CN112462698A - Intelligent factory control system and method based on big data - Google Patents
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Abstract
The invention relates to an intelligent factory control system and method based on big data, the system includes: the intelligent management module is used for obtaining control instructions according to the obtained equipment operation data and the actual production data; the intelligent logistics module is used for dynamically tracking and acquiring external logistics data and internal logistics data in the process of assembling internal materials of a factory; the intelligent storage module is used for distributing storage arrangement of materials to obtain storage data; the intelligent production module is used for carrying out intelligent production scheduling to obtain production scheduling data; the database module is used for acquiring and storing the data; the intelligent control module is used for making and adjusting the shift time of the scheduling; the intelligent operation module is used for arranging monitoring equipment inside a factory and on a logistics vehicle. The invention can realize the systematic operation of cross-department, multi-system and full flow through the seven modules of the system, improve the comprehensive supervision capability of the factory park, effectively reduce the enterprise operation cost, and realize the refinement of management, scientific decision and high-efficiency service.
Description
Technical Field
The invention relates to the technical field of intelligent manufacturing, in particular to an intelligent factory control system and method based on big data.
Background
The manufacturing industry is the main body of national economy, along with the rapid development of industrial technology, the promotion of national basic appeal, the timeliness and the functionality of products are gradually improved, and a series of problems are exposed to corresponding manufacturing factories.
At present, the following problems still exist for intelligent production enterprises, one is that a large part of small and medium-sized manufacturing enterprises do not have a clear management system suitable for the enterprises, and because production differentiation is serious, the enterprises are mostly expressed as small batches, multiple product types, field production is disordered, the interactivity among production information, an order system and warehouse logistics system information is poor, the production efficiency is seriously influenced, and the quality is difficult to control. Secondly, small and medium-sized enterprises are thin in lean production awareness, data analysis, data statistics and data feedback are lacked in the production process, so that the production process is seriously dependent on personal experience, and the production based on experience is not beneficial to the reasonalization of product benefits, the quality standardization and the profit maximization of the enterprises. Thirdly, small and medium-sized enterprises lack effective management and control tools and methods for production, and product quality is difficult to guarantee.
In summary, the intelligent production system in the prior art has low automation and intelligence efficiency and needs to improve the management level, and the above drawbacks are expected to be overcome by those skilled in the art.
Disclosure of Invention
Technical problem to be solved
In order to solve the above problems in the prior art, the present invention provides an intelligent plant control system and method based on big data, so as to overcome, at least to a certain extent, the problem of low efficiency caused by the fact that the flow control management of the intelligent plant in the prior art is not fine enough.
(II) technical scheme
In order to achieve the purpose, the invention adopts the main technical scheme that:
an embodiment of the present invention provides an intelligent factory control system based on big data, including:
the intelligent management module is used for obtaining a control instruction according to the equipment operation data and the actual production data obtained through monitoring;
the intelligent logistics module is used for dynamically tracking and acquiring external logistics data and internal logistics data in factory internal material assembly from supplier supply;
the intelligent storage module is used for distributing storage arrangement of materials by adopting an intelligent storage management technology in a factory to obtain storage data;
the intelligent production module is used for carrying out intelligent production scheduling according to the internal logistics data, the warehousing data and the acquired order data to obtain production scheduling data;
the database module is used for being connected with the intelligent management module, the intelligent logistics module, the intelligent warehousing module and the intelligent production module, and acquiring and storing equipment operation data, actual production data, external logistics data, internal logistics data, warehousing data and production arrangement data;
the intelligent control module is used for automatically identifying the acquired logistics vehicle entering time and the acquired worker entering time, and making and adjusting the scheduling time of scheduling;
and the intelligent operation module is used for arranging monitoring equipment inside a factory and on a logistics vehicle.
In an embodiment of the present invention, the intelligent management module includes:
the remote receiving submodule is used for monitoring the operation condition of equipment and the product output condition in the factory through a remote terminal to obtain equipment operation data and actual production data;
the VR construction sub-module is used for constructing a VR view according to the real-time equipment operation data and the actual production data in a combined manner, and simulating the production process of a factory in the VR view;
the data analysis submodule is used for analyzing according to the equipment operation data, the actual production data, the production arrangement data, the maintenance data and the scrapped data to generate a control instruction;
and the loss management and control sub-module is used for generating a loss management scheme according to the material input data, the actual production data, the maintenance data, the scrapping data and the energy consumption data.
In an embodiment of the present invention, the intelligent logistics module includes:
the external logistics sub-module is used for matching the material list after the order data is disassembled with the data of the supply management base, issuing the matched material list to a corresponding supplier, and dynamically tracking the transportation information of the logistics vehicle after the supplier finishes goods distribution to obtain external logistics information;
and the internal logistics submodule is used for performing material warehousing arrangement according to the external logistics information, performing data identification and coding on the parts in the material warehousing process, and obtaining internal logistics information by monitoring dynamic information of the materials in the factory internal assembly process.
