CN115081728A - Multi-source heterogeneous textile equipment scheduling management and optimization system of textile factory - Google Patents
Multi-source heterogeneous textile equipment scheduling management and optimization system of textile factory Download PDFInfo
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Abstract
The invention discloses a multi-source heterogeneous textile equipment scheduling management and optimization system of a textile factory, belonging to the technical field of textile industry automation, comprising textile equipment, a control system and a control system, wherein the textile equipment is used for carrying out specific actual production and production process flow of the factory; the data acquisition module is used for acquiring data of multi-source heterogeneous textile equipment in a factory; the data platform is used for stripping the scheduling and control in the running process of the textile equipment to a data layer for processing; and the application optimization module is used for making autonomous optimization adjustment or giving out reasonable working suggestions of operators through high-efficiency calculation of the data platform. The invention highly joints multi-source heterogeneous equipment, bears a digital model containing numerous industrial knowledge and micro-services, and greatly improves the reuse level of the industrial knowledge; reverse innovation and performance optimization improvement of the textile equipment are promoted, and the improved sites and links of the textile equipment are beneficially excavated and identified; the support number diversifies the structured data analysis, and accelerates the process from data to value.
Description
Technical Field
The invention belongs to the technical field of automation of textile industry, and particularly relates to a multi-source heterogeneous textile equipment scheduling management and optimization system for a textile factory.
Background
The intelligent factory is a new stage of modern factory informatization development. On the basis of a digital factory, information management and service are enhanced by using the technology of the Internet of things and the equipment monitoring technology; the production and marketing process is clearly mastered, the controllability of the production process is improved, manual intervention on a production line is reduced, production line data are timely and correctly acquired, and production planning and production progress are reasonable.
However, there are many problems in the practical application process of the intelligent factory, especially in the textile field:
(1) free flow of data and application are problematic.
The method is characterized in that multi-source heterogeneous equipment and a split information system/module are built, and a traditional relational database cannot adapt to the mass data growth change and the service promotion requirement of a modern intelligent factory. The intelligent factory has large-scale equipment access, new access layer message processing and message delivery capabilities, a storage structure of the intelligent factory cannot be well matched with multi-source heterogeneous equipment data, a bottleneck also appears in the aspect of reading and writing efficiency, and the intelligent factory is not matched with the intelligent factory with mass data and quick response.
Data cannot be called and applied with high efficiency and high value according to the production operation requirement of equipment. Textile equipment in a factory is various, control logic, communication protocols and data formats of the textile equipment are different, how to unify the textile equipment to a data platform for marking, and high-efficiency scheduling is carried out according to various working conditions and sudden problems which may occur to the equipment in the production process, which is an important problem, a special module is correspondingly needed for processing, and the access, storage and data processing flow and mode of the textile equipment operation data also need to be improved.
(2) A large amount of textile equipment is bought and operated according to set work until being scrapped, a more scientific method for systematic improvement, optimization and performance improvement by combining production attributes is lacked, and core production operation data of relevant and combined processes cannot be subjected to targeted equipment optimization and improvement. If a large number of machine devices exist in a complex working scene, the operating efficiency of the same machine device is different due to the change of operators or working conditions, such as power consumption and part loss, regular equipment control behaviors are difficult to form, personnel training of certain textile machinery takes several months, and the optimal state of the control performance of the textile machinery cannot be ensured.
(3) A large amount of textile equipment is lacking in the aspect of failure prediction, and once the textile equipment fails, the stability of the process and the quality of products are seriously affected, and thus huge economic losses are generated. However, in many current factories, measures for acquiring and analyzing the real-time data of all the states of the textile equipment, comparing the data with historical data, predicting the possible accidents of the equipment and dynamically reflecting the running state of the equipment are lacked.
(4) How to dynamically process the cooperation of the mobile device and the static device system needs innovative ways and methods. Nowadays, more mobile production equipment participates in the logistics and production in a factory, such as AGVs, but the control logic of these equipment is greatly different, the control logic of the magnetic guidance AGVs is developed based on PLC, and the control logic of the laser guidance AGVs is developed based on embedded systems, and these two AGVs are completely different, when such similar production equipment exists in a production workshop, if there is no effective data collection, data analysis and scheduling system based on data processing, the complexity of the system itself will be increased, and the efficiency of the system operation will be reduced. This can lead to more complex scheduling work and greater cost input, especially when the system is adding new production equipment.
