Production equipment data acquisition method based on data intensive intelligent calculation

Abstract

The invention relates to the technical field of industrial big data, and discloses a production equipment data acquisition method based on data intensive intelligent computing, which comprises the following steps: data acquisition request receiving and instruction generation refinement, establishment of an acquisition method system, and when the system receives a specific data acquisition request from production equipment, sending the specific data acquisition request to the production equipment; the method comprises the following steps: firstly analyzing key information, a data acquisition time range, a data type and data precision in a request, and then matching and determining an associated data acquisition instruction completely corresponding to the request in a preset data acquisition instruction library according to the information, so as to ensure the accuracy and pertinence of the instruction. The production equipment data acquisition method based on data intensive intelligent calculation aims to realize acquisition of associated data of multiple types of databases in a single acquisition task, avoids the tedious process of configuring multiple databases during traditional multi-database associated data acquisition, and improves the acquisition operation efficiency while reducing the data acquisition cost.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial big data, and in particular to a production equipment data collection method based on data-intensive intelligent computing. Background Art

[0002] With the in-depth transformation from digitalization to intelligence, enterprises are entering a new era of data-driven decision-making. In this process, a core challenge is how to collect and efficiently integrate data in real time from many heterogeneous databases with different structures and wide distribution. These databases may include relational databases, non-relational databases, big data platforms and even various cloud storage services. They each have different data formats, storage mechanisms and access interfaces. In order to achieve comprehensive data interconnection and intelligent analysis, enterprises urgently need to build a flexible and scalable data integration framework. The framework should be able to automatically adapt to various data sources, capture and clean data in real time, and then integrate it into the data center or data lake. This will not only help improve data processing efficiency, but also provide a solid foundation for subsequent data insights and intelligent decision-making, and accelerate the intelligent process of enterprises.

[0003] Traditional methods usually only support single-type database collection in each collection process. It is necessary to pre-configure multiple libraries and store the collected data in the corresponding tables. Finally, assembly calculations are performed based on the data in multiple tables to obtain the desired related data. This method is not only cumbersome, but also occupies a lot of computing resources. In addition, in many industries such as medicine and manufacturing, the multi-dimensionality and complexity of data make it difficult for traditional data collection methods to meet the requirements of real-time and accuracy. Summary of the invention

[0004] The purpose of the present invention is to realize the collection of associated data from multiple types of databases in a single collection task, avoiding the cumbersome process of configuring multiple databases during traditional multi-database associated data collection, reducing data collection costs while improving collection efficiency, and proposing a production equipment data collection method based on data-intensive intelligent computing.

[0005] The technical solution of the present invention to solve the above technical problems is as follows:

[0006] A production equipment data collection method based on data-intensive intelligent computing comprises the following steps:

[0007] S10, data collection request reception and instruction generation refinement, build a collection method system, when the system receives a specific data collection request from a production device, first parse the key information in the request, the time range, data type, and data accuracy of the data collection, and then match and determine the associated data collection instructions that completely correspond to the request in the preset data collection instruction library based on this information, to ensure the accuracy and pertinence of the instructions;

[0008] S20, data query statement generation and database precise positioning, according to the associated data collection instructions determined in step S10, the system automatically constructs at least two data query statements related to the data to be collected, these statements accurately reflect the specific needs and conditions of data collection, and then the system uses database connection technology to accurately locate and send these query statements to at least two different types of databases to be collected, relational databases and non-relational databases, to meet diverse data collection needs;

[0009] S30, data query execution and response refinement. After receiving the data query statement, the computing engine of each database to be collected will immediately parse and execute it. During the execution process, the system will monitor the query progress and results in real time to ensure the smooth progress of the query operation. Once the query is completed, the system will automatically generate data integration instructions based on the query results to provide guidance for subsequent data integration work;

[0010] S40, data integration and storage refinement. According to the data integration instructions generated in step S30, the system performs detailed cleaning, conversion and integration operations on the query results of each database to be collected. The integrated data will be stored in the target database in strict accordance with the preset format and standards for subsequent data analysis and application use.

