Background data monitoring system

Abstract

The invention relates to the technical field of data monitoring, and particularly discloses a background data monitoring system, which is characterized in that a data acquisition module is used for acquiring equipment parameters, product information and process parameters in real time, and transmitting the equipment parameters, the product information and the process parameters to a background server in real time by adopting 5G; the data storage and analysis center is fused with a machine learning algorithm, analyzes the data from the aspects of time, equipment and products, mines the law and trend of the data, and performs multi-dimensional correlation analysis; the intelligent early warning system is used for judging whether the input new data is abnormal or not, identifying abnormal points in the data and immediately triggering an alarm after the abnormality is detected; and the visual management module displays the early warning information in a pop-up window form and is used for notifying an engineer to carry out maintenance processing and popping up an exception processing result after maintenance is completed. According to the method, mass data can be automatically analyzed, and key information and potential problems in the data can be identified; according to the monitoring result, optimization suggestions and solutions can be recommended to related personnel, and the efficiency and accuracy of problem processing are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data monitoring, and in particular to a background data monitoring system. Background Art

[0002] In fields like industrial production, effective monitoring of data such as equipment operating status and product production processes is crucial. Existing data monitoring systems suffer from numerous issues, including inadequate real-time and comprehensive data collection, making it difficult to quickly and accurately obtain equipment parameters, product information, and process parameters. Data storage and analysis methods are limited, preventing in-depth exploration of patterns and trends underlying the data and hindering multi-dimensional correlation analysis. Early warning systems are not intelligent enough to promptly and accurately identify data anomalies and lack flexible early warning mechanisms. Visual management modules have limited functionality and cannot intuitively and clearly display data and early warning information, making it difficult for engineers to respond to and address abnormal situations in a timely manner. These issues severely impact production efficiency and product quality, increasing the difficulty and cost of production management.

[0003] Therefore, a background data monitoring system has become an issue that needs to be solved urgently. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a background data monitoring system that can realize real-time collection, efficient storage and in-depth analysis of equipment parameters, product information and process parameters, timely detect anomalies and trigger alarms through an intelligent early warning system, and use a visual management module to intuitively display information, so as to improve the efficiency and accuracy of production management and ensure the stable operation of the production process.

[0005] In order to solve the above technical problems, the present invention provides a technical solution as follows: a background data monitoring system, including a data acquisition module, a data storage and analysis center, an intelligent early warning system and a visual management module;

[0006] The data acquisition module is used to collect equipment parameters, product information and process parameters in real time, and transmit them back to the backend server in real time using 5G;

[0007] The data storage and analysis center integrates machine learning algorithms to analyze data from the perspectives of time, equipment, and products, explore data patterns and trends, and conduct multi-dimensional correlation analysis;

[0008] The intelligent early warning system is used to determine whether the new data input is abnormal, identify abnormal points in the data, and immediately trigger an alarm when an abnormality is detected;

[0009] The visual management module displays warning information in the form of a pop-up window to notify engineering personnel to perform maintenance and will pop up the exception handling results after the maintenance is completed.

[0010] Furthermore, the data acquisition module collects equipment parameters, product information and process parameters in real time through network devices arranged on the production line.

[0011] Furthermore, the data acquisition module performs denoising, filtering and normalization processing on the collected raw data.

[0012] Furthermore, the data storage and analysis center adopts a distributed storage architecture to classify and store the collected data according to time series, device type and product category.

[0013] Furthermore, the intelligent early warning system is provided with multi-level early warning thresholds, which are divided into general early warning, serious early warning and emergency early warning according to the degree of data anomaly, and correspond to different alarm modes and processing procedures.

[0014] Furthermore, the intelligent early warning system interacts with the data storage and analysis center in real time, and dynamically adjusts the early warning threshold and abnormality judgment rules according to the data analysis results.

[0015] Furthermore, the visual management module displays the equipment operation status, product quality indicators and change trends of production process parameters in the form of charts and curves.

[0016] The advantages of this invention over existing technologies include: it can automatically analyze massive amounts of data, identify key information and potential issues within the data, and provide data support for system optimization and expansion. Based on the monitoring results, it can recommend optimization suggestions and solutions to relevant personnel, improving the efficiency and accuracy of problem resolution. It also provides an intuitive data visualization interface, helping even non-technical personnel quickly understand data status. This optimizes resource allocation and reduces costs, creating greater commercial value for enterprises. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a system block diagram of a background data monitoring system of the present invention. DETAILED DESCRIPTION

[0018] Various exemplary embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be noted that unless otherwise specifically stated, the relative arrangements of components and steps, numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present invention.

[0019] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.

[0020] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered part of the specification.

[0021] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not limiting. Therefore, other examples of the exemplary embodiments may have different values.

[0022] The following is a further detailed description of a background data monitoring system of the present invention with reference to the accompanying drawings.

[0023] Combined with attachment Figure 1 , the present invention is introduced in detail.

[0024] A background data monitoring system includes a data acquisition module, a data storage and analysis center, an intelligent early warning system and a visual management module.

