CN113298487A - Intelligent manufacturing execution process management system and method based on cloud big data platform - Google Patents
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Abstract
The invention discloses an intelligent manufacturing execution process management system based on a cloud big data platform, which comprises the following steps: the system comprises an intelligent equipment production line, a production data and instruction interaction layer, a production execution process local management center, a cloud big data and a management platform. The invention also discloses an intelligent manufacturing execution process management method executed by the system. The invention aims at the personalized mass production based on intelligent manufacturing, a cloud platform is built on the basis of on-site big data, and the effective scheduling and management of the complex production execution process of diversified products are realized on the basis of big data analysis, evaluation, prediction and early warning, so that the optimization of resources and time is achieved. And the production line can be flexibly and quickly customized to meet the management requirement of the production execution process, so that the high efficiency and the low cost are kept.
Description
Technical Field
The invention relates to the field of intelligent manufacturing, in particular to an intelligent manufacturing execution process management system and method based on a cloud big data platform.
Background
At present, intelligent manufacturing represented by industry 4.0 is rapidly developing, and a production management system and ecology are greatly changed. The intelligent manufacturing is realized by the aid of technical means such as Internet of things, RFID (radio frequency identification devices), mode control, various industrial robots and flexible conveyor belts, datamation and refinement of a production process are realized, and diversity and controllability are improved. Compared with the traditional industrial production, the advantages of intelligent manufacturing are various, and particularly, the personalized customized production can be realized.
The traditional mass production uses a fixed production line to produce products of a single variety, reduces raw material purchasing cost and production cost through scale benefit, and has large capacity and high efficiency. However, a single type of product cannot meet the individual requirements of consumers, the product homogeneity is serious, and differential competition cannot be formed. To change this situation, large-scale customized production has appeared, and based on technologies such as mechatronics, computer networks, production management information systems, etc., on a plurality of processes of a large-scale production line, alternatives are respectively placed, for example, 4 alternatives exist in the process 1, 2 alternatives exist in the process 2, 2 alternatives exist in the process 3, 2 alternatives exist in the process 4, and 4 alternatives exist in the process 5, so that 4 × 2 × 4 ═ 128 types of products can be performed on the whole production line; the options of the product types are provided for the customer to select, and the user can select and match the options according to which of the options meets the requirements of the user, so that the customization is realized. However, customization for mass-customized production is limited customization, and consumers cannot actually participate in the design and production process of products, and can only passively select the type of product in the selection, and thus is not essentially personalized production that is adapted to the individual characteristics and needs of each consumer.
On the basis of intelligent manufacturing such as industrial 4.0, the real personalized customized production aims at designing and producing products according to personalized characteristics and requirements of each consumer. In the personalized customization production process, factors such as component parts, structural forms, parameters, materials, manufacturing processes, assembly, packaging and the like of the product are adaptively adjusted or regenerated according to the personalized customization requirements of each consumer, namely, various optional schemes are not preset by manufacturers any more, but a set of independent and specific product manufacturing scheme meeting the requirements is really generated from the personalized customization requirements of the consumers.
Personalized production based on intelligent manufacturing also leads to the fact that the product diversity is increased in magnitude order, the production process is complicated, and the execution process changes frequently, so that the management of the execution of the production process must be enhanced, and the original advantages of mass production are reserved to the greatest extent. Hopefully, the production process can be digitalized, fine and quantitative regulation and control can be realized, the optimization of resources and time can be achieved, the production process needs can be adapted, and the production line can be flexibly and quickly switched, so that the high efficiency and the low cost are kept.
However, the existing datamation and production management of the intelligent manufacturing technology still do not completely achieve the above goals, and still stay at the relatively shallow layer of data acquisition, statistics, progress tracking and tracing and the like.
Disclosure of Invention
In view of the problems, the invention aims to establish a cloud platform on the basis of field big data for individualized mass production based on intelligent manufacturing, and realize effective scheduling and management of complex production execution processes of diversified products on the basis of big data analysis, evaluation, prediction and early warning, so as to achieve optimization of resources and time. And the production line can be flexibly and quickly customized to meet the management requirement of the production execution process, so that the high efficiency and the low cost are kept.
The invention provides an intelligent manufacturing execution process management system based on a cloud big data platform, which comprises the following steps: the system comprises an intelligent equipment production line, a production data and instruction interaction layer, a production execution process local management center, a cloud big data and management platform;
the intelligent equipment production line specifically comprises production equipment, sensing equipment, flexible conveying equipment and transportation loading and unloading equipment; the production equipment is used for executing a specific procedure to realize the production and processing of the product; the sensing equipment is used for sensing parameter data related to production realization; the flexible conveying equipment realizes the conveying of materials or products connecting each process; the transportation loading and unloading equipment realizes the loading and unloading of materials and products;
the production data and instruction interaction layer is used as a production execution process local management center and one or more uplink and downlink transition interfaces of the intelligent equipment production line, so that the isomorphism of uplink and downlink transmission data, messages and instructions between the production execution process local management center and the intelligent equipment production line is realized, and an industrial field network safety mechanism is provided;
the production execution process local management center comprises a production execution process-oriented local job scheduling center, a local data center, a local state management center and a local fault response center; the local operation scheduling center is used for resolving a production order issued by a local or cloud big data and management platform into a production execution process according to the production order, and issuing a command to the intelligent equipment production line through the production data and command interaction layer; the local data center is used for acquiring data and messages from the production data and instruction interaction layer, and performing data management, monitoring, storage and visual display; the local state management center is used for acquiring real-time data, historical data and real-time and historical information from the local data center so as to realize management, analysis and monitoring of the running state of the production line of the intelligent equipment and the production execution process; the local fault response center realizes the fault prediction and maintenance of an intelligent equipment production line and a production execution process;
the cloud big data and management platform is used for extracting production process big data indexes of all production execution processes of an intelligent factory based on interaction with a production execution process local management center, and executing scheduling, peripheral support and safety audit of the cloud.
