CN117493452A - Manufacturing site full-flow data management system - Google Patents
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Abstract
The embodiment of the application provides a manufacturing site full-flow data management system. The system comprises: a production manufacturing station comprising a plurality of production devices of different types; the production and manufacturing application system comprises a plurality of sub-application systems and is used for collecting different types of information management data of a production and manufacturing station in a preset time period; the data acquisition module is used for acquiring production line data of a plurality of production devices in real time, receiving information management data and transmitting the information management data to the first data platform; the first data platform comprises a data cache module and a data calculation module, wherein the data cache module is used for storing production line data and information management data in a classified mode, and the data calculation module is used for carrying out real-time and/or off-line calculation on the production line data and the information management data according to the production and manufacturing requirements of the production and manufacturing stations so as to obtain index data and production condition data of the production and manufacturing stations in a preset time period; performing fusion calculation on the index data and the production condition to obtain comprehensive index data; and storing the comprehensive index data into a distributed database.
Description
Technical Field
The application relates to the technical field of intelligent manufacturing, in particular to a manufacturing field full-flow data management system.
Background
With the digital transformation, the construction of intelligent factories and lighthouses is in progress, and the digital transformation is not just a strategic option for most enterprises, but a necessary trend for survival and development of organizations. In order to carry out digital transformation, a large number of management systems such as SAP, MES, WMS, APS, CRM, SRM, EAM, CSS, ECC, QMS, PLM and the like which relate to the whole flow of research, production, marketing and service are introduced into most industrial manufacturing enterprises in order to achieve the purposes of cost reduction, efficiency enhancement and intelligent manufacturing.
In the prior art, the management of the production site of the excavator is mainly carried out by a management mode of a traditional factory mainly based on human factors, although various informationized systems are assisted, management systems such as MES, WMS, TMS, EAM and the like are usually provided by different manufacturers, each system is a closed system, the systems are isolated from each other, manual management, operation, analysis, operation and decision are needed, and then analysis results are fed back to the production site. The manual mode can not acquire real-time statistical analysis data of the whole production process in real time for decision making, so that the production process is not transparent and untimely to manage, the punctual arrival of materials can not be ensured, and the production efficiency can not be ensured.
Disclosure of Invention
An object of an embodiment of the present application is to provide a manufacturing site full flow data management system.
To achieve the above object, a first aspect of the present application provides a manufacturing site full-flow data management system, including:
a production manufacturing station comprising a plurality of production devices of different types;
the production and manufacturing application system comprises a plurality of sub-application systems, wherein different sub-application systems are used for collecting different types of information management data of the production and manufacturing station in a preset time period;
the data acquisition module is used for acquiring production line data of a plurality of production devices in real time, receiving information management data acquired by the production and manufacturing application system, and transmitting the production line data and the information management data to the first data platform, wherein the production line data comprises working condition data and operation data of the plurality of production devices;
the first data platform comprises a data cache module, a data calculation module and a distributed database, wherein the data cache module is used for storing production line data and information management data in a classified mode, and the data calculation module is used for calculating the production line data and the information management data in real time and/or off line according to the production and manufacturing requirements of the production and manufacturing station so as to obtain index data and production condition data of the production and manufacturing station in a preset time period; performing fusion calculation on the index data and the production condition to obtain comprehensive index data; and storing the comprehensive index data into a distributed database.
In the embodiment of the application, the data caching module comprises a distributed publishing and subscribing message module and a big data storage module, wherein the distributed publishing and subscribing message module is used for caching production line data into different topics, and the topics are divided according to the type of production equipment; the big data storage module is used for caching information management data and preprocessing the information management data.
In an embodiment of the present application, the data calculation module includes: the first distributed computing engine is used for dividing the production line data into first production line data and second production line data according to the production and manufacturing requirements, and analyzing and processing the first production line data in real time to obtain first current production condition data of the production and manufacturing station; the off-line data bin is used for acquiring the second production line data and the information management data preprocessed in the big data storage module, and performing off-line processing on the second production line data and the information management data preprocessed so as to respectively obtain index data and second production condition data of the production and manufacturing station in a preset time period; and the second distributed computing engine is used for computing the index data and the second production condition data to obtain comprehensive index data of the production manufacturing station in a preset time period.
In an embodiment of the present application, the first data platform further includes a data lake, configured to obtain, according to an instruction triggered by a user, data corresponding to the instruction from the offline data bin, and display the data to the user in a form of a table.
