CN113849483A - Real-time database system architecture for intelligent factory - Google Patents
Real-time database system architecture for intelligent factory Download PDFInfo
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- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
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Abstract
The invention discloses a real-time database system architecture for an intelligent factory, which comprises a high-reliability real-time data acquisition and storage middleware, a database cluster, a data service layer, a database system operation management module and a database system safety management module; the high-reliability real-time data acquisition and storage middleware is used as the bottom layer of a real-time database system architecture and is used for acquiring production and manufacturing related data of an intelligent factory in real time from different data sources; the data service layer is used as the top layer of the real-time database system architecture and is used for outputting data processed, extracted, stored and analyzed by the high-reliability real-time data acquisition and storage middleware, the database cluster, the data service layer, the database system operation management module and the database system safety management module to the data application layer for use by various intelligent factory information systems. The invention adopts the modular design, improves the universality and the interchangeability, shortens the system fault repairing time, and can carry out targeted improvement and optimization on weak links in the expansibility.
Description
Technical Field
The invention belongs to the technical field of intelligent manufacturing, and particularly relates to a real-time database system architecture for an intelligent factory.
Background
In the white paper of intelligent manufacturing capability maturity promulgated by 2016 at the institute of electronic technology standardization in China, the capability maturity model of intelligent manufacturing is divided into five levels, namely a planned level, a standard level, an integrated level, an optimized level and a leading level. Definition at the second level of specification: at this level, enterprises have formed intelligently manufactured drawings, invested in equipment and systems supporting core services, and the equipment has data acquisition and communication capabilities through technical transformation, so that automation and digital upgrading covering important links of the core services are realized. By formulating standardized interfaces and data formats, internal integration of part of information systems supporting production operations can be realized, data and information are shared in business, and enterprises begin to step forward to the threshold of intelligent manufacturing.
In the third level of integration, mention is made of: the information system integration of core business components such as design, production, sales, logistics, service and the like can be realized, the data sharing in a factory range is focused, and the enterprise finishes the preparation work of intelligent promotion.
In the fourth optimization stage it is mentioned that: the method realizes factory-level digital modeling, analyzes data collected by personnel, equipment, products and environments and data formed in the production process, optimizes production processes and business processes through a knowledge base, an expert base and the like, and can realize interaction between the information world and the physical world.
In the fifth lead stage, mention is made of: the lead level is the highest level of intelligent manufacturing capability construction, at which the analytical use of data has been through the aspects of the enterprise.
As can be seen from the above explanation, the collection, processing, analysis, application and service capabilities of the factory data are the prerequisite guarantee for intelligent manufacturing and upgrading, and are the key links for realizing the intelligent factory.
For modern industrial enterprises, how to obtain various data of factory production fields and serve for analysis and decision of production, research and development, sales, operation and other activities is a key of intelligent factory construction. Among various resources concerned by an intelligent factory, production workshop data is an important resource, and if the problems of real-time and effective uploading, efficient processing and comprehensive service of production process data cannot be solved, the conditions of production, inventory, logistics, equipment and the like cannot be timely and comprehensively mastered, so that the resource utilization rate and the production reliability are improved, the core competitiveness of an enterprise is enhanced, and the goal of the intelligent factory is achieved.
The currently common real-time database system mainly comprises a commercial system and a customized system, and the commercial real-time database system mainly comprises: the PI real-time database system of the American OSI Software company, which is a commercial Software application platform based on a C/S, B/S structure, is the real-time database system with the largest machine loading amount all over the world at present. Wonderware's InSQL Server, a factory-oriented, high-performance, real-time relational database. The InSQL integrates and expands the MS SQL Server of Microsoft corporation, not only has high data acquisition speed, high-efficiency data compression, data storage and other real-time database performances, but also can integrate events, summaries, production, configuration and other factory data generally stored in a relational database. With the strategic development of China, some representative domestic real-time databases are emerged successively, for example: openPlant of Shanghai Maijie science, force-controlled pSpace, Agilor real-time database system of software institute of Chinese academy of sciences, and the like.