Disclosure of Invention
The invention aims to provide a multi-source heterogeneous textile equipment scheduling management and optimization system for a textile factory.
In order to solve the problems, the technical scheme adopted by the invention is as follows: a multi-source heterogeneous textile equipment scheduling management and optimization system of a textile factory comprises textile equipment, a data acquisition module, a data platform and an application optimization module,
the textile equipment is used for carrying out specific actual production and production process flow of a factory;
the data acquisition module is used for acquiring data of multi-source heterogeneous textile equipment in a factory;
the data platform is used for separating the scheduling and control in the running process of the textile equipment to the data layer for processing;
and the application optimization module is used for making autonomous optimization adjustment or giving out reasonable working suggestions of operators through high-efficiency calculation of the data platform.
The data acquisition module is built by adopting an industrial Ethernet and an Internet of things, is butted with a communication protocol with various devices and multisource isomerism at the bottom layer through OPC UA, TCP/IP and MQTT protocols, acquires and transmits the operating data of the textile equipment in the whole process, and stores the data into a data platform;
aiming at equipment without redundant communication interfaces, a 485/Ethernet conversion module of a PLC is required to be added to expand the communication interfaces of the PLC, then the 485-Ethernet conversion module is adopted to convert signals into an RJ45 port of the Ethernet, PLC networking is realized, OPC acquisition software is adopted to realize PLC data acquisition, and an OPC-client monitoring program is compiled by adopting Java to write acquired data into a data processing system in a data platform;
the method supports the online acquisition based on an industrial Internet of things technology sensor and the offline acquisition based on mobile operation and wireless positioning technology for the acquisition of the state data of the textile equipment.
The data platform comprises
Firstly, App development based on a cloud native system,
a B/S framework is adopted to support the elastic business APP development of the front end, so that the front end application is separated from background data management, humanized APP development and application of the front end are supported through a background intensive data platform, and personnel operation data and expert knowledge data are better precipitated.
Secondly, a large amount of real-time field data is shunted through Kafka,
the operation data and the external data of the bottom layer of the intelligent factory are distributed to MongoDB and Hbase and Flink through a Kafka message queue, the Kafka is used as a cache module of real-time data, and the real-time data is cached to different theme partitions of the message queue through a message producer interface in the Kafka message queue;
the Flink cluster is used as a consumer of the message, the data reading interface pulls cache data from the theme of the Kafka message queue for flow calculation, and the Flink carries out real-time intake, analysis and processing on continuous real-time data in process production; flink has the processing characteristics of throughput, high performance, low latency in terms of outgoing data. Flink is a distributed system, which divides streaming data in continuous time into a series of tiny batch jobs for data processing, can simultaneously realize batch processing and stream processing, and is used for performing stateful computation on unbounded and bounded data streams;
thirdly, data cleaning is done through data standardization and main data management, data quality and data uniqueness are guaranteed,
establishing a standard data service directory, extracting and calling target data from a data service directory system according to the data requirement of a front-end App, calling all data from a data plane, supporting real-time access and batch processing, separating data storage from computing application, and performing different analysis applications on the same data according to business requirements; the Flink performs conversion on the data set from the Kafka, performs data conversion operations such as filtering, mapping, adding, grouping and aggregation, and can be quickly recovered from faults while maintaining the state; aiming at the problems that workshop production equipment is complicated, the production environment is complex, the conditions of all the production equipment are difficult to monitor, and the requirements of high efficiency, real time and quick response of modern manufacture cannot be met, the operation data of each equipment of an enterprise is automatic, real time and accurate by utilizing a field bus, an industrial Ethernet, a wireless sensor network technology and an Internet of things technology, and is transmitted to MongoDB through Kafka and Flink for analysis and insight and optimization of equipment operation and process parameters;
fourthly, by taking MongoDB as a core and combining components such as Kafka message queues, Flink real-time data processing and the like, the data are collected into a MongoDB database to construct an enterprise-level data lake system,
the method has the advantages that multisource heterogeneous data of enterprises are uniformly stored in a large data layer, data are unique and shared, information islands are eliminated, calculation and data storage are separated, the same data can be analyzed and applied differently, different scaling is carried out on the storage size and the calculation scale, self-service integration of various data assets, data management and catalog generation, real-time access and batch processing are achieved, a NoSQL database represented by MongoDB is adopted, and more choices and technical implementation are provided for manufacturing enterprises to process mass data. The MongoDB is oriented to distributed computing and storage, so that the MongoDB has good support for operations such as cloud computing, real-time storage, data query and the like. The MongoDB horizontally expands the data through a fragmentation mechanism, and builds a storage server and a configuration server by adopting a copy set to realize the high availability of the whole cluster; when the data cluster faces a large amount of read-write operations, the MongoDB fragmentation mechanism can realize load balance and cannot cause great read-write pressure on a certain server; meanwhile, MongoDB expands the storage capacity of the whole platform by increasing the number of servers,
based on the data processing system, the operation data of the textile equipment is communicated with the external data of the enterprise, the internal production operation data of the enterprise and the production activity data of the enterprise, the data stored by the data storage and analysis module is acquired according to the requirements and application requirements of different businesses of the enterprise, and the acquired data is used for supporting the application program of enterprise management.
