Intelligent input management method for chemical production

Abstract

The invention discloses an intelligent input management method for chemical production, and relates to the technical field of chemical production. The intelligent input management method is based on a system architecture of virtual reality, and comprises the steps that data collection is carried out, collected data comprises structured data and unstructured data, and data is extracted from a third-party system; the collected data are stored, and the data are divided into three-dimensional data, attribute data and Pamp according to data types during storage; iD data and key document data; according to the intelligent input management method for chemical production, the system architecture is based on virtual reality, the time and labor cost are greatly saved through automatic data acquisition and processing, and meanwhile, the system can quickly process and analyze the acquired data, timely feed back production state information and help operators to quickly make decisions, so that the production efficiency is improved. The production efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of chemical production, and in particular to an intelligent input management method for chemical production. Background Art

[0002] With the continuous advancement of science and technology, the concepts of Industry 4.0 and intelligent manufacturing have gradually become popular. The chemical industry has also introduced intelligent technologies to achieve automation and intelligent management of production processes, which has become an important means for the chemical industry to enhance its competitiveness. A large amount of data is generated during the chemical production process, including equipment operation data, process parameter data, quality inspection data, etc. Traditional data management methods are mostly aimed at the simple storage and one-way use of small batches of data. They are suitable for small projects, but when dealing with large projects in the chemical field, it is difficult to efficiently process and analyze data, and it is impossible to fully explore the value behind the data. The degree of cooperation in chemical production is poor. At this time, an intelligent input management method is needed to cope with the explosive growth of data volume; to this end, this solution proposes an intelligent input management method for chemical production. Summary of the Invention

[0003] The purpose of the present invention is to provide an intelligent input management method for chemical production to solve the problems raised in the background technology.

[0004] The present invention is achieved through the following technical solutions: The present invention is an intelligent input management method for chemical production. The intelligent input management method is based on a virtual reality system architecture and includes the following steps: S1, data collection, including structured data and unstructured data, also includes extracting data from third-party systems; S2, storing the collected data, and classifying the data into three-dimensional data, attribute data, P&ID data and key document data according to the data type; S3: Identify and import drawings, digitize chemical production-related drawings, including process flow charts, equipment layout diagrams, and piping and instrumentation flow charts, and process initial data; S4, perform data correction. For the data imported by drawing identification, check the accuracy and rationality of the data by comparing it with actual production conditions, historical data or other relevant information; S5, applying the data, including integrated data display, data distribution and data export; S6: During the application of the data in S5, machine learning and deep learning AI algorithms are used to deeply mine and analyze the data in the application, and process monitoring and optimization are performed to obtain digitally delivered virtual reality system data. S7, the virtual reality system data is integrated into the system. After being centrally extracted from the enterprise comprehensive database, it is connected to the virtual reality system through the data interface.

[0005] Preferably, the system architecture of the virtual reality adopts a microservice architecture, and its overall functions are divided into application layer, capability layer, platform layer and resource layer. The application layer builds application modules or subsystems based on the engineering digital delivery business, establishes business rules and presentation layer according to business needs, and the application layer will complete the execution of its own business rules based on the capability layer. The capability layer unifies technical labels and entrances, unifies security rules and performance constraints, and provides common components or microservice interfaces. The platform layer is based on middleware or other system platforms and is constructed as a platform service layer to unify platform capabilities, abstract platform dependencies, and achieve modularization and moderate control of service granularity through microservices to improve service integration and agile management capabilities.

[0006] Preferably, the data collection plan in S1 covers all types of data in the chemical production process, including raw material information, equipment operating parameters, process flow parameters, product quality data, and determines the collection frequency and accuracy requirements. It also includes sensors, instruments, database records and various drawing materials of production equipment. When extracting data from a third-party system, the third-party system must provide a data interface through the office intranet; when it comes to collecting monitoring data from a third-party system, the system will functionally set a manual supplement function (the relevant third-party system must provide a historical data interface) to supplement the incomplete data caused by abnormal interruption of the interface.

[0007] Preferably, in said S3, the paper drawings are converted into electronic image format by means of a scanner or other image acquisition device, and the text, symbols, graphics and other information in the digitized drawings are identified and extracted using optical character recognition technology and image recognition algorithms, and the identified information is converted into computer-understandable text and graphic data for subsequent processing and analysis, and the identified drawing data is formatted and integrated so that it can be integrated with other production data into a unified database or data platform for convenient unified management and call.

[0008] Preferably, the initial data is processed in S3. First, the collected raw data, including the drawing recognition and import, are cleaned to remove noise, outliers and missing values. Any recognition errors or incomplete information that may exist in the drawing data are manually checked and supplemented and corrected. Then, the data is standardized and normalized to convert data of different types and magnitudes to a unified scale to ensure data consistency and comparability.