In the industrial field, commercial systems are adopted, and have the advantages of mature technology, rapid deployment, stable use and the like, but in practical application, because production characteristics of various factories are different, factory equipment, environments, logistics and the like are greatly different, a large amount of non-standard equipment and non-standard systems exist, and the commercial real-time database system is adopted, the following defects often exist:
(1) to meet the needs of most users, business software typically provides standardized interfaces, but there are compatibility issues with interfaces to non-standard devices and systems.
(2) Commercial software often contains a large amount of bulky and unnecessary components or functions, has high requirements on hardware configuration, and cannot fully utilize hardware resources.
(3) The use of business software may eventually result in the enterprise being overly dependent on the supplier, locked into a closed system.
(4) The business software also has the problems of implementation and deployment, operation and maintenance, secondary development, version upgrading and the like, and also has personnel training and personnel storage requirements on users.
(5) Commercial software tends to be expensive, especially in the case of plants with small data volumes, and performance costs are low.
In the aspect of a conventional customized database system, because a unified integrated framework and a standard guidance system are not designed and developed, a developer usually performs system design and construction from the practical requirements of user data services, and the system is limited by the experience and level of the developer, the customized and developed system usually only solves the local problem, and the consideration of systematicness, safety and integrity on data acquisition, processing, storage and analysis is lacked.
Disclosure of Invention
In view of the above, the present invention provides a real-time database system architecture for an intelligent plant. The invention adopts a modularized structure aiming at the common data acquisition, processing, storage and service modes of an intelligent factory, reduces the service coupling degree in the system and can support developers to quickly build a real-time database system.
The invention is realized by the following technical scheme:
a real-time database system architecture for an intelligent factory comprises a high-reliability real-time data acquisition and storage middleware, a database cluster, a data service layer, a database system operation management module and a database system safety management module;
the high-reliability real-time data acquisition and storage middleware is used as the bottom layer of the real-time database system architecture and is used for acquiring production and manufacturing related data of an intelligent factory in real time from different data sources;
the data service layer is used as the top layer of the real-time database system architecture and used for outputting data processed, extracted, stored and analyzed by the high-reliability real-time data acquisition and storage middleware, the database cluster, the data service layer, the database system operation management module and the database system safety management module to the data application layer for use by various intelligent factory information systems.
Preferably, the high-reliability real-time data acquisition and storage middleware supports a multi-level distributed deployment structure, the data is guaranteed to be unified, synchronous and accurate to update, and multi-thread reading and writing are adopted for reading and writing of a single acquisition and storage middleware node.
Preferably, the high-reliability real-time data acquisition and storage middleware comprises a point configuration unit, a data driving and interface unit, a data caching unit and a data storage unit;
the data driving and interface unit integrates various industrial data drivers and various interface protocols for establishing communication with different data sources so as to acquire data of the different data sources in real time;
the point configuration unit associates the acquisition object with the data point and realizes the hierarchical management of the data through a built-in logic grouping structure;
the data cache unit adopts a service program to read unprocessed write-in failure logs at regular time, when write-in failure data are found, the data are written into a master database and a slave database of a data center, and if the write-in failure data are successfully written, the logs are deleted or a log device is changed into processed; if the writing fails, the operation is not carried out, and the next processing is waited;
and the data storage unit stores all the acquired real-time data into a real-time database of the database cluster.
Preferably, the real-time data storage process of the present invention specifically includes:
caching data into a data queue;
comparing the newly inserted data with the previous data, and updating the newly inserted data into the queue of the previous data when the data are inconsistent;
and writing the updated data into a real-time database cache queue, embedding a high-efficiency compression algorithm in the real-time database, and efficiently compressing and storing the data in the cache queue.
Preferably, the database cluster of the invention comprises a real-time database, a historical database, an alarm database and an analysis database;
the real-time data collected by the high-reliability real-time data collection and storage middleware are directly stored in the real-time database, the real-time data are stored in the historical database after being durably stored, and then the real-time data are respectively stored in the analysis data and the alarm database according to data properties or data processing.
