Automatic data acquisition intelligent analysis system

Abstract

The invention discloses an automatic data acquisition and intelligent analysis system, which relates to the technical field of factory management, and comprises a data acquisition module, a data storage module, a data analysis module, a result display module and a system management module. The data value is deeply mined through the advanced analysis module, the comprehensive processing mode helps to monitor the production state in real time, timely discover potential problems and improve the controllability and predictive ability of the production process, and the analysis module and the display module are combined, so that deep data insight is provided, and the production efficiency is improved. The analysis result is visually displayed to a user through a visual tool, so that the safety and the stability are improved, the management, the operation and the maintenance of the system are more efficient, all modules of the system are tightly matched, the process from data acquisition to result display is highly automatic, a real-time feedback mechanism helps to quickly identify and solve problems, and the system is more convenient to use. And continuous improvement and optimization are promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of factory management, and in particular to an automatic data acquisition and intelligent analysis system. Background Art

[0002] With the continuous development of industry, the scale and complexity of factories are increasing. With the expansion of factory scale and the complexity of production process, the amount of data in traditional production methods has exploded. Some detection steps in existing factories have used sensor technology, but the entire factory management process still relies on traditional manual operation. The speed and accuracy of manual data processing are difficult to keep up, and errors and omissions are prone to occur. At the same time, traditional data processing methods cannot conduct in-depth analysis of massive data, and it is difficult to dig out the key information hidden in the data, so it cannot provide effective support for production decisions and it is difficult to meet the needs of large-scale production;

[0003] Thanks to the rapid development of information technology, sensor technology, artificial intelligence and other fields, the emergence of automated data acquisition and intelligent analysis systems has provided a technical foundation. Advanced sensors can accurately collect various physical quantities and state parameters, high-speed data transmission and storage technology can ensure timely processing and preservation of data, and artificial intelligence algorithms can perform in-depth analysis and prediction of massive data. The maturity of these technologies has made it possible for factories to realize automated data acquisition and intelligent analysis. To this end, we propose an automated data acquisition and intelligent analysis system to solve the above problems. Summary of the invention

[0004] The purpose of the present invention is to provide an automated data acquisition and intelligent analysis system to solve the problems raised in the above background technology.

[0005] In order to achieve the above-mentioned invention object, the present invention adopts the following technical scheme:

[0006] The present invention provides an automated data collection and intelligent analysis system, comprising the following modules:

[0007] S101, data acquisition module, connects various sensors, equipment controllers and factory databases, collects production parameters, equipment operation status, production progress and product quality test results in real time through communication protocols and interfaces, and has data verification and preprocessing functions;

[0008] S102, data storage module, establishes a large-capacity database, adopts distributed storage and backup strategies to prevent data loss, stores different types of data in a classified manner, separates historical and real-time data, supports data compression and encryption, and ensures security and privacy;

[0009] S103, data analysis module, uses regression algorithm to analyze stored data, can perform statistical analysis, understand data characteristics, use machine learning to mine potential pattern trends, predict equipment failures, optimize processes and improve quality, and provide real-time analysis to support production decisions;

[0010] S104, result display module, displays analysis results, can generate reports, charts, bars, lines and pie charts, supports user-defined display, and enables users to grasp production dynamics through large screens and mobile devices;

[0011] S105, system management module, is responsible for system configuration, maintenance and management, including user management, permission setting, parameter adjustment, data backup and recovery, monitoring operation status, troubleshooting, optimizing performance, improving response speed and processing capabilities.

[0012] Preferably, the data acquisition module includes a connection with a sensor, and is connected to different types of sensors and device controllers through a communication protocol. These sensors include temperature sensors, pressure sensors, humidity sensors, etc., which are connected to the device controller through an interface to obtain the operating status, switch status, and operating speed of the device.