Preferably, the production data and instruction interaction layer is provided with a data message pool, a plurality of groups of data stacks and message stacks are established in the data message pool, and messages and data reported by the intelligent equipment production line are converted according to a uniform format, then are respectively added into the data stacks and the message stacks, and are sequentially uploaded to the production execution process local management center.
Preferably, the production data and instruction interaction layer establishes an equipment configuration form for each production equipment, sensing equipment, flexible conveying equipment, transportation loading and unloading equipment on each intelligent equipment production line, a currently effective operation mode for each equipment operation is recorded in a configuration item in the configuration form, and the production execution process local management center can manage, maintain and change the configuration item in each equipment configuration form according to the requirement of personalized customized production, so as to adjust the operation mode of each equipment.
Preferably, the local job scheduling center determines a basic module and a matching module of the product and parameters of each module from order product information based on preset key information extraction rules aiming at a production order recorded with the personalized customization requirements of the consumer; and then, determining the configuration items of all devices related to the basic module, the matching module and the parameters of each module in the intelligent device production line, thereby defining the production execution process of each module and issuing the operation configuration instruction.
Preferably, the local fault response center pre-judges whether a fault risk exists according to data and information reported by an intelligent device production line and a preset fault prediction rule, and carries out fault rating.
Preferably, the cloud big data and management platform comprises: the system comprises a big data aggregation analysis center and a production support scheduling advanced platform; the big data aggregation analysis center extracts big data indexes of a production process facing all production execution processes of the whole intelligent factory; the advanced platform for production support and scheduling comprises: the system comprises a cloud process scheduling unit, a cloud peripheral supporting unit, a cloud security auditing unit and a mobile terminal interface unit.
On the basis of the intelligent manufacturing execution process management system based on the cloud big data platform, the invention provides a corresponding intelligent manufacturing execution process management method, which comprises the following steps:
analyzing the production order issued by the local management center of the production execution process or the cloud big data and management platform into the production execution process according to the production order, generating an operation configuration instruction facing an intelligent equipment production line according to each production execution process, and transmitting the operation configuration instruction to a production data and instruction interaction layer through a local bus interface;
on the production data and instruction interaction layer, for each intelligent device on the intelligent device production line, establishing a device configuration form, wherein the currently effective operation mode for each device operation is recorded in the configuration items in the configuration form;
changing the configuration items of the configuration form through the operation configuration instruction, translating the configuration items into a series of operation instruction sequences and sending the operation instruction sequences to each intelligent device;
establishing a plurality of groups of data stacks and message stacks in a data message pool of the production data and instruction interaction layer, converting messages and data reported by an intelligent equipment production line according to a uniform format, adding the messages and the data stacks into the data stacks and the message stacks respectively, and uploading the messages and the data stacks to a production execution process local management center in sequence;
according to the acquired real-time data, historical data and real-time and historical information, management, analysis and monitoring of the running state of the production line of the intelligent equipment and the production execution process are further realized;
and extracting production process big data indexes aiming at all production execution processes of the intelligent factory at the cloud, and executing scheduling, peripheral support and safety audit of the cloud.
Aiming at a production order recording the personalized customization requirements of consumers, determining a basic module and a matching module of the product and parameters of each module from order product information based on a preset key information extraction rule; and then, determining the configuration items of all devices related to the basic module, the matching module and the parameters of each module in the intelligent device production line, thereby defining the production execution process of each module and issuing the operation configuration instruction.
The personalized demand description in the order information is converted into a personalized module of a product based on an expert rule knowledge base, configuration items of each device related to the personalized module in an intelligent device production line are determined according to the personalized module of the product, a production execution process is generated according to the personalized module, and an operation configuration instruction is issued; and for the individualized modules which cannot support the production of the local intelligent equipment production line, the local operation scheduling center generates an outsourcing production execution process and uploads the outsourcing production execution process to the cloud.
And pre-judging whether fault risks exist or not according to data and information reported by the intelligent equipment production line and a preset fault prediction rule, and grading the faults.
When the intelligent equipment production line reports that irregular production operation needs to be executed, special safety assessment authentication is carried out by cloud safety audit according to data and information of the intelligent equipment production line, and a result that the authentication is passed or not passed is generated and issued.