In an embodiment of the present application, the plurality of sub-application systems includes: a manufacturing execution management information system for collecting manufacturing execution management data of the production manufacturing station; the supply chain management information system is used for collecting supply chain management data; the manpower management information system is used for collecting manpower management data; the warehouse management information system is used for collecting warehouse management data; the quality management information system is used for collecting quality management data; and the equipment management information system is used for collecting equipment management data. In an embodiment of the present application, the data acquisition module includes: the system comprises an Internet of things data acquisition module, a data management module and a data management module, wherein the Internet of things data acquisition module is used for acquiring production line data, a manufacturing execution management information system and information management data in a supply chain management information system; the distributed acquisition module is used for acquiring information management data in the human management information system, the warehouse management information system, the quality management information system and the equipment management information system.
In the embodiment of the application, the data caching module comprises a distributed publishing and subscribing message module, and the data acquisition module of the internet of things is further used for transmitting the production line data to the gateway in an encrypted mode through a preset protocol after the production line data are acquired, and transmitting the encrypted production line data to the distributed publishing and subscribing message module through the gateway.
In an embodiment of the present application, a distributed database includes: the report analysis module is used for carrying out multidimensional analysis processing on the index data and the production condition data so as to generate a corresponding multidimensional visual report; the early warning module is used for generating a corresponding warning signal under the condition that any one of the index data and the production condition data is abnormal; and the data modeling module is used for carrying out real-time aggregation and updating operation on the comprehensive index data based on the aggregation and updating data model according to the instruction triggered by the user.
In an embodiment of the present application, the system further comprises: and the report system is used for acquiring the comprehensive index data and the production condition data from the distributed database, and generating a visual report for display according to the comprehensive index data and a preset template.
In an embodiment of the present application, the production manufacturing station further comprises: and the man-machine interaction device is used for displaying the visual report generated by the report system to the terminal so that a user can make a production plan for the production manufacturing station according to the visual report.
The technical scheme provides a manufacturing site full-flow data management system, which comprises a production manufacturing station and a production management system, wherein the production manufacturing station comprises a plurality of production devices of different types; the production and manufacturing application system comprises a plurality of sub-application systems, wherein different sub-application systems are used for collecting different types of information management data of the production and manufacturing station in a preset time period; the data acquisition module is used for acquiring production line data of a plurality of production devices in real time, receiving information management data acquired by the production and manufacturing application system, and transmitting the production line data and the information management data to the first data platform, wherein the production line data comprises working condition data and operation data of the plurality of production devices; the first data platform comprises a data cache module, a data calculation module and a distributed database, wherein the data cache module is used for storing production line data and information management data in a classified mode, and the data calculation module is used for calculating the production line data and the information management data in real time and/or off line according to the production and manufacturing requirements of the production and manufacturing station so as to obtain index data and production condition data of the production and manufacturing station in a preset time period; performing fusion calculation on the index data and the production condition to obtain comprehensive index data; and storing the comprehensive index data into a distributed database. The data acquisition module acquires the production data and the information management data of the manufacturing site in real time, and transmits the production data and the information management data to the data platform for unified analysis and management, so that the data fusion and intercommunication among different subsystems are realized, meanwhile, the real-time statistical analysis data of the whole production process is acquired in real time for decision making, the production efficiency is effectively improved, and the manufacturing cost is reduced.
Additional features and advantages of embodiments of the present application will be set forth in the detailed description that follows.
Drawings
The accompanying drawings are included to provide a further understanding of embodiments of the present application and are incorporated in and constitute a part of this specification, illustrate embodiments of the present application and together with the description serve to explain, without limitation, the embodiments of the present application. In the drawings:
FIG. 1 schematically illustrates a block diagram of a manufacturing site full flow data management system according to an embodiment of the present application;
FIG. 2 schematically illustrates a system architecture diagram of a manufacturing site full flow data management system according to an embodiment of the present application;
fig. 3 schematically illustrates a flow chart for IOT data acquisition and analysis for a production facility in accordance with an embodiment of the present application.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it should be understood that the specific implementations described herein are only for illustrating and explaining the embodiments of the present application, and are not intended to limit the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present application based on the embodiments herein.
Fig. 1 schematically shows a system architecture diagram of a manufacturing site full flow data management system according to an embodiment of the present application. As shown in fig. 1, in one embodiment of the present application, there is provided a manufacturing site full flow data management system, comprising:
a production manufacturing station 110 comprising a plurality of production devices of different types;
the manufacturing application system 120 includes a plurality of sub-application systems, and different sub-application systems are used for collecting different types of information management data of the manufacturing station in a preset time period;
the data acquisition module 130 is configured to acquire production line data of a plurality of production devices in real time, receive information management data acquired by a production and manufacturing application system, and transmit the production line data and the information management data to the first data platform, where the production line includes working condition data and operation data of the plurality of production devices;
the first data platform 140 includes a data cache module, a data calculation module and a distributed database, wherein the data cache module is used for storing production line data and information management data in a classified manner, and the data calculation module is used for performing real-time and/or off-line calculation on the production line data and the information management data according to the production and manufacturing requirements of the production and manufacturing station so as to obtain index data and production status data of the production and manufacturing station in a preset time period; performing fusion calculation on the index data and the production condition to obtain comprehensive index data; and storing the comprehensive index data into a distributed database.