In an embodiment of the present invention, the warehousing data includes a stacking arrangement table, a warehousing arrangement table, and the intelligent warehousing module includes:
the warehouse static management submodule is used for acquiring a stacking arrangement table according to input basic material information, wherein the basic material information comprises an identity, a code, a type, a quantity, a batch, a specification, a quality guarantee period, an upper limit value of inventory quantity and a lower limit value of inventory quantity;
and the warehouse dynamic arrangement submodule is used for dynamically arranging in real time according to the stacking arrangement table in the production process by combining the order data and the residual storage space to generate an in-warehouse arrangement table and an out-warehouse arrangement table.
In an embodiment of the present invention, the intelligent production module includes:
the intelligent production scheduling submodule is used for performing intelligent production scheduling according to the internal logistics data, the warehousing data and the acquired order data in combination with the material ratio required by assembly to obtain production scheduling data;
the simple automation sub-module is used for comparing the order data with the automation process flow, screening and determining simple processing steps which can adopt a simple device to replace automation equipment, disassembling the automation process flow by combining the shift arrangement data of workers, and partially or completely processing the simple processing steps by adopting the simple device.
In an embodiment of the present invention, the intelligent production module further includes:
and the intelligent line-changing submodule is used for analyzing product raw materials, processing processes and process characteristics required by finishing different order data and performing station integration on process steps of different types of products.
In an embodiment of the present invention, the database module includes:
the data collection submodule is used for acquiring and storing equipment operation data, actual production data, external logistics data, internal logistics data, warehousing data and production arrangement data by being connected with the intelligent management module, the intelligent logistics module, the intelligent warehousing module and the intelligent production module;
and the data output sub-module is used for outputting the equipment operation data, the actual production data, the external logistics data, the internal logistics data, the warehousing data and the production arrangement data in a form of single form or combined form.
In an embodiment of the present invention, the intelligent control module includes:
the intelligent identification submodule is used for automatically identifying the time of the logistics vehicle entering the factory and the time of a worker entering the factory, matching and identifying the time of the logistics vehicle entering the factory with the preset time of the access reservation system, and appointing or adjusting the scheduling time of scheduling the worker and the visit time of the visitor;
the intelligent authorization submodule is used for determining whether the access of a factory entrance and a factory exit and the access of different workshops can be passed according to the identification results of logistics vehicles and workers;
in an embodiment of the present invention, the intelligent operation module includes:
hardware monitoring equipment comprises positioning equipment arranged on a logistics vehicle, and entrance guard management equipment, security monitoring equipment, passing monitoring equipment, monitoring management equipment, energy consumption monitoring equipment and workshop environment monitoring equipment are arranged in a factory.
Another embodiment of the present invention further provides an intelligent factory control method based on big data, including:
obtaining a control instruction according to the equipment operation data and the actual production data obtained by monitoring;
the method comprises the steps of dynamically tracking and acquiring external logistics data and internal logistics data in the process of assembling internal materials of a factory from a supplier;
distributing the warehousing arrangement of the materials by adopting an intelligent warehousing management technology in a factory to obtain warehousing data;
performing intelligent production scheduling according to the internal logistics data, the warehousing data and the acquired order data to obtain production scheduling data;
acquiring and storing equipment operation data, actual production data, external logistics data, internal logistics data, warehousing data and production arrangement data;
and automatically identifying the acquired logistics vehicle factory entering time and the worker factory entering time, and making and adjusting the scheduling time of scheduling.
(III) advantageous effects
The invention has the beneficial effects that: according to the intelligent factory control system and method based on big data, in the management of the manufacturing industry of an intelligent factory, the systematic operation of cross-department, multi-system and full flow can be realized through seven modules of the system, the comprehensive supervision capability of a factory park is improved, the enterprise operation cost is effectively reduced, and the management refinement, decision-making scientification and service high-efficiency are realized.
Drawings
FIG. 1 is a schematic view of a smart house according to the present invention;
FIG. 2 is a schematic diagram illustrating a big data based intelligent plant control system according to an embodiment of the present invention;
FIG. 3 is a diagram of the intelligent management module in FIG. 2 according to an embodiment of the present invention;
FIG. 4 is a schematic flow chart of a data analysis submodule in an embodiment of the invention;
FIG. 5 is a schematic flow diagram of a VR construction sub-module and an energy consumption management sub-module in an embodiment of the present invention;
FIG. 6 is a schematic diagram of the intelligent logistics module of FIG. 2 according to an embodiment of the present invention;
FIG. 7 is a schematic flow diagram of external logistics in an embodiment of the present invention;
FIG. 8 is a schematic flow diagram of internal logistics in an embodiment of the present invention;
FIG. 9 is a schematic diagram of the smart warehousing module of FIG. 2 according to an embodiment of the present invention;
FIG. 10 is a schematic diagram of the smart production module of FIG. 2 according to an embodiment of the present invention;
FIG. 11 is a schematic diagram of the database module in FIG. 2 according to an embodiment of the present invention;
FIG. 12 is a diagram of the intelligent control module of FIG. 2 according to an embodiment of the present invention;
fig. 13 is a flowchart of a big data based intelligent plant control method according to another embodiment of the present invention.
Detailed Description
For the purpose of better explaining the present invention and to facilitate understanding, the present invention will be described in detail by way of specific embodiments with reference to the accompanying drawings.
All technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
Based on the above, the present invention provides a digital factory management platform, which performs interactive analysis and overall planning by using data to complete intelligent upgrade and modification of a factory. The technical scheme of the invention can be represented by a lean room shown in fig. 1, and the lean room shown in fig. 1 mainly comprises two aspects of lean production and intelligent manufacturing:
1) lean production is a production mode with the main goals of minimizing resources occupied by enterprise production and reducing enterprise management and operation costs. It is a mental force supporting the lives of individuals and enterprises, and is a boundary for self-satisfaction in the endless learning process. Lean production is realized by utilizing minimum resources to maximize income, is especially a predecessor for carrying out intelligent modification and upgrading on enterprises, does not have a lean production idea, is standardized in operation, is standardized in flow and has smaller and smaller enterprise competitiveness. Through carrying out a large amount of investigations and research to current production, discover the problem that exists at present, then participate in improvement and rationalization activity through the wholesaler, take improvement measure, including eliminating extravagant reduce cost, carry out flexible production and promote enterprise competitiveness, adopt JIT production mode, carry out quality assurance through the billboard management, the small batch production of many varieties, the mode of balanced production carries out strict management and control, adopt the mistake proofing system in the production, LICIA, SMED and operation standardization, raise the efficiency, make the whole profit of enterprise increase.
2) The intelligent manufacturing part carries out data management in the whole production process based on big data and can provide an optimization scheme from the data perspective, namely the intelligent factory management system is built by the following seven modules.
Fig. 2 is a schematic composition diagram of an intelligent plant control system based on big data according to an embodiment of the present invention, and as shown in fig. 2, the system includes: the intelligent management module 210, the intelligent logistics module 220, the intelligent warehousing module 230, the intelligent production module 240, the database module 250, the intelligent control module 260 and the intelligent operation module 270.
The intelligent management module 210 is configured to obtain a control instruction according to the monitored and obtained device operation data and actual production data; the intelligent logistics module 220 is used for dynamically tracking and acquiring external logistics data and internal logistics data in factory internal material assembly from supplier supply; the intelligent warehousing module 230 is used for distributing warehousing arrangement of materials by adopting an intelligent warehousing management technology in a factory to obtain warehousing data; the intelligent production module 240 is configured to perform intelligent production scheduling according to the internal logistics data, the warehousing data and the acquired order data to obtain production scheduling data; the database module 250 is used for being connected with the intelligent management module, the intelligent logistics module, the intelligent warehousing module and the intelligent production module, and acquiring and storing equipment operation data, actual production data, external logistics data, internal logistics data, warehousing data and production arrangement data; the intelligent control module 260 is used for automatically identifying the acquired logistics vehicle entering time and the worker entering time, and making and adjusting the scheduling time of scheduling; the intelligent operations module 270 is used to monitor the plant environment by deploying monitoring equipment inside the plant and on the logistics vehicles.
According to the intelligent factory management system based on the big data, provided by the embodiment of the invention, on the basis of the process standard and production digitization in the lean production background, the mode of production on demand, intelligent scheduling and intelligent output is adopted to maximize the revenue of enterprises, the enterprises are helped to carry out intelligent upgrade, various modernization technologies are utilized to realize the automation of office work, management and production of factories, and the purposes of strengthening and standardizing enterprise management, reducing working errors, blocking various bugs, improving the working efficiency, carrying out safe production, providing decision reference, strengthening external connection and widening the international market are achieved.
Each of the modules of the system shown in fig. 2 is described in detail below:
(1) intelligent management module
The module collects data mainly through a mobile phone APP, VR glasses, data analysis, and loss management and control, fig. 3 is a schematic diagram of the intelligent management module in fig. 2 according to the embodiment of the present invention, and as shown in fig. 3, the intelligent management module includes: a remote reception sub-module 211, a VR construction sub-module 212, a data analysis sub-module 213, and a wear leveling sub-module 214.
The remote receiving submodule 211 is used for monitoring the operation condition of equipment and the output condition of products in the factory through a remote terminal to obtain the operation data and the actual production data of the equipment, and the submodule can also output the plan completion degree, the quality control degree, the yield efficiency and the like.
The VR construction sub-module 212 is configured to combine and construct a VR view according to real-time device operation data and actual production data, simulate a production process of a factory in the VR view (which can be checked by wearing VR glasses), so as to check a whole and local dynamic change flow of a device operation condition in the factory and a product manufacturing process execution condition, and perform remote fault maintenance, debugging, monitoring and the like on the device through the VR glasses.
The data analysis submodule 213 is configured to analyze the data according to the equipment operation data, the actual production data, the production schedule data, the maintenance data, and the scrapped data, and generate a control command. The sub-module determines whether the number of workers or the number of production lines is increased on site through basic data analysis when the traffic is changed so as to perform optimal solution logical operation, performs the data analysis on the basis of establishing a data interaction center, gives suggestions for improvement in production, avoids adverse factors and improves product quality.