Therefore, the intelligent manufacturing execution process management system and method based on the cloud big data platform can realize effective scheduling by taking the production execution process as a unit aiming at diversified products in the personalized customized production process, include the scheduling of the outsourcing production execution process, and realize transparent data management on the production execution process and the conditions of an intelligent equipment production line; according to the invention, high-efficiency state management, fault response and data monitoring can be realized locally in an intelligent factory, and large data aggregation analysis, high-level scheduling, full-supply chain support and safety audit can also be realized based on a cloud platform; the invention meets the management requirement of the production execution process, and can flexibly and quickly define the production line, thereby keeping high efficiency and low cost.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a block diagram of an intelligent manufacturing execution process management system according to an embodiment of the present invention;
fig. 2 is a specific schematic diagram of a production support scheduling advanced platform according to an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
Referring to fig. 1, an intelligent manufacturing execution process management system based on a cloud big data platform provided in an embodiment of the present invention specifically includes: the system comprises an intelligent equipment production line, a production data and instruction interaction layer, a production execution process local management center, a cloud big data and a management platform.
The intelligent equipment production line specifically comprises production equipment, sensing equipment, flexible conveying equipment, transportation loading and unloading equipment and the like.
The production equipment is used for executing specific procedures to realize production and processing of products, such as machine tools, mechanical arms and the like. In order to meet the requirement of intelligent manufacturing personalized production, the production equipment should have a data operation capability, that is, relevant data of the working state and the working record of the production equipment can be generated and uploaded, and corresponding messages can be generated and uploaded under the condition that equipment or operation is abnormal. The production equipment can also switch the production working mode according to the production instruction, so that the adjustment of the processing sequence, the flow, the feeding and the process parameter setting within an allowable range is realized in a specific working procedure which is responsible for the production equipment, the differentiation of the final product is caused, the individual requirements of users are met, and the personalized production is realized.
The sensing device is used for sensing parameter data related to production, such as environmental parameter data of temperature, humidity, dust particle density and the like in a production workshop environment, positioning parameter data of materials and the like. The sensing device may also generate and upload an alarm message when the production related parameter data deviates from a normal interval.
The flexible conveying equipment realizes the conveying of materials or products connecting each process, and can be a conveying belt connecting production equipment on each process, or a trolley, a conveying robot and the like capable of conveying materials between the production equipment of each process. The flexible conveying device may also generate conveying-related parameter data, such as the conveying quantity of the material, the initiating process, the target process, etc. And the flexible conveying equipment can also switch the conveying path and the conveying sequence according to the production instruction, so that the dynamic adjustment and assembly among the working procedures are carried out according to the requirement of personalized production, and the form of the product is increased. The transportation loading and unloading equipment mainly realizes loading and unloading of materials and products, and can also generate loading and unloading related parameter data. The flexible transport device and the transport handling device may also generate corresponding messages.
Production equipment, sensing equipment, flexible conveying equipment and transportation handling equipment of the intelligent equipment production line are all connected into the Internet of things hotspot, so that bidirectional interaction with a production data and instruction interaction layer is realized. The data and the message can be uploaded to a production data and instruction interaction layer, and the production data and instruction interaction layer can also issue operation instructions aiming at the equipment above the intelligent equipment production line.
The production data and instruction interaction layer is used as a production execution process local management center of an intelligent manufacturing factory and an uplink and downlink transition interface of one or more intelligent equipment production lines. The interaction layer of the production data and the instructions has the function of shielding the bottom layer difference of the production line of the intelligent equipment and realizing the isomorphism of the data, the messages and the instructions. Because the intelligent device production line is a variety of devices of different manufacturers, the data formats, message formats and instruction sets adopted by the devices are heterogeneous; heterogeneous and diverse data, messages and instructions need to be isomorphic through unified transformation by the layer. The production data and instruction interaction layer also provides an industrial field network safety mechanism, and for the operation instruction issued to the intelligent equipment production line by the production execution process local management center, authority and information safety verification is carried out according to the network safety mechanism, so that the instruction of an unauthorized party is prevented from being issued to the intelligent equipment production line, or the instruction containing risk codes such as viruses is issued to the intelligent equipment production line.