[0013] Preferably, the data acquisition module needs to exchange data with the factory database, collect information on production progress, equipment status and product quality inspection results, obtain and store production parameters, equipment operating status, production progress and product quality inspection results, and verify and pre-process the data during the data acquisition process to provide real-time monitoring of the production process.

[0014] Preferably, the data storage module needs to receive real-time and historical data from the data acquisition module, classify the data and store them in different databases or storage locations according to the data type of real-time data and historical data, and distribute and store the data on multiple nodes.

[0015] Preferably, the data storage module backs up data regularly and stores the backups in different geographical locations, uses encryption technology to protect data security, provides a data recovery mechanism, encryption technology and backup strategies to ensure data privacy and security, and a distributed storage structure supports system expansion.

[0016] Preferably, the data analysis module includes extracting real-time and historical data that need to be analyzed from the data storage module, cleaning the extracted data, processing missing values, outliers and noise, and applying statistical methods to analyze the basic characteristics of the data, such as mean, standard deviation and distribution, to understand the overall situation of the data.

[0017] Preferably, the data analysis module includes machine learning modeling, which uses machine learning algorithm regression analysis and classification models to mine potential patterns and trends in factory data, perform regression analysis, predict equipment failures and performance issues based on machine learning models, and provide early warnings and maintenance recommendations.

[0018] Preferably, the result display module includes receiving analysis results and reports from the data analysis module, formatting and organizing the analysis results, preparing display data, and generating visual charts such as bar charts, line charts, pie charts, etc. according to user needs, including data overviews, charts and explanatory text.

[0019] Preferably, the result display module allows users to customize the display method according to their needs, select the chart type, data range and layout, and push the generated charts and reports to users through large screens and mobile devices to help users understand the analysis results.

[0020] Preferably, the system management module includes adding, modifying and deleting user accounts, assigning roles and permissions, configuring access rights and operating permissions for different users, adjusting system configuration parameters to optimize performance and functions, regularly backing up system data, restoring data in the event of data loss, real-time monitoring of the system's operating status, detecting anomalies and performance bottlenecks, and performing subsequent diagnosis and resolution of system failures.

[0021] Compared with the prior art, one or more of the above technical solutions have the following beneficial effects:

[0022] The present invention conducts comprehensive data collection in the factory management process. The system can collect various data in the production process, including temperature, pressure, humidity and equipment status, through a variety of sensors and equipment controllers. This multi-dimensional data collection method ensures comprehensive monitoring of the production process, helps to track production parameters, equipment operating status and product quality in real time, and improves the accuracy and integrity of the data.

[0023] The data storage module stores real-time and historical data in a classified manner and adopts a distributed storage and backup strategy to ensure the scalability and high availability of the system. Data encryption and backup mechanisms effectively ensure data security and reduce the risk of data loss and leakage. These measures not only improve the efficiency of data management, but also enhance the reliability of the system.

[0024] The data analysis module cleans and processes data, applies statistical methods and machine learning algorithms, and deeply explores potential patterns and trends. The application of regression analysis models can predict equipment failures and production problems, and provide early warnings and maintenance suggestions. This data-based analysis helps identify production bottlenecks, optimize production processes, improve product quality, and reduce defects and return rates, ultimately enhancing the accuracy and effectiveness of production decisions.

[0025] The result display module presents the analysis results in the form of visual charts and reports, and supports users to customize the display method to ensure the clear communication of information. The large screen and mobile device push function enable users to obtain the latest production dynamics in real time, helping users to quickly understand the data and make timely decisions and adjustments.

[0026] The system management module provides comprehensive user management, authority control, system configuration and performance monitoring functions, ensuring the security and stability of the system. Regular backup and fault recovery functions ensure data continuity and normal operation of the system, optimize system performance, and improve response speed and processing capabilities.

