CN115659832B - Enterprise operation analysis and early warning monitoring method and system based on big data analysis - Google Patents
Enterprise operation analysis and early warning monitoring method and system based on big data analysis Download PDFInfo
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Abstract
The invention provides an enterprise operation analysis and early warning method and system based on big data analysis, which are characterized in that analysis data required by enterprise operation analysis and early warning monitoring are collected, the collected analysis data are converted into a standard format, a dynamic evaluation index system is determined by combining manual selection and big data clustering extraction analysis data, an enterprise operation analysis result is obtained by adopting single index analysis and multi-index comprehensive evaluation, and a new early warning network is formed by integrating four independent neural networks of historical data, industry data, outlier data, policies and market data, and then risk early warning monitoring is carried out. The invention can improve the reliability and accuracy of operation analysis and risk monitoring.
Description
Technical Field
The invention relates to the field of data analysis, in particular to an enterprise operation analysis and early warning monitoring method and system based on big data analysis.
Background
The operation monitoring platform of the enterprise provides a management means for production operation management monitoring, and can realize the integrated evaluation of the enterprise development index and the project health index. Along with the rapid development of society, enterprise operation data risks are generated, and a method for enterprise operation analysis and risk early warning monitoring is required to be formed, so that benign guarantee of enterprise operation is realized. The method can provide reliable information basis for relevant management personnel and supervision personnel in time, thereby facilitating the relevant personnel to carry out subsequent management work or supervision work.
The existing enterprise operation and risk monitoring method has the problems that the reliability of operation analysis is low, the risk monitoring effect is poor, and the accuracy of a risk analysis result is low.
Disclosure of Invention
The invention aims to solve the problems in the prior art, and provides an enterprise operation analysis and early warning monitoring method and system based on big data analysis, so as to solve the problems of low reliability and low accuracy of operation analysis.
In order to achieve the above objective, the present invention provides an enterprise operation analysis and early warning monitoring method based on big data analysis, step S1: collecting analysis data required by enterprise operation analysis and early warning monitoring; step S2: converting the collected analysis data into a standard format, and transmitting the standard format to a big data center for storage; step S3: uniformly managing the stored analysis data; step S4: combining manual selection and big data clustering to extract the analysis data to determine a dynamic evaluation index system; step S5: obtaining enterprise operation analysis results by adopting single index analysis and multi-index comprehensive evaluation; step S6: and forming a new early warning network by fusing four independent neural networks of historical data, industry data, outlier data, policies and market data, and then carrying out risk early warning monitoring.
Preferably, the step S5 specifically includes the following step S5.1: determining the enterprise operation analysis input index byBig data clustering extraction, fusion expert experience index data, and common formation of input indexes of enterprise operation analysis, and marking an index system formed by m indexes as I= { I 1 ,I 2 ,…,I m Each index has a weight of w= [ W ] 1 ,w 2 ,…,w m ]Let each index value in the index system at time t be X t ＝[X t1 ,X t2 ,…,X tm ]The method comprises the steps of carrying out a first treatment on the surface of the Obtaining index I of normal operation range according to historical statistics i Minimum, recommended, maximum, noted asWherein i=1, 2, …, m; .
Preferably, after the step S5.1, the method further includes a step S5.2 of evaluating the single index to obtain a single index evaluation value, and specifically includes: if it isThe index is within the normal range, and the score value is [0.6,1 ]]The calculation is performed according to the following formula
Where parameter a=0.4, e (X ti ) A single index evaluation value for the index i at time t,respectively index I i Minimum, recommended, maximum of the operating normal range, where i=1, 2, …, m; .
If it isOr->The index is not within the normal range, the scoring value 0,0.6,
wherein E (X) ti ) A single index evaluation value for the index i at time t,respectively index I i Minimum, recommended, maximum of the operating normal range, parameter b=0.6, where i=1, 2, …, m; .