Fifthly, constructing a PaaS platform based on the cloud native technology, establishing a management and application development environment of a data system,
a PaaS platform is constructed through Kubernetes, the Kubernetes provides high-efficiency arrangement and scheduling capability for cloud native application, and the advantages of good isolation, resource allocation and arrangement management of containers are exerted to the greatest extent through distribution and cluster formation; kubernetes and Docker construct a foundation framework of the landing of a big data governance tool, and a foundation environment is provided for flexible, elastic and light App application development landing; kubernets manages the creation and high availability of a plurality of Docker containers, controls the connection of the Docker containers to construct corresponding application programs from a plurality of micro-service containers, and further accelerates the software and hardware decoupling of the enterprise digital system, flexibly realizes the development of applications and micro-services, and realizes more flexible configuration and management of the applications. The textile equipment is configured to the micro-services of different functional modules, such as production scheduling, cost accounting, capacity planning, operation and maintenance management and control, so as to meet different management requirements. Through the system management and the sufficient flow of data, loose coupling, efficient fusion and rapid innovation among textile equipment assets, APPs and production services are realized.
The application optimization module comprises
Firstly, optimizing the operation/control,
through historical data precipitation, a knowledge base system is built, a performance knowledge base, a fault early warning knowledge base, an operation knowledge base, a maintenance knowledge base, a product quality knowledge base and the like are solidified into a transplantable and reusable industrial micro-service component base, a universal micro-service and module is provided, an industrial mechanism model is built by combining data deposited by textile equipment and expert knowledge data, the equipment control parameters are adjusted according to real-time acquired data through a Kubernets upper deployment model algorithm, knowledge, process theory and operation experience are mined, cooperative driving modeling based on fusion of big data and knowledge is developed, an expert system data model is optimized, online identification and adaptive adjustment of model process parameters are realized, the optimization control of equipment process parameters is achieved, and the production operation control is optimized through data operation and dynamic scheduling supported by a cloud platform, the cluster cooperation of the textile equipment is promoted, and the system cooperation efficiency is improved.
Secondly, the equipment hardware is innovated reversely,
through multi-dimensional data analysis of production operation parameters, corresponding product performance parameters, product yield, process stability, staff controllability and the like of textile equipment with different performances at the same process site, on the basis of a knowledge base, a model and an algorithm on a data platform deployed on Kubernets, through comparison analysis of real-time data of textile equipment operation and data in the knowledge base, collaborative driving modeling based on fusion of big data and knowledge is developed to discover functional modules and units with improved equipment for reversely researching, developing and innovating the textile equipment;
thirdly, capturing and allocating the optimal parameters in the equipment production process,
combining raw material data, product quality data, energy consumption data and optimal working parameters matched with the textile equipment, wherein the parameter points are not isolated but collected to form a knowledge base and a model formed by multidimensional data, and the optimal parameters are used for optimal parameter allocation of the textile equipment in a similar production environment in the future;
and fourthly, through a cloud control platform + data + App mode, comprehensive fusion of the textile equipment, a digital system and a service system is realized, the data of the textile equipment is communicated, the interconnection and the intercommunication with each element and link of factory production and operation are realized, the digital landing of the lightweight SaaS on the enterprise full-value chain is supported, the multi-source heterogeneous textile equipment supports elastic access, the flexible production process, factory-wide intelligent logistics, an order system and production scheduling are combined, the material allocation and production plan are fully automatic, orders are automatically converted into production demands in a factory, production tasks of specific production lines are decomposed, the real-time global visualization of production and operation data, and the quality of the whole process can be monitored and traced.