[0027] Through automated data collection and intelligent analysis, the production management level has been significantly improved, production efficiency has been optimized, product quality control has been enhanced, and it is suitable for the development of modern factories. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0029] Figure 1 It is the framework diagram of the automatic data acquisition and intelligent analysis system of the present invention;

[0030] Figure 2 It is the data acquisition module architecture diagram of the present invention;

[0031] Figure 3 It is a data storage module architecture diagram of the present invention;

[0032] Figure 4 It is the data analysis module architecture diagram of the present invention;

[0033] Figure 5 It is a result display module framework diagram of the present invention;

[0034] Figure 6 It is the system management module architecture diagram of the present invention. DETAILED DESCRIPTION

[0035] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0036] See also Figure 1-Figure 6The present invention provides an automated data acquisition and intelligent analysis system, including a data acquisition module, a data storage module, a data analysis module, a result display module and a system management module. The production process is monitored in real time through a variety of sensors to ensure the accuracy and integrity of the data. Distributed storage, backup and encryption strategies are adopted to ensure data security and system reliability. Statistics and machine learning algorithms are applied to predict faults, optimize processes, and improve product quality and production efficiency. Through visual charts and mobile push functions, data is displayed in real time to facilitate decision-making and adjustment. Comprehensive user management and system monitoring are provided to ensure the safe, stable and efficient operation of the system. The system improves the production management level, optimizes the production process and quality control, and ensures data security and system stability.

[0037] The data acquisition module is connected through sensors and various communication protocols to different types of sensors and equipment controllers. These sensors can include temperature sensors, pressure sensors, humidity sensors, etc. The equipment controller is connected to the equipment controller through an interface to obtain the operating status of the equipment, such as switch status and operating speed, and exchange data with the factory database to collect information such as production progress, equipment status and product quality test results. The system obtains and stores production parameters, equipment operating status, production progress and product quality test results through real-time data acquisition functions. During the data acquisition process, the data is verified to ensure its accuracy and preprocessed to improve data quality. The data is transmitted from the sensor, equipment controller and factory database to the central server or cloud platform for storage and further processing, providing real-time monitoring of the production process and being able to quickly respond to problems and changes in the production process. The data verification and preprocessing functions ensure the accuracy and completeness of the collected data, reduce the impact of human errors and data noise, and monitor product quality test results in real time, which helps to promptly discover and correct quality problems in the production process, thereby improving product quality, integrating new sensors and equipment, adapting to changing production needs, and accurately collecting and analyzing production data, significantly improving production management level and product quality.

[0038] In the data storage module, it is necessary to receive real-time and historical data from the data acquisition module, classify the data into different databases or storage locations according to the data type, and distribute the data on multiple nodes to ensure the scalability and high availability of the system. Back up the data regularly and store the backup in different geographical locations to prevent data loss. Use encryption technology to protect the security of data and ensure that the data remains confidential during storage and transmission. Provide a data recovery mechanism to quickly restore data in the event of data loss or damage. Encryption technology and backup strategies effectively protect data privacy and security and prevent data leakage or loss. The distributed storage structure supports system expansion and can flexibly adapt to data growth. Classified storage and compression technology optimize data management, improve storage efficiency and retrieval speed, and the backup and recovery mechanism ensures data reliability and reduces the risk of data loss due to system failures. Data compression reduces storage requirements, improves access performance, and improves the overall efficiency of the system.

[0039] In addition, in the data analysis module, it is necessary to extract the real-time and historical data that need to be analyzed from the data storage module, clean the extracted data, deal with missing values, outliers and noise to ensure the accuracy of the analyzed data, and apply statistical methods to analyze the basic characteristics of the data, such as mean, standard deviation, distribution, etc., to understand the overall situation of the data.

[0040] Thus, machine learning modeling is established, and machine learning algorithm regression analysis and classification models are used to mine potential patterns and trends in factory data. When performing regression analysis:

[0041] Let γ be the time from the equipment failure to the occurrence of failure (or the frequency index of failure occurrence), x 1 is the device operating temperature, x 2 is the equipment operating pressure, etc., and the regression formula is:

[0042] γ=β 0 +β 1 x 1 +β 2 x 2 +∈

[0043] β 0 is the intercept, which represents the basic expected time or frequency of equipment failure without considering factors such as temperature and pressure; β 1 and β 2 are the regression coefficients corresponding to temperature and pressure, respectively, reflecting the degree of influence of temperature and pressure changes on equipment failure time or frequency. ∈ is the error term, indicating the influence of other unconsidered factors on equipment failure. Based on the machine learning model, equipment failure or performance problems are predicted, and early warning and maintenance suggestions are provided.

[0044] By considering multiple factors such as equipment operating temperature and pressure, the regression analysis algorithm can more comprehensively capture the relationship between the equipment's operating status and the occurrence of failures. Compared with traditional single indicator judgments, multivariate-based regression models can more accurately predict the time until equipment failures occur or the frequency of failures.

[0045] When the equipment temperature and pressure increase, the regression model can be used to calculate the coefficient β 1 and β 2 Accurately adjust predictions of failure times so that factories can prepare for them in advance.

[0046] Early warning

[0047] With accurate fault prediction, factories can receive early warning before serious equipment failure occurs. This allows maintenance personnel to have enough time to arrange maintenance plans and avoid production interruptions and economic losses caused by sudden equipment downtime.

[0048] When the regression model predicts that equipment may fail in the next few hours, the factory can adjust the production plan in advance, transfer the affected production tasks to other equipment, or arrange emergency repairs.

[0049] Through the analysis results, production bottlenecks and optimization points are identified, and improvement measures are recommended to improve the efficiency of the production process. Product quality data is analyzed to find factors affecting quality and propose improvement plans to improve product quality. Real-time data analysis results are provided to support production decisions and immediate adjustment of production strategies. Advanced algorithms provide a deep understanding of the data, revealing potential patterns and trends. Machine learning models can predict equipment failures and production problems and prevent them in advance. Data-driven analysis optimizes the production process, improves production efficiency and resource utilization, improves product quality through analysis, reduces defects and return rates, and real-time analysis results provide timely and accurate data support for production decisions, enhancing decision-making effectiveness.

[0050] In the result display module, it is necessary to receive analysis results and reports from the data analysis module, format and organize the analysis results, prepare to display data, generate visual charts such as bar charts, line charts, pie charts, etc. according to user needs, and make detailed analysis reports, including data overviews, charts, and explanatory texts, allowing users to customize the display method according to their needs, such as selecting chart types, data ranges, and layouts, and push the generated charts and reports to users through large screens and mobile devices to ensure real-time updates and access. Clear charts and reports help users better understand the analysis results. The large screen and mobile push functions ensure that users can instantly obtain the latest production dynamics. Users can customize the display content and methods as needed to improve the relevance and practicality of information. Through visualized results, users can make production decisions and adjustments more effectively, obtain and view production data in a timely manner, and help respond to and solve problems quickly.

[0051] Finally, there is the system management module. In user management, you can add, modify and delete user accounts, assign roles and permissions, configure access rights and operation permissions for different users, ensure data security, adjust system configuration parameters to optimize performance and functions, adapt to business needs, and regularly back up system data to ensure data security; restore data when it is lost, monitor the system's operating status in real time, detect anomalies and performance bottlenecks, diagnose and resolve system failures, fix errors, ensure stable system operation, analyze system performance, make necessary adjustments and optimizations to improve response speed and processing capabilities, ensure the security of the system and data through permission settings and user management, ensure stable system operation and reduce downtime through real-time monitoring and troubleshooting functions, improve the system's response speed and processing capabilities through parameter adjustment and performance optimization, data backup and recovery functions prevent data loss and ensure business continuity, and system configuration and management functions are flexible and adjustable to adapt to different business needs and changes.

