CN110490486B - Enterprise big data management system - Google Patents
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- CN110490486B CN110490486B CN201910795104.9A CN201910795104A CN110490486B CN 110490486 B CN110490486 B CN 110490486B CN 201910795104 A CN201910795104 A CN 201910795104A CN 110490486 B CN110490486 B CN 110490486B
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Abstract
The invention provides an enterprise big data management system which comprises a big data acquisition subsystem, a big data classification subsystem, a big data processing subsystem, a big data early warning subsystem and a visualization subsystem, wherein the big data acquisition subsystem is used for acquiring enterprise data, the big data classification subsystem divides the enterprise data into production equipment data and market data, the big data processing subsystem is used for analyzing and processing the production equipment data and the market data, the big data early warning subsystem is used for respectively monitoring and early warning the production equipment and the market based on a production equipment data analysis result and a market data analysis result, and the visualization subsystem is used for displaying a production equipment data analysis result and a market data analysis result. Compared with the conventional single data management system, the enterprise big data management system realizes multidimensional and massive enterprise data management.
Description
Technical Field
The invention relates to the technical field of big data, in particular to an enterprise big data management system.
Background
With the coming of the information era, data management becomes a difficult problem in front of enterprises, multi-dimensional and massive data bring new challenges and opportunities to the enterprises, and if the data can be effectively managed, the production efficiency of the enterprises can be improved, and the decision level of the enterprises can be improved. However, the existing enterprise data management system can only manage data in a single aspect, and is inefficient.
Disclosure of Invention
In order to solve the technical problem, the invention provides an enterprise big data management system, which realizes multidimensional and massive enterprise data management compared with the traditional single data management system.
The invention particularly provides an enterprise big data management system which comprises a big data acquisition subsystem, a big data classification subsystem, a big data processing subsystem, a big data early warning subsystem and a visualization subsystem, wherein the big data acquisition subsystem is used for acquiring enterprise data, the big data classification subsystem divides the enterprise data into production equipment data and market data, the big data processing subsystem is used for analyzing and processing the production equipment data and the market data, the big data early warning subsystem is used for respectively monitoring and early warning the production equipment and the market based on a production equipment data analysis result and a market data analysis result, and the visualization subsystem is used for displaying a production equipment data analysis result and a market data analysis result.
Optionally, the big data processing subsystem includes a production device data processing module and a market data processing module, the production device data processing module is configured to perform fault detection on the production device according to production device data, the market data processing module is configured to predict a market according to market data, the production device data processing module includes a preprocessing module, a first feature extraction module, a first fusion analysis module and a first evaluation module, the preprocessing module is configured to perform standardized processing on collected production device data, the first feature extraction module is configured to determine a feature vector of the production device, the first fusion analysis module detects a production device fault according to the feature vector of the production device, and the first evaluation module is configured to evaluate accuracy of the production device fault detection.
Optionally, the market data processing module includes a second feature extraction module and a second fusion analysis module, the second feature extraction module determines a feature vector of the market according to the market data, and the second fusion analysis module predicts the market according to the feature vector of the market.
Optionally, the preprocessing module is configured to perform standardized processing on the acquired production equipment data, and specifically includes:
the collected production equipment data is standardized using 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.
Optionally, the first feature extraction module is configured to determine a feature vector of the production device, and specifically includes:
for the normalized production equipment data, a first characteristic value T of the production equipment data is calculated according to the following formula1：
Calculating a second characteristic value T of the production equipment data according to the following formula2：
Calculating a third characteristic value T of the production equipment data according to the following formula3：
Determining a feature vector X of the production equipment according to the first feature value, the second feature value and the third feature value of the production equipment data: x ═ T1，T2，T3]。
Optionally, the first fusion analysis module detects the fault of the production equipment according to the feature vector of the production equipment, and specifically includes:
training the characteristic vector of the production equipment by adopting a neural network model to obtain the characteristic vector when the production equipment fails, carrying out standardized processing on the collected real-time data of the production equipment, inputting the data into the trained neural network model, and detecting the failure of the production equipment.
Optionally, the first evaluation module is configured to evaluate accuracy of fault detection of the production equipment, and specifically includes:
determining an evaluation factor of the fault detection accuracy:
P＝0.5A+0.5/B
in the formula, P represents an evaluation factor of the fault detection accuracy, A represents the percentage of the number of correct fault detection times to the total number of fault detection times, B represents the percentage of the number of missed fault detection times to the total number of fault detection times, and the larger P is, the more accurate fault detection is represented.
