CN113220799A - Big data early warning management system - Google Patents
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Abstract
The invention discloses a big data early warning management system, which comprises a big data acquisition module, a big data early warning module and a big data early warning module, wherein the big data acquisition module is used for acquiring various data in an enterprise; the data classification module is used for classifying various data in the enterprise, which are acquired by the big data acquisition module; the database is used for storing the enterprise data classified by the data classification module; the data processing module is used for reading the classified enterprise data from the database, analyzing and processing the enterprise data and extracting abnormal data; the data early warning module is used for generating early warning information according to the abnormal data extracted by the data processing module; and the data reporting module is used for reporting the early warning information generated by the data early warning module to the enterprise management terminal. The system can collect, classify, process, early warn and report the mass data in the enterprise, can monitor and early warn the enterprise data in real time, and finds abnormal conditions in time, thereby realizing the high-efficiency and orderly management of the enterprise data.
Description
Technical Field
The invention relates to the technical field of big data, in particular to a big data early warning management system.
Background
At present, with the rapid popularization of social informatization and the internet, the challenges are brought to the orderly management work of enterprises by multi-dimensional and massive data, how to effectively classify the massive data, how to timely find abnormality and give early warning from the classified data, and the problems become the main problems facing the enterprises. The existing enterprise data management mode has low enterprise data management efficiency and difficult reliability guarantee due to lack of real-time effective monitoring and early warning measures.
Therefore, how to provide an efficient and reliable big data early warning management system is a problem that needs to be solved urgently by those skilled in the art.
Disclosure of Invention
In view of the above, the invention provides a big data early warning management system, which effectively solves the problems that the existing enterprise data processing mode lacks monitoring and early warning measures, the management efficiency is low, the reliability is difficult to guarantee, and the like.
In order to achieve the purpose, the invention adopts the following technical scheme:
a big data early warning management system, the system comprising:
the big data acquisition module is used for acquiring various data in an enterprise;
the data classification module is used for classifying various data in the enterprise, which are acquired by the big data acquisition module;
the database is used for storing the enterprise data classified by the data classification module;
the data processing module is used for reading the classified enterprise data from the database, analyzing and processing the enterprise data and extracting abnormal data;
the data early warning module is used for generating early warning information according to the abnormal data extracted by the data processing module; and
and the data reporting module is used for reporting the early warning information generated by the data early warning module to an enterprise management terminal.
Furthermore, the big data early warning management system further comprises an early warning self-removing module, wherein the early warning self-removing module is used for analyzing and judging the abnormal data extracted by the data processing module in real time, and controlling the data early warning module to automatically remove corresponding early warning after judging that the abnormal data recovers to a normal value.
After the early warning information is reported to the enterprise management terminal, the enterprise management terminal can take corresponding measures aiming at the early warning content to enable the early warning content to be recovered to a normal state, and the early warning self-removing module can automatically remove the early warning in time after the abnormal data is detected to be recovered to a normal state, so that the accuracy and the timeliness of the early warning information are improved.
Further, the data classification module classifies various types of data within the enterprise into device data, personnel data, and market data. The data classification module can divide mass enterprise data into three types of equipment data, personnel data and market data, so that the data can be conveniently classified and stored, the subsequent data classification processing is convenient, and the data management efficiency is improved.
Further, the data processing module comprises:
the data reading unit is used for reading the classified enterprise data from the database;
the analysis processing unit is used for analyzing and processing the read enterprise data according to the classification type to obtain an analysis processing result;
an anomaly extraction unit for extracting anomaly data from the analysis processing result; and
and the data sending unit is used for sending the analysis processing result and the abnormal data to the database for storage and sending the abnormal data to the data early warning module.
Still further, the analysis processing unit includes:
the equipment data processing subunit is used for preprocessing the equipment data and detecting the fault of the production equipment according to the preprocessed equipment data;
the personnel data processing subunit is used for carrying out post gap analysis on the enterprise staff according to the personnel data; and
a market data processing subunit to predict market trends from market data.
Further, the equipment data processing subunit performs preprocessing on the equipment data, including data cleaning and data standardization.