Preferably, the database system operation management module of the invention comprises a data management unit, a collection network health monitoring unit, a data alarm unit, a historical data retrieval and report unit and a data standardization unit;
the data management unit is used for managing multi-dimensional operation data and state data of the intelligent factory, providing functions of data query, data processing and data export, supporting comparison and display of data of the same measuring point in different time periods or different measuring points and supporting setting of a data early warning range;
the acquisition network health monitoring unit realizes the monitoring of the key state of the IT infrastructure in the real-time data transmission process, has a data reporting function and realizes data interaction with other service systems;
the data alarm unit displays the whole alarm structure and a specific monitoring point by creating a layered information tree, and realizes data alarm when finding an event exceeding a normal parameter range;
the historical data retrieval and reporting unit generates a data analysis report by performing statistical analysis on the retrieved historical data;
the data standardization unit comprises a reading interface compatible with various types of original collected data of the intelligent factory, unified calculation parameter configuration, a standardized calculation formula, a user-defined calculation formula and visual output file format configuration.
Preferably, the data service layer of the present invention provides data services to users of internal and external systems.
Preferably, the database system security management module of the present invention implements the comprehensive security management of the highly reliable real-time data acquisition and storage middleware, the database cluster, the data service layer, and the database operation management module, and includes a database access authority authentication unit, a data security policy unit, a sensitive data security protection unit, and a data backup and recovery unit;
the database access authority authentication unit provides a traditional autonomous access control mechanism and a mandatory access control mechanism;
the data security policy unit realizes the functions of modification authority control and data encryption on data in the database;
the sensitive data safety protection unit is used for realizing safety management on sensitive data;
the data backup and recovery unit is used for performing full backup and recovery of the database.
Preferably, the data sources of the present invention include warehouse control system data, logistics scheduling system data, production line control system data, inspection instrument data, safety and protection system data, and environmental monitoring system data.
Preferably, the data application layer of the invention can adopt an intelligent factory application system of a B/S architecture or a C/S architecture.
The invention has the following advantages and beneficial effects:
the invention provides a data base for the construction of intelligent factories, particularly realizes the modularized integration through the multi-service of real-time data acquisition, processing, storage, forwarding, management, safety and the like, supports developers to quickly realize the system and complete system design and development, and carries out pertinence on key and core system modules according to the service and data characteristics of different intelligent factories so as to adapt to the requirements of data acquisition, processing and service of different intelligent factories. The invention adopts the modular design, improves the universality and the interchangeability, shortens the system fault repairing time, and can carry out targeted improvement and optimization on weak links in the expansibility.
The invention adopts a real-time database technology, can be applied to the real-time acquisition, processing and storage of full link data such as personnel, equipment, materials, processes, environment, quality, logistics and the like in the production and manufacturing process of an intelligent factory, and can provide customized data service. The full utilization of data is realized, the digital application of production, research and development, market, operation and the like is effectively promoted, and a solid foundation is established for realizing an intelligent factory.
Compared with a commercial database system, the system architecture provided by the invention can customize and modify source codes, and has richer system flexibility and environmental adaptability. The system architecture of the invention can realize the core performance index and the main performance equivalent to those of a commercial software real-time database system, and can carry out iterative optimization upgrading.
The system architecture provided by the invention supports linkage with a database and various interfaces in the aspect of a third-party interface, can customize the interface at a source code level, and provides better support for an upper-layer application system.
The invention has better cost advantage, only focuses on developing the service function needed by the user, and the expenses saved on the software can be invested in other places.
Drawings
The accompanying drawings, which are included to provide a further understanding of the embodiments of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the principles of the invention. In the drawings:
FIG. 1 is a system architecture diagram of the present invention.
Fig. 2 is a real-time data acquisition and storage data flow of the present invention.
FIG. 3 is a schematic diagram of a data caching process according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to examples and accompanying drawings, and the exemplary embodiments and descriptions thereof are only used for explaining the present invention and are not meant to limit the present invention.