Fig. 4 is a schematic flow chart of a data analysis submodule in an embodiment of the present invention, and as shown in fig. 4, when a traffic volume is changed, the number of on-site personnel is increased or the number of production lines is increased through basic data analysis, and an optimal solution logical operation includes: on one hand, IE standard man-hour is periodically measured to obtain a standard man-hour table, and manufacturing value analysis is carried out according to the standard man-hour table; on the other hand, abnormal man-hour judgment is carried out through abnormal man-hour statistics, the number of people, man-hour and output are obtained according to daily input and output statistics, and then the production efficiency is calculated according to the man-hour table, the abnormal man-hour, the number of people, the man-hour and the output. The repair personnel collect the defective products to carry out the counting and maintenance work of the defective products to obtain a defective product counting table, and the production yield is further obtained by combining the number of people, the working hours and the output with the defective product counting table; and scrapping the materials which cannot be qualified in maintenance as bad materials, counting the reported wastes to obtain a scrap statistical table, and calculating according to the production yield and the scrap statistical table to obtain the manufacturing cost. Data analysis was performed overall.
The loss management and control sub-module 214 is configured to generate a loss management scheme according to the material input data, the actual production data, the maintenance data, the scrapping data, and the energy consumption data. This submodule piece is through establishing loss management system, and the energy consumption of equipment such as according to production arrangement, workshop place, light, water, gas, electricity is makeed statistics, combines the product output condition can only reduce energy consumption, reduces the loss under the same output condition, reduce cost.
Fig. 5 is a schematic flow chart of a VR construction sub-module and an energy consumption management and control sub-module in an example of the present invention, and as shown in fig. 5, in the VR construction, TPM equipment preservation, equipment failure history, vulnerable area calibration, camera installation, and 3D model modeling are sequentially performed, and finally displayed through VR glasses. Energy loss monitored control system can judge whether unusual according to the actual power consumption that standard power consumption and equipment ammeter installation were gathered, perhaps detects whether unusual in order to judge equipment through the current characteristic of external test point pair key node, if unusual, then carries out the energy consumption management and control through the remote control switch. The TPM equipment is preserved by carrying out corresponding operation according to the result of whether the equipment is abnormal or not.
(2) Intelligent logistics module
The module collects data mainly through internal logistics and external logistics, fig. 6 is a schematic composition diagram of the intelligent logistics module in fig. 2 according to the embodiment of the invention, and as shown in fig. 6, the intelligent logistics module comprises: the external logistics sub-module 221 and the internal logistics sub-module 222, wherein the external logistics sub-module 221 is configured to match the bill of materials after the order data is disassembled with the data of the supply management base and issue the matched bill of materials to a corresponding supplier, and start to dynamically track the transportation information of the logistics vehicle after the supplier finishes the distribution, so as to obtain external logistics information; the internal logistics sub-module 222 is configured to perform material warehousing arrangement according to external logistics information, perform data identification and encoding on parts in a material warehousing process, and obtain internal logistics information by monitoring dynamic information of materials in a factory internal assembly process.
Fig. 7 is a schematic flow chart of external logistics in the embodiment of the present invention, and as shown in fig. 7, a bill of material is issued to a supplier, the supplier performs dynamic tracking on logistics car information after completing distribution, and arranges logistics for warehousing according to dynamic logistics information. The method mainly comprises the steps of positioning and tracing the whole material supply system, positioning a General Packet Radio Service (GPRS) of a vehicle to and from, and monitoring the position and the arrival time in real time; and calculating the completion number or yield and the like according to a supplier production data uploading system to obtain a supplier capacity comparison distribution order, then delivering according to the order by the supplier, recording the delivery quantity and the vehicle information into the system, calculating the positioning of the freight vehicle, and intelligently scheduling according to the fed-back vehicle positioning to obtain the arrangement of the external logistics. The south navigation of the project can be used for planned distribution of materials to be put in storage, production is intelligently arranged in a production workshop, the risk of shutdown of the materials to be treated is avoided, the operation efficiency of a factory is improved, the cost is reduced, and flexible production is realized.
Fig. 8 is a schematic flow chart of internal logistics in the embodiment of the present invention, and as shown in fig. 8, engineering analysis and logistics analysis are performed according to a layout diagram of layout of; calculating turnover batch and turnover frequency according to the standard bom and operation standard man-hour, and calculating to obtain a turnover mode confirmation material/finished product based on the turnover batch and turnover frequency and a process flow chart; and confirming the calculation of the materials/finished products according to layout change and turnover modes to obtain standard container formulation, further determining AGV application, air suspension and a three-dimensional assembly line, and finishing arrangement of internal logistics. Through the method, the planned order is acquired through internal logistics, the bill of materials of the type, the model and the quantity of the parts are obtained through disassembly, the parts are subjected to data identification and coding after being put in storage, dynamic information (adverse factors of production such as material loss and material breakage) of logistics in the assembling process is monitored, material breakage is prevented from being lost, the C/T flowing in and out of each assembling process is dynamically monitored, the production lifting space can be found, and the production efficiency is improved.
(3) Intelligent storage module
The module collects data mainly through a precise warehouse and a three-dimensional warehouse, and the obtained warehouse data comprise a stacking arrangement table, a warehouse-in arrangement table and a warehouse-out arrangement table. Fig. 9 is a schematic diagram of the smart warehousing module shown in fig. 2 according to an embodiment of the present invention, and as shown in fig. 9, the smart warehousing module includes: a warehouse static management submodule 231 and a warehouse dynamic orchestration submodule 232.