On one hand, due to the existence of noise data and irrelevant data in the equipment data, the noise data and the irrelevant data can be removed by the data cleaning process; on the other hand, different production equipment has different dimensions and the numerical values are different greatly, so that the accuracy of subsequent data processing can be ensured through standardized processing.
Further, the data early warning module comprises:
the equipment fault early warning unit is used for generating equipment fault early warning information according to the equipment fault state in the abnormal data;
the post gap early warning unit is used for generating post gap early warning information according to the state of the post gap in the abnormal data; and
and the market risk early warning unit is used for generating market risk early warning information according to the market trend deviation state in the abnormal data.
Compared with the prior art, the big data early warning management system can acquire, classify, process, early warn and report mass data in an enterprise, can monitor and early warn the enterprise data in real time, and finds abnormal conditions in time, so that the high-efficiency and ordered management of the enterprise data is realized.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
Fig. 1 is a schematic structural diagram of a big data early warning management system according to the present invention;
FIG. 2 is a block diagram of a data processing module according to an embodiment of the present invention;
FIG. 3 is a block diagram of an exemplary architecture of an analysis processing unit;
fig. 4 is a schematic structural diagram of a data early warning module according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, an embodiment of the present invention discloses a big data early warning management system, which includes:
the big data acquisition module 1 is used for acquiring various data in an enterprise;
the data classification module 2 is used for classifying various data in the enterprise, which are acquired by the big data acquisition module 1;
the database 3 is used for storing the enterprise data classified by the data classification module 2;
the data processing module 4 is used for reading the classified enterprise data from the database 3, analyzing and processing the enterprise data and extracting abnormal data;
the data early warning module 5 is used for generating early warning information according to the abnormal data extracted by the data processing module 4; and
and the data reporting module 6 is used for reporting the early warning information generated by the data early warning module 5 to the enterprise management terminal.
Preferably, the big data early warning management system further comprises an early warning self-removing module 7, wherein the early warning self-removing module 7 is used for analyzing and judging the abnormal data extracted by the data processing module 4 in real time, and controlling the data early warning module 5 to automatically remove corresponding early warning after judging that the abnormal data recovers to a normal value.
In this embodiment, the data classification module 3 may classify various types of data in the enterprise into device data, personnel data, and market data. Enterprise data is divided into three types, and subsequent data processing and data early warning are carried out in a classified mode, so that the processing efficiency of the enterprise data can be guaranteed.
Specifically, referring to fig. 2, the data processing module 4 includes:
a data reading unit 401, where the data reading unit 401 is configured to read the classified enterprise data from the database 3;
the analysis processing unit 402 is used for analyzing and processing the read enterprise data according to the classification type to obtain an analysis processing result;
an anomaly extraction unit 403, the anomaly extraction unit 403 being used for extracting anomaly data from the analysis processing result; and
and the data sending unit 404 is configured to send the analysis processing result and the abnormal data to the database 3 for storage, and send the abnormal data to the data early warning module 5.
Specifically, referring to fig. 3, the analysis processing unit 402 includes:
the device data processing subunit 4021, the device data processing subunit 4021 is configured to perform preprocessing on the device data, and perform fault detection on the production device according to the preprocessed device data;
the personnel data processing subunit 4022, the personnel data processing subunit 4022 is configured to perform post gap analysis on the enterprise staff according to the personnel data; and
the market data processing subunit 4023, and the market data processing subunit 4023 is configured to predict market trends based on the market data.
Specifically, the device data processing subunit 4021 performs pre-processing on the device data, including data cleaning and data normalization. The data cleaning can remove noise data and irrelevant data in the equipment data, and the data standardization can convert data with different dimensions to the same dimension so as to eliminate the influence of the dimensions on the subsequent data processing effect.
In this embodiment, the device data is normalized by the following formula:
in the formula, ciData representing the ith production facility after the normalization process, bi、bjRespectively representing the ith and jth production facility data collected, i, j e [1, 2, …, n]And n represents the number of collected production equipment data.