The textile equipment comprises a warping machine, a cloth slitting machine, a shearing machine, a scalding and shearing combination machine, a brushing machine, a tentering and forming machine, a flat machine, a large warp knitting machine, a small warp knitting machine, a stranding machine, an AGV and a cloth inspecting and packaging machine.
Due to the adoption of the technical scheme, the invention has the following beneficial effects:
(1) the invention can highly butt joint multi-source heterogeneous equipment, can bear a digital model and micro-service containing numerous industrial knowledge, and greatly improves the reuse level of the industrial knowledge. The reverse innovation and the performance optimization promotion of the textile equipment are promoted, and the improved sites and links of the textile equipment are excavated and identified. Supporting data predictive analysis, cross-domain analysis, active analysis, real-time analysis, and multivariate structured data analysis can accelerate the process from data to value.
(2) The method is characterized in that the textile equipment is based on a data platform in a production operation environment system, the scheduling and control in the running process of the textile equipment are separated to the processing of a data layer, and the autonomous optimization adjustment is made or the rationalization work suggestion of an operator is given through the high-efficiency calculation of the data platform.
(3) The butt joint and the storage of bottom data and a cloud platform are realized, and the vertical access of the running data of the textile equipment and the inside of an enterprise such as a process flow and production operation and the horizontal access of the outside of the enterprise are realized. And researching a mining technology based on space-time data, fault diagnosis based on a fault knowledge base and a graph chart of equipment state data.
(4) Cloud-based native has formed a huge open-source technology system covering IaaS and PaaS layers, and has a plurality of selectable open-source components and technical schemes with mature applications and stable operation. By flexibly developing and deploying App according to business scenes and functional requirements of enterprises through micro-services, textile equipment and a digital system are fully integrated, the problem of insufficient mutual cooperation is solved, and the processes of field perception, data acquisition, storage and analysis, operation insights and decision making are simplified.
(5) And analyzing the abnormal conditions and positioning fault points, adjusting in time and taking relevant measures, keeping the functional precision of key equipment stable, and ensuring high-quality product production. The development and operation of the textile equipment management and control App are supported conveniently, and the functions of equipment reference management, point inspection management, work order management, abnormal fault management, equipment shutdown management, spare part cooperative management and the like are realized.
(6) The efficient, reliable, stable and safe production and operation of the textile equipment are guaranteed; the coordination and fusion of the process, the equipment and the flow are accelerated, the textile equipment flexibly responds to the production demand, the order system automatically converts the production demand into the production demand in a factory, and the specific production task of decomposing the order system into the textile equipment is more efficient, systematized and synergistic.
Drawings
The invention is described in detail below by way of example with reference to the accompanying drawings, in which:
figure 1 is a block diagram of the architecture of the present invention,
FIG. 2 is a block diagram of the architecture of the data platform of the present invention.
Detailed Description
The invention is further described below with reference to examples and figures thereof.
As shown in figures 1 and 2, the multi-source heterogeneous textile equipment scheduling management and optimization system of the textile factory comprises
(1) The process is constructed by the following steps of,
the textile factory of this embodiment is equipped with the following machinery according to the process flow from raw material warehousing to production processing to finished product ex-warehouse: the automatic cloth inspecting and cutting device comprises a warping machine, a cloth slitting machine, a shearing machine, a hot shearing combination machine, a brushing machine, a tentering and setting machine, a flat machine, a large warp knitting machine, a small warp knitting machine, a stranding machine, an AGV and a cloth inspecting and packaging machine.
(2) A data acquisition module for acquiring the data of the user,
the system is used for obtaining data of multisource heterogeneous textile equipment in a factory, remote control over bottom layer equipment is an important attribute for achieving interconnection manufacturing, the textile equipment of the factory is various in types, the communication protocols are rich, a network access and data acquisition system capable of widely supporting various protocols and achieving stable network access and comprehensive data acquisition can be constructed. According to the embodiment, a safe, reliable and high-speed network system is built by adopting an industrial Ethernet, an Internet of things and the like, various devices at the bottom layer and a multi-source heterogeneous communication protocol are butted through OPC UA, TCP/IP and MQTT protocols, the running data of the textile equipment is collected and transmitted in the whole process, and the data is stored in a data platform.