[0025] The data acquisition module collects equipment parameters, product information, and process parameters in real time, and transmits them back to the backend server using 5G. Specifically, this module collects relevant data in real time through network equipment deployed on the production line. It then performs denoising, filtering, and normalization on the collected raw data to improve data quality and usability, ensuring that the data transmitted to the backend server is accurate and valid.

[0026] The Data Storage and Analysis Center integrates machine learning algorithms to analyze data from the perspectives of time, equipment, and product, uncovering patterns and trends in the data and conducting multi-dimensional correlation analysis. The center utilizes a distributed storage architecture to categorize and store collected data by time series, equipment type, and product category, facilitating data management, query, and analysis. Machine learning algorithms are also used to deeply process large amounts of data, providing strong support for production decision-making.

[0027] The intelligent early warning system determines whether new input data is abnormal, identifies anomalies within the data, and triggers an alarm immediately upon detection. The system has multiple warning thresholds, divided into general warnings, severe warnings, and emergency warnings based on the severity of the data anomaly. Different alarm methods and processing procedures are used to address the needs of different levels of abnormality. Furthermore, the intelligent early warning system interacts in real time with the data storage and analysis center, dynamically adjusting warning thresholds and anomaly judgment rules based on data analysis results. This makes the early warning system more intelligent and flexible, capable of adapting to different production scenarios and data changes.

[0028] The visual management module displays early warning information in pop-up windows, notifying engineers to initiate repairs. Once repairs are complete, it also displays the results of abnormality handling. Furthermore, the module uses charts and graphs to display equipment operating status, product quality indicators, and trends in production process parameters. This allows engineers to intuitively and clearly understand all data and operational status during the production process, enabling them to identify problems and take appropriate measures promptly.

[0029] The specific implementation process of a background data monitoring system of the present invention is as follows:

[0030] First, the network equipment for data acquisition modules is rationally arranged on the production line to ensure comprehensive, real-time collection of equipment parameters, product information, and process parameters. The collected raw data is then de-noised, filtered, and normalized before being rapidly transmitted back to the data storage and analysis center on the backend server via the 5G network.

[0031] The data storage and analysis center classifies and stores data according to time series, equipment type and product category, and uses machine learning algorithms to conduct in-depth analysis of data from multiple dimensions such as time, equipment, and products to explore data patterns and trends. For example, it analyzes the operating efficiency of equipment in different time periods and the impact of different equipment on product quality.

[0032] The intelligent early warning system receives new data in real time and determines whether the data is abnormal based on predefined multi-level warning thresholds. When an anomaly is detected, it immediately triggers different alarm methods based on the severity of the anomaly. For example, general warnings can be alerted by sound, severe warnings can be notified via text message, and emergency warnings can be contacted by phone. The intelligent early warning system also interacts with the data storage and analysis center in real time, dynamically adjusting warning thresholds and anomaly detection rules based on data analysis results to adapt to changes in the production process.

[0033] Upon receiving an early warning, the visual management module promptly notifies engineering personnel via a pop-up window to initiate repairs. After the engineer completes the repair, the module displays the results of the abnormality handling. Furthermore, the module displays real-time charts and graphs showing equipment operating status, product quality indicators, and trends in production process parameters, enabling engineers to monitor production status and effectively manage production.

[0034] The present invention and its embodiments are described above. This description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. In short, if a person skilled in the art is inspired by this and, without departing from the purpose of the present invention, designs structures and embodiments similar to this technical solution without inventiveness, they shall fall within the scope of protection of the present invention.

Claims

1. A background data monitoring system, characterized by: It includes data acquisition module, data storage and analysis center, intelligent early warning system and visual management module; The data acquisition module is used to collect equipment parameters, product information and process parameters in real time, and transmit them back to the backend server in real time using 5G; The data storage and analysis center integrates machine learning algorithms to analyze data from the perspectives of time, equipment, and products, explore data patterns and trends, and conduct multi-dimensional correlation analysis; The intelligent early warning system is used to determine whether the new data input is abnormal, identify abnormal points in the data, and immediately trigger an alarm when an abnormality is detected; The visual management module displays warning information in the form of a pop-up window to notify engineering personnel to perform maintenance and will pop up the exception handling results after the maintenance is completed.

2. A background data monitoring system according to claim 1, characterized in that: The data acquisition module collects equipment parameters, product information and process parameters in real time through network devices arranged on the production line.

3. A background data monitoring system according to claim 2, characterized in that: The data acquisition module performs denoising, filtering and normalization processing on the collected raw data.

4. A background data monitoring system according to claim 3, characterized in that: The data storage and analysis center adopts a distributed storage architecture to classify and store the collected data according to time series, device type and product category.

5. A background data monitoring system according to claim 4, characterized in that: The intelligent early warning system is equipped with multi-level warning thresholds, which are divided into general warning, serious warning and emergency warning according to the degree of data anomaly, and correspond to different alarm methods and processing procedures.

6. A background data monitoring system according to claim 5, characterized in that: The intelligent early warning system interacts with the data storage and analysis center in real time and dynamically adjusts the early warning threshold and abnormality judgment rules according to the data analysis results.

7. A background data monitoring system according to claim 6, characterized in that: The visual management module displays the equipment operation status, product quality indicators and change trends of production process parameters in the form of charts and curves.

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