[0011] Based on the above technical solution, the present invention can also be improved as follows.

[0012] Furthermore, in step S10, after receiving the data collection request, the system automatically checks the legitimacy and integrity of the request. In the preset data collection instruction library, the system uses an intelligent matching algorithm to quickly locate the associated data collection instructions that completely correspond to the request, while considering the priority and execution efficiency of the instructions to ensure the accuracy and timeliness of data collection.

[0013] Furthermore, in step S20, the system not only constructs a data query statement, but also automatically adjusts the format and syntax of the query statement according to the type and characteristics of the database to be collected. Before sending the query statement, the system will perform a preliminary check to ensure the correctness and executability of the statement. At the same time, the system supports multiple database connection technologies JDBC and ODBC to adapt to the connection requirements of different databases.

[0014] Furthermore, in step S30, the system monitors the query progress and results in real time, and displays them intuitively in the form of a progress bar or a percentage. After the query is completed, the system automatically generates data integration instructions based on the query result set R = {(x1, y1), (x2, y2), ..., (xn, yn)}, where xi is the record ID and yi is the data content. The instructions clearly include specific rules and methods for data cleaning, conversion and integration to ensure the accuracy and efficiency of data integration. The intersection operation (∩) in set theory is also used to screen common data items, that is, Rcommon = R1∩R2∩...∩Rn, for subsequent data analysis and application.

[0015] Furthermore, in step S40, before data integration, the system will first perform a quality assessment on the query results, including the completeness, accuracy, and consistency of the data. During the integration process, the system supports a variety of data cleaning and conversion methods, including regular expression matching and data mapping. The integrated data will be stored in the target database according to the preset format and standards, and a data integration report will be generated at the same time, recording in detail the key information and operations in the integration process.

[0016] Furthermore, the method also includes a data quality monitoring and feedback mechanism. In each link of data collection, query, integration and storage, the system will record key information and operation logs in real time. Once data quality problems or abnormal operations are found, the system will immediately issue an alarm and generate a detailed error report. Users can make corresponding processing or adjustments based on the information in the report to improve the accuracy and reliability of data collection.

[0017] Furthermore, the method also supports multiple data acquisition modes, including real-time acquisition, timed acquisition and manually triggered acquisition. Users can select the appropriate acquisition mode according to actual needs and set corresponding acquisition parameters and conditions. The system will automatically execute the acquisition task according to the user's settings and provide real-time feedback on the acquisition progress and results.

[0018] Furthermore, the method also provides data visualization function, and users can intuitively view the progress and results of each link of data collection, query, integration and storage through the visualization interface provided by the system. At the same time, the system also supports a variety of data visualization charts and tools such as line charts, bar charts, and pie charts.

[0019] Furthermore, the method supports integration and docking with other systems. Users can integrate and dock the method with other systems through an API interface or a data exchange platform to achieve data sharing and exchange.

[0020] Compared with the prior art, the technical solution of this application has the following beneficial technical effects:

[0021] The present invention refines the data collection request reception and instruction generation, and the system can accurately parse the specific data collection requests from the production equipment to ensure the pertinence and accuracy of data collection. At the same time, through the preset data collection instruction library, the system can automatically match and generate related data collection instructions that completely correspond to the request, further ensuring the accuracy and efficiency of data collection. Secondly, it can automatically generate data query statements related to the data to be collected, and accurately locate and send them to at least two different types of databases to be collected, including relational databases and non-relational databases. This feature breaks the limitation of traditional methods that only support the collection of a single type of database, and realizes compatibility and adaptation with multiple heterogeneous databases. At the same time, through database connection technology, the system can access and query various data sources in real time, meet diverse data collection needs, and improve data. The comprehensiveness and real-time nature of the collection. Furthermore, by real-time monitoring of the query progress and results, the smooth progress of the query operation is ensured. Once the query is completed, the system can automatically generate data integration instructions based on the query results, providing guidance for subsequent data integration work. This feature not only improves the reliability of data query, but also simplifies the data integration process and reduces dependence on manual intervention. Finally, the query results of each database to be collected are cleaned, converted and integrated in detail, and the integrated data is stored in the target database in strict accordance with the preset format and standards. This feature not only ensures the accuracy and consistency of the data, but also facilitates subsequent data analysis and application use. At the same time, through unified data storage formats and standards, the system can improve the efficiency of data processing, reduce the complexity of data management, and provide strong support for the intelligent decision-making of enterprises. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 The present invention is a method flow chart of a production equipment data collection method based on data-intensive intelligent computing. DETAILED DESCRIPTION

[0023] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions 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. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0024] Combination Figure 1 As shown, a production equipment data collection method based on data-intensive intelligent computing of the present invention comprises the following steps:

[0025] S10, data collection request reception and instruction generation refinement, build a collection method system, when the system receives a specific data collection request from a production device, first parse the key information in the request, the time range, data type, and data accuracy of the data collection, and then match and determine the associated data collection instructions that completely correspond to the request in the preset data collection instruction library based on this information, to ensure the accuracy and pertinence of the instructions;

[0026] S20, data query statement generation and database precise positioning, according to the associated data collection instructions determined in step S10, the system automatically constructs at least two data query statements related to the data to be collected, these statements accurately reflect the specific needs and conditions of data collection, and then the system uses database connection technology to accurately locate and send these query statements to at least two different types of databases to be collected, relational databases and non-relational databases, to meet diverse data collection needs;

[0027] S30, data query execution and response refinement. After receiving the data query statement, the computing engine of each database to be collected will immediately parse and execute it. During the execution process, the system will monitor the query progress and results in real time to ensure the smooth progress of the query operation. Once the query is completed, the system will automatically generate data integration instructions based on the query results to provide guidance for subsequent data integration work;

[0028] S40, data integration and storage refinement. According to the data integration instructions generated in step S30, the system performs detailed cleaning, conversion and integration operations on the query results of each database to be collected. The integrated data will be stored in the target database in strict accordance with the preset format and standards for subsequent data analysis and application use.

[0029] In a preferred embodiment, the present invention can be further configured as follows: in step S10, after the system receives the data collection request, it also automatically checks the legitimacy and integrity of the request. In the preset data collection instruction library, the system uses an intelligent matching algorithm to quickly locate the associated data collection instruction that completely corresponds to the request, while considering the priority and execution efficiency of the instruction to ensure the accuracy and timeliness of data collection. In step S10, after the system receives the data collection request, it not only parses the key information in the request, but also automatically checks the legitimacy and integrity of the request. The addition of this step effectively avoids the collection failure or data error caused by incomplete request information or incorrect format, thereby improving the accuracy and reliability of data collection. At the same time, by quickly locating the associated data collection instruction that completely corresponds to the request in the preset data collection instruction library through the intelligent matching algorithm, the system can efficiently and accurately perform the collection task, reduce the time cost of manual intervention and configuration, and improve the automation level of data collection. In practical applications, different data collection requests may have different urgency and importance. By setting priorities for instructions, the system can give priority to urgent and important requests to ensure the timely collection of key data. At the same time, the algorithm will evaluate the execution efficiency of each instruction and select the optimal execution path to reduce resource consumption and improve overall acquisition efficiency.

[0030] The algorithm is based on machine learning or deep learning models, and continuously learns and adjusts matching strategies to adapt to changes in different data sources and data formats. At the same time, the algorithm can also dynamically adjust the priority and execution efficiency of instructions based on historical data and user feedback to achieve better collection results. These optimization measures not only improve the intelligence level of data collection, but also provide enterprises with more flexible and efficient data collection solutions.

[0031] In a preferred embodiment, the present invention can be further configured as follows: in step S20, the system not only constructs data query statements, but also automatically adjusts the format and syntax of the query statements according to the type and characteristics of the database to be collected. Before sending the query statement, the system will perform a pre-check to ensure the correctness and executability of the statement. At the same time, the system supports a variety of database connection technologies JDBC and ODBC to adapt to the connection requirements of different databases. In step S20, the system is not only responsible for constructing data query statements, but also automatically adjusts the format and syntax of the query statements according to the type and characteristics of the database to be collected. This feature greatly improves the compatibility and flexibility of data collection, allowing the system to seamlessly connect to a variety of heterogeneous databases, including relational databases and non-relational databases, thereby meeting the diverse data collection needs of enterprises. By automatically adjusting the query statement, the system ensures the accuracy and integrity of the data, avoids data loss or collection failure due to syntax errors or format mismatches, and improves the efficiency and reliability of data collection.

[0032] It also emphasizes the importance of the system's pre-checking steps before conducting data queries and supporting multiple database connection technologies. The pre-checking step ensures the correctness and executableness of the query statement. By verifying the syntax and logic of the statement, the system can promptly detect and correct potential problems before sending the query, thereby avoiding errors and delays in the query process. This feature not only improves the accuracy and stability of data collection, but also reduces the waste of resources and increased costs caused by query failures. At the same time, the system supports multiple database connection technologies, such as JDBC and ODBC, which provides flexible options for connecting to different databases, allowing the system to easily cope with various complex database environments, further enhancing the adaptability and scalability of data collection. These optimization measures not only improve the intelligence level of data collection, but also provide enterprises with more efficient and stable data collection solutions, helping enterprises achieve digital transformation and intelligent upgrades.

[0033] In a preferred embodiment, the present invention can be further configured as follows: in step S30, the system monitors the query progress and results in real time, and displays them visually in the form of a progress bar or a percentage. After the query is completed, the system automatically generates a data integration instruction based on the query result set R = {(x1, y1), (x2, y2), ..., (xn, yn)}, where xi is the record ID and yi is the data content. The instruction clearly contains specific rules and methods for data cleaning, conversion and integration to ensure the accuracy and efficiency of data integration. The intersection operation (∩) in set theory is also used to screen common data items, that is, Rcommon = R1∩R2∩...∩Rn, for subsequent data analysis and application, and monitors the query progress and results in real time, and displays them visually in the form of a progress bar or a percentage. This feature not only improves the user experience, enables users to understand the progress of data collection in real time, but also enhances the transparency and controllability of the system. Users can adjust the collection strategy or handle emergencies in a timely manner according to the monitoring information, thereby ensuring the smooth progress of data collection. At the same time, the system can automatically generate data integration instructions based on the query result set. This automated process greatly simplifies the data integration process, reduces manual intervention, and improves the efficiency and accuracy of data processing.

[0034] The specific content of the data integration instructions is also described in detail, including the specific rules and methods for data cleaning, conversion and integration. These rules and methods ensure the accuracy and consistency of the data during the integration process, and avoid problems caused by data format mismatch or content errors. Through clear data integration rules, the system can automatically clean redundant data, convert data formats, and integrate related data items, thereby generating high-quality data sets, providing a solid foundation for subsequent data analysis and applications. In addition, the system also uses the intersection operation in set theory to screen common data items, that is, calculate the intersection of multiple query result sets to obtain a common set of data items. This step is of great significance for data deduplication, data association analysis, etc., and can further improve the quality and availability of data. By screening common data items, the system can provide more accurate and valuable data support for subsequent data analysis and applications.

[0035] In a preferred embodiment, the present invention can be further configured as follows: In step S40, before data integration, the system will first perform a quality assessment on the query results, including the completeness, accuracy, and consistency of the data. During the integration process, the system supports a variety of data cleaning and conversion methods, such as regular expression matching and data mapping. The integrated data will be stored in the target database according to the preset format and standard, and a data integration report will be generated at the same time, recording the key information and operations in the integration process in detail. The system will perform a quality assessment on the query results before data integration. This step ensures that the data before integration has completeness, accuracy, and consistency, thereby effectively avoiding errors and deviations in subsequent analysis and application caused by data quality issues. This feature not only improves the reliability of data integration, but also reduces the risks and costs caused by data errors.

[0036] The data cleaning and conversion methods supported by the system during the integration process are also described in detail, including regular expression matching and data mapping. Regular expression matching can efficiently identify and replace specific patterns in data, such as phone numbers, email addresses, etc., to ensure the uniformity and accuracy of the data format. Data mapping can convert and map data from different data sources according to preset rules to achieve seamless docking and integration of data. The introduction of these methods not only enhances the system's ability to clean and convert data, but also improves the flexibility and adaptability of data integration. In addition, the integrated data will be stored in the target database according to the preset format and standards. This step ensures the normalization and standardization of the data, which facilitates subsequent data analysis and application. At the same time, the system also generates a data integration report that records the key information and operations in the integration process in detail. This feature not only helps to trace and audit the process of data integration, but also provides a basis for continuous monitoring and improvement of data quality.

[0037] In a preferred embodiment, the present invention can be further configured as follows: the method also includes a data quality monitoring and feedback mechanism. In each link of data collection, query, integration and storage, the system will record key information and operation logs in real time. Once data quality problems or abnormal operations are found, the system will immediately issue an alarm and generate a detailed error report. Users can perform corresponding processing or adjustments based on the information in the report to improve the accuracy and reliability of data collection. The data quality monitoring and feedback mechanism brings significant beneficial effects to the entire process of data collection, query, integration and storage. The introduction of this mechanism ensures the accuracy and reliability of data in each processing link, and effectively avoids errors and deviations in subsequent analysis and application caused by data quality problems. By recording key information and operation logs in real time, the system can comprehensively monitor every link of data processing, promptly discover and report potential data quality problems or abnormal operations, thereby greatly reducing the risk of data errors.

[0038] The data quality monitoring and feedback mechanism is not only real-time and comprehensive, but also provides detailed error reports and user feedback channels. Once a data quality problem is found, the system will immediately issue an alarm and generate a detailed error report containing key information such as error details, occurrence time, and impact range. Users can quickly locate the source of the problem based on the specific information in the report and take corresponding processing or adjustment measures. In addition, users can also interact with the system through feedback channels to provide suggestions or opinions on error handling, further optimizing the effectiveness of the data quality monitoring and feedback mechanism. This two-way communication method not only enhances the flexibility and adaptability of the system, but also improves user participation and satisfaction. Through continuous monitoring, feedback and improvement, the system can continuously improve the accuracy and reliability of data processing, and provide more solid data support for the intelligent decision-making of enterprises.

[0039] In a preferred embodiment, the present invention can be further configured as follows: the method also supports multiple data acquisition modes, including real-time acquisition, timed acquisition and manually triggered acquisition. Users can select a suitable acquisition mode according to actual needs and set corresponding acquisition parameters and conditions. The system will automatically execute the acquisition task according to the user's settings, and provide real-time feedback on the acquisition progress and results. It supports multiple data acquisition modes, which brings significant beneficial effects to the data acquisition process. This feature not only enhances the flexibility and adaptability of the system, but also meets the diverse needs of users in different scenarios. By providing multiple modes such as real-time acquisition, timed acquisition and manually triggered acquisition, users can select a suitable acquisition mode according to actual needs and data characteristics, and set corresponding acquisition parameters and conditions. This flexible selection method not only improves the efficiency and accuracy of data acquisition, but also reduces the waste of resources and cost increase caused by improper acquisition modes.

[0040] The system will automatically execute the collection task according to the user's settings, and provide real-time feedback on the collection progress and results. This feature allows users to understand the progress of data collection in real time, promptly discover and deal with problems that may arise during the collection process, thereby ensuring the smooth progress of data collection. At the same time, the real-time feedback mechanism also helps users adjust the collection strategy or parameters based on the collection results to further optimize the collection effect. In addition, the system also supports users to manage and monitor collection tasks, including operations such as starting, stopping, pausing and resuming tasks, as well as viewing the history and execution status of tasks. These functions not only improve the user experience, but also facilitate the full monitoring and tracing of data collection.

[0041] In a preferred embodiment, the present invention can be further configured as follows: the method also provides a data visualization function, and the user can intuitively view the progress and results of each link of data collection, query, integration and storage through the visualization interface provided by the system. At the same time, the system also supports a variety of data visualization charts and tools such as line charts, bar charts, and pie charts, providing data visualization functions, which brings significant beneficial effects to each link of data processing. This feature not only enhances the transparency and traceability of the system, but also significantly improves the user's experience and decision-making efficiency. Through the visualization interface provided by the system, users can intuitively view the progress and results of each link of data collection, query, integration and storage, so as to grasp the dynamic situation of data processing in real time. This intuitive visualization display method enables users to understand and analyze data more easily, discover and deal with potential problems in a timely manner, and ensure the accuracy and efficiency of data processing.

[0042] The data visualization function is not limited to simple progress and result display, but also supports a variety of data visualization charts and tools, such as line charts, bar charts, pie charts, etc. These charts and tools can display the changing trend, distribution and proportional relationship of data in a more intuitive and vivid way according to different data characteristics and user needs. For example, a line chart can clearly show the changing trend of data over time, a bar chart can intuitively compare the quantitative differences of different categories of data, and a pie chart can vividly show the proportional distribution of data. These diverse visualization charts and tools not only enrich users' data display methods, but also improve users' understanding and analysis capabilities of data. At the same time, the system also supports users to customize visualization charts and tools according to actual needs, further enhancing the flexibility and adaptability of the system.

[0043] In a preferred embodiment, the present invention can be further configured as follows: the method supports integration and docking with other systems. Users can integrate and dock the method with other systems through an API interface or a data exchange platform to achieve data sharing and exchange, support integration and docking with other systems, and bring significant beneficial effects to data management and application. This feature greatly enhances the interoperability and scalability of the system, allowing users to easily integrate the method with other business systems to achieve seamless sharing and exchange of data. This not only breaks down information silos and promotes the flow of data between different systems, but also improves the utilization and value of data, providing more comprehensive and accurate data support for corporate decision-making.

[0044] The system integration and docking method is highly flexible and convenient. Users can integrate and dock this method with other systems through various methods such as API interface or data exchange platform. The API interface provides standardized data access and interaction methods, making data interaction between different systems simple and efficient. The data exchange platform, as an intermediate bridge, realizes the conversion, transmission and synchronization of data between different systems, further reducing the technical threshold and cost of system integration. In addition, the system also supports users to customize the rules and processes of integration and docking according to actual needs to meet the diverse needs of different business scenarios. This highly flexible and convenient system integration and docking method not only reduces the complexity of enterprise informatization construction, but also improves the overall performance of the system and user experience.

[0045] This production equipment data collection method based on data-intensive intelligent computing aims to efficiently and accurately collect the required data from production equipment to meet the subsequent data analysis and application needs. The entire process starts from the receipt of data collection requests to the integration, storage and visualization of data, covering multiple refined steps and features;

[0046] First, the system has built a collection method system. When receiving a specific data collection request from a production device, the system will immediately parse the key information in the request, such as the time range, data type, and data accuracy of the data collection. Based on this information, the system uses an intelligent matching algorithm to quickly locate the associated data collection instructions that completely correspond to the request in the preset data collection instruction library. This step not only ensures the accuracy and pertinence of the instructions, but also further improves the reliability of data collection by checking the legitimacy and integrity of the request.

[0047] Next, the system automatically constructs data query statements related to the data to be collected based on the determined associated data collection instructions. These statements accurately reflect the specific needs and conditions of data collection, and automatically adjust the format and syntax for different types of databases (such as relational databases and non-relational databases). Before sending the query statement, the system will perform a pre-check to ensure the correctness and executability of the statement. Subsequently, through various database connection technologies such as JDBC and ODBC, the system will accurately locate the query statement and send it to the corresponding database to be collected;

[0048] During the data query execution phase, the computing engine of each database to be collected will immediately parse and execute the query statement after receiving it. The system will monitor the query progress and results in real time and display them to the user in the form of a progress bar or percentage. Once the query is completed, the system will automatically generate data integration instructions based on the query result set. These instructions clearly include specific rules and methods for data cleaning, conversion and integration. At the same time, the system also uses the intersection operation in set theory to screen common data items, providing strong support for subsequent data analysis and application.

[0049] In the data integration and storage stage, the system first evaluates the quality of the query results, including the completeness, accuracy and consistency of the data. Then, the system supports a variety of data cleaning and conversion methods (such as regular expression matching, data mapping, etc.) to perform detailed cleaning, conversion and integration operations on the query results. The integrated data will be stored in the target database in strict accordance with the preset format and standards for subsequent data analysis and application use. At the same time, the system will also generate a data integration report to record in detail the key information and operations in the integration process.

[0050] In addition, this method also has a data quality monitoring and feedback mechanism. In each link of data collection, query, integration and storage, the system will record key information and operation logs in real time. Once data quality problems or abnormal operations are found, the system will immediately issue an alarm and generate a detailed error report. Users can make corresponding processing or adjustments based on the information in the report to improve the accuracy and reliability of data collection.

[0051] In order to meet the data collection needs in different scenarios, this method also supports multiple data collection modes (such as real-time collection, timed collection and manual trigger collection). Users can select the appropriate collection mode according to actual needs and set the corresponding collection parameters and conditions. The system will automatically execute the collection task according to the user's settings and provide real-time feedback on the collection progress and results.

[0052] Finally, the method also provides data visualization function. Users can intuitively view the progress and results of each link of data collection, query, integration and storage through the visualization interface provided by the system. At the same time, the system also supports a variety of data visualization charts and tools (such as line charts, bar charts, pie charts, etc.), providing users with a more intuitive and clear way to display data;

[0053] In addition, this method also supports integration and docking with other systems. Users can integrate and dock this method with other systems through API interfaces or data exchange platforms to achieve data sharing and exchange. This feature not only enhances the interoperability and scalability of the system, but also provides strong support for the digital transformation and intelligent upgrading of enterprises.

[0054] In summary, this production equipment data collection method based on data-intensive intelligent computing realizes efficient and accurate data collection, query, integration, storage and visualization functions through a series of refined steps and feature designs, providing comprehensive and reliable data support for enterprises.

[0055] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0056] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A production equipment data collection method based on data-intensive intelligent computing, characterized in that: The following steps are involved: S10, data collection request reception and instruction generation refinement, build a collection method system, when the system receives a specific data collection request from a production device, first parse the key information in the request, the time range, data type, and data accuracy of the data collection, and then match and determine the associated data collection instructions that completely correspond to the request in the preset data collection instruction library based on this information, to ensure the accuracy and pertinence of the instructions; S20, data query statement generation and database precise positioning, according to the associated data collection instructions determined in step S10, the system automatically constructs at least two data query statements related to the data to be collected, these statements accurately reflect the specific needs and conditions of data collection, and then the system uses database connection technology to accurately locate and send these query statements to at least two different types of databases to be collected, relational databases and non-relational databases, to meet diverse data collection needs; S30, data query execution and response refinement. After receiving the data query statement, the computing engine of each database to be collected will immediately parse and execute it. During the execution process, the system will monitor the query progress and results in real time to ensure the smooth progress of the query operation. Once the query is completed, the system will automatically generate data integration instructions based on the query results to provide guidance for subsequent data integration work; S40, data integration and storage refinement. According to the data integration instructions generated in step S30, the system performs detailed cleaning, conversion and integration operations on the query results of each database to be collected. The integrated data will be stored in the target database in strict accordance with the preset format and standards for subsequent data analysis and application use.