The manufacturing site may refer to an excavator intelligent manufacturing site, and thus, the plurality of production devices of different types may refer to a plurality of excavator devices of different types, and specifically may include a press-and-add excavator, an machine-and-excavator, a cutting excavator, a welding excavator, an AGV excavator, a blanking excavator, a cross-over excavator, an electric spanner excavator, and the like. The manufacturing application 120 is wirelessly connected to the manufacturing station 110 and collects various types of information management data of the manufacturing station over a preset period of time. It should be appreciated that the preset time period may be freely set according to actual needs, for example, set to one hour, 12 pm to 3 pm in the same day, or one day. The production equipment is different in types, the required raw materials, raw material cost, operation mode, finished products and the like, and in order to support the organic connection, reasonable scheduling and systematic operation of materials, logistics equipment and stations, support various material distribution modes, coordinate the material distribution to a production line on time, ensure the timely arrival of the material, ensure the production efficiency and need to transparently manage all data of the production equipment. Meanwhile, the manufacturing site is manually managed, and the manual work relates to manual attendance, performance, workload and other data, the equipment and the manual data can be collectively called information management data, and for different types of information management data, each type of information management data is collected and managed by different sub-application systems. The information management data collected by each sub-application system is different in type, and each sub-application system is usually provided by different suppliers, so that the sub-application systems cannot be linked, the compatibility between the sub-application systems is poor, serious problems of data island and industry barriers exist, the application systems cannot be opened transversely through means such as an API (application program interface) directly, and unified management cannot be performed after the information management data of a production manufacturing station are uploaded. Therefore, in order to realize unified management of information management data, in the present embodiment, the data acquisition module 130 acquires and uploads different types of information management data stored in each sub-application system to the first data platform 140 with high fault tolerance and high throughput for unified management.
It should be appreciated that for a production facility, when the production facility is operating, corresponding production line data including operational data OT and operating condition data IOT may be generated. For example, taking a welding excavator as an example, the welding excavator welds two workpieces together after starting, this data may be referred to as operation data of the welding excavator, and the hydraulic and pneumatic power consumed by the welding excavator during the welding process, the temperature, the power rotation speed, the pressure, the measurement value and the welding operation duration during the welding process belong to the working condition data of the welding excavator. In order to display the data of production operation management, materials, storage, logistics, financial performance and the like on the production site of the excavator, cost data barriers such as yield, electric energy, working hours, materials, manufacturing cost and the like need to be opened, loss abnormality is deeply excavated, and the assistance of financial cost assessment and workshop cost reduction are realized. Therefore, the production line data of the production and manufacturing stations are linked with the information management data, and in this technical solution, the production line data of each production device can be collected and uploaded to the first data platform 140 through the data collection module 130.
In this technical solution, the first data platform 140 may refer to Hadoop, where the Hadoop is a distributed file system, and a core framework of the Hadoop includes HDFS and MapReduce, where the HDFS may provide a cache function for massive data, and the MapReduce may provide a computation function for massive data. The first data platform 140 includes a data caching module, a data calculating module and a distributed database, after the data collecting module 130 uploads the production line data and the information management data of the production and manufacturing stations, the data caching module firstly classifies and stores the production line data and the information management data, and the data calculating module performs real-time and/or off-line calculation on the production line data and the information management data according to the production and manufacturing requirements of the production and manufacturing stations so as to obtain index data and production condition data of the production and manufacturing stations within a preset time period, and performs fusion calculation on the index data and the production condition so as to obtain comprehensive index data. It should be understood that the production line data includes, but is not limited to, data such as water, electricity, gas, temperature, power rotation speed, pressure, measurement value, welding operation duration, etc. of the production equipment, and the information management data includes, but is not limited to, data such as materials, logistics, manual attendance, manual performance, etc., and the comprehensive index data should relate to analysis index data of a plurality of service modules such as manual attendance, manual efficiency, delivery yield, production order, purchase order, electric energy, production man-hour, expense, single cost, etc. After the data calculation module calculates the comprehensive index data, the comprehensive index data is stored in a distributed database for unified management.
According to the technical scheme, the data acquisition module is used for acquiring the production data and the information management data of the manufacturing site in real time and transmitting the production data and the information management data to the data platform for unified analysis and management, so that the data fusion and intercommunication among different subsystems are realized, meanwhile, the real-time statistical analysis data of the whole production process is acquired in real time for decision making, the production efficiency is effectively improved, and the manufacturing cost is reduced.
In one embodiment, after the data acquisition module uploads the acquired production line data and information management data to the first data platform, the production line data and the information management data are stored through a data cache module in the first data platform. The data caching module comprises a distributed publishing and subscribing message module and a big data storage module, wherein the distributed publishing and subscribing message module is used for caching production line data into different topics, the topics are divided according to the type of production equipment, and the big data storage module is used for caching information management data and preprocessing the information management data. In particular, the distributed publish-subscribe message module may refer to Kafka, which is a high-throughput distributed publish-subscribe message system, which can process all action flow data of a user in a website, provide real-time messages through a cluster, and each message published to the Kafka cluster has a category, namely Topic, also referred to as a Topic, and the Topic can be freely set. In this technical scheme, the line data of gathering comes from different production line equipment, consequently, can divide different themes according to the type of production equipment, including press add Topic, machine add Topic, cut Topic, welding Topic, AGVTopic, unloading Topic, intermodulation Topic, electric spanner Topic etc.. The big data caching module may refer to a storage system hadoop-hdfs of the first data platform, and some blank and repeated error data may exist in the original information management data, so that in order to ensure accuracy and integrity of the data, data cleaning is performed first after the information management data is cached to the hadoop-hdfs, and error data such as invalid values and missing values are cleaned.
In one embodiment, the production line data and the information management data are respectively classified and cached in the distributed publishing and subscribing message module and the big data cache module, and after preprocessing, the production line data classified and stored in different topics of kafka are subjected to real-time and/or offline calculation through the data calculation module. The data calculation module comprises a first distributed calculation engine for processing data in real time, and an offline data bin and a second distributed calculation engine for processing the data offline. Specifically, real-time analysis processing is performed on the production line data through a first distributed computing engine, so that current production condition data of the production manufacturing station is obtained. The first distributed computing engine may refer to a Flink, which is an open source stream processing framework, and executes any stream data program in a data parallel and pipeline mode, and when the pipeline of the Flink runs, the system may execute batch processing and stream processing programs, which provide rich APIs and libraries, and is an ideal choice for real-time data processing. In general, the production status data of the production equipment is real-time data, so that the real-time calculation is performed through the link, and if the production status data of a certain type of equipment only needs to be counted once in one day or a period of time, the corresponding production line data needs to be sent to an offline data bin for offline processing. Therefore, in the present technical solution, the first line data may refer to line data that needs to be processed in real time, and the second line data may refer to line data that needs to be processed offline.
Further, the offline data bin is configured to obtain the second production line data and the information management data after preprocessing in the big data storage module, and perform offline processing on the second production line data and the information management data after preprocessing, so as to obtain the index data and the second production status data of the production and manufacturing station in the preset time period respectively. The offline bins may refer to hive offline bins, and the conventional hive offline bins generally consist of six layers of architecture, including an ODS data original layer, a DWD data detail layer, a DWS data service layer, an ADS data application layer, a DIM data operation layer, and a TEMP bridge layer. And the second production line data and the information management data are subjected to cleaning processing, standardization, association aggregation and summarization of different domain data layer by layer in the offline data bin structure sequence, and finally, index data and second production condition data of the production and manufacturing station in a preset time period are obtained in the ADS layer.
Further, the second production condition data and the index data after offline processing of the offline data bin are sent to a second distributed computing engine, and the index data and the second production condition data are computed in the second distributed computing engine so as to obtain comprehensive index data of the production manufacturing station in a preset time period. The second distributed computing engine can be referred to as spark, which is a fast and general computing engine designed for large-scale data processing, and has the characteristics of high efficiency, universality, usability, compatibility and fault tolerance, and is suitable for batch processing of mass data. Specifically, the data after hive number bin analysis processing is periodically submitted to the Spark cluster through the scheduling system to perform periodic calculation, namely offline calculation, and the comprehensive index data after calculation is periodically stored in a database corresponding to the ADS layer.
In one embodiment, the first data platform further comprises a data lake for acquiring data in the offline data bin according to a user-triggered instruction and displaying the data to the user in a form of a table. The data in the offline bins usually process the data in one day in units of one day, and when a user wants to query the data at a certain time point of one day, the offline bins cannot provide a user-visualized interface, so that when the user has real-time requirements, a data lake can be started, the data in the current offline bins can be called through the data lake, and the data can be displayed to the user in the form of a table. Where a data lake may be referred to as Hudi, which is an open source data lake tool for managing data in a large scale data lake. Hudi aims to address some of the challenges common in data lakes, such as incremental updating, deleting, querying, etc. of data. The method provides a set of API and tools which can help users to write, update, delete, inquire and the like in the data lake, and provides an efficient data indexing and storage mechanism to accelerate the access and processing of data.
In one embodiment, each type of information management data in a production manufacturing station is collected and managed by a different sub-application system in a production manufacturing application system, wherein the plurality of sub-application systems includes a manufacturing execution management information system, a supply chain management information system, a human management information system, a warehouse management information system, a quality management information system, and a facility management information system. The manufacturing execution management information system may refer to an MES system for collecting manufacturing execution management data of the manufacturing stations. The supply chain management information system may refer to an SAP system for collecting the supply chain management data. The human management information system may be referred to as HR system for collecting the human management data. The warehouse management information system may be referred to as a WMS system for collecting the warehouse management data. The quality management information system may refer to a QMS system for collecting said quality management data. The device management information system may refer to a WAM system for collecting the device management data.
In one embodiment, the data collection module is configured to collect production line data of a production manufacturing station and information management data of a production manufacturing application system, and for different types of information management data, each type of information management data is collected and managed by a different sub-application system. Specifically, in the technical scheme, the manufacturing execution management information system collects manufacturing execution management data, the supply chain management information system collects supply chain management data, the human management information system collects human management data, the warehouse management information system collects warehouse management data, the quality management information system collects quality management data, and the equipment management information system collects equipment management data. Since each sub-application system is usually provided by a different provider, when data of each sub-application system is collected, a suitable module is selected according to the system characteristics of each sub-application system to collect the data of each sub-application system. In the technical scheme, the data acquisition module comprises an internet of things data acquisition module and a distributed acquisition module, wherein the internet of things data acquisition module is used for acquiring production line data, manufacturing execution management information system and information management data in a supply chain management information system. The distributed acquisition module is used for acquiring information management data in the human management information system, the warehouse management information system, the quality management information system and the equipment management information system. Specifically, the data acquisition module of the internet of things can refer to NIFI, which is an easy-to-use, powerful and reliable data processing and distributing system, which is provided with a plurality of built-in processing modules, and the functions of the data processing and distributing system can be realized only by modifying conditions or variables by a user, and the data processing and distributing system is used for processing, summarizing, distributing, filtering, screening, combining and the like. The distributed acquisition module can be a Flink CDC or DATAX, the DataX is a heterogeneous data source offline synchronization tool, data with low real-time requirements can be acquired in an incremental or full mode, the Flink CDC is a data capture component of a distributed computing engine Flink, and data with high real-time can be acquired in an incremental mode.
In one embodiment, the data acquisition module of the internet of things is further configured to encrypt and transmit the line data to the gateway through a preset protocol after the line data is acquired, and transmit the line data to the distributed publish and subscribe message module through the gateway. Specifically, because the production equipment has more data parameters and a complex structure and high acquisition frequency, the technical scheme can adopt the Mqtt lightweight publishing/subscribing message transfer protocol to encrypt the production line data, the production line data of the production equipment in the working process can be transmitted to an edge box through a PLC at a certain frequency, the edge end is assembled through a 5G network by the Mqtt protocol, then the data is transmitted to a HiveMqtt gateway of the cloud, and the gateway sends the production line data of the production equipment to an appointed topic of a message queue kafka after receiving the data.
In one embodiment, the data collection module collects the different types of information management data stored in each sub-application system and the production line data of the production and manufacturing station in a unified way, and uploads the collected data to the first data platform with high fault tolerance and high throughput to be managed in a unified way. After the data acquisition module uploads the acquired production line data and information management data to the first data platform, the production line data and the information management data are stored in a classified mode through a data cache module in the first data platform. After real-time and/or off-line calculation is performed according to the production and manufacturing requirements of the production and manufacturing stations through the data calculation module, index data and production condition data of the production and manufacturing stations in a preset time period are obtained, and fusion calculation is further performed on the index data and the production condition data to obtain comprehensive index data. After the comprehensive index data is obtained, the comprehensive index data can be stored in a distributed database, specifically, in the technical scheme, the distributed database can be Dorisdb, which is a high-performance distributed relational column database with multiple data analysis scenes, mySQL protocol compatibility and high performance, has the advantages of easy deployment, easy maintenance and extremely simple architecture design, can effectively reduce the complexity and maintenance cost of the system, and can improve the reliability and expansibility of the system. The Dorisdb can meet various data analysis scenes of users, support various data models such as detail tables and aggregation tables, support various importing modes, and can be integrated into various existing systems such as spark, flink and Hive. Therefore, in the technical scheme, the distributed database can comprise a report analysis module, an early warning module and a data modeling module. The report analysis module is used for carrying out multidimensional analysis processing on the index data and the production condition data so as to generate a corresponding multidimensional visual report, the early warning module is used for generating a corresponding alarm signal under the condition that any one of the index data and the production condition data is abnormal, and the data modeling module is used for carrying out real-time aggregation and updating operation on the comprehensive index data based on the aggregation and updating data model according to an instruction triggered by a user.
In one embodiment, the system further comprises a reporting system, wherein the reporting system is used for acquiring comprehensive index data and production condition data from the distributed database, and generating a visual report for display according to the comprehensive index data and a preset template. The report system can be a BI system, which is also called a data analysis system, and is used for positioning, analyzing and diagnosing the comprehensive health degree of each service side platform, and simultaneously, can also pre-judge and pre-warn potential risks, and is a data analysis display system for acquiring, integrating, maintaining and storing data from various data sources and realizing flexible calling and visual display based on basic data. Specifically, after the comprehensive index data is stored in the dorisdb database in a centralized manner, the user cannot directly query the database to perform production management and operation. Therefore, the comprehensive index data can be further summarized, analyzed and mined through the BI report, then the visual report development is carried out, and all relevant data such as the whole production field and manufacturing, materials, order quantity, production progress, attendance man-hour, production efficiency, investigation plan, material nesting property, logistics efficiency, storage condition, product quality, safety and the like are displayed in the form of a graphical report.
Further, since the BI system service is still in the form of a desktop service, or the production management situation cannot be intuitively presented to all users, only some management personnel and business personnel can access the data. Therefore, in the technical scheme, the production and manufacturing station further comprises a man-machine interaction device, and the man-machine interaction device is used for displaying the visual report generated by the report system to the terminal, so that a user can make a production plan for the production and manufacturing station according to the visual report.
In one embodiment, as shown in FIG. 2, a system architecture diagram of a manufacturing site full flow data management system is provided. The full-flow data management system comprises a production and manufacturing site, a production and manufacturing application system, a data acquisition, a data storage, a data calculation, a data storage, a BI report analysis system, a production site visual management module and the like. The production and manufacturing site can comprise different types of workshops such as an assembly workshop, a blanking workshop, a machining workshop, a welding workshop, a coating workshop, a intermodulation workshop, a material preparation workshop and the like, and in each type of workshops, a plurality of excavator equipment of the same type are included for providing production line data and different types of information management data. The production and manufacturing application system comprises IOT, MES, WMS, QMS, SAP, HR and other information management systems, and the different types of information management systems collect production line data and different types of information management data of a production and manufacturing site and transmit the data to a data big data platform Hadoop for unified management. The multi-dimensional analysis is carried out through the BI report analysis system, wherein the multi-dimensional analysis comprises electric energy analysis, abnormality diagnosis, time and labor output, manufacturing statistics, working hour unit price, material consumption, working hours of attendance, direct labor and single cost, and finally the multi-dimensional analysis enters the production field visual management module and is displayed to all users through various visual signboards, and the multi-dimensional analysis comprises a workshop signboard, a production line signboard, a station signboard, an office signboard and a mobile signboard.
In order to get through cost data barriers such as yield, electric energy, working hours, materials, manufacturing cost and the like, build a factory-end multi-level cost cockpit, analyze working hours, manufacturing cost, single cost constitution and target actual differences, deeply mine abnormal loss, assist financial cost assessment and workshop cost reduction, alarm processing on abnormal production conditions, real-time Internet of things data of all production equipment of an excavator manufacturing production line such as water and electricity meter values, temperature, power, rotation speed, pressure, metering values, time and other working condition parameters are required to be acquired in real time and stored in a hadoop storage system for large data analysis. Because the production equipment has more data parameters and a complex structure and high acquisition frequency, the production line data is transmitted and encrypted by adopting the Mqtt lightweight publish/subscribe message transmission protocol, working condition data of the production equipment in the working process can be transmitted to an edge box through a PLC (programmable logic controller) at a certain frequency, the edge end is assembled by the data through a 5G network by adopting the MQTT protocol and then transmitted to a HiveMQTT gateway of a cloud, and the gateway receives the data and then transmits the production equipment IOT data to an appointed topic of a message queue kafka.
As shown in fig. 3, a flow chart for IOT data collection and analysis for a production facility is provided. In order to support the organic connection, reasonable scheduling and systematic operation of materials, logistics equipment and stations, support various material distribution modes, coordinate the timely delivery of materials to a production line, ensure the timely arrival of the materials, ensure the production efficiency, and require the transparent management of production condition data of the production equipment, the acquired production line internet of things data are stored in a hadoop file system on one hand so as to facilitate the subsequent batch analysis of the level Zhou Yue by a spark batch processing engine to obtain a historical statistical analysis result, and on the other hand, the real-time analysis of data of kafka specified subjects by a real-time calculation engine flink is carried out to reflect the current real-time production condition of factories.