Preferably, after step S5.2, step S5.3 further includes obtaining an operation analysis and evaluation result by using a multi-index comprehensive method, which specifically includes: the ideal values of the index evaluation values are set as follows:negative ideal value of
The operation analysis and evaluation result is that
Wherein D () is a distance measurement function, and is calculated by absolute value methodAccording to the operation analysis and evaluation result P t And measuring the operation condition of the enterprise.
Preferably, the step S6 specifically includes:
s6.1, determining the input data of the neural network, wherein the comprehensive data of the big data processing center and the operation analysis evaluation result is the input of the neural network, and performing data standardization processing before the comprehensive data enters a network model;
s6.2, establishing a network architecture model, wherein the network architecture model is divided into two layers of neural networks, and the first layer of neural network analyzes four dimensions of historical data, same industry data, outlier data, policies and market data to construct four independent prediction networks; the second layer neural network takes output data of the first layer neural network as input to carry out fusion prediction;
step S6.3, outputting risk early warning data, wherein the first layer neural network and the second layer neural network are both of three-output design, and the output value is Boolean variable [ O ] 1 ,O 2 ,O 3 ]Wherein O is i And 0 or 1, and outputting a result to provide basis for risk early warning and alarming.
Preferably, the analysis data includes operation data, domestic market data, international market data, policy regulation data, industry development data.
Another aspect of the present invention provides an enterprise operation analysis and early warning monitoring system based on big data analysis, comprising:
the data collection module is used for collecting analysis data required by enterprise operation analysis and early warning monitoring;
the data conversion module converts the collected analysis data into a standard format and transmits the standard format to a big data center for storage;
the data management module is used for uniformly managing the stored analysis data;
the dynamic evaluation module is used for combining manual selection and big data clustering to extract the analysis data to determine a dynamic evaluation index system;
the enterprise operation analysis module is used for obtaining an enterprise operation analysis result by adopting single index analysis and multi-index comprehensive evaluation;
the risk early warning module adopts four independent neural networks of historical data, industry data, outlier data, policies and market data to form a new early warning network after fusion, and then carries out risk early warning monitoring, and the early warning process comprises early warning setting, early warning monitoring, early warning notification, early warning treatment and alarm elimination modules.
Compared with the prior art, the invention has the beneficial effects that:
1) Through the single index analysis evaluation and multi-index fusion evaluation methods, the relations between the index values and the minimum value, the recommender and the maximum value of the normal operation range are fully considered, quantification is reasonably carried out, the evaluation result is easy to understand, and the operability is high.
2) The invention adopts two layers of fusion networks, which can reduce the overall parameters of the model and is convenient for training and optimizing; secondly, each model is relatively independent, can be independently optimized, and improves the robustness of the system;
thirdly, the enterprise operation condition is monitored in an early warning mode from different dimensions, and higher accuracy is achieved through data fusion.
Drawings
FIG. 1 is a schematic diagram of an enterprise operation analysis and early warning monitoring method based on big data analysis according to an embodiment of the present invention;
FIG. 2 is a flowchart of a method for analyzing and monitoring enterprise operations based on big data analysis according to an embodiment of the present invention;
fig. 3 is a block diagram of input and output of a neural network according to a first embodiment of the present invention.
Detailed Description
The invention is described in further detail below with reference to the attached drawing figures:
for a better understanding of the present invention, embodiments of the present invention are explained in detail below with reference to the drawings.
The first embodiment of the invention provides an enterprise operation analysis and early warning monitoring method based on big data analysis, wherein a schematic diagram of the method is shown in fig. 1, a specific flow is shown in fig. 2, and the method specifically comprises the following steps:
step S1: and collecting data required by enterprise operation analysis and early warning monitoring, wherein data sources comprise network public data, enterprise report data and enterprise internal networks, and data types comprise operation data, domestic market data, international market data, policy and regulation data and industry development data.
Step S2: and converting the collected data into a standard format, and transmitting the data to a large data center for storage.