The warehouse static management submodule 231 is configured to obtain a stacking arrangement table according to the input basic material information, where the basic material information includes an identity, a code, a type, a quantity, a batch, a specification, a quality guarantee period, an upper limit value of the stock quantity, and a lower limit value of the stock quantity. The submodule conducts information interaction through a data center, and materials to be arrived are guided into an MES in an EXCEL, EDI or manual input mode, so that intelligent data storage is achieved, query is facilitated, and the shelf life of the prefabricated materials and the upper limit and the lower limit of the stock are accurately recorded.
The warehouse dynamic arrangement submodule 232 is used for dynamically arranging in real time according to the stacking arrangement table, the order data and the residual storage space in the production process to generate a warehouse-in arrangement table and a warehouse-out arrangement table. The submodule intelligently arranges storage positions in a warehouse by adopting an RFID intelligent warehouse management technology through data center interactive information, realizes intelligent management and high-efficiency management of logistics storage according to intelligent arrangement, ensures quick acquisition of data and data accuracy of each link of goods warehouse management, reasonably keeps and controls enterprise inventory, and is convenient to manage batches, quality guarantee periods and the like of materials.
(4) Intelligent production module
The module mainly collects data through intelligent production scheduling, LCIA and intelligent line changing,
fig. 10 is a schematic diagram of the intelligent production module in fig. 2 according to the embodiment of the present invention, and as shown in fig. 10, the intelligent production module includes: an intelligent scheduling submodule 241, a simple automation submodule 242 and an intelligent line changing submodule 243.
The intelligent scheduling submodule 241 is used for performing intelligent scheduling according to the internal logistics data, the warehousing data and the acquired order data by combining the material proportion required by assembly, so as to obtain production scheduling data. This submodule piece is by central control, carries out intelligent scheduling according to customer order volume, warehouse memory space, material ratio condition, loss, place utilization ratio and workman's condition of arriving at the post, mainly includes two parts: firstly, the issuing and finishing progress of all planned orders is displayed in a Gantt chart form and is convenient to adjust; secondly, scheduling is to perform intelligent scheduling through the big data of the past production, for example: a certain type of machine client places an order, and through analysis of data machine types of all previous production lines, the production efficiency and yield of the machine type on which line are highest, and the system automatically distributes the machine type to be produced by which line.
The simple automation sub-module 242 is used for comparing order data with an automation process flow, screening and determining simple processing steps which can adopt a simple device to replace automation equipment, disassembling the automation process flow by combining worker scheduling data, partially or completely adopting the simple device to process the simple processing steps, realizing functional requirements, reducing the input cost of production equipment, synchronously improving the assembly efficiency of products and ensuring the quality.
The intelligent line-changing submodule 243 is used for analyzing product raw materials, processing technologies and process characteristics required by completing different order data, and performing station integration on process steps of different types of products. The production plan can be arranged according to the order of the customer, and the time for changing the line integrally is saved.
Based on intelligent production module can combine production scheduling and vacant position in storehouse to arrange and match according to commodity circulation size, quantity, guarantor's production, according to order volume, the storage in warehouse, material ratio, loss ratio and place utilization ratio, personnel arrive the post condition and carry out intelligent scheduling, and the scheduling segmentation is analyzed, confirms which stage can adopt LCIA to replace, and reduce cost, intelligence line changing is gone on in a station to different models and the close technology of model integration not.
(5) Database module
This module mainly gathers data through leading-in of control production, in the maintenance process export, fig. 11 is the schematic diagram of database module in fig. 2 of the embodiment of this invention, as shown in fig. 11, this database module includes: a data gathering sub-module 251 and a data output sub-module 252.
The data collection submodule 251 is used for acquiring and storing equipment operation data, actual production data, external logistics data, internal logistics data, warehousing data and production arrangement data by being connected with the intelligent management module, the intelligent logistics module, the intelligent warehousing module and the intelligent production module. Data collection of the sub-modules can be carried out through multiple channels, for example, production process data are produced, data in a maintenance process are produced, quality detection data are produced, work orders are put in and out of a warehouse, dynamic tracking of an MES system and an ERP system and the like can be used as information sources, processing is carried out through a data center, dynamic management production and manufacturing are carried out by feeding back to a central control, waste is eliminated, cost is reduced, and balanced production is achieved.
The data output sub-module 252 is used for outputting the equipment operation data, the actual production data, the external logistics data, the internal logistics data, the warehousing data and the production schedule data in a form of single form or combined form. The submodule can guarantee the yield and efficiency of operation to the maximum extent, such as real-time detection of order completion rate, equipment input rate and output ratio, and dynamic production management is achieved. In addition, the quality of the product can be guaranteed to the maximum extent, for example, flexible production can be realized through aging of wearing parts of input equipment, and the input ratio of an enterprise is reduced.
(6) Intelligent control module
The module summarizes data through intelligent identification and intelligent authorization, fig. 12 is a schematic diagram of the intelligent control module in fig. 2 according to the embodiment of the present invention, and as shown in fig. 12, the intelligent control module includes: an intelligent identification submodule 261 and an intelligent authorization submodule 262.