Specifically, the production data and instructions interaction layer has two mechanisms, a data message pool and a device configuration form. Establishing a plurality of groups of data stacks and message stacks in a data message pool, converting messages and data reported by production equipment, sensing equipment, flexible conveying equipment and transportation loading and unloading equipment according to a uniform format, adding the messages and the data into the data stacks and the message stacks respectively, and uploading the messages and the data to a local management center of a production execution process in sequence; stacks of different priorities may be set according to the urgency level of the data and the message. The production data and instruction interaction layer establishes an equipment configuration form for each production equipment, sensing equipment, flexible conveying equipment and transportation loading and unloading equipment on each intelligent equipment production line, a currently effective operation mode for each equipment operation is recorded in a configuration item in the configuration form, and a production execution process local management center can manage, maintain and change the configuration item in each equipment configuration form according to the requirement of personalized customized production so as to adjust the operation mode of each equipment. And the equipment coordinator corresponding to each configuration form translates the configuration items into a series of operation instruction sequences according to the instruction set and the industrial control protocol supported by each equipment and the format and the time sequence mechanism agreed by the instruction set and the industrial control protocol, and achieves production equipment, sensing equipment, flexible conveying equipment, transportation loading and unloading equipment and the like in series or in parallel. For example, the local management center of the production execution process may change the configuration items of the configuration form of a certain production device, so as to switch the production operation mode of the production device, including but not limited to changing the configuration items of the processing sequence, flow, feeding, process parameter setting, and the like of a specific process for which the production device is responsible; the changed configuration items are translated into a series of operation instruction sequences by the equipment coordinator, and then the operation instruction sequences are issued to start production. Therefore, the local management center for the production execution process does not need to face various heterogeneous and complex devices on the production line, can realize regulation and control on the production line of the intelligent device by only updating the configuration items facing the configuration form with the uniform format, is favorable for effectively scheduling and managing the complex production execution processes of various products, and is suitable for the management of the production execution process to flexibly and quickly define the production line.
The production execution process local management center comprises a production execution process-oriented local job scheduling center, a local data center, a local state management center and a local fault response center. In order to facilitate the communication interaction between each local center and the data message pool and the equipment configuration form of the production data and instruction interaction layer, the communication interaction between each local center and the cloud big data and management platform, and the communication interaction between each local center, unified data, instruction and message interfaces, namely local bus interfaces, are established. Therefore, the communication interaction architecture is simplified, mutual coupling among each local center, the production data and instruction interaction layer and the cloud is eliminated, the building difficulty of an intelligent factory is greatly reduced, and the expansibility is improved. Through the bus, data and messages provided by the production data and instruction interaction layer can be directly provided for each local center and even the cloud, and data transparency is improved.
The local operation scheduling center is used for analyzing the production order issued by the local or cloud big data and the management platform into production execution processes, generating operation configuration instructions facing the intelligent equipment production line according to each production execution process, and transmitting the operation configuration instructions to the production data and instruction interaction layer through a local bus interface so as to maintain and change the configuration items in the equipment configuration form. For the requirement of personalized customization, the local job scheduling center can obtain a production order recording the personalized customization requirements of the customer from an order source interface (such as an e-commerce website and an APP). The expression form of the personalized demand of the consumer comprises the following steps: selecting a configurable matching module on the basis of a product prototype template provided by a manufacturer; inputting self-defined parameters and requirements aiming at one or more basic modules and matching modules of the product; the customer can also realize self-definition by inputting the personalized demand description information. For example, a personalized and customized children bicycle product, the base module comprises a frame, front and rear wheels, a handlebar, front and rear fenders, a wheel shaft, a chain, pedals, a saddle shock absorber, a brake and the like; the matching module can comprise a front basket, a rear seat, a bell, a battery car lamp, a battery horn, decorative strips with various colors on the frame, fluorescent strips and the like; the consumer selects one or more matching modules on the product prototype template composed of the basic modules; the consumer can further customize the size and specification parameters of the basic modules and the matching modules of the frame, the front and rear wheels, the saddle, the basket and the like according to the personal data and the requirements of the height, the weight, the placed articles and the like of the child, and the consumer is not limited to a plurality of solidified optional specifications; the consumer may also input a personalized demand description, for example by mounting a three-dimensional emblem on the front fender, the 3D shape of which is the shape of the consumer's own design, such as a cartoon animal or character. The method comprises the steps that a local job scheduling center determines basic modules and matching modules required by a product and parameters of each module from order product information according to a preset key information extraction rule aiming at a production order recorded with the personalized customization requirements of consumers; and then, the local job scheduling center determines the configuration items of each device related to the basic module, the matching module and the parameters of each module in the intelligent device production line, thereby defining the production execution process of each module and issuing the job configuration instruction. For the personalized requirement description, the local job scheduling center can convert the personalized requirement description into a personalized module of a product based on an expert rule knowledge base, determine configuration items of each device related to the personalized module in an intelligent device production line aiming at the personalized module of the product, generate a production execution process aiming at the personalized module and issue a job configuration instruction; for the individualized modules which cannot support the production of the local intelligent equipment production line, the local operation scheduling center generates an outsourcing production execution process and uploads the outsourcing production execution process to the cloud big data and management platform through a local bus interface. For example, the three-dimensional car logo serves as an individualized module, a local job scheduling center inquires based on an expert rule knowledge base, the car logo is determined to be required to be achieved by adopting 3D printing equipment, and a punch for mounting the car logo is reserved on a front fender; the local operation scheduling center generates an outsourcing production execution process for the size and the shape of the three-dimensional car logo and uploads the outsourcing production execution process to the cloud big data and management platform; and the front mudguard with the holes also belongs to the personalized module, and the local operation scheduling center determines the configuration items such as the hole drilling machine, the hole diameter, the hole distance and the like related to the intelligent equipment production line, generates a production execution process aiming at the personalized front mudguard and issues an operation configuration instruction.
