaborative intelligent manufacturing system based on the internet of things according to claim 1, wherein the amount of raw materials to be purchased which do not meet production requirements is obtained according to a calculation formula, and the method comprises the following steps:
the data statistics module acquires the order quantity and the stock quantity of the raw materials which do not meet the production requirements;
the data statistics module marks the order quantity of the raw materials which do not meet the production requirement as n n ；
The data statistics module marks the stock of the raw materials which do not meet the production requirements as A Library n ；
Wherein N is the number of the raw material, and the value of N is 1,2,3, 8230, 8230N;
obtaining the quantity to be purchased of the raw materials which do not meet the production requirement according to the order quantity and the inventory quantity of the raw materials which do not meet the production requirement, and marking the quantity to be purchased as delta A n ；
The calculation formula of the amount to be purchased is as follows: delta A n ＝A n -A Library n 。
5. The IOT-based workshop collaborative intelligent manufacturing system according to claim 1, wherein the preset rule is that an inventory amount of raw materials in a raw material supply plant is at least half of a to-be-purchased amount of corresponding raw materials.
6. The Internet of things-based workshop collaborative intelligent manufacturing system according to claim 1, wherein the target raw material supply plant coefficient is obtained through a calculation formula, and the method comprises the following steps:
the raw material purchasing module numbers the target raw material supply plant; the number of the target raw material supply plant is represented by I, and the value of I is 1,2,3, 8230; I;
the unit price of the raw material in the target raw material supply plant and the distance from the target raw material supply plant to the workshop are respectively marked as O in And L in ；
Obtaining a target raw material supply plant coefficient through a calculation formula, and marking the target raw material supply plant coefficient as P in 。
7. The Internet of things-based workshop collaborative intelligent manufacturing system according to claim 6, wherein the target raw material supply plant coefficient is calculated by the formula: p is in ＝αO in +βL in ；
In the formula, α and β are correction coefficients of unit price and distance, respectively.
8. The Internet of things-based workshop collaborative intelligent manufacturing system according to claim 1, wherein the data acquisition module is in communication and/or electrical connection with the data statistics module;
the data statistics module is in communication and/or electrical connection with the database;
the data statistics module is in communication and/or electrical connection with the raw material purchasing module;
the raw material procurement module is in communication and/or electrical connection with the database.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN118333519A (en) * | 2024-04-19 | 2024-07-12 | 山东鹰联光电科技股份有限公司 | Cable distribution plate raw material storage intelligent management system based on artificial intelligence |
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| CN112381482A (en) * | 2020-11-16 | 2021-02-19 | 广东全程云科技有限公司 | Material management method and device, electronic equipment and storage medium |
| CN114331291A (en) * | 2022-01-05 | 2022-04-12 | 陈艳红 | Raw material management method and system for intelligent manufacturing |
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