Examples
The traditional factory real-time database system architecture has the problems of insufficient expansibility, incomplete data acquisition types, especially low data real-time performance, incapability of effectively cleaning, extracting and managing acquired data, incapability of timely providing accurate and comprehensive data services and the like. The data of personnel, equipment, materials, processes, environment, quality, logistics and the like in the factory production and manufacturing process cannot be linked completely in real time, the digital services of production management, research and development, service and the like are supported, and the digital and intelligent upgrading of a factory becomes a great obstacle.
The embodiment provides a real-time database system architecture for an intelligent factory, which perfects and expands a traditional data acquisition system architecture, decomposes the system into modules such as a high-reliability real-time data acquisition and storage middleware, a database cluster, a data service, database system operation management, database system safety management and the like, ensures the real-time performance of data, provides high expansibility, supports the configurability of data management and data service, and provides data safety guarantee. The system can be used for rapidly customizing a database system for an intelligent factory and providing all-around services such as data acquisition, storage, processing, service and the like.
Specifically, as shown in fig. 1, the system architecture of this embodiment mainly includes a high-reliability real-time data acquisition and storage middleware, a database cluster, a data service layer, a database system operation management module, and a database system security management module.
The data source layer is used as data input of the real-time database system, and input data are output to the data application layer after being processed, extracted, stored and analyzed by the real-time database system and are used by various intelligent factory informatization systems.
The data source layer is the data input of a database management system, comprises core elements of intelligent factory production and manufacturing, and mainly comprises storage control system data, logistics scheduling system data, production line control system data, inspection instrument equipment data, safety precaution system data, environment monitoring system data and other data sources. The various systems herein refer not only to a narrowly defined software system, but also to an integrated system including hardware, software, and other facilities to implement a certain type of specific functions.
The data application layer acquires data output by the database system through interface modes such as WebAPI, WebSocket, MQTT and the like, and can construct an intelligent factory application system adopting a B/S architecture, such as a production operation management system, a warehousing operation management system, a quality operation management system, a safety and environmental protection management system and the like; application systems with high real-time performance, such as field operation systems, safety monitoring systems, equipment monitoring systems and the like, adopting the C/S architecture can also be constructed.
The high-reliability real-time data acquisition and storage middleware of the embodiment is positioned at the bottommost layer of a system architecture and comprises functions of data acquisition, low-delay data release, data caching and the like. The middleware supports multi-level distributed deployment, and dedicated connection is used as a communication link among all nodes so as to quickly build a distributed architecture.
The acquisition and storage middleware supports a distributed deployment structure and can ensure unified, synchronous and accurate updating of data, and multithreading reading and writing is adopted for reading and writing of a single acquisition and storage middleware node.
The acquisition and storage middleware has a data caching function, does not influence real-time data acquisition when network interruption occurs and a central machine room server needs to be offline for upgrading or maintenance, and has certain data buffering time. When the data link is restored, the cached data can be automatically uploaded to a real-time database, and the real-time data acquisition and storage data flow is shown in fig. 2.
The acquisition and storage middleware can be realized by a C + + programming language, and the data acquisition, transmission and storage adopt a low-delay design. The acquisition and storage middleware of the embodiment mainly comprises a point configuration unit, a data driving and interface unit, a data cache unit and the like.
Data driving and interface unit: in order to establish communication with a data source layer system and equipment, the storage middleware is used for installing a driver program for each different system and equipment so as to establish proper communication, and comprises a plurality of industrial data drivers such as Mitsubishi, Beifu, Siemens and the like. The system comprises a plurality of interface protocols such as Modbus, OPC UA/DA, IEC 104, WebService, WebAPI, MQTT and the like.
A point configuration unit: the operation of creating, editing, deleting and the like of the access data points is realized, and the management of all data of the database system, such as the names, numerical values, measurement units, conversion names and the like of the collected data points is realized. The data management method supports the association of the collected objects and data points, realizes the hierarchical management of data through a built-in logic grouping structure, and regroups the collected objects. And supporting the setting of a data value alarm rule, and setting alarm attributes, including state turnover alarm, data value overrun alarm and multiple data value composite alarm.
