CN112951415A - Time series abnormity detection system based on deep learning - Google Patents
Time series abnormity detection system based on deep learning Download PDFInfo
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- CN112951415A CN112951415A CN202110356544.1A CN202110356544A CN112951415A CN 112951415 A CN112951415 A CN 112951415A CN 202110356544 A CN202110356544 A CN 202110356544A CN 112951415 A CN112951415 A CN 112951415A
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- 238000001514 detection method Methods 0.000 title claims abstract description 35
- 238000013135 deep learning Methods 0.000 title claims abstract description 17
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Abstract
The invention discloses a time series abnormity detection system based on deep learning, which comprises a user, a detection module, a display module, an alarm module and a power supply module, wherein the user is connected with the detection module, the display module and the alarm module are both connected with the detection module, and the power supply module respectively supplies power to the alarm module, the detection module and the display module.
Description
Technical Field
The invention relates to the technical field of anomaly detection systems, in particular to a time series anomaly detection system based on deep learning.
Background
Deep learning is a new research direction in the field of machine learning, and is introduced into machine learning to make it closer to the original target, artificial intelligence. Deep learning is the intrinsic law and expression level of the learning sample data, and the information obtained in the learning process is very helpful for the interpretation of data such as characters, images and sounds. The final aim of the method is to enable the machine to have the analysis and learning capability like a human, and to recognize data such as characters, images and sounds. Deep learning is a complex machine learning algorithm, and achieves the effect in speech and image recognition far exceeding the prior related art.
The time-series abnormality can often reflect systematic abnormality, for example, in electrocardiographic data, abnormality caused by heart disease can also be reflected in electrocardiographic data, but the existing abnormality detection for time-series data cannot help people to find abnormality as early as possible and take appropriate measures to avoid abnormality, so we propose a time-series abnormality detection system based on deep learning to solve the above-mentioned proposed problems.
Disclosure of Invention
Based on the technical problems in the background art, the invention provides a time series abnormity detection system based on deep learning.
The invention provides a time series abnormity detection system based on deep learning, which comprises a user, a detection module, a display module, an alarm module and a power supply module, wherein the user is connected with the detection module, the display module and the alarm module are both connected with the detection module, and the power supply module is used for supplying power to the alarm module, the detection module and the display module respectively.
Further, the operation of the detection module comprises the following steps:
s1, determining and storing the data abnormal standard;
s2, determining and storing the abnormal threshold value of the data;
s3, inputting real-time sequence data and judging whether the data are abnormal or not;
s4, if the data are not abnormal, returning to S3, and if the data are abnormal, carrying out the next step;
s5, comparing the time sequence data with the previous N time sequence data, judging the data change trend, and exceeding the time required by the threshold value in time;
s6, judging whether the time is less than T, if so, returning to S2, and if not, performing primary alarm and performing the next step;
and S7, judging whether the data exceed the threshold value, if not, returning to S2, and if so, performing secondary alarm.
Further, the data abnormality criterion in step S1 may be modified according to actual conditions.
Further, the threshold of the data abnormality in step S2 may be modified according to actual situations.
Further, N in step S5 may be modified according to actual situations.
Further, T in step S6 can be modified according to the condition of the patient and the time that the medical staff can arrive in time.
According to the invention, the time sequence data is detected in real time by keeping the data abnormity standard and the threshold, and the time required for exceeding the threshold can be calculated according to the trend of the current time sequence, and an alarm is given in advance, so that the safety of the patient is further ensured, and a large amount of manpower and material resources are saved.
Drawings
FIG. 1 is a flow chart of the operation of the present invention;
fig. 2 is a block diagram of the system of the present invention.
Detailed Description
The present invention will be further illustrated with reference to the following specific examples.
As shown in fig. 1-2, a time series anomaly detection system based on deep learning includes a user, a detection module, a display module, an alarm module, and a power module, wherein the user is connected with the detection module, the display module and the alarm module are both connected with the detection module, and the power module respectively supplies power to the alarm module, the detection module, and the display module.