Aiming at equipment without redundant communication interfaces of old equipment, a 485/Ethernet conversion module of PLC is additionally arranged to expand the communication interfaces of the PLC, then the 485-Ethernet conversion module is adopted to convert signals into an RJ45 port of the Ethernet (network cables are used for collecting equipment data in a factory to be directly connected to ensure collection stability, and meanwhile WIFI coverage is used for mobile application operation), PLC networking is realized in a wired (or wireless) mode, PLC data collection is realized by OPC collection software, and OPC-client monitoring programs are compiled by adopting Java to write collected data into a data processing system in a data platform.
The method is used for collecting state data of the textile equipment, such as body temperature, bearing temperature, vibration, operation process, energy consumption parameters and the like, supports on-line collection based on an industrial internet of things technology sensor and off-line collection based on mobile operation and wireless positioning technology,
(3) app development based on a cloud native system supports small program development, increases the usability and the habituation of workers,
the B/S framework is adopted to support the elastic business APP development of the front end, so that the front end application and the background data are controlled and separated, and the humanized APP development and application of the front end are supported through the background intensive data platform, so that the personnel operation data and the expert knowledge data are better precipitated.
(4) By splitting large amounts of real-time field data by Kafka,
kafka is a distributed message queue, and plays the roles of decoupling, peak clipping and asynchronous processing in the enterprise data circulation process. Kafka has high performance, persistence, multi-copy backup, lateral expansion capabilities. The operation data and the external data of the intelligent factory bottom layer comprise: production equipment basic information, process data, sensor data such as temperature, pressure and vibration and energy consumption data are distributed to MongoDB, Hbase and Flink through Kafka messages. Kafka is used as a real-time data caching module, and real-time data is cached into different subject partitions of the message queue through a message producer interface in the Kafka message queue.
The Flink cluster is used as a consumer of the information, the data reading interface pulls cache data from the theme of the Kafka information queue for flow calculation, and the Flink carries out real-time ingestion, analysis and processing on continuous real-time data in process production. Flink has the processing characteristics of throughput, high performance, low latency in terms of outgoing data. Flink is a distributed system that splits streaming data in continuous time into a series of tiny batch jobs for data processing, enabling simultaneous batch and stream processing for stateful computation of unbounded and bounded data streams
(5) Data cleaning is well done through data standardization and main data management, data quality and data uniqueness are ensured,
the method comprises the steps of establishing a standard data service directory, extracting and calling target data from a data service directory system according to the data requirements of a front-end App, calling all data from a data plane, supporting real-time access and batch processing, separating data storage from computing application, and carrying out different analysis applications on the same data according to business requirements. The Flink performs conversion on the data sets from the Kafka, performs data conversion operations such as filtering, mapping, adding, grouping and aggregation, and can quickly recover from faults while maintaining the state. Aiming at the problems that workshop production equipment is complicated, the production environment is complex, the conditions of all the production equipment are difficult to monitor, and the requirements of high efficiency, real time and quick response of modern manufacturing cannot be met, the operation data of each equipment of an enterprise is automatic, real time and accurate by using a field bus, an industrial Ethernet, a wireless sensor network technology and an Internet of things technology, and is transmitted to MongoDB through Kafka and Flink for analysis and insight and optimization of equipment operation and process parameters.
(6) By taking MongoDB as a core and combining components such as Kafka message queues, Flink real-time data processing and the like, data are collected into a MongoDB database to construct an enterprise-level data lake system.
The multi-source heterogeneous data of an enterprise are uniformly stored in a big data layer, so that the data are unique and shared, and an information island is eliminated. The method realizes the separation of calculation and data storage, different analysis and application of the same data can be carried out, different scaling of the storage size and the calculation scale is carried out, and the self-service integration of various data assets, the management of data and the generation, the real-time access and the batch processing of catalogues are realized. The advent of the NoSQL database represented by MongoDB provides more choices and technical implementation for manufacturing enterprises to process mass data. The MongoDB is oriented to distributed computing and storage, so that the MongoDB has good support for operations such as cloud computing, real-time storage, data query and the like. The MongoDB horizontally expands the data through a fragmentation mechanism, and builds a storage server and a configuration server by adopting a copy set, so that the high availability of the whole cluster is realized. When the data cluster faces a large amount of read-write operations, the MongoDB fragmentation mechanism can realize load balance and cannot cause great read-write pressure on a certain server; meanwhile, MongoDB expands the storage capacity of the whole platform by increasing the number of servers.