A data caching unit: the acquisition and storage middleware has a data caching function so as to deal with the situations that network interruption occurs, a central computer room server needs to be offline for upgrading or maintenance, and the like, a special background service is adopted to process the information, a service program reads unprocessed write-in failure logs at regular time, when write-in failure data are found, the data are tried to be written into a master database and a slave database of a data center, if the write-in failure is successful, the logs are deleted or the log state is changed into processed, if the write-in failure is not carried out, the operation is not carried out, and the next processing is waited, as shown in fig. 3.
A data storage unit: all the collected real-time data are stored in a real-time database of the database cluster, and the real-time database selects Influx DB. The method comprises the steps that after real-time data are acquired through a drive or an interface by a storage middleware, the data are firstly cached in a data queue, newly inserted data are compared with previous data, when the data are inconsistent, the new data are updated in the queue of the previous data, then the data are written into a cache queue of a real-time database, a high-efficiency data compression algorithm is embedded in the real-time database, and the data in the cache queue are efficiently compressed and stored.
The database cluster of the embodiment comprises a real-time database, a historical database, an alarm database, an analysis database and other types of databases. Logically speaking, from data processing: the real-time data acquired by the data acquisition and storage middleware are directly stored in a real-time database, the real-time data are stored in a historical database after being duralized, and then the real-time data are respectively stored in various databases such as an analysis database and an alarm database according to data properties or data processing. The database can be mainly divided into a real-time database and a relational database.
The real-time data is time series data, a time series database Influx DB is adopted for storage, and the data format supported by the database at least comprises the following four data formats: int, Float, String, boot. The database supports secondary data filtering and data compression, the data storage efficiency is high, the occupied space of a disk is small, the recovery precision is good, and different compression algorithms can be selected according to different data types when the data of types such as timestamps, Boolean quantities, integer values, floating point numbers, character strings and the like are stored.
And storing the acquired real-time data in a local temporary storage and forwarding the data to an original numerical table in a relational database such as an SQL Server, an Oracle, a My SQL, a Dameng and the like to form a historical database. The relational database realizes storage of analysis data and alarm data, and simultaneously realizes storage of historical data such as factory operation management data, manufacturing process management data, maintenance management data, warehouse logistics data, standardized data and the like.
The database system operation management module of the embodiment mainly and effectively manages various types of data of an intelligent factory, meets the requirements of storage, processing and analysis of the data, and mainly comprises a data management unit, a collection network health monitoring unit, a data alarm unit, a historical data retrieval and report unit and a data standardization unit. The database system operation management module is developed by adopting C #, and information input, information acquisition, background data management, data processing service, WebAPI interface and the like can be integrated with the existing information system standard framework of the intelligent factory.
The data management unit provides management of multidimensional operation data and state data of a factory, provides functions of data query, data processing and data export, supports comparison and display of data of the same measuring point in different time periods or different measuring points, supports setting of a data early warning range and the like.
The acquisition network health monitoring unit realizes the monitoring of the key state of the IT infrastructure in the real-time data transmission process, eliminates or reduces data loss caused by power failure, infrastructure faults and performance problems as much as possible, has a data reporting function and realizes data interaction with other service systems.
The data alarm unit displays the whole alarm structure and specific monitoring points by creating a layered information tree, and realizes data alarm when finding out an event exceeding the normal parameter range.
The historical data retrieval and reporting unit generates a data analysis report by performing a summation, averaging, mean square error, or other statistical analysis calculation on the retrieved historical data.
The data standardization unit comprises a reading interface compatible with each original collected data of the factory, a unified calculation parameter configuration, a standardized calculation formula, a user-defined calculation formula, a visual output file format configuration and the like, and meets the requirements of various types of data processing of the intelligent factory.
The data service layer of the embodiment provides data service for internal and external system users. The data service layer is developed by adopting C #, and comprises a real-time data service unit, a historical data service unit, a data statistical analysis unit, a real-time data extraction unit and the like.