In the invention, the operation of the detection module comprises the following steps:
s1, determining and storing the data abnormal standard;
s2, determining and storing the abnormal threshold value of the data;
s3, inputting real-time sequence data and judging whether the data are abnormal or not;
s4, if the data are not abnormal, returning to S3, and if the data are abnormal, carrying out the next step;
s5, comparing the time sequence data with the previous N time sequence data, judging the data change trend, and exceeding the time required by the threshold value in time;
s6, judging whether the time is less than T, if so, returning to S2, and if not, performing primary alarm and performing the next step;
and S7, judging whether the data exceed the threshold value, if not, returning to S2, and if so, performing secondary alarm.
In the present invention, the data abnormality criterion in step S1 may be modified according to actual situations.
In the present invention, the threshold of the data anomaly in step S2 may be modified according to actual situations.
In the present invention, N in step S5 may be modified according to actual situations.
In the present invention, T in step S6 can be modified according to the condition of the patient and the time that the medical staff can arrive in time.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.
Claims (6)
1. The time series abnormity detection system based on deep learning is characterized by comprising a user, a detection module, a display module, an alarm module and a power module, wherein the user is connected with the detection module, the display module and the alarm module are both connected with the detection module, and the power module is used for supplying power to the alarm module, the detection module and the display module respectively.
2. The deep learning based time series anomaly detection system according to claim 1, wherein the detection module is operated by the following steps:
s1, determining and storing the data abnormal standard;
s2, determining and storing the abnormal threshold value of the data;
s3, inputting real-time sequence data and judging whether the data are abnormal or not;
s4, if the data are not abnormal, returning to S3, and if the data are abnormal, carrying out the next step;
s5, comparing the time sequence data with the previous N time sequence data, judging the data change trend, and exceeding the time required by the threshold value in time;
s6, judging whether the time is less than T, if so, returning to S2, and if not, performing primary alarm and performing the next step;
and S7, judging whether the data exceed the threshold value, if not, returning to S2, and if so, performing secondary alarm.
3. The deep learning-based time series abnormality detection system according to claim 2, wherein the data abnormality criterion in step S1 can be modified according to actual conditions.
4. The deep learning-based time series abnormality detection system according to claim 2, wherein the threshold of the data abnormality in step S2 can be modified according to actual conditions.
5. The deep learning based time series abnormality detection system according to claim 2, wherein N in step S5 can be modified according to actual conditions.
6. The deep learning based time series abnormality detection system according to claim 1, wherein T in step S6 is modified according to the condition of the patient and the time that the medical staff can arrive in time.
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| CN202110356544.1A CN112951415A (en) | 2021-04-01 | 2021-04-01 | Time series abnormity detection system based on deep learning |
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107951475A (en) * | 2017-12-14 | 2018-04-24 | 上海工程技术大学 | A kind of medical treatment real-time monitoring locating alarm device, system and method |
| CN108309263A (en) * | 2018-02-24 | 2018-07-24 | 乐普（北京）医疗器械股份有限公司 | Multi-parameter monitoring data analysing method and multi-parameter monitoring system |
| CN110934566A (en) * | 2019-09-09 | 2020-03-31 | 精华隆智慧感知科技（深圳）股份有限公司 | Intelligent detection, automatic sleep-aiding and emergency alarm device |
| CN112148768A (en) * | 2020-09-14 | 2020-12-29 | 北京基调网络股份有限公司 | Index time series abnormity detection method, system and storage medium |
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- 2021-04-01 CN CN202110356544.1A patent/CN112951415A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107951475A (en) * | 2017-12-14 | 2018-04-24 | 上海工程技术大学 | A kind of medical treatment real-time monitoring locating alarm device, system and method |
| CN108309263A (en) * | 2018-02-24 | 2018-07-24 | 乐普（北京）医疗器械股份有限公司 | Multi-parameter monitoring data analysing method and multi-parameter monitoring system |
| CN110934566A (en) * | 2019-09-09 | 2020-03-31 | 精华隆智慧感知科技（深圳）股份有限公司 | Intelligent detection, automatic sleep-aiding and emergency alarm device |
| CN112148768A (en) * | 2020-09-14 | 2020-12-29 | 北京基调网络股份有限公司 | Index time series abnormity detection method, system and storage medium |
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