Based on the data processing system, the operation data of the textile equipment is communicated with the external data of the enterprise, the internal production operation data of the enterprise and the production activity data of the enterprise, the data stored by the data storage and analysis module is acquired according to the requirements and application requirements of different businesses of the enterprise, and the acquired data is used for supporting the application program of enterprise management.
(7) A PaaS platform constructed based on a cloud native technology builds the governance and application development environment of a data system,
a PaaS platform is constructed through Kubernets, the Kubernets provide high-efficiency arrangement and scheduling capability for cloud native application, and the advantages of good isolation, resource allocation and arrangement management of containers are exerted to the greatest extent through distribution and cluster formation. Kubernetes and Docker construct a foundation framework of large data governance tool landing, and provide a foundation environment for flexible, elastic and light App application development landing. Kubernets manages the creation and high availability of multiple Docker containers, controlling Docker container connections to build respective applications from multiple microservice containers. Kubernets and Docker further accelerate software and hardware decoupling of the enterprise digital system, flexibly realize application and development of micro-service, and realize more flexible configuration and management of the application. The textile equipment is configured to the micro-services of different functional modules, such as production scheduling, cost accounting, capacity planning, operation and maintenance management and control, so as to meet different management requirements. Through the system management and the sufficient flow of data, loose coupling, efficient fusion and rapid innovation among textile equipment assets, APPs and production services are realized.
(8) The optimization of the operation/control is carried out,
through historical data precipitation, a knowledge base system, an operation performance knowledge base, a fault early warning knowledge base, an operation knowledge base, a maintenance knowledge base, a product quality knowledge base and the like are established. The method has the advantages that the resources such as technology, knowledge, experience and the like are solidified into the transplantable and reusable industrial micro-service component library, the universal micro-service and module are provided, and the industrial mechanism model is constructed by combining the data deposited by the textile equipment and the expert knowledge data. By deploying a model algorithm on the basis of Kubernetes, adjusting equipment control parameters according to data acquired in real time, mining knowledge, process theory and control experience, developing cooperative driving modeling based on fusion of big data and knowledge, optimizing an expert system data model, realizing online identification and adaptive adjustment of model process parameters, and achieving optimal control of equipment process parameters. And through data operation and dynamic scheduling optimization production operation control supported by the cloud platform, cluster cooperation of textile equipment is promoted, and system cooperation efficiency is improved. For example, the mobile production equipment of the AGV is scheduled through a cloud algorithm to replace manual control and physical space position limitation, and the limitation that the equipment is fixed at a certain physical position and the equipment is separated by physical space in a factory in the past is broken.
(9) The reverse innovation of the equipment hardware is realized,
the method is characterized in that a device improved functional module and a device improved function unit are discovered by performing multidimensional data analysis on production operation parameters, corresponding product performance parameters, product yield, process stability, employee operability and the like of textile equipment with different performances at the same process site, deploying a knowledge base, a model and an algorithm on a data platform on the basis of Kubernets, and performing cooperative driving modeling based on big data and knowledge fusion by comparing and analyzing real-time data of textile equipment operation and data in the knowledge base, so as to reversely research, develop and innovate the textile equipment.
Capturing and allocating the optimal parameters in the equipment production process. The optimal working parameters of the matched textile equipment are combined with raw material data, product quality data and energy consumption data, meanwhile, the parameter points are not isolated, but are collected to form a knowledge base and a model which are formed by multidimensional data, and the optimal parameters are used for optimal parameter allocation of the textile equipment in a similar production environment in the future.
According to the invention, through a mode of 'cloud control platform + data + App', the comprehensive fusion of textile equipment, a digital system and a service system is realized, the data of the textile equipment is communicated, the interconnection and the intercommunication with each element and link of factory production and operation are realized, the digital landing of light-weight SaaS on an enterprise full-value chain is supported, the multi-source heterogeneous textile equipment supports elastic access, the flexible production process, the factory intelligent logistics, the order system and the production scheduling are combined, the material allocation and production plan are fully automatic, the order is automatically converted into the production demand in a factory, the production task of a specific production line is decomposed, the real-time global visualization of production and operation data and the quality of the whole process can be monitored and traced.
The present invention has been described in detail with reference to the embodiments, but the invention is not limited to the embodiments, and the embodiments are only illustrative and not restrictive, and all changes and modifications that come within the scope of the invention are desired to be protected.