Optionally, the second feature extraction module determines a feature vector of the market according to the market data, and specifically includes: determining a feature vector Y for the market using: y ═[S1，S2，S3]In the formula, S1Sales, S, representing a period of time2Indicating sales volume, S, for a period of time3Indicating sales profits for a certain period of time.
Optionally, the second fusion analysis module predicts the market according to the feature vector of the market, and specifically includes: collecting m market characteristic vectors adjacent to different time periods to obtain a prediction matrix Z of the market:
in the formula, Y1、Y2、…、YmFeature vector, Y, representing market for 1 st, 2 nd, … th, m th time periodk＝[Sk1，Sk2，Sk3]，Sk1、Sk2、Sk3Respectively representing sales, sales volume and sales profits for the kth time period, where k e [1, 2, …, m]；
Determining a feature vector Y for the market for the next time period using the following equation0：
Y0＝[S01，S02，S03]
Wherein the content of the first and second substances,
S01、S02、S03respectively, the predicted values of sales, sales volume, and sales profits for the next time period.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
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The invention is further illustrated by means of the attached drawings, but the embodiments in the drawings do not constitute any limitation to the invention, and for a person skilled in the art, other drawings can be obtained on the basis of the following drawings without inventive effort.
FIG. 1 is a schematic view of the structure of the present invention.
Reference numerals:
the system comprises a big data acquisition subsystem 1, a big data classification subsystem 2, a big data processing subsystem 3, a big data early warning subsystem 4 and a visualization subsystem 5.
Detailed Description
The invention is further described with reference to the following examples.
With reference to fig. 1, an embodiment of the present invention provides an enterprise big data management system, which includes a big data collecting subsystem 1, a big data classifying subsystem 2, a big data processing subsystem 3, a big data early warning subsystem 4, and a visualization subsystem 5, where the big data collecting subsystem 1 is configured to collect enterprise data, the big data classifying subsystem 2 divides the enterprise data into production equipment data and market data, the big data processing subsystem 3 analyzes and processes the production equipment data and the market data, the big data early warning subsystem 4 monitors and early warns production equipment and a market respectively based on a production equipment data analysis result and a market data analysis result, and the visualization subsystem 5 is configured to display a production equipment data analysis result and a market data analysis result.
The big data acquisition system 1 accessible multiple sensor gathers production facility data, and the production facility data of gathering include production facility's vibration data, temperature data, sound data, load data etc, and simultaneously, big data acquisition system 1 also can obtain market data through each sales terminal or user terminal, market data includes sales volume, sales profit etc. according to the enterprise difference, and production facility is different, and the data index is also different, can gather the data of different production facility, and according to market demand difference, the market data of gathering also can adjust, for example, to a certain region or, a certain sales force, the relevant data of a certain customer.
For production equipment data, the analysis result can be applied to product quality detection, production equipment fault early warning and the like. The method is sent to a manager in an alarm or suggestion mode for adjustment in time, a production equipment abnormity detection early warning model based on a relevant big data algorithm is established, and a characteristic mode in a time sequence is mined, so that the production process management and control performance is improved;
with respect to market data, the analysis results may be used for enterprise market forecasting to achieve a steady increase in manufacturing enterprise revenue.
The visualization subsystem 5 may display the analysis result of the production equipment data and the analysis result of the market data by using different terminal devices, such as a computer, a tablet computer, etc., or may present the analysis result of the production equipment data and the analysis result of the market data in different forms, such as a table, a histogram, etc.
The preferred embodiment realizes the collection, classification and analysis processing of different data of an enterprise, monitors and warns production equipment and the market through the data, improves the data management level of the enterprise, and is beneficial to ensuring the normal operation of the production equipment and making correct market decisions.
Preferably, the big data processing subsystem 3 includes a production equipment data processing module and a market data processing module, the production equipment data processing module is configured to perform fault detection on production equipment according to production equipment data, the market data processing module is configured to predict a market according to market data, the production equipment data processing module includes a preprocessing module, a first feature extraction module, a first fusion analysis module and a first evaluation module, the preprocessing module is configured to perform standardization processing on collected production equipment data, the first feature extraction module is configured to determine a feature vector of the production equipment, the first fusion analysis module detects a production equipment fault according to the feature vector of the production equipment, and the first evaluation module is configured to evaluate accuracy of the production equipment fault detection.
The market data processing module comprises a second feature extraction module and a second fusion analysis module, the second feature extraction module determines the feature vector of the market according to the market data, and the second fusion analysis module predicts the market according to the feature vector of the market.
The preferred embodiment analyzes the collected data, and can detect the faults of the production equipment and predict the market by reasonably determining the characteristic vectors of the production equipment and the market. The determination of the eigenvectors is based on the collected production equipment data and market data, and can be adjusted according to actual conditions and requirements.