2. The method for collecting production equipment data based on data-intensive intelligent computing according to claim 1, characterized in that: In step S10, after receiving the data collection request, the system automatically checks the legitimacy and integrity of the request. In the preset data collection instruction library, the system uses an intelligent matching algorithm to quickly locate the associated data collection instructions that completely correspond to the request, while considering the priority and execution efficiency of the instructions to ensure the accuracy and timeliness of data collection.

3. The method for collecting production equipment data based on data-intensive intelligent computing according to claim 1, characterized in that: In step S20, the system not only constructs a data query statement, but also automatically adjusts the format and syntax of the query statement according to the type and characteristics of the database to be collected. Before sending the query statement, the system will perform a preliminary check to ensure the correctness and executability of the statement. At the same time, the system supports multiple database connection technologies JDBC and ODBC to adapt to the connection requirements of different databases.

4. The method for collecting production equipment data based on data-intensive intelligent computing according to claim 1, characterized in that: In step S30, the system monitors the query progress and results in real time, and displays them intuitively in the form of a progress bar or a percentage. After the query is completed, the system automatically generates data integration instructions based on the query result set R = {(x1, y1), (x2, y2), ..., (xn, yn)}, where xi is the record ID and yi is the data content. The instructions clearly include specific rules and methods for data cleaning, conversion and integration to ensure the accuracy and efficiency of data integration. The intersection operation (∩) in set theory is also used to screen common data items, that is, Rcommon = R1∩R2∩...∩Rn, for subsequent data analysis and application.

5. The method for collecting production equipment data based on data-intensive intelligent computing according to claim 1, characterized in that: In step S40, before data integration, the system will first perform a quality assessment on the query results, including the completeness, accuracy, and consistency of the data. During the integration process, the system supports a variety of data cleaning and conversion methods, including regular expression matching and data mapping. The integrated data will be stored in the target database according to the preset format and standards, and a data integration report will be generated at the same time, recording in detail the key information and operations during the integration process.

6. The method for collecting production equipment data based on data-intensive intelligent computing according to claim 1, characterized in that: The method also includes a data quality monitoring and feedback mechanism. In each link of data collection, query, integration and storage, the system will record key information and operation logs in real time. Once data quality problems or abnormal operations are found, the system will immediately issue an alarm and generate a detailed error report. Users can perform corresponding processing or adjustments based on the information in the report to improve the accuracy and reliability of data collection.

7. The method for collecting production equipment data based on data-intensive intelligent computing according to claim 1, characterized in that: The method also supports multiple data collection modes, including real-time collection, timed collection and manually triggered collection. Users can select a suitable collection mode according to actual needs and set corresponding collection parameters and conditions. The system will automatically execute the collection task according to the user's settings and provide real-time feedback on the collection progress and results.

8. The method for collecting production equipment data based on data-intensive intelligent computing according to claim 1, characterized in that: The method also provides a data visualization function. Users can intuitively view the progress and results of each link of data collection, query, integration and storage through the visualization interface provided by the system. At the same time, the system also supports a variety of data visualization charts and tools such as line charts, bar charts, and pie charts.

9. The method for collecting production equipment data based on data-intensive intelligent computing according to claim 1, characterized in that: The method supports integration and docking with other systems. Users can integrate and dock the method with other systems through an API interface or a data exchange platform to achieve data sharing and exchange.

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