The real-time data service unit can design a data service interface to provide data service for a related data application system according to the data content, format and interface requirements of an external system. And carrying out SOA service encapsulation on the real-time data, and providing functions of real-time data query, real-time data write-in and the like in a data service form. According to the characteristics of various service system data interfaces of the intelligent factory, various types of data service interfaces can be provided, and the data service interfaces comprise RESTful interfaces (WebAPI), WebSocket (SignalR), MQTT and other modes.
The historical data service unit is completed by adopting a RESTful style WebAPI interface. According to the data adding, deleting, modifying and checking requirements of a service caller, a data service provides corresponding Get, Post, Put and Delete methods, the service interface needs to provide authority authentication, and only a user passing the authentication can call the interface.
The data statistical analysis service unit defines a series of interfaces to complete the statistical analysis of various data according to different data statistical analysis requirements. The statistical analysis function needs to inquire and calculate a large amount of data, is long in time consumption, and does not change the analysis statistical result of the historical data, so that the function can be completed by using a special statistical analysis service.
The real-time data extraction unit can be realized in the high-reliability real-time data acquisition and storage middleware, and can also extract from a real-time database at regular time by using background service. If implemented in the acquisition middleware, the time interval, data item, etc. information is specified in the point configuration. If a background service implementation is used, a series of data extraction methods (scalable) can be defined, and then the methods are executed at respective specified time intervals to complete the extraction and storage of data.
The database system security management module of the embodiment is developed by adopting C + +, realizes comprehensive security management of a high-reliability acquisition and storage middleware, a database cluster, a data service and a database operation management module, and mainly comprises a database access authority authentication unit, a data security policy unit, a sensitive data security protection unit, a data backup and recovery unit and the like.
The database access authority authentication unit provides a traditional autonomous access control mechanism and a forced access control, wherein the former is responsible for managing an access protection strategy of a data object (object) by an owner or a system administrator, and the latter is mainly completed by adding a security label to a user and the object.
The data security policy unit mainly implements functions including modification authority control and data encryption on data in the database.
The sensitive data security protection unit includes but is not limited to implement the following functions: passwords, keys, certificates, session identification, License, privacy data, authorization credentials, and the like. And for sensitive data, three modes of encryption, fine-grained authority and auditing are adopted to strengthen management.
The data backup and recovery unit executes the complete backup of the database, and the recovery method of the database adopts three recovery methods according to different requirements: database level recovery, Tablespace (Tablespace) recovery, data file recovery.
In the aspect of a conventional customized database system, because a unified integrated framework and a standard guide system are not developed, a developer usually performs system design and construction from the practical requirements of user data services, the system is limited by the experience and level of the developer, and the customized developed system usually only solves the local problem. For example, the functions of data acquisition, storage, and service are also realized, modules such as database security management and database operation management are integrated into the system architecture provided in this embodiment, and the data processing, analysis, and extraction are implemented in the database system, so that a more efficient and high-quality data support service can be provided for the service system better on the premise of effectively ensuring data security.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are merely exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. A real-time database system architecture for an intelligent factory is characterized by comprising a high-reliability real-time data acquisition and storage middleware, a database cluster, a data service layer, a database system operation management module and a database system safety management module;
the high-reliability real-time data acquisition and storage middleware is used as the bottom layer of the real-time database system architecture and is used for acquiring production and manufacturing related data of an intelligent factory in real time from different data sources;
the data service layer is used as the top layer of the real-time database system architecture and used for outputting data processed, extracted, stored and analyzed by the high-reliability real-time data acquisition and storage middleware, the database cluster, the data service layer, the database system operation management module and the database system safety management module to the data application layer for use by various intelligent factory information systems.
2. The real-time database system architecture for the intelligent factory as claimed in claim 1, wherein the high-reliability real-time data collection and storage middleware supports a multi-level distributed deployment structure, uniform, synchronous and accurate updating of data is guaranteed, and multi-thread reading and writing are adopted for reading and writing of a single collection and storage middleware node.
3. The real-time database system architecture for the intelligent factory as claimed in claim 2, wherein the high-reliability real-time data acquisition and storage middleware comprises a point configuration unit, a data driving and interface unit, a data caching unit and a data warehousing unit;
the data driving and interface unit integrates various industrial data drivers and various interface protocols for establishing communication with different data sources so as to acquire data of the different data sources in real time;
the point configuration unit associates the acquisition object with the data point and realizes the hierarchical management of the data through a built-in logic grouping structure;
the data cache unit adopts a service program to read unprocessed write-in failure logs at regular time, when write-in failure data are found, the data are written into a master database and a slave database of a data center, and if the write-in failure data are successfully written, the logs are deleted or a log device is changed into processed; if the writing fails, the operation is not carried out, and the next processing is waited;
and the data storage unit stores all the acquired real-time data into a real-time database of the database cluster.
4. The real-time database system architecture for intelligent plants according to claim 3, wherein said real-time data storage process specifically comprises:
caching data into a data queue;
comparing the newly inserted data with the previous data, and updating the newly inserted data into the queue of the previous data when the data are inconsistent;
and writing the updated data into a real-time database cache queue, embedding a high-efficiency compression algorithm in the real-time database, and efficiently compressing and storing the data in the cache queue.
5. A real-time database system architecture for intelligent plants according to any of claims 1-4, characterized in that said database cluster comprises a real-time database, a history database, an alarm database and an analysis database;
the real-time data collected by the high-reliability real-time data collection and storage middleware are directly stored in the real-time database, the real-time data are stored in the historical database after being durably stored, and then the real-time data are respectively stored in the analysis data and the alarm database according to data properties or data processing.
6. A real-time database system architecture for intelligent plants according to any of claims 1-4, wherein said database system operation management module comprises a data management unit, a collection network health monitoring unit, a data alarm unit, a historical data retrieval and reporting unit, a data standardization unit;
the data management unit is used for managing multi-dimensional operation data and state data of the intelligent factory, providing functions of data query, data processing and data export, supporting comparison and display of data of the same measuring point in different time periods or different measuring points and supporting setting of a data early warning range;
the acquisition network health monitoring unit realizes the monitoring of the key state of the IT infrastructure in the real-time data transmission process, has a data reporting function and realizes data interaction with other service systems;
the data alarm unit displays the whole alarm structure and a specific monitoring point by creating a layered information tree, and realizes data alarm when finding an event exceeding a normal parameter range;
the historical data retrieval and reporting unit generates a data analysis report by performing statistical analysis on the retrieved historical data;
the data standardization unit comprises a reading interface compatible with various types of original collected data of the intelligent factory, unified calculation parameter configuration, a standardized calculation formula, a user-defined calculation formula and visual output file format configuration.
7. The real-time database system architecture for intelligent plants according to any of claims 1-4, wherein said data services layer enables providing data services to internal and external system users.
8. The real-time database system architecture for the intelligent factory as claimed in any one of claims 1-4, wherein the database system security management module implements comprehensive security management of the high-reliability real-time data acquisition middleware, the database cluster, the data service layer, and the database operation management module, and comprises a database access authority authentication unit, a data security policy unit, a sensitive data security protection unit, and a data backup and recovery unit;
the database access authority authentication unit provides a traditional autonomous access control mechanism and a mandatory access control mechanism;
the data security policy unit realizes the functions of modification authority control and data encryption on data in the database;
the sensitive data safety protection unit is used for realizing safety management on sensitive data;
the data backup and recovery unit is used for performing full backup and recovery of the database.
9. The real-time database system architecture for intelligent plants according to any one of claims 1-4, wherein the data sources include warehouse control system data, logistics scheduling system data, production line control system data, instrumentation data, safety precaution system data, environmental monitoring system data.
10. The real-time database system architecture for intelligent plants according to any one of claims 1-4, wherein the data application layer can adopt an intelligent plant application system of B/S architecture or C/S architecture.
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