[0009] Preferably, the accuracy and rationality of the data are checked in S4, including checking whether the parameters in the process flow diagram are consistent with the actual production records, whether the dimensions in the equipment layout diagram are consistent with the actual equipment dimensions, and using data verification algorithms and rules to automatically verify and correct the data, including checking whether the range and accuracy settings of the instruments in the pipeline instrument flow diagram meet the process requirements, marking and correcting the data that does not meet the requirements, and organizing professionals to conduct manual analysis and judgment for some data errors that cannot be corrected by automatic methods, and make modifications and adjustments based on actual conditions.

[0010] Preferably, in S6, based on the results of intelligent perception and analysis, the chemical production process is monitored in real time. By establishing a monitoring indicator system and thresholds, when the data in the production process exceeds the normal range or an abnormal pattern occurs, an alarm is promptly issued to notify relevant personnel, and optimization algorithms and models are used to optimize the production process according to production goals (such as improving product quality, reducing costs, saving energy and reducing emissions, etc.). For example, by adjusting process parameters, equipment operating status, etc., the production process reaches the optimal operating state, achieving efficient utilization of resources and maximizing production benefits.

[0011] Preferably, system integration is performed in S7, including video surveillance system, oil refining and chemical operation system, safety and environmental protection management system, equipment integrated management system, enterprise production command system, corrosion monitoring system and VOCs traceability and early warning system, enterprise comprehensive database, including operation and maintenance data and real-time data.

[0012] Preferably, the present invention also includes S8, conducting systematic evaluation and continuous improvement of virtual reality system data, establishing a system evaluation index system, and regularly evaluating the intelligent input management system from multiple dimensions such as data quality, intelligent analysis accuracy, monitoring effect, optimization benefit, and user satisfaction. Based on the evaluation results, problems and deficiencies in the system are identified, and corresponding improvement measures are formulated to better meet the actual needs of chemical production.

[0013] The present invention has the following beneficial effects: The intelligent input management method for chemical production of the present invention is based on a virtual reality system architecture and greatly saves time and labor costs by automating data collection and processing. At the same time, the system can quickly process and analyze the collected data, and timely feedback production status information to help operators make decisions quickly and improve production efficiency. In addition, by analyzing a large amount of production data, the intelligent input management system can discover bottlenecks and potential problems in the production process, provide a basis for optimizing the production process, and improve product quality.

[0014] The intelligent input management method for chemical production of the present invention can standardize and normalize data by identifying and correcting drawings during data processing, thereby ensuring the consistency and comparability of data and improving the refinement of the entire chemical production process.

[0015] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 This is a diagram of the virtual reality system architecture for chemical production according to the present invention; Figure 2 This is a flow chart of the intelligent input management method for chemical production according to the present invention. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solutions and advantages of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary persons in this field without making creative work are within the scope of protection of the present invention. The preferred embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although the preferred embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments described here. On the contrary, these embodiments are provided to make the present invention more thorough and complete, and to be able to fully convey the scope of the present invention to those skilled in the art.