The intelligent identification submodule 261 is configured to automatically identify the time when the logistics vehicle enters the factory and the time when a worker enters the factory, match and identify the time when the logistics vehicle enters the factory with the preset time of the access reservation system, and specify or adjust the scheduling time of scheduling for the worker and the visit time of the visitor. This submodule piece provides a visual management platform for the mill, advances the factory discernment to commodity circulation car, workman, and management such as reservation is carried out through intelligent recognition to customer position, and the reasonable arrangement advances the factory, reduces to block up, staggers access time, promotes the managerial efficiency of enterprise.
The intelligent authorization submodule 262 is used for determining whether the access of the entrance and exit of the factory and the access between different workshops can be passed according to the identification result of the logistics vehicles and the workers. The submodule manages different work types in a workshop, only workers of authorized work types can enter the specific workshop, the problem that the safety is caused by confusion due to mistaken entry of non-professionals and the like is prevented, the materials can be conveniently managed in and out, and smoothness of a production channel and a safety channel is guaranteed.
(7) Intelligent operation module
This module is mainly through basic platform, remote client gathers data, realize through basic platform and remote end, set up various hardware supervisory equipment on the basic platform, including the positioning device who lays on the commodity circulation car, lay entrance guard's management equipment in the mill inside, security protection monitoring facilities, current monitoring facilities, monitoring management equipment, energy consumption monitoring facilities, workshop environment monitoring facilities etc. utilize the 5G network, the thing networking, cloud computing, big data, artificial intelligence etc. to synthesize the situation monitoring to the garden, realize management operation's promptness and validity. Remote end is as basic platform's execution end, comprises garden control, cell-phone APP, parking stall, light illumination, air conditioner etc. and the unified management to garden people, affairs, thing is realized to the whole operation situation of supplementary management mill, promotes infrastructure fortune dimension efficiency, improves work efficiency.
In summary, by adopting the technical scheme provided by the embodiment of the invention, in the management of the intelligent factory manufacturing industry, the systematic operation of cross-department, multi-system and full flow can be realized through seven modules of the system, the comprehensive supervision capability of the factory park is improved, the enterprise operation cost is effectively reduced, and the management refinement, decision-making scientification and service high-efficiency are realized.
Fig. 13 is a flowchart of an intelligent factory control method based on big data according to another embodiment of the present invention, as shown in fig. 13, including the following steps:
in step S1, obtaining a control command according to the monitored and obtained device operation data and actual production data;
in step S2, dynamically tracking and acquiring external logistics data and internal logistics data in assembling internal materials of the factory from the supplier;
in step S3, the intelligent warehousing management technology is used to distribute the warehousing arrangement of the materials in the factory to obtain warehousing data;
in step S4, performing intelligent production scheduling according to the internal logistics data, the warehousing data and the acquired order data to obtain production scheduling data;
in step S5, acquiring and storing equipment operation data, actual production data, external logistics data, internal logistics data, warehousing data, and production arrangement data;
in step S6, the obtained logistics vehicle entering time and the obtained worker entering time are automatically identified, and the scheduling time of the scheduling is formulated and adjusted.
The specific implementation process of the steps is described in the embodiment of the system, and through the steps, the whole process of intelligent production is comprehensively managed and controlled, so that the systematic operation of cross-department, multi-system and whole process can be realized, the comprehensive supervision capability of a factory park is improved, the enterprise operation cost is effectively reduced, and the management refinement, decision-making scientification and service high efficiency are realized.
The units described in the embodiments of the present invention may be implemented by software, or may be implemented by hardware, and the described units may also be disposed in a processor. Wherein the names of the elements do not in some way constitute a limitation on the elements themselves.
It should be noted that although in the above detailed description several modules or units of the device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit, according to embodiments of the invention. Conversely, the features and functions of one module or unit described above may be further divided into embodiments by a plurality of modules or units.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiment of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the embodiment of the present invention.
Other embodiments of the invention will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the invention being indicated by the following claims.
It will be understood that the invention is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the invention is limited only by the appended claims.
Claims (10)
1. An intelligent factory control system based on big data, comprising:
the intelligent management module is used for obtaining a control instruction according to the equipment operation data and the actual production data obtained through monitoring;
the intelligent logistics module is used for dynamically tracking and acquiring external logistics data and internal logistics data in factory internal material assembly from supplier supply;
the intelligent storage module is used for distributing storage arrangement of materials by adopting an intelligent storage management technology in a factory to obtain storage data;
the intelligent production module is used for carrying out intelligent production scheduling according to the internal logistics data, the warehousing data and the acquired order data to obtain production scheduling data;
the database module is used for being connected with the intelligent management module, the intelligent logistics module, the intelligent warehousing module and the intelligent production module, and acquiring and storing equipment operation data, actual production data, external logistics data, internal logistics data, warehousing data and production arrangement data;
the intelligent control module is used for automatically identifying the acquired logistics vehicle entering time and the acquired worker entering time, and making and adjusting the scheduling time of scheduling;
and the intelligent operation module is used for arranging monitoring equipment inside a factory and on a logistics vehicle.