The local data center obtains data and information from a data information pool of a production data and instruction interaction layer through a local bus interface, performs data management, monitoring, storage and visual display, and realizes transparentization. The local data center can be provided with a visual interface, such as a large central screen, so that monitoring of real-time data and historical data and display of messages are realized, and transparency of an intelligent construction site is realized.
The local state management center acquires real-time data, historical data and real-time and historical information from the local data center through a local bus interface, and further realizes management, analysis and monitoring of the operating state of the intelligent equipment production line and the production execution process. Based on data and information, the local state management center can visually and transparently monitor the equipment operation condition, the production environment condition, the conveying, loading and unloading condition, the material condition and the like on the intelligent equipment production line in real time, and can check real-time data and historical data curves of the equipment, the environment, the conveying, the materials and the like. The local state management center monitors, records, tracks and traces the source of each production execution process established by the local job scheduling center based on the data and the information, so that the production execution processes are realized, and the progress deadline monitoring, scheduling conflict monitoring, full life cycle record tracing and the like of the production execution processes can be performed.
And the local fault response center realizes the fault prediction and maintenance of the intelligent equipment production line and the production execution process. And according to the data and the message, the local fault response center judges whether fault risks exist or not in advance according to a preset fault prediction rule, and carries out fault grading to avoid accidents and damage and shutdown of intelligent production equipment. The failure prediction rules include: static threshold rules, dynamic threshold rules, statistical distribution rules, message event rules, and engineer-defined rules.
The cloud big data and management platform is a big data aggregation analysis center and a high-level production support scheduling platform which are established at the cloud. The platform is accessed to the local bus interface of each intelligent factory through a communication network, so that the interaction with a local management center of a production execution process is realized
The big data aggregation analysis center has cloud computing big data analysis capacity, can be used for all production execution processes of the whole intelligent factory and extracting big data indexes of the production processes. The center carries out data sharing with local data centers of all intelligent manufacturing factories, and accumulates massive equipment operation data, production environment data and material data. Therefore, on the basis of massive big data, aiming at the production execution process of the whole intelligent factory, the discovery and analysis of big data indexes are realized by combining an AI data analysis algorithm and an expert rule system, and references are provided for mode flow optimization, operation condition evaluation, product upgrading decision and the like of the whole intelligent factory.
The advanced platform for production support scheduling, as specifically shown in fig. 2, may specifically include: the system comprises a cloud process scheduling unit, a cloud peripheral supporting unit, a cloud security auditing unit and a mobile terminal interface unit. The cloud process scheduling unit may be configured to schedule the outsourced production execution process reported by the local job scheduling center to an intelligent factory manufacturer capable of meeting the requirements of the outsourced production execution process, for example, the outsourced production execution process of the three-dimensional emblem may be scheduled to a production execution process local management center of a manufacturer having the 3D printing device, and the production job may be performed by the production execution process local management center according to the manner described above. The cloud peripheral support unit is used for realizing a peripheral support supply chain including a plurality of intelligent factories, logistics, maintenance and customer service, for example, realizing support in logistics delivery for an outsourcing production execution process, and scheduling a maintenance party of an external dimension for a local unsolvable fault reported by a local fault response center of the intelligent factory. The cloud security audit unit is used for acquiring the production execution process condition from a local state management center of any intelligent factory, and data and information acquired from a local data center, so as to realize security assessment and audit, and issuing a security alarm once the security audit deems that the security audit is not in accordance, and transmitting the security alarm to a supervisor; when the local state management center reports that the intelligent equipment production line needs to execute irregular production operation, the cloud security audit unit also carries out special security assessment and authentication according to data and information obtained by the local data center, and the result that the authentication is passed or not passed is issued to the local state management center. The mobile terminal interface unit is used for realizing connection and interaction with mobile terminals such as mobile phones and the like, so that the mobile terminals can be accessed into the cloud process scheduling unit, the cloud peripheral supporting unit and the cloud security auditing unit, and necessary cloud support can be obtained anytime and anywhere.
On the basis of the intelligent manufacturing execution process management system based on the cloud big data platform, the invention provides a corresponding intelligent manufacturing execution process management method, which comprises the following steps:
analyzing the production order issued by the local management center of the production execution process or the cloud big data and management platform into the production execution process according to the production order, generating an operation configuration instruction facing an intelligent equipment production line according to each production execution process, and transmitting the operation configuration instruction to a production data and instruction interaction layer through a local bus interface;
on the production data and instruction interaction layer, for each intelligent device on the intelligent device production line, establishing a device configuration form, wherein the currently effective operation mode for each device operation is recorded in the configuration items in the configuration form;
changing the configuration items of the configuration form through the operation configuration instruction, translating the configuration items into a series of operation instruction sequences and sending the operation instruction sequences to each intelligent device;
establishing a plurality of groups of data stacks and message stacks in a data message pool of the production data and instruction interaction layer, converting messages and data reported by an intelligent equipment production line according to a uniform format, adding the messages and the data stacks into the data stacks and the message stacks respectively, and uploading the messages and the data stacks to a production execution process local management center in sequence;
according to the acquired real-time data, historical data and real-time and historical information, management, analysis and monitoring of the running state of the production line of the intelligent equipment and the production execution process are further realized;
and extracting production process big data indexes aiming at all production execution processes of the intelligent factory at the cloud, and executing scheduling, peripheral support and safety audit of the cloud.
Aiming at a production order recording the personalized customization requirements of consumers, determining a basic module and a matching module of the product and parameters of each module from order product information based on a preset key information extraction rule; and then, determining the configuration items of all devices related to the basic module, the matching module and the parameters of each module in the intelligent device production line, thereby defining the production execution process of each module and issuing the operation configuration instruction.
The personalized demand description in the order information is converted into a personalized module of a product based on an expert rule knowledge base, configuration items of each device related to the personalized module in an intelligent device production line are determined according to the personalized module of the product, a production execution process is generated according to the personalized module, and an operation configuration instruction is issued; and for the individualized modules which cannot support the production of the local intelligent equipment production line, the local operation scheduling center generates an outsourcing production execution process and uploads the outsourcing production execution process to the cloud.
And pre-judging whether fault risks exist or not according to data and information reported by the intelligent equipment production line and a preset fault prediction rule, and grading the faults.
When the intelligent equipment production line reports that irregular production operation needs to be executed, special safety assessment authentication is carried out by cloud safety audit according to data and information of the intelligent equipment production line, and a result that the authentication is passed or not passed is generated and issued.
Therefore, the intelligent manufacturing execution process management system and method based on the cloud big data platform can realize effective scheduling by taking the production execution process as a unit aiming at diversified products in the personalized customized production process, include the scheduling of the outsourcing production execution process, and realize transparent data management on the production execution process and the conditions of an intelligent equipment production line; according to the invention, high-efficiency state management, fault response and data monitoring can be realized locally in an intelligent factory, and large data aggregation analysis, high-level scheduling, full-supply chain support and safety audit can also be realized based on a cloud platform; the invention meets the management requirement of the production execution process, and can flexibly and quickly define the production line, thereby keeping high efficiency and low cost.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (10)
1. Intelligent manufacturing execution process management system based on cloud big data platform, which is characterized by comprising: the system comprises an intelligent equipment production line, a production data and instruction interaction layer, a production execution process local management center, a cloud big data and management platform;
the intelligent equipment production line specifically comprises production equipment, sensing equipment, flexible conveying equipment and transportation loading and unloading equipment; the production equipment is used for executing a specific procedure to realize the production and processing of the product; the sensing equipment is used for sensing parameter data related to production realization; the flexible conveying equipment realizes the conveying of materials or products connecting each process; the transportation loading and unloading equipment realizes the loading and unloading of materials and products;
the production data and instruction interaction layer is used as a production execution process local management center and one or more uplink and downlink transition interfaces of the intelligent equipment production line, so that the isomorphism of uplink and downlink transmission data, messages and instructions between the production execution process local management center and the intelligent equipment production line is realized, and an industrial field network safety mechanism is provided;
the production execution process local management center comprises a production execution process-oriented local job scheduling center, a local data center, a local state management center and a local fault response center; the local operation scheduling center is used for resolving a production order issued by a local or cloud big data and management platform into a production execution process according to the production order, and issuing a command to the intelligent equipment production line through the production data and command interaction layer; the local data center is used for acquiring data and messages from the production data and instruction interaction layer, and performing data management, monitoring, storage and visual display; the local state management center is used for acquiring real-time data, historical data and real-time and historical information from the local data center so as to realize management, analysis and monitoring of the running state of the production line of the intelligent equipment and the production execution process; the local fault response center realizes the fault prediction and maintenance of an intelligent equipment production line and a production execution process;
the cloud big data and management platform is used for extracting production process big data indexes of all production execution processes of an intelligent factory based on interaction with a production execution process local management center, and executing scheduling, peripheral support and safety audit of the cloud.
2. The cloud big data platform-based intelligent manufacturing execution process management system according to claim 1, wherein the production data and instruction interaction layer is provided with a data message pool, a plurality of groups of data stacks and message stacks are built in the data message pool, and messages and data reported by the intelligent device production line are converted according to a uniform format, added to the data stacks and the message stacks, and uploaded to a local production execution process management center in sequence.
3. The cloud big data platform-based intelligent manufacturing execution process management system according to claim 2, wherein the production data and instruction interaction layer establishes an equipment configuration form for each production equipment, sensing equipment, flexible transmission equipment, transportation, loading and unloading equipment on each intelligent equipment production line, an operation mode currently effective for operation of each equipment is recorded in a configuration item in the configuration form, and the production execution process local management center can manage, maintain and change the configuration item in each equipment configuration form according to the requirement of personalized customized production, so as to adjust the operation mode of each equipment.
4. The cloud big data platform-based intelligent manufacturing execution process management system according to claim 3, wherein the local job scheduling center determines a basic module and a matching module of a product and parameters of each module from order product information for a production order in which personalized customization needs of consumers are recorded based on preset key information extraction rules; and then, determining the configuration items of all devices related to the basic module, the matching module and the parameters of each module in the intelligent device production line, thereby defining the production execution process of each module and issuing the operation configuration instruction.