[0019] Please refer to Figure 1-Figure 2 As shown, the present invention is an intelligent input management method for chemical production, based on a virtual reality system architecture. The virtual reality system architecture complies with national and industry information technology standards and information system security standards and specifications, and can fully utilize the data resources and big data analysis and processing capabilities of the big data center. The system technology architecture adopts a microservice architecture, using the microservice architecture and front-end and back-end separation technology to make each part independent and build an engineering delivery subsystem to ensure the practicality, reliability and advancement of the system, including the following steps: S1, data collection, including structured data and unstructured data, also includes extracting data from third-party systems; Data collection planning covers all types of data in the chemical production process, including raw material information, equipment operating parameters, process parameters, and product quality data, while also determining the collection frequency and accuracy requirements. It also includes sensors, instruments, database records, and various drawings and materials for production equipment. When extracting data from a third-party system, the third-party system must provide a data interface through the office intranet. Regarding the collection of monitoring data from a third-party system, the system will include a manual data supplement function (requiring the relevant third-party system to provide a historical data interface) to supplement incomplete data caused by abnormal interface interruptions. S2, storing the collected data, and classifying the data into three-dimensional data, attribute data, P&ID data and key document data according to the data type; S3: Identify and import drawings, digitize chemical production-related drawings, including process flow charts, equipment layout diagrams, and piping and instrumentation flow charts, and process initial data; Convert paper drawings into electronic image formats using scanners or other image acquisition devices. Utilize optical character recognition technology and image recognition algorithms to identify and extract text, symbols, graphics, and other information from digitized drawings. Convert the identified information into computer-understandable text and graphic data for subsequent processing and analysis. Convert and integrate the identified drawing data so that it can be integrated with other production data into a unified database or data platform for easy unified management and access. During processing, first clean the collected raw data, including drawing recognition import, to remove noise, outliers, and missing values. Manually check and supplement any possible recognition errors or incomplete information in the drawing data. Then, standardize and normalize the data, converting data of different types and magnitudes to a unified scale to ensure data consistency and comparability. S4, perform data correction. For the data imported by drawing identification, check the accuracy and rationality of the data by comparing it with actual production conditions, historical data or other relevant information; This includes verifying whether the parameters in the process flow diagram are consistent with actual production records, and whether the dimensions in the equipment layout diagram are consistent with the actual equipment dimensions. Data verification algorithms and rules are used to automatically verify and correct data. This includes checking whether the range and accuracy settings of instruments in the piping and instrument flow diagrams meet process requirements, marking and correcting data that does not meet the requirements, and organizing professionals to conduct manual analysis and judgment for data errors that cannot be corrected through automatic methods, and making modifications and adjustments based on actual conditions. S5, applying the data, including integrated data display, data distribution and data export; S6: During the application of the data in S5, machine learning and deep learning AI algorithms are used to deeply mine and analyze the data in the application, and process monitoring and optimization are performed to obtain digitally delivered virtual reality system data. Based on the results of intelligent perception and analysis, the chemical production process is monitored in real time. By establishing a monitoring indicator system and thresholds, when the data in the production process exceeds the normal range or shows abnormal patterns, an alarm is promptly issued to notify relevant personnel. Optimization algorithms and models are used to optimize the production process according to production goals (such as improving product quality, reducing costs, and saving energy and reducing emissions). For example, by adjusting process parameters and equipment operating status, the production process can reach the optimal operating state, achieving efficient resource utilization and maximizing production benefits. S7: Virtual reality system data is integrated. After being centrally extracted from the enterprise comprehensive database, it is connected to the virtual reality system through a data interface. The system integration includes video surveillance systems, refining and chemical operation systems, safety and environmental protection management systems, equipment integrated management systems, enterprise production command systems, corrosion monitoring systems, and VOCs traceability and early warning systems. The enterprise comprehensive database includes operation and maintenance data and real-time data. S8, conduct systematic evaluation and continuous improvement of virtual reality system data, establish a system evaluation indicator system, and regularly evaluate the intelligent input management system from multiple dimensions such as data quality, intelligent analysis accuracy, monitoring effect, optimization benefit, and user satisfaction. Based on the evaluation results, identify problems and deficiencies in the system and formulate corresponding improvement measures to better meet the actual needs of chemical production.

[0020] The system architecture of virtual reality adopts a microservice architecture, in which the microservice architecture provides microservice governance capabilities, and at the same time provides DevOps development and deployment integrated operation and maintenance delivery processes and container management services on the platform. Its overall functions are divided into application layer, capability layer, platform layer and resource layer. The application layer builds application modules or subsystems based on the digital delivery of engineering services, establishes business rules and presentation layers according to business needs, and the application layer will complete the execution of its own business rules based on the capability layer. The capability layer unifies technical labels and entrances, unifies security rules and performance constraints, and provides shared components or microservice interfaces. The platform layer is based on middleware or other system platforms and is built as a platform service layer. It unifies platform capabilities, abstracts platform dependencies, and realizes modularization and moderate control of service granularity through microservices to improve service integration and agile management capabilities.

[0021] In this solution, the virtual reality system uses Spring Boot as the microservice development framework, fully inheriting the advantages of the Spring series framework, simplifying the initial construction and development process of the application, and can be effectively integrated with Spring Cloud and Docker technologies to quickly realize the microservice development of business needs. It uses etcd as the service registration center of the platform to realize service registration and service discovery, helping service providers and users to establish connections. The API gateway provides a unified access entry for services and business applications, shielding the client from problems related to directly calling microservices, maintaining the independence of the client and backend microservices, managing the mapping between client requests and specific backend services, and being responsible for service request routing, combination and protocol conversion; At the same time, the system implements service governance functions, supports service degradation, current limiting, circuit breaking, fault tolerance, fault isolation, etc., and effectively reduces future operation and maintenance pressure by solving real problems such as complex service configuration and multiple dependencies between services. Including: service degradation, current limiting, circuit breaking, fault tolerance, fault isolation and other mechanisms; the microservice framework provides underlying support for the containerization and service-oriented development of pipeline collaborative research business, which can realize the creation and operation and maintenance of microservices, the application and orchestration management of microservice operations, and at the same time, the system provides microservice elastic scaling functions. Through the user's pre-defined automatic scaling strategy, resources can be automatically scaled without manual intervention, initially realizing partial operation and maintenance automation and improving resource utilization; Furthermore, the chemical production virtual reality system aggregates data from multiple sources, including technical documentation and business application systems. By receiving and processing digitally delivered data from plant-wide equipment, tank farms, and supporting facilities, it builds a digital factory synchronized with the physical plant. This digital factory also enables business expansion and application, as well as data integration with related systems. The system comprises eight primary modules, 20 secondary modules, and 65 functional areas: homepage, management platform, digital archives, P&ID management, 3D inspection, 3D operation, assisted maintenance, and system management.