Claims (7)
1. The utility model provides a multisource heterogeneous weaving of weaving mill equips dispatch management and optimization system which characterized in that: comprises textile equipment, a data acquisition module, a data platform and an application optimization module,
the textile equipment is used for carrying out specific actual production and production process flow of a factory;
the data acquisition module is used for acquiring data of multi-source heterogeneous textile equipment in a factory;
the data platform is used for separating the scheduling and control in the running process of the textile equipment to the data layer for processing;
and the application optimization module is used for making autonomous optimization adjustment or giving out reasonable working suggestions of operators through high-efficiency calculation of the data platform.
2. The multi-source heterogeneous textile equipment scheduling management and optimization system of a textile factory of claim 1, wherein: the data acquisition module is built by adopting an industrial Ethernet and an Internet of things, is butted with various devices on the bottom layer and a multi-source heterogeneous communication protocol through OPC UA, TCP/IP and MQTT protocols, collects and transmits the operating data of the textile equipment in the whole process, and stores the data into a data platform.
3. The multi-source heterogeneous textile equipment scheduling management and optimization system of a textile factory of claim 2, wherein: aiming at equipment without redundant communication interfaces, a 485/Ethernet conversion module of a PLC is required to be added to expand the communication interfaces of the PLC, then the 485-Ethernet conversion module is adopted to convert signals into an RJ45 port of the Ethernet, PLC networking is realized, OPC acquisition software is adopted to realize PLC data acquisition, and Java is adopted to compile an OPC-client monitoring program to write acquired data into a data processing system in a data platform.
4. The multi-source heterogeneous textile equipment scheduling management and optimization system of a textile factory of claim 3, wherein: the method supports the online acquisition based on an industrial Internet of things technology sensor and the offline acquisition based on mobile operation and wireless positioning technology for the acquisition of the state data of the textile equipment.
5. The multi-source heterogeneous textile equipment scheduling management and optimization system of a textile factory according to claim 1, wherein: the data platform comprises
The method comprises the steps that firstly, App development based on a cloud native system supports front-end elastic business App development by adopting a B/S framework, so that front-end application and background data governance are separated, and front-end humanized APP development and application are supported through a background intensive data platform;
secondly, distributing a large amount of real-time field data, operation data and external data of the bottom layer of the intelligent factory through Kafka, distributing the data to MongoDB, Hbase and Flink through a Kafka message queue, wherein the Kafka is used as a cache module of the real-time data, and caching the real-time data into different theme partitions of the message queue through a message producer interface in the Kafka message queue;
the Flink cluster is used as a consumer of the message, the data reading interface pulls cache data from the theme of the Kafka message queue for flow calculation, and the Flink carries out real-time intake, analysis and processing on continuous real-time data in process production; flink is a distributed system, which divides streaming data in continuous time into a series of tiny batch jobs for data processing, can simultaneously realize batch processing and stream processing, and is used for performing stateful computation on unbounded and bounded data streams;
thirdly, data cleaning is well performed through data standardization and main data management, data quality and data uniqueness are guaranteed, a standard data service directory is established, target data are extracted and called from a data service directory system according to the data requirements of a front-end App, all data are called from a data plane, real-time access and batch processing are supported, data storage and calculation application are separated, and different analysis applications can be performed on the same data according to business requirements; the Flink performs conversion on the data set from the Kafka, performs data conversion operations such as filtering, mapping, adding, grouping and aggregation, and can be quickly recovered from faults while maintaining the state; the operating data of each device of an enterprise is automatically, accurately in real time by using a field bus, an industrial Ethernet, a wireless sensor network technology and an Internet of things technology, and is transmitted to MongoDB through Kafka and Flink for analysis and insight and optimization of device operation and process parameters;
fourthly, by taking MongoDB as a core and combining Kafka message queues and Flink real-time data processing, collecting the data into a MongoDB database to construct an enterprise-level data lake system;
the PaaS platform is constructed based on the cloud native technology, governance and application development environments of a data system are constructed, the PaaS platform is constructed through Kubernets, a foundation framework of a large data governance tool ground is constructed through Kubernets and Dockers, the Kubernets manage creation and high availability of a plurality of Docker containers, and the Docker containers are controlled to be connected to construct corresponding application programs from the micro-service containers.