Preferably, the preprocessing module is configured to perform standardized processing on the acquired production equipment data, and specifically includes:
the collected production equipment data is standardized using 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.
Because different production equipment data have different dimensions and have very large size difference, the preferred embodiment carries out standardized processing on the production equipment data, is convenient for carrying out subsequent data analysis processing, improves the calculation efficiency and improves the enterprise data management level.
Preferably, the first feature extraction module is configured to determine a feature vector of the production device, and specifically includes:
for the normalized production equipment data, a first characteristic value T of the production equipment data is calculated according to the following formula1：
The characteristic vector first characteristic value can effectively reflect the fluctuation degree of the data, the smaller the first characteristic value is, the smaller the fluctuation of the data is, the larger the first characteristic value is, the larger the fluctuation of the data is, and the higher the possibility of failure is.
Calculating a second characteristic value T of the production equipment data according to the following formula2：
The feature vector second feature value can effectively reflect the steepness degree of data, and the steepness degree is increased along with the occurrence of faults.
Calculating a third characteristic value T of the production equipment data according to the following formula3：
The feature vector third feature value can reflect the instantaneous maximum value of the data, and when the instantaneous maximum value of the data changes, the possibility of failure increases.
Determining a feature vector X of the production equipment according to the first feature value, the second feature value and the third feature value of the production equipment data: x ═ T1，T2，T3]。
Preferably, the first fusion analysis module detects the fault of the production equipment according to the feature vector of the production equipment, and specifically comprises:
training the characteristic vector of the production equipment by adopting a neural network model to obtain the characteristic vector when the production equipment fails, carrying out standardized processing on the collected real-time data of the production equipment, inputting the data into the trained neural network model, and detecting the failure of the production equipment.
Classifying the working state of the production equipment according to the characteristic vector of the production equipment, dividing the state of the production equipment into normal state and fault state, determining the characteristic vector in the corresponding state, acquiring real-time data of the production equipment for standardization, generating the corresponding characteristic vector according to the acquired data, performing similarity measurement with the characteristic vector in the corresponding state, and determining the real-time state of the production equipment, wherein the similarity measurement of the characteristic vector can be performed by adopting Euclidean distance.
Preferably, the first evaluation module is configured to evaluate accuracy of fault detection of the production equipment, and specifically includes:
determining an evaluation factor of the fault detection accuracy:
P＝0.5A+0.5/B
in the formula, P represents an evaluation factor of the fault detection accuracy, A represents the percentage of the number of correct fault detection times to the total number of fault detection times, B represents the percentage of the number of missed fault detection times to the total number of fault detection times, and the larger P is, the more accurate fault detection is represented.
In the fault detection accuracy evaluation process, the preferred embodiment not only adopts the correct detection ratio, but also considers the proportion of missed detection, thereby realizing the comprehensive evaluation of fault detection.
Preferably, the second feature extraction module determines a feature vector of the market according to the market data, and specifically includes: determining a feature vector Y for the market using: y ═ S1，S2，S3]In the formula, S1Sales, S, representing a period of time2Indicating sales volume, S, for a period of time3Indicating sales profits for a certain period of time.
The second fusion analysis module predicts the market according to the characteristic vector of the market, and specifically comprises the following steps: collecting m market characteristic vectors adjacent to different time periods to obtain a prediction matrix Z of the market:
in the formula, Y1、Y2、…、YmFeature vector, Y, representing market for 1 st, 2 nd, … th, m th time periodk＝[Sk1，Sk2，Sk3]，Sk1、Sk2、Sk3Respectively represent the k-thSales, sales volume and sales profit for a period of time, where k e [1, 2, …, m]；
Determining a feature vector Y for the market for the next time period using the following equation0：
Y0＝[S01，S02，S03]
Wherein the content of the first and second substances,
S01、S02、S03respectively, the predicted values of sales, sales volume, and sales profits for the next time period.
The market prediction is carried out by selecting the sales volume, the sales volume and the sales profit of the time period in the preferred embodiment, the average market level in the past period and the market growth rate in the recent time period are fully considered in the prediction process, and the prediction result has guiding significance for market decision.
From the above description of embodiments, it is clear for a person skilled in the art that the embodiments described herein can be implemented in hardware, software, firmware, middleware, code or any appropriate combination thereof. For a hardware implementation, a processor may be implemented in one or more of the following units: an Application Specific Integrated Circuit (ASIC), a Digital Signal Processor (DSP), a Digital Signal Processing Device (DSPD), a Programmable Logic Device (PLD), a Field Programmable Gate Array (FPGA), a processor, a controller, a microcontroller, a microprocessor, other electronic units designed to perform the functions described herein, or a combination thereof. For a software implementation, some or all of the procedures of an embodiment may be performed by a computer program instructing associated hardware. In practice, the program may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media may be any available media that can be accessed by a computer. Computer-readable media can include, but is not limited to, RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer.