[0052] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. An automated data collection and intelligent analysis system, characterized in that: Specifically, it includes the following modules: S101, data acquisition module, connects various sensors, equipment controllers and factory databases, collects production parameters, equipment operation status, production progress and product quality test results in real time through communication protocols and interfaces, and has data verification and preprocessing functions; S102, data storage module, establishes a large-capacity database, adopts distributed storage and backup strategies to prevent data loss, stores different types of data in a classified manner, separates historical and real-time data, supports data compression and encryption, and ensures security and privacy; S103, data analysis module, uses regression algorithm to analyze stored data, can perform statistical analysis, understand data characteristics, use machine learning to mine potential pattern trends, predict equipment failures, optimize processes and improve quality, and provide real-time analysis to support production decisions; S104, result display module, displays analysis results, can generate reports, charts, bars, lines and pie charts, supports user-defined display, and enables users to grasp production dynamics through large screens and mobile devices; S105, system management module, is responsible for system configuration, maintenance and management, including user management, permission setting, parameter adjustment, data backup and recovery, monitoring operation status, troubleshooting, optimizing performance, improving response speed and processing capabilities.

2. The automated data collection and intelligent analysis system according to claim 1, characterized in that: The data acquisition module includes a connection with a sensor, and is connected to different types of sensors and device controllers through a communication protocol. These sensors include temperature sensors, pressure sensors, humidity sensors, etc., which are connected to the device controller through an interface to obtain the operating status, switch status, and operating speed of the device.

3. The automated data collection and intelligent analysis system according to claim 1, characterized in that: The data acquisition module needs to exchange data with the factory database, collect information on production progress, equipment status and product quality inspection results, obtain and store production parameters, equipment operating status, production progress and product quality inspection results, and verify and pre-process the data during the data acquisition process to provide real-time monitoring of the production process.

4. The automated data collection and intelligent analysis system according to claim 1, characterized in that: The data storage module needs to receive real-time and historical data from the data acquisition module, classify the data and store them in different databases or storage locations according to the data types of real-time data and historical data, and distribute and store the data on multiple nodes.

5. The automated data collection and intelligent analysis system according to claim 1, characterized in that: The data storage module backs up data regularly and stores the backups in different geographical locations, uses encryption technology to protect data security, provides a data recovery mechanism, encryption technology and backup strategies to ensure data privacy and security, and a distributed storage structure supports system expansion.

6. The automated data collection and intelligent analysis system according to claim 1, characterized in that: The data analysis module includes extracting real-time and historical data that need to be analyzed from the data storage module, cleaning the extracted data, processing missing values, outliers and noise, and applying statistical methods to analyze the basic characteristics of the data, such as mean, standard deviation and distribution, to understand the overall situation of the data.

7. The automated data collection and intelligent analysis system according to claim 1, characterized in that: The data analysis module includes machine learning modeling, which uses machine learning algorithm regression analysis and classification models to mine potential patterns and trends in factory data, perform regression analysis, predict equipment failures and performance issues based on machine learning models, and provide early warnings and maintenance recommendations.

8. The automated data collection and intelligent analysis system according to claim 1, characterized in that: The result display module includes receiving analysis results and reports from the data analysis module, formatting and organizing the analysis results, preparing display data, and generating visual charts such as bar charts, line charts, pie charts, etc. according to user needs, including data overviews, charts and explanatory texts.

9. The automated data collection and intelligent analysis system according to claim 1, characterized in that: The result display module allows users to customize the display method according to their needs, select the chart type, data range and layout, and push the generated charts and reports to users through large screens and mobile devices to help users understand the analysis results.

10. The automated data collection and intelligent analysis system according to claim 1, characterized in that: The system management module includes adding, modifying and deleting user accounts, assigning roles and permissions, configuring access rights and operation permissions for different users, adjusting system configuration parameters to optimize performance and functions, regularly backing up system data, restoring data in the event of data loss, real-time monitoring of the system's operating status, detecting anomalies and performance bottlenecks, and performing subsequent diagnosis and resolution of system failures.

Images