Further, the informatization system SAP, ECC, HR, MES, WMS, EAM, QMS, PLM of the manufacturing enterprises is usually provided by different suppliers, the compatibility between the systems is poor, serious problems of data islands and industry barriers exist, and it is almost impossible to transversely open each application system directly through the API. The data of the systems are transmitted to a big data storage system hadoop-hdfs through big data acquisition tools such as NIFI or DATAX or FLINK CDC by the local tertiary scheme, specifically, which acquisition tool is adopted is needed to be selected according to timeliness of the data, and the data with high timeliness such as materials, warehouse, orders, finance, cost, labor, faults and the like are acquired in an increment mode through the Flink CDC, and the data with unnecessary timeliness is acquired in full quantity or in increment mode through the NIFI or DATAX, so that data of all different fields related to the manufacturing of the digging machine such as manufacturing, logistics, warehouse, supply chain, quality, finance, cost, manpower, performance, materials and the like are stored in a concentrated mode, and then the data are subjected to association fusion analysis according to business rules, so that interconnection and intercommunication of the data of the different fields are realized.
Further, after the data of all the subject domains, including the IOT data of the production line, are stored in the hdfs in a centralized manner, the data are loaded into the hive number bin, and cleaning, standardization and fusion of all the data are realized through structured SQL programming. Specifically, the method for gradually performing data processing calculation according to the number bin layering is to perform cleaning processing of different domain data layer by layer according to the sequence of ODS- & gtDWD- & gtDWS- & gtADS (with the addition of a dimension layer DMI and a bridging layer TEMP), standardizing, associating aggregation and summarization, index integration and the like, and finally obtaining index data about the whole production process at the ADS layer, wherein the index data comprise a plurality of business modules and analysis index data such as personnel on duty, attendance, manual efficiency, delivery yield, production order, purchase order, electric energy, production man-hour, cost, single cost and the like. After the comprehensive summary index data is developed according to the hive number bin analysis processing flow, the calculation process codes of the indexes are periodically submitted to the Spark cluster for periodic calculation according to the scheduling frequency of days, weeks and months by a scheduling system. Finally, the calculated fusion index data are periodically stored in a database corresponding to the ADS layer, and the data can effectively guide the management of a production site and the cooperation of production processes.
Furthermore, the summarized data of the hive number bin ADS is required to be exported to a database supporting quick query before visual display, dorisdb can be selected as a storage system of final index data, and the dorisdb supports quick insertion query under the condition of mass data storage, atomic-level data update and aggregation, so that the requirement of a report system can be met. According to the technical scheme, the data of the ADS layer can be periodically exported to the dorisdb according to the day through the function of the brooker-load of the dorisdb. The multi-service data related to the production site are systematically summarized and integrated, the data not only comprise historical data summarized according to the day Zhou Yue, but also comprise latest statistical data at the current moment, such as energy consumption, stock quantity, part consumption condition, orders, production site monitoring indexes, labor cost and real-time data related to finance, and the analyzed index data are stored in a dorisdb database of an application layer in a centralized manner, so that management staff, business staff and first-line workers cannot query the database to carry out production management and operation. Therefore, the data are further summarized, analyzed, mined and then subjected to visual report development through the BI report, and all relevant data such as the whole production field, manufacturing, materials, order quantity, production progress, attendance man-hour, production efficiency, investigation plan, material nesting property, logistics efficiency, storage condition, product quality, safety and the like are displayed in the form of a graphical report.
Further, since the BI system service is still in the form of a desktop service, or the production management situation cannot be intuitively displayed to all producers, only some managers and business personnel can call the data. In order to make the production process more transparent, realize timely, the comprehensive control to mill production progress, realize the timely discovery of abnormal information, in time solve, need embody the intelligent manufacturing ability that can't see through big data technology, show the wisdom that digs intelligent factory "can't see" with the vision. By calling the api interface of the BI system, the report in the BI is embedded into the web page of the large screen, so that remote real-time presentation of the BI report is realized. The problems of the workshop management mode of the traditional factory mainly based on human factors, business management informatization, production plan variation collaboration, production and material shortage collaboration management, untimely material distribution, opaque production process and the like are solved.
According to the technical scheme, the real-time incremental acquisition of the data of each production and manufacturing service system is realized through a big data technology, the total acquisition and the centralized storage are realized, and the aeipathia that the data island and the data are difficult to interconnect and communicate among different service systems is broken through. Real-time collection and uploading of mass heterogeneous IOT data and OT data of production line production equipment are realized through an advanced Internet of things technology, association fusion analysis of mass Internet of things data and business data is realized based on a digital bin and data lake technology, and data values in different business fields are fully mined. And the statistical analysis result data in the data bins are exported and stored from the data bins to a database system dorisdb supporting real-time insertion, updating, aggregation, real-time quick query and aggregation analysis of mass data, so that the requirements of a BI report system on quick query, summarization analysis and real-time response of the data are met. And through carrying out system integration on various production operation visual reports developed by the BI system on a workshop large screen system, the three-dimensional presentation of the BI report is realized, the effect of the report system is fully exerted, the production operation efficiency and the management level are improved.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises an element.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and changes may be made to the present application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc. that fall within the spirit and principles of the present application are intended to be included within the scope of the claims of the present application.
Claims (10)
1. A manufacturing site full flow data management system, the system comprising:
a production manufacturing station comprising a plurality of production devices of different types;
the production and manufacturing application system comprises a plurality of sub-application systems, wherein different sub-application systems are used for collecting different types of information management data of the production and manufacturing station in a preset time period;
The data acquisition module is used for acquiring production line data of the plurality of production devices in real time, receiving information management data acquired by the production and manufacturing application system, and transmitting the production line data and the information management data to the first data platform, wherein the production line data comprise working condition data and operation data of the plurality of production devices;
the first data platform comprises a data cache module, a data calculation module and a distributed database, wherein the data cache module is used for storing the production line data and the information management data in a classified mode, and the data calculation module is used for carrying out real-time and/or off-line calculation on the production line data and the information management data according to the production and manufacturing requirements of the production and manufacturing station so as to obtain index data and production condition data of the production and manufacturing station in the preset time period; performing fusion calculation on the index data and the production condition to obtain comprehensive index data; and storing the comprehensive index data into a distributed database.
2. The manufacturing site full flow data management system of claim 1, wherein the data caching module comprises a distributed publish-subscribe message module and a big data storage module, the distributed publish-subscribe message module configured to cache the production line data into different topics, the topics being partitioned according to the type of the production facility;
The big data storage module is used for caching the information management data and preprocessing the information management data.
3. The manufacturing site full flow data management system of claim 2, wherein the data calculation module comprises:
the first distributed computing engine is used for dividing the production line data into first production line data and second production line data according to the production and manufacturing requirements, and analyzing and processing the first production line data in real time to obtain first current production condition data of the production and manufacturing station;
the offline data bin is used for acquiring the second production line data and the information management data after pretreatment in the big data storage module, and performing offline processing on the second production line data and the information management data after pretreatment so as to respectively obtain index data and second production condition data of the production manufacturing station in the preset time period;
and the second distributed computing engine is used for computing the index data and the second production condition data to obtain comprehensive index data of the production manufacturing station in the preset time period.
4. The manufacturing site full flow data management system of claim 3, wherein the first data platform further comprises a data lake for retrieving data corresponding to the instructions from the offline digital storage according to the instructions triggered by the user and presenting the data to the user in the form of a table.
5. The manufacturing site full flow data management system of claim 1, wherein the plurality of sub-application systems comprises:
a manufacturing execution management information system for collecting manufacturing execution management data of the production manufacturing station;
the supply chain management information system is used for collecting supply chain management data;
the manpower management information system is used for collecting manpower management data;
the warehouse management information system is used for collecting warehouse management data;
the quality management information system is used for collecting quality management data;
and the equipment management information system is used for collecting equipment management data.
6. The manufacturing site full flow data management system of claim 5, wherein the data collection module comprises:
the internet of things data acquisition module is used for acquiring the production line data, the manufacturing execution management information system and the information management data in the supply chain management information system;
and the distributed acquisition module is used for acquiring information management data in the human management information system, the warehouse management information system, the quality management information system and the equipment management information system.
7. The system according to claim 6, wherein the data caching module comprises a distributed publish-subscribe message module, and the data collection module of the internet of things is further configured to encrypt and transmit the line data to a gateway through a preset protocol after the line data is collected, and transmit the encrypted line data to the distributed publish-subscribe message module through the gateway.
8. The manufacturing site full flow data management system of claim 1, wherein the distributed database comprises:
the report analysis module is used for carrying out multidimensional analysis processing on the index data and the production condition data so as to generate a corresponding multidimensional visual report;
the early warning module is used for generating a corresponding warning signal under the condition that any one of the index data and the production condition data is abnormal;
and the data modeling module is used for carrying out real-time aggregation and updating operation on the comprehensive index data based on the aggregation and updating data model according to the instruction triggered by the user.
9. The manufacturing site full flow data management system of claim 1, wherein the system further comprises:
and the report system is used for acquiring the comprehensive index data and the production condition data from the distributed database, and generating a visual report for display according to a preset template by the comprehensive index data.
10. The manufacturing site full flow data management system of claim 9, wherein the production manufacturing station further comprises:
and the man-machine interaction device is used for displaying the visual report generated by the report system to a terminal so that a user can make a production plan aiming at the production manufacturing station according to the visual report.
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