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the protection scope of the present invention, although the present invention is described in detail with reference to the preferred embodiments, it should be understood by the ordinary technical destination in the art that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (5)
1. An enterprise big data management system is characterized by comprising a big data acquisition subsystem, a big data classification subsystem, a big data processing subsystem, a big data early warning subsystem and a visualization subsystem, wherein the big data acquisition subsystem is used for acquiring enterprise data, the big data classification subsystem divides the enterprise data into production equipment data and market data, the big data processing subsystem analyzes and processes the production equipment data and the market data, the big data early warning subsystem respectively monitors and early warns production equipment and a market based on a production equipment data analysis result and a market data analysis result, and the visualization subsystem is used for displaying a production equipment data analysis result and a market data analysis result;
the big data processing subsystem comprises a production equipment data processing module and a market data processing module, the production equipment data processing module is used for carrying out fault detection on production equipment according to production equipment data, the market data processing module is used for predicting markets according to market data, the production equipment data processing module comprises a preprocessing module, a first feature extraction module, a first fusion analysis module and a first evaluation module, the preprocessing module is used for carrying out standardized processing on collected production equipment data, the first feature extraction module is used for determining a feature vector of the production equipment, the first fusion analysis module is used for detecting the fault of the production equipment according to the feature vector of the production equipment, and the first evaluation module is used for evaluating the accuracy of the fault detection of the production equipment;
the market data processing module comprises a second feature extraction module and a second fusion analysis module, the second feature extraction module determines a feature vector of a market according to market data, and the second fusion analysis module predicts the market according to the feature vector of the market;
the preprocessing module is used for carrying out standardized processing on the acquired production equipment data, and specifically comprises the following steps:
the collected production equipment data is standardized using 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]N represents the number of the collected production equipment data;
the first feature extraction module is used for determining a feature vector of the production equipment, and specifically comprises the following steps:
for the normalized production equipment data, a first characteristic value T of the production equipment data is calculated according to the following formula1：
Calculating a second characteristic value T of the production equipment data according to the following formula2：
Calculating a third characteristic value T of the production equipment data according to the following formula3：
Determining a feature vector X of the production equipment according to the first feature value, the second feature value and the third feature value of the production equipment data: x ═ T1，T2，T3]。
2. The enterprise big data management system according to claim 1, wherein the first fusion analysis module detects a fault of the production equipment according to a feature vector of the production equipment, and specifically comprises:
training the characteristic vector of the production equipment by adopting a neural network model to obtain the characteristic vector when the production equipment fails, carrying out standardized processing on the collected real-time data of the production equipment, inputting the data into the trained neural network model, and detecting the failure of the production equipment.
3. The enterprise big data management system according to claim 2, wherein the first evaluation module is configured to evaluate accuracy of fault detection of the production equipment, and specifically:
determining an evaluation factor of the fault detection accuracy:
P＝0.5A+0.5/B
in the formula, P represents an evaluation factor of the fault detection accuracy, A represents the percentage of the number of correct fault detection times to the total number of fault detection times, B represents the percentage of the number of missed fault detection times to the total number of fault detection times, and the larger P is, the more accurate fault detection is represented.
4. The enterprise big data management system according to claim 1, wherein the second feature extraction module determines a feature vector of a market according to market data, specifically: determining a feature vector Y for the market using: y is＝[S1，S2，S3]In the formula, S1Sales, S, representing a period of time2Indicating sales volume, S, for a period of time3Indicating sales profits for a certain period of time.
5. The enterprise big data management system according to claim 4, wherein the second fusion analysis module predicts the market according to the feature vector of the market, specifically: collecting m market characteristic vectors adjacent to different time periods to obtain a prediction matrix Z of the market:
in the formula, Y1、Y2、…、YmFeature vector, Y, representing market for 1 st, 2 nd, … th, m th time periodk＝[Sk1，Sk2，Sk3]，Sk1、Sk2、Sk3Respectively representing sales, sales volume and sales profits for the kth time period, where k e [1, 2, …, m]；
Determining a feature vector Y for the market for the next time period using the following equation0：
Y0＝[S01，S02，S03]
Wherein the content of the first and second substances,
S01、S02、S03respectively, the predicted values of sales, sales volume, and sales profits for the next time period.
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