Data monitoring system based on artificial intelligence

Abstract

The invention provides a data monitoring system based on artificial intelligence, and the system comprises a data collection module which collects a plurality of data sources in real time; the data preprocessing module receives the data from the data acquisition module and preprocesses the acquired data; the data analysis module carries out training on historical data through an artificial intelligence platform to form a model, and abnormal behaviors are detected through model comparison; and the alarm module receives the data output from the data analysis module and triggers an alarm notification. According to the invention, related data streams of a production line test production line can be monitored in real time, abnormal data can be automatically identified, potential risks can be predicted, and the accuracy, real-time performance and intelligent level of monitoring can be improved.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence, and in particular to the field of AI-based data monitoring technology, and specifically to an artificial intelligence-based data monitoring system. Background Art

[0002] With the rapid development of big data technology, data monitoring systems are playing an increasingly important role in various industries. Traditional data monitoring systems rely mainly on rule engines and static thresholds to detect anomalies, which has the following problems:

[0003] 1. Lack of flexibility: The rule engine requires manual pre-set rules and is difficult to cope with complex and changing exception scenarios.

[0004] 2. High false alarm rate: Static thresholds cannot adapt to dynamic changes in data, resulting in false alarms and missed alarms.

[0005] 3. Poor real-time performance: Traditional systems have slow response times when processing large-scale data streams and cannot meet real-time monitoring requirements.

[0006] Therefore, there is an urgent need for an AI-based data monitoring system that can automatically learn data features, detect anomalies in real time, and predict potential risks. Summary of the Invention

[0007] In view of the above-mentioned shortcomings of the prior art, an object of the present invention is to provide a data monitoring system based on artificial intelligence to solve the difficulties of the prior art.

[0008] To achieve the above and other related purposes, the present invention provides an artificial intelligence-based data monitoring system, comprising:

[0009] A data acquisition module, which collects multiple data sources in real time;

[0010] A data preprocessing module, which receives data from the data acquisition module and preprocesses the acquired data;

[0011] A data analysis module, which uses an artificial intelligence platform to train historical data to form a model and detect abnormal behavior through model comparison;

[0012] An alarm module receives the data output from the data analysis module and triggers an alarm notification.

[0013] According to the preferred solution, the ways to trigger the alarm notification include sound and light alarm, email, text message, and instrument panel.

[0014] According to the preferred solution, a visualization module is also included, which performs real-time data display, anomaly annotation and trend analysis on the data obtained by the data analysis module.

[0015] According to a preferred solution, the multiple data sources include data from sensors, databases, and log files.

[0016] According to the preferred solution, access to multiple data sources can be through API interfaces, databases, and file systems.

[0017] According to the preferred solution, the data analysis module also predicts anomalies or risks by combining time series analysis.

[0018] According to a preferred embodiment, preprocessing includes cleaning, normalizing and feature extraction of data.

[0019] According to the preferred solution, cleaning includes removing noise and invalid data, normalization processing includes unifying data format and range, and feature extraction includes timestamps and numerical change trends.

[0020] According to the preferred solution, the models within the artificial intelligence platform include machine learning models, deep learning models and rule engines.

[0021] According to a preferred embodiment, the alarm module triggers alarms of different levels according to the severity of the abnormality.

[0022] The present invention can monitor the relevant data streams of the production line test lot in real time through the acquisition module, data preprocessing module, data analysis module and alarm module, automatically identify abnormal data, and predict potential risks, thereby improving the accuracy, real-time nature and intelligent level of monitoring;

[0023] 1. High accuracy: AI models automatically learn data features to reduce false positives and false negatives;

[0024] 2. Strong real-time performance: The system can quickly process large-scale data streams to meet real-time monitoring needs;

[0025] 3. Intelligence: Supports anomaly detection and risk prediction, helping users take proactive measures;

[0026] 4. Scalability: The system is modularly designed and can be flexibly applied to different industries and scenarios.

[0027] Hereinafter, the best embodiment for carrying out the present invention will be described in more detail with reference to the accompanying drawings so that the features and advantages of the present invention can be easily understood. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 Shown is a schematic structural diagram of the present invention;

[0029] Figure 2It is a schematic diagram showing the working of the present invention. DETAILED DESCRIPTION

[0030] In order to make the purpose, technical solution and advantages of the technical solution of the present invention clearer, the technical solution of the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings of specific embodiments of the present invention. The same figure marks in the drawings represent the same components. It should be noted that the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the described 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.

[0031] Compared to the embodiments shown in the drawings, feasible embodiments within the scope of protection of the present invention may have fewer components, additional components not shown in the drawings, different components, differently arranged components, or differently connected components, etc. In addition, two or more components in the drawings may be implemented in a single component, or a single component shown in the drawings may be implemented as multiple separate components.

[0032] Unless otherwise defined, the technical or scientific terms used herein shall have the ordinary meaning understood by persons of ordinary skill in the field to which the invention belongs. The words "first", "second" and similar terms used in the patent application specification and claims of the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. Similarly, words such as "a" or "an" do not necessarily indicate a quantity limitation. Words such as "include" or "comprising" mean that the elements or objects preceding the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. Words such as "connected" or "connected" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the object being described changes, the relative positional relationship may also change accordingly.

[0033] The present invention proposes an artificial intelligence-based data monitoring system for use in semiconductors. The present invention does not limit the type of memory stick, but the artificial intelligence-based data monitoring system is particularly suitable for data monitoring scenarios in fields such as the semiconductor industry.

[0034] In general, the artificial intelligence-based data monitoring system proposed in the present invention mainly includes a data acquisition module, a data preprocessing module, a data analysis module, and an alarm module. Figure 1 , which shows the layout relationship of the acquisition module, data preprocessing module, data analysis module, and alarm module.

[0035] In order to achieve the purpose of real-time monitoring, analysis and prediction of abnormal behavior in data streams, and to solve the following problems in the background technology, the traditional data monitoring system mainly relies on rule engines and static thresholds to detect anomalies: the rule engine requires manual preset rules, which is difficult to cope with complex and changeable abnormal scenarios; the static threshold cannot adapt to dynamic changes in data, resulting in false alarms and missed alarms; when the traditional system processes large-scale data streams, the response speed is slow and cannot meet the real-time monitoring needs. Therefore, in the technical solution provided in this embodiment, the acquisition module, data preprocessing module, data analysis module, and alarm module can monitor the relevant data streams of the production line test lot in real time, automatically identify abnormal data, and predict potential risks, thereby improving the accuracy, real-time nature and intelligence level of monitoring.

[0036] Specifically, such as Figure 1 As shown, the data acquisition module is used to collect data in real time from multiple data sources, such as sensors, databases, and log files. It is accessed through multiple data sources, including API interfaces, databases, and file systems, and adopts a distributed architecture to ensure the stability of high-concurrency data acquisition.

[0037] Next, the data preprocessing module receives the data from the data acquisition module, cleans the original data, removes noise and invalid data, normalizes the data, unifies the data format and range, and extracts key features of the data, such as timestamps and numerical change trends, so that the data analysis module can obtain data without impurities and redundant parts, thereby improving the data analysis speed of the data analysis module.

[0038] As mentioned above, the data analysis module uses an artificial intelligence platform to train historical data to form a model, and detects abnormal behavior through model comparison. This mainly includes using artificial intelligence models to train historical data and learn the normal patterns of data streams. The models within the artificial intelligence platform include machine learning models, deep learning models, and rule engines to cope with the analysis of different data. In addition, it can also monitor data streams in real time, calculate the deviation between current data and normal patterns, and detect anomalies. At the same time, combined with time series analysis, it can predict possible anomalies or risks in the future.

[0039] On this basis, preferably, the alarm module receives data output from the data analysis module, such as anomalies or risks, and triggers alarm notifications. Different levels of alarms are triggered according to the severity of the anomaly, and notifications can be sent via email, SMS, or dashboard.

[0040] In addition, a visualization module is also provided, which displays the data obtained by the data analysis module in real time, marks anomalies and performs trend analysis.

[0041] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the present invention. Anyone skilled in the art may modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by one of ordinary skill in the art without departing from the spirit and technical principles disclosed herein are intended to be covered by the claims of the present invention.

Claims

1. A data monitoring system based on artificial intelligence, characterized in that: include: A data acquisition module, which collects multiple data sources in real time; A data preprocessing module, which receives data from the data acquisition module and preprocesses the acquired data; A data analysis module, which uses an artificial intelligence platform to train historical data to form a model and detect abnormal behavior through model comparison; An alarm module receives the data output from the data analysis module and triggers an alarm notification.

2. The artificial intelligence-based data monitoring system according to claim 1, characterized in that: It also includes a visualization module, which performs real-time data display, anomaly annotation and trend analysis on the data obtained by the data analysis module.

3. The artificial intelligence-based data monitoring system according to claim 2, characterized in that: The multiple data sources include data from sensors, databases, and log files.

4. The artificial intelligence-based data monitoring system according to claim 3, characterized in that: The data analysis module also predicts anomalies or risks by combining time series analysis.

5. The artificial intelligence-based data monitoring system according to claim 4, characterized in that: The preprocessing includes cleaning, normalizing and feature extraction of data.

6. The artificial intelligence-based data monitoring system according to claim 5, characterized in that: The models within the artificial intelligence platform include machine learning models, deep learning models and rule engines.

7. The artificial intelligence-based data monitoring system according to claim 6, characterized in that: The alarm module triggers alarms of different levels according to the severity of the abnormality.

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