[0022] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0023] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to specific embodiments. Obviously, many modifications and variations are possible based on the contents of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. An intelligent input management method for chemical production, characterized in that: The intelligent input management method is based on a virtual reality system architecture and includes the following steps: S1, data collection, including structured data and unstructured data, also includes extracting data from third-party systems; S2, storing the collected data, and classifying the data into three-dimensional data, attribute data, P&ID data and key document data according to the data type; S3: Identify and import drawings, digitize chemical production-related drawings, including process flow charts, equipment layout diagrams, and piping and instrumentation flow charts, and process initial data; S4, perform data correction. For the data imported by drawing identification, check the accuracy and rationality of the data by comparing it with actual production conditions, historical data or other relevant information; S5, applying the data, including integrated data display, data distribution and data export; S6: During the application of the data in S5, machine learning and deep learning AI algorithms are used to deeply mine and analyze the data in the application, and process monitoring and optimization are performed to obtain digitally delivered virtual reality system data. S7, the virtual reality system data is integrated into the system. After being centrally extracted from the enterprise comprehensive database, it is connected to the virtual reality system through the data interface.

2. The intelligent input management method for chemical production according to claim 1, characterized in that: The system architecture of virtual reality adopts a microservice architecture, and its overall functions are divided into application layer, capability layer, platform layer and resource layer. The application layer builds application modules or subsystems based on the engineering digital delivery business, establishes business rules and presentation layer according to business needs, and the application layer will complete the execution of its own business rules based on the capability layer. The capability layer unifies technical labels and entrances, unifies security rules and performance constraints, and provides shared components or microservice interfaces. The platform layer is based on middleware or other system platforms and is constructed as a platform service layer to unify platform capabilities and abstract platform dependencies.

3. The intelligent input management method for chemical production according to claim 1, characterized in that: The data collection plan in S1 covers all types of data in the chemical production process, including raw material information, equipment operating parameters, process flow parameters, and product quality data, while determining the collection frequency and accuracy requirements. It also includes sensors, instruments, database records, and various drawings and materials of production equipment. When data is extracted by a third-party system, the third-party system must provide a data interface through the office intranet.

4. The intelligent input management method for chemical production according to claim 1, characterized in that: In S3, the paper drawings are converted into electronic image formats by using a scanner or other image acquisition equipment, and the text, symbols, graphics and other information in the digitized drawings are recognized and extracted using optical character recognition technology and image recognition algorithms. The recognized information is then converted into computer-understandable text and graphic data for subsequent processing and analysis.

5. The intelligent input management method for chemical production according to claim 1, characterized in that: In the S3, the initial data is processed. First, the collected raw data, including the drawing recognition and import, are cleaned to remove noise, outliers and missing values. Then, the data is standardized and normalized to convert data of different types and magnitudes to a unified scale to ensure data consistency and comparability.

6. The intelligent input management method for chemical production according to claim 1, characterized in that: In said S4, data verification algorithms and rules are used to automatically verify and correct data, mark and correct data that does not meet the requirements, and organize professionals to conduct manual analysis and judgment on some data errors that cannot be corrected by automatic methods, and make modifications and adjustments based on actual conditions.

7. The intelligent input management method for chemical production according to claim 1, characterized in that: In S6, based on the results of intelligent perception and analysis, the chemical production process is monitored in real time, and the production process is optimized according to the production target using optimization algorithms and models.

8. The intelligent input management method for chemical production according to claim 1, characterized in that: The S7 performs system integration, including video surveillance system, oil refining and chemical operation system, safety and environmental protection management system, equipment integrated management system, enterprise production command system, corrosion monitoring system and VOCs traceability and early warning system, and enterprise comprehensive database, including operation and maintenance data and real-time data.

9. The intelligent input management method for chemical production according to claim 1, characterized in that: It also includes S8, which conducts systematic evaluation and continuous improvement of virtual reality system data, establishes a system evaluation indicator system, and regularly evaluates the intelligent input management system from multiple dimensions such as data quality, intelligent analysis accuracy, monitoring effect, optimization benefits, and user satisfaction. Based on the evaluation results, it identifies problems and deficiencies in the system and formulates improvement measures.

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