5. The cloud big data platform-based intelligent manufacturing execution process management system according to claim 4, wherein the local fault response center pre-determines whether a fault risk exists according to data and messages reported by an intelligent device production line and a preset fault prediction rule, and performs fault rating.
6. The cloud big data platform-based intelligent manufacturing execution process management system according to claim 5, wherein the cloud big data and management platform comprises: the system comprises a big data aggregation analysis center and a production support scheduling advanced platform; the big data aggregation analysis center extracts big data indexes of a production process facing all production execution processes of the whole intelligent factory; the advanced platform for production support and scheduling comprises: the system comprises a cloud process scheduling unit, a cloud peripheral supporting unit, a cloud security auditing unit and a mobile terminal interface unit.
7. An intelligent manufacturing execution process management method, which is executed by the intelligent manufacturing execution process management system based on the cloud big data platform according to any one of claims 1 to 6, is characterized by comprising the following steps:
analyzing the production order issued by the local management center of the production execution process or the cloud big data and management platform into the production execution process according to the production order, generating an operation configuration instruction facing an intelligent equipment production line according to each production execution process, and transmitting the operation configuration instruction to a production data and instruction interaction layer through a local bus interface;
on the production data and instruction interaction layer, for each intelligent device on the intelligent device production line, establishing a device configuration form, wherein the currently effective operation mode for each device operation is recorded in the configuration items in the configuration form;
changing the configuration items of the configuration form through the operation configuration instruction, translating the configuration items into a series of operation instruction sequences and sending the operation instruction sequences to each intelligent device;
establishing a plurality of groups of data stacks and message stacks in a data message pool of the production data and instruction interaction layer, converting messages and data reported by an intelligent equipment production line according to a uniform format, adding the messages and the data stacks into the data stacks and the message stacks respectively, and uploading the messages and the data stacks to a production execution process local management center in sequence;
according to the acquired real-time data, historical data and real-time and historical information, management, analysis and monitoring of the running state of the production line of the intelligent equipment and the production execution process are further realized;
and extracting production process big data indexes aiming at all production execution processes of the intelligent factory at the cloud, and executing scheduling, peripheral support and safety audit of the cloud.
8. The intelligent manufacturing execution process management method according to claim 7, wherein for a production order in which the customer personalized customization needs are recorded, a basic module and a matching module of the product and parameters of each module are determined from order product information based on preset key information extraction rules; and then, determining the configuration items of all devices related to the basic module, the matching module and the parameters of each module in the intelligent device production line, thereby defining the production execution process of each module and issuing the operation configuration instruction.
9. The intelligent manufacturing execution process management method according to claim 8, wherein the personalized requirement description in the order information is converted into a personalized module of the product based on an expert rule knowledge base, configuration items of each device related to the personalized module in the intelligent device production line are determined for the product personalized module, a production execution process is generated for the personalized module, and a job configuration instruction is issued; and for the individualized modules which cannot support the production of the local intelligent equipment production line, the local operation scheduling center generates an outsourcing production execution process and uploads the outsourcing production execution process to the cloud.
10. The intelligent manufacturing execution process management method of claim 9, wherein when the intelligent device production line reports that irregular production needs to be executed, the cloud security audit also performs special security assessment and authentication according to the data and messages of the intelligent device production line, and generates and issues a result that the authentication is passed or not passed.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114035516A (en) * | 2021-09-30 | 2022-02-11 | 宜宾天原集团股份有限公司 | Double-layer industrial cooperative control method and system |
| CN114742487A (en) * | 2022-06-13 | 2022-07-12 | 成都秦川物联网科技股份有限公司 | Production task management and control method and system based on industrial Internet of things |