2. The big-data based intelligent plant control system of claim 1, wherein the intelligent management module comprises:
the remote receiving submodule is used for monitoring the operation condition of equipment and the product output condition in the factory through a remote terminal to obtain equipment operation data and actual production data;
the VR construction sub-module is used for constructing a VR view according to the real-time equipment operation data and the actual production data in a combined manner, and simulating the production process of a factory in the VR view;
the data analysis submodule is used for analyzing according to the equipment operation data, the actual production data, the production arrangement data, the maintenance data and the scrapped data to generate a control instruction;
and the loss management and control sub-module is used for generating a loss management scheme according to the material input data, the actual production data, the maintenance data, the scrapping data and the energy consumption data.
3. The intelligent big data-based plant control system of claim 1, wherein the intelligent logistics module comprises:
the external logistics sub-module is used for matching the material list after the order data is disassembled with the data of the supply management base, issuing the matched material list to a corresponding supplier, and dynamically tracking the transportation information of the logistics vehicle after the supplier finishes goods distribution to obtain external logistics information;
and the internal logistics submodule is used for performing material warehousing arrangement according to the external logistics information, performing data identification and coding on the parts in the material warehousing process, and obtaining internal logistics information by monitoring dynamic information of the materials in the factory internal assembly process.
4. The intelligent big data based plant control system according to claim 1, wherein the warehousing data includes a palletizing schedule, an entering schedule and an exiting schedule, the intelligent warehousing module comprising:
the warehouse static management submodule is used for acquiring a stacking arrangement table according to input basic material information, wherein the basic material information comprises an identity, a code, a type, a quantity, a batch, a specification, a quality guarantee period, an upper limit value of inventory quantity and a lower limit value of inventory quantity;
and the warehouse dynamic arrangement submodule is used for dynamically arranging in real time according to the stacking arrangement table in the production process by combining the order data and the residual storage space to generate an in-warehouse arrangement table and an out-warehouse arrangement table.
5. The big-data based intelligent plant control system of claim 1, wherein the intelligent production module comprises:
the intelligent production scheduling submodule is used for performing intelligent production scheduling according to the internal logistics data, the warehousing data and the acquired order data in combination with the material ratio required by assembly to obtain production scheduling data;
the simple automation sub-module is used for comparing the order data with the automation process flow, screening and determining simple processing steps which can adopt a simple device to replace automation equipment, disassembling the automation process flow by combining the shift arrangement data of workers, and partially or completely processing the simple processing steps by adopting the simple device.
6. The big-data based intelligent plant control system of claim 5, wherein the intelligent production module further comprises:
and the intelligent line-changing submodule is used for analyzing product raw materials, processing processes and process characteristics required by finishing different order data and performing station integration on process steps of different types of products.
7. The intelligent big-data-based plant control system as claimed in claim 1, wherein the database module comprises:
the data collection submodule is used for acquiring and storing equipment operation data, actual production data, external logistics data, internal logistics data, warehousing data and production arrangement data by being connected with the intelligent management module, the intelligent logistics module, the intelligent warehousing module and the intelligent production module;
and the data output sub-module is used for outputting the equipment operation data, the actual production data, the external logistics data, the internal logistics data, the warehousing data and the production arrangement data in a form of single form or combined form.
8. The big-data based intelligent plant control system of claim 1, wherein the intelligent control module comprises:
the intelligent identification submodule is used for automatically identifying the time of the logistics vehicle entering the factory and the time of a worker entering the factory, matching and identifying the time of the logistics vehicle entering the factory with the preset time of the access reservation system, and appointing or adjusting the scheduling time of scheduling the worker and the visit time of the visitor;
and the intelligent authorization submodule is used for determining whether the access of a factory entrance and a factory exit and the access between different workshops can pass according to the identification results of the logistics vehicles and workers.
9. The big-data based intelligent plant control system of claim 1, wherein the intelligent operations module comprises:
hardware monitoring equipment comprises positioning equipment arranged on a logistics vehicle, and entrance guard management equipment, security monitoring equipment, passing monitoring equipment, monitoring management equipment, energy consumption monitoring equipment and workshop environment monitoring equipment are arranged in a factory.
10. An intelligent factory control method based on big data is characterized by comprising the following steps:
obtaining a control instruction according to the equipment operation data and the actual production data obtained by monitoring;
the method comprises the steps of dynamically tracking and acquiring external logistics data and internal logistics data in the process of assembling internal materials of a factory from a supplier;
distributing the warehousing arrangement of the materials by adopting an intelligent warehousing management technology in a factory to obtain warehousing data;
performing intelligent production scheduling according to the internal logistics data, the warehousing data and the acquired order data to obtain production scheduling data;
acquiring and storing equipment operation data, actual production data, external logistics data, internal logistics data, warehousing data and production arrangement data;
and automatically identifying the acquired logistics vehicle factory entering time and the worker factory entering time, and making and adjusting the scheduling time of scheduling.