Specifically, referring to fig. 4, the data early warning module 5 includes:
the device fault early warning unit 501, the device fault early warning unit 501 is used for generating device fault early warning information according to the device fault state in the abnormal data;
the post gap early warning unit 502, the post gap early warning unit 502 is used for generating post gap early warning information according to the post gap state in the abnormal data; and
and the market risk early warning unit 503 is used for generating market risk early warning information according to the market trend deviation state in the abnormal data.
Specifically, the device data collected in this embodiment may include operating parameters of the production device, such as voltage, current, power, and the like, and also include information such as vibration data, temperature data, sound data, and load data. The detection of the equipment fault can compare the data with the corresponding standard threshold value, and detect whether the equipment has the fault or not according to the deviation state of each data and the standard threshold value.
The personnel data mainly comprises information such as the number of people on each post, the number of attendance, the number of departures, the number of trainees and the like. Comparing the data with a standard threshold preset for each post, and determining whether a post gap occurs according to the comparison result of the actual data and the preset threshold, so as to fill the gap by injecting human resources into the post with the post gap in time.
The market data mainly includes sales, sales volume, sales profits, and the like. By comparing the market data with a preset target value, the market deviation state can be determined, for example, the sales volume of a certain area is suddenly reduced, and the problem of the sales market of an enterprise in the area can be preliminarily judged. Meanwhile, the enterprise market trend can be predicted according to the obtained market data so as to realize the stable increase of the manufacturing enterprise income. Of course, according to different conditions of each enterprise, the data may be added, deleted and adjusted appropriately according to actual needs, and is not limited specifically herein.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (7)
1. A big data early warning management system, characterized by comprising:
the big data acquisition module is used for acquiring various data in an enterprise;
the data classification module is used for classifying various data in the enterprise, which are acquired by the big data acquisition module;
the database is used for storing the enterprise data classified by the data classification module;
the data processing module is used for reading the classified enterprise data from the database, analyzing and processing the enterprise data and extracting abnormal data;
the data early warning module is used for generating early warning information according to the abnormal data extracted by the data processing module; and
and the data reporting module is used for reporting the early warning information generated by the data early warning module to an enterprise management terminal.
2. The big data early warning management system according to claim 1, further comprising an early warning self-removing module, wherein the early warning self-removing module is configured to analyze and judge the abnormal data extracted by the data processing module in real time, and control the data early warning module to automatically remove a corresponding early warning after judging that the abnormal data recovers to a normal value.
3. The big data early warning management system according to claim 1 or 2, wherein the data classification module classifies various types of data in an enterprise into equipment data, personnel data and market data.
4. The big data early warning management system according to claim 3, wherein the data processing module comprises:
the data reading unit is used for reading the classified enterprise data from the database;
the analysis processing unit is used for analyzing and processing the read enterprise data according to the classification type to obtain an analysis processing result;
an anomaly extraction unit for extracting anomaly data from the analysis processing result; and
and the data sending unit is used for sending the analysis processing result and the abnormal data to the database for storage and sending the abnormal data to the data early warning module.
5. The big data early warning management system according to claim 4, wherein the analysis processing unit comprises:
the equipment data processing subunit is used for preprocessing the equipment data and detecting the fault of the production equipment according to the preprocessed equipment data;
the personnel data processing subunit is used for carrying out post gap analysis on the enterprise staff according to the personnel data; and
a market data processing subunit to predict market trends from market data.
6. The big data early warning management system according to claim 5, wherein the equipment data processing subunit performs preprocessing on the equipment data, including data cleaning and data standardization.
7. The big data early warning management system according to claim 5, wherein the data early warning module comprises:
the equipment fault early warning unit is used for generating equipment fault early warning information according to the equipment fault state in the abnormal data;
the post gap early warning unit is used for generating post gap early warning information according to the state of the post gap in the abnormal data; and
and the market risk early warning unit is used for generating market risk early warning information according to the market trend deviation state in the abnormal data.
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| CN115858715A (en) * | 2022-09-29 | 2023-03-28 | 安徽中科新辰技术有限公司 | Early warning data management system |
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