| CN114971416A (en) * | 2022-07-13 | 2022-08-30 | 宁波创元信息科技有限公司 | Data acquisition and command scheduling system and method based on intelligent form |
Citations (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110282476A1 (en) * | 2010-05-07 | 2011-11-17 | Skinit, Inc. | Systems and methods of on demand manufacturing of customized products |
| CN105511430A (en) * | 2015-12-02 | 2016-04-20 | 西安交通大学 | Socialization and personalized manufacturing environment-oriented CPSS system |
| CN105988450A (en) * | 2015-03-16 | 2016-10-05 | 洛克威尔自动控制技术股份有限公司 | Cloud-based industrial controller |
| CN106527384A (en) * | 2016-12-19 | 2017-03-22 | 华南理工大学 | Production control mechanism based on cloud platform assisted switching strategy |
| CN107222527A (en) * | 2017-05-16 | 2017-09-29 | 浙江工业大学 | It is a kind of that the method that internet of things equipment merotype is run is controlled by configuration file |
| CN107392686A (en) * | 2017-01-03 | 2017-11-24 | 珠海伊斯佳科技股份有限公司 | A kind of the personalized customization intelligence system of makeups product, method and complete manufacture system |
| US9858606B1 (en) * | 2016-11-06 | 2018-01-02 | Conrad Sing Mar | Integrated knitted textiles system and method |
| CN108985711A (en) * | 2018-06-29 | 2018-12-11 | 北京仿真中心 | A kind of intelligent plant dynamic planning scheduling system |
| US20190155262A1 (en) * | 2017-11-17 | 2019-05-23 | Accenture Global Solutions Limited | Digital manufacturing |
| CN109933010A (en) * | 2017-12-15 | 2019-06-25 | 中国科学院沈阳自动化研究所 | A kind of industrial CPS system and implementation method towards personalized customization |
| CN111090263A (en) * | 2018-10-23 | 2020-05-01 | 杨宇 | Customized intelligent production line control system and control method |
| CN111242445A (en) * | 2020-01-06 | 2020-06-05 | 广州熔科机电技术有限公司 | Method, device and readable storage medium for testing products based on configuration production line |
| CN111319035A (en) * | 2018-12-14 | 2020-06-23 | 中国科学院沈阳自动化研究所 | Robot control system construction method for realizing modular communication |
| CN111782184A (en) * | 2020-06-30 | 2020-10-16 | 北京百度网讯科技有限公司 | Apparatus, method, apparatus and medium for performing a customized artificial intelligence production line |
| CN111857065A (en) * | 2020-06-08 | 2020-10-30 | 北京邮电大学 | Intelligent production system and method based on edge calculation and digital twinning |
| CN112418820A (en) * | 2020-12-08 | 2021-02-26 | 蒲惠智造科技有限公司 | Cloud system and method for supporting customized industrial process production management |
-
2021
- 2021-04-25 CN CN202110449053.1A patent/CN113298487B/en active Active
Patent Citations (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110282476A1 (en) * | 2010-05-07 | 2011-11-17 | Skinit, Inc. | Systems and methods of on demand manufacturing of customized products |
| CN105988450A (en) * | 2015-03-16 | 2016-10-05 | 洛克威尔自动控制技术股份有限公司 | Cloud-based industrial controller |
| CN105511430A (en) * | 2015-12-02 | 2016-04-20 | 西安交通大学 | Socialization and personalized manufacturing environment-oriented CPSS system |
| US9858606B1 (en) * | 2016-11-06 | 2018-01-02 | Conrad Sing Mar | Integrated knitted textiles system and method |
| CN106527384A (en) * | 2016-12-19 | 2017-03-22 | 华南理工大学 | Production control mechanism based on cloud platform assisted switching strategy |
| CN107392686A (en) * | 2017-01-03 | 2017-11-24 | 珠海伊斯佳科技股份有限公司 | A kind of the personalized customization intelligence system of makeups product, method and complete manufacture system |
| CN107222527A (en) * | 2017-05-16 | 2017-09-29 | 浙江工业大学 | It is a kind of that the method that internet of things equipment merotype is run is controlled by configuration file |
| US20190155262A1 (en) * | 2017-11-17 | 2019-05-23 | Accenture Global Solutions Limited | Digital manufacturing |
| CN109933010A (en) * | 2017-12-15 | 2019-06-25 | 中国科学院沈阳自动化研究所 | A kind of industrial CPS system and implementation method towards personalized customization |
| CN108985711A (en) * | 2018-06-29 | 2018-12-11 | 北京仿真中心 | A kind of intelligent plant dynamic planning scheduling system |
| CN111090263A (en) * | 2018-10-23 | 2020-05-01 | 杨宇 | Customized intelligent production line control system and control method |
| CN111319035A (en) * | 2018-12-14 | 2020-06-23 | 中国科学院沈阳自动化研究所 | Robot control system construction method for realizing modular communication |
| CN111242445A (en) * | 2020-01-06 | 2020-06-05 | 广州熔科机电技术有限公司 | Method, device and readable storage medium for testing products based on configuration production line |
| CN111857065A (en) * | 2020-06-08 | 2020-10-30 | 北京邮电大学 | Intelligent production system and method based on edge calculation and digital twinning |
| CN111782184A (en) * | 2020-06-30 | 2020-10-16 | 北京百度网讯科技有限公司 | Apparatus, method, apparatus and medium for performing a customized artificial intelligence production line |
| CN112418820A (en) * | 2020-12-08 | 2021-02-26 | 蒲惠智造科技有限公司 | Cloud system and method for supporting customized industrial process production management |
Non-Patent Citations (1)
| Title |
|---|
| 李雪霞等: "基于智能化缝制单元的服装柔性生产线的构建", 《毛纺科技》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114035516A (en) * | 2021-09-30 | 2022-02-11 | 宜宾天原集团股份有限公司 | Double-layer industrial cooperative control method and system |
| CN114742487A (en) * | 2022-06-13 | 2022-07-12 | 成都秦川物联网科技股份有限公司 | Production task management and control method and system based on industrial Internet of things |
| CN114971416A (en) * | 2022-07-13 | 2022-08-30 | 宁波创元信息科技有限公司 | Data acquisition and command scheduling system and method based on intelligent form |
| CN114971416B (en) * | 2022-07-13 | 2022-11-25 | 宁波创元信息科技有限公司 | Data acquisition and command scheduling system and method based on intelligent form |
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