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Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113341903A (en) * | 2021-06-28 | 2021-09-03 | 国家工业信息安全发展研究中心 | Intelligent manufacturing safety test bed |
| CN113570336A (en) * | 2021-07-28 | 2021-10-29 | 上海致景信息科技有限公司 | Production scheduling method and device for textile industry and processor |
| CN113721566A (en) * | 2021-08-06 | 2021-11-30 | 苏州长城开发科技有限公司 | Workshop-level multi-closed-loop management system and management method |
| CN113902336A (en) * | 2021-10-28 | 2022-01-07 | 广域铭岛数字科技有限公司 | Intelligent flexible manufacturing factory and production method |
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| TWI771971B (en) * | 2021-03-31 | 2022-07-21 | 宇辰系統科技股份有限公司 | Plant management system |
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104700211A (en) * | 2015-03-11 | 2015-06-10 | 上海金能安全科技有限责任公司 | RFID management system for site construction worker safety |
| CN106934528A (en) * | 2017-02-24 | 2017-07-07 | 浙江红旗机械有限公司 | A kind of discrete type manufacture process management system |
| CN108171422A (en) * | 2017-12-28 | 2018-06-15 | 鞍钢集团自动化有限公司 | A kind of platform construction method of steel intelligent plant |
| CN108197833A (en) * | 2018-02-01 | 2018-06-22 | 南京航空航天大学 | A kind of complete Real-time dispatch system and dispatching method towards discrete workshop |
| CN108985711A (en) * | 2018-06-29 | 2018-12-11 | 北京仿真中心 | A kind of intelligent plant dynamic planning scheduling system |
| CN110580026A (en) * | 2019-09-18 | 2019-12-17 | 工业云制造(四川)创新中心有限公司 | intelligent manufacturing MES system |
| CN111653023A (en) * | 2020-05-22 | 2020-09-11 | 深圳欧依云科技有限公司 | Intelligent factory supervision method |
-
2020
- 2020-10-29 CN CN202011182842.5A patent/CN112462698A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104700211A (en) * | 2015-03-11 | 2015-06-10 | 上海金能安全科技有限责任公司 | RFID management system for site construction worker safety |
| CN106934528A (en) * | 2017-02-24 | 2017-07-07 | 浙江红旗机械有限公司 | A kind of discrete type manufacture process management system |
| CN108171422A (en) * | 2017-12-28 | 2018-06-15 | 鞍钢集团自动化有限公司 | A kind of platform construction method of steel intelligent plant |
| CN108197833A (en) * | 2018-02-01 | 2018-06-22 | 南京航空航天大学 | A kind of complete Real-time dispatch system and dispatching method towards discrete workshop |
| CN108985711A (en) * | 2018-06-29 | 2018-12-11 | 北京仿真中心 | A kind of intelligent plant dynamic planning scheduling system |
| CN110580026A (en) * | 2019-09-18 | 2019-12-17 | 工业云制造(四川)创新中心有限公司 | intelligent manufacturing MES system |
| CN111653023A (en) * | 2020-05-22 | 2020-09-11 | 深圳欧依云科技有限公司 | Intelligent factory supervision method |
Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| TWI771971B (en) * | 2021-03-31 | 2022-07-21 | 宇辰系統科技股份有限公司 | Plant management system |
| CN113341903A (en) * | 2021-06-28 | 2021-09-03 | 国家工业信息安全发展研究中心 | Intelligent manufacturing safety test bed |
| CN113570336A (en) * | 2021-07-28 | 2021-10-29 | 上海致景信息科技有限公司 | Production scheduling method and device for textile industry and processor |
| CN113721566A (en) * | 2021-08-06 | 2021-11-30 | 苏州长城开发科技有限公司 | Workshop-level multi-closed-loop management system and management method |
| CN113902336A (en) * | 2021-10-28 | 2022-01-07 | 广域铭岛数字科技有限公司 | Intelligent flexible manufacturing factory and production method |
| CN114219400B (en) * | 2021-12-15 | 2022-08-05 | 浙江省邮电工程建设有限公司 | Material supervision system and method of intelligent factory |
| CN114219400A (en) * | 2021-12-15 | 2022-03-22 | 浙江省邮电工程建设有限公司 | Material supervision system and method of intelligent factory |
| CN114237190A (en) * | 2021-12-21 | 2022-03-25 | 江西捷配工业互联网有限公司 | 3D digital factory equipment management method and system |
| CN114548774A (en) * | 2022-02-23 | 2022-05-27 | 扬州云易信息技术有限公司 | Intelligent manufacturing production control system and control method for workshop scheduling |
| CN114548774B (en) * | 2022-02-23 | 2023-12-15 | 深圳市小励科技有限公司 | Intelligent manufacturing production control system and control method for workshop scheduling |
| CN116384944A (en) * | 2023-05-26 | 2023-07-04 | 中建中新建设工程有限公司 | Execution management system and execution management method for intelligent production line of wiring cassette |
| CN116579720A (en) * | 2023-07-12 | 2023-08-11 | 机械工业教育发展中心 | Service-oriented supply chain service method and system for intelligent manufacturing factory |
| CN117875680A (en) * | 2024-03-13 | 2024-04-12 | 南京理工大学 | Flexible control method for hydraulic pump production flow based on process atomic model |
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Application publication date: 20210309 |