Step S3: and uniformly managing the stored data. The large data processing center processes the acquired data novel, and specifically comprises data mining, data cleaning, data association and data clustering, so that a data basis is provided for enterprise operation analysis and early warning monitoring.
Step S4: and determining a dynamic evaluation index system by combining manual selection and big data clustering extraction data. When the enterprise operation analysis is carried out, due to huge data volume and different data relativity, related indexes are required to be automatically clustered according to a big data clustering method. Meanwhile, in order to integrate expert experience, different analysis emphasis is added to different enterprises, partial indexes are added manually, and the two are combined to form a final evaluation index system.
Step S5: and obtaining an enterprise operation analysis result by adopting single-index analysis and multi-index comprehensive evaluation.
Step S5.1: and determining an enterprise operation analysis input index.
According to the index system determining method, the input index of enterprise operation analysis is formed by automatically extracting through big data clustering and fusing expert experience index data. The index system formed by m indexes is I= { I 1 ,I 2 ,…,I m Each index has a weight of w= [ W ] 1 ,w 2 ,…,w m ]Let each index value in the index system at time t be X t ＝[X t1 ,X t2 ,…,X tm ]。
According to the historical statistics, the index I of the normal operation range can be obtained i Minimum, recommended, maximum, noted asNational committee industry evaluation criteria, such as excellent, average, benchmark, where i=1, 2, …, m; .
Step S5.2: and evaluating the single index to obtain a single index evaluation value.
If it isThe index is within the normal range, and the score value is [0.6,1 ]]The closer to the recommended value, the larger the index evaluation value thereof is, the calculation is performed according to the following formula:
preferably, a=0.4;
if it isOr->The index is not within the normal range and the score value is 0,0.6.
Wherein X is ti For the value of the i-th index at time t,respectively index I i Minimum, recommended, maximum of the operating normal range, where i=1, 2, …, m; .
Preferably, b=0.6.
Step S5.3: and calculating by adopting a TOPSIS (approach to ideal solution ordering method) method to obtain the final evaluation condition.
The ideal values of the index evaluation values are set as follows:negative ideal value of
The final operational condition evaluation value is:
wherein D () is a distance measurement function, and is calculated by absolute value methodE(X ti ) Single index evaluation value, w, which is the ith index at time t i Is the weight of index i.
According to the final evaluation value P t Measuring the operation condition of enterprises, P t A larger value indicates a better enterprise operation, and a smaller value indicates a worse operation. By P t Whether the operation of the enterprise is normal or not can be judged, and the actual operation is convenient. A certain threshold value P can be selected in general 0 P as a reference for measuring whether enterprise operation is in a reasonable state t >P 0 The enterprise operates normally, otherwise it is abnormal. The threshold values can be classified into yellow early warning, orange early warning and red early warning according to the classified early warning management and control mechanism, and the early warning condition of the single index is judged according to the corresponding threshold values. And (3) adopting multidimensional deviation analysis, timely performing early warning and deviation correction, and timely reminding the enterprise operation condition of the risk control.
Step S6: and forming a new early warning network by fusing four independent neural networks of historical data, industry data, outlier data, policies and market data, and then carrying out risk early warning monitoring. The input/output mode of the neural network is shown in fig. 3.
Step S6.1: neural network input data is determined. The data of the large data processing center and the enterprise operation evaluation result are input into the neural network, and before the data enter the network model, data normalization processing is carried out.
Step S6.2: and establishing a network architecture model. Because the running condition of enterprises is related to various factors, the false alarm rate is high in one aspect, and the false alarm rate is greatly different from the actual false alarm rate, a novel early warning monitoring network architecture is adopted. The whole network is divided into two layers, wherein the first layer respectively analyzes four dimensions of historical data, same industry data, outlier data, policies and market data to construct four independent prediction networks; the second layer takes the output data of the first layer as input to carry out fusion prediction.