6. The multi-source heterogeneous textile equipment scheduling management and optimization system of a textile factory of claim 1, wherein: the application optimization module comprises
The method comprises the steps of firstly, optimizing operation/control, constructing a knowledge base system, operating a performance knowledge base, a fault early warning knowledge base, an operation knowledge base, a maintenance knowledge base and a product quality knowledge base through historical data precipitation, solidifying technical, knowledge and experience resources into a transplantable and reusable industrial micro-service component base, providing universal micro-service and modules, combining data deposited by textile equipment and expert knowledge data, constructing an industrial mechanism model, deploying a model algorithm on the basis of Kubernetes, adjusting equipment control parameters according to data acquired in real time, mining knowledge, process theory and operation experience, developing collaborative driving modeling based on fusion of big data and knowledge, optimizing an expert system data model, realizing online identification and self-adaptive adjustment of model process parameters, and achieving optimized control of equipment process parameters;
secondly, equipment hardware is reversely innovated, through multi-dimensional data analysis of textile equipment with different performances at the same process site, knowledge base, model and algorithm on a data platform are deployed on the basis of Kubernets, and through comparison and analysis of real-time data of textile equipment operation and data in the knowledge base, cooperative driving modeling based on fusion of big data and knowledge is developed to discover functional modules and units of equipment improvement for reversely researching and developing innovative textile equipment;
thirdly, capturing and allocating optimal parameters in the equipment production process, matching the optimal working parameters of the textile equipment by combining data, and simultaneously collecting the data to form a knowledge base and a model which are formed by multi-dimensional data at the parameter point, wherein the optimal parameters are used for allocating the optimal parameters of the textile equipment in the similar production environment in the future;
and fourthly, through a cloud control platform + data + App mode, comprehensive fusion of the textile equipment, a digital system and a service system is realized, the data of the textile equipment is communicated, the interconnection and the intercommunication with each element and link of factory production and operation are realized, the digital landing of the lightweight SaaS on the enterprise full-value chain is supported, the multi-source heterogeneous textile equipment supports elastic access, the flexible production process, factory-wide intelligent logistics, an order system and production scheduling are combined, the material allocation and production plan are fully automatic, orders are automatically converted into production requirements in a factory, production tasks of specific production lines are decomposed, the real-time global visualization of production and operation data, and the quality of the whole process can be monitored and traced.
7. The multi-source heterogeneous textile equipment scheduling management and optimization system of a textile plant of any one of claims 1 to 6, wherein: the textile equipment comprises a warping machine, a cloth slitting machine, a shearing machine, an ironing and shearing combination machine, a brushing machine, a tentering and setting machine, a flat machine, a large warp knitting machine, a small warp knitting machine, a stranding machine, an AGV and a cloth inspecting and packaging machine.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115936405A (en) * | 2023-01-10 | 2023-04-07 | 东莞盟大集团有限公司 | Internet of things platform based on big data technology |
| CN117408502A (en) * | 2023-12-15 | 2024-01-16 | 成都川油瑞飞科技有限责任公司 | Data stream arrangement method and system applied to oil and gas production system |
| CN117726160A (en) * | 2024-02-09 | 2024-03-19 | 厦门碳基翱翔数字科技有限公司 | Textile flow management method and system based on virtual reality and evolution reinforcement learning |
| CN117726080A (en) * | 2024-02-05 | 2024-03-19 | 南京迅集科技有限公司 | Multi-source heterogeneous data driven intelligent manufacturing decision system and method |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115936405A (en) * | 2023-01-10 | 2023-04-07 | 东莞盟大集团有限公司 | Internet of things platform based on big data technology |
| CN117408502A (en) * | 2023-12-15 | 2024-01-16 | 成都川油瑞飞科技有限责任公司 | Data stream arrangement method and system applied to oil and gas production system |
| CN117408502B (en) * | 2023-12-15 | 2024-03-15 | 成都川油瑞飞科技有限责任公司 | Data stream arrangement method and system applied to oil and gas production system |
| CN117726080A (en) * | 2024-02-05 | 2024-03-19 | 南京迅集科技有限公司 | Multi-source heterogeneous data driven intelligent manufacturing decision system and method |
| CN117726080B (en) * | 2024-02-05 | 2024-04-26 | 南京迅集科技有限公司 | Multi-source heterogeneous data driven intelligent manufacturing decision system and method |
| CN117726160A (en) * | 2024-02-09 | 2024-03-19 | 厦门碳基翱翔数字科技有限公司 | Textile flow management method and system based on virtual reality and evolution reinforcement learning |
| CN117726160B (en) * | 2024-02-09 | 2024-04-30 | 厦门碳基翱翔数字科技有限公司 | Textile flow management method and system based on virtual reality and evolution reinforcement learning |
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