Step S6.3: and outputting risk early warning data. The first layer and the second layer of the network are both three-output design, and the output value is Boolean variable [ O ] 1 ,O 2 ,O 3 ]Wherein O is i 0 or 1. Output is four results [1,0]、[0,1,0]、[0,0,1]、[0,0,0]Respectively represent the lighting of red, orange, yellow and green lamps. And risk early warning and alarming are carried out in red, orange and yellow states, and alarming is not needed in a green state.
The two-layer fusion network has the advantages that overall parameters of the model are reduced, and training and optimization are facilitated. And secondly, each model is relatively independent, can be independently optimized, and improves the robustness of the system. Thirdly, the enterprise operation condition is monitored in an early warning mode from different dimensions, and higher accuracy is achieved through data fusion.
The invention further provides an enterprise operation analysis and early warning monitoring system based on big data analysis, and the data collection module is used for collecting analysis data required by enterprise operation analysis and early warning monitoring;
the data conversion module converts the collected analysis data into a standard format and transmits the standard format to a big data center for storage;
the data management module is used for uniformly managing the stored analysis data;
the dynamic evaluation module is used for determining a dynamic evaluation index system by combining manual selection and big data clustering extraction analysis data, wherein the big data clustering extraction indexes are generally data aggregation, such as the completion progress, the peer-to-peer alignment, the peer-to-peer growth and other dimensionalities, and are generally net asset yield, economic increase value, new contract amount completion rate, net profit completion rate, business income completion rate and the like; the manual selection indexes are divided into: first, key indexes such as national resource commission assessment indexes of labor productivity of the whole staff and the like; and secondly, special indexes, such as special indexes of increasing the same-proportion increase of the incumbent project, the proportion of management fee in the incumbent and the like according to the requirement of leading speaking in the annual work meeting.
The enterprise operation analysis module is used for obtaining an enterprise operation analysis result by adopting single index analysis and multi-index comprehensive evaluation; the corresponding analysis standards of different types of business of enterprises are also different, and the specific application can analyze the business according to the businesses such as outer warp, capital construction, design consultation science and technology service, investment operation, real estate, equipment manufacturing, financial service, commerce service and the like.
The risk early warning module adopts four independent neural networks of historical data, industry data, outlier data, policies and market data to form a new early warning network after fusion, and then carries out risk early warning monitoring, wherein the early warning process mainly comprises 5 processes of early warning setting, early warning monitoring, early warning notification, early warning treatment and alarm elimination, the early warning setting process mainly carries out index definition and rule setting, the early warning monitoring process mainly carries out information acquisition, index treatment and index research, the early warning notification process mainly carries out comprehensive assessment of early warning grades and issues early warning information to related units, the early warning treatment process mainly comprises processing early warning conditions by groups, secondary units, tertiary units, quaternary units and project groups, and the alarm elimination process mainly comprises achieving the aim of releasing an expected target alarm after finishing the rectification of the related units.
In the description of the present invention, it should be noted that, unless explicitly specified and limited otherwise, the terms "connected," "connected," and "connected" are to be construed broadly, and may be either fixedly connected, detachably connected, or integrally connected, for example; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art according to the specific circumstances.
In the description of the present invention, unless otherwise indicated, the terms "upper," "lower," "left," "right," "inner," "outer," and the like are used for convenience in describing the present invention and simplifying the description based on the orientation or positional relationship shown in the drawings, and do not denote or imply that the devices or elements in question must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present invention.
Finally, it should be noted that the above-mentioned technical solution is only one embodiment of the present invention, and various modifications and variations can be easily made by those skilled in the art based on the application methods and principles disclosed in the present invention, and are not limited to the methods described in the above-mentioned specific embodiments of the present invention, therefore, the foregoing description is only preferred, and not meant to be limiting.
Claims (3)
1. An enterprise operation analysis and early warning monitoring method based on big data analysis is characterized in that:
step S1: collecting analysis data required by enterprise operation analysis and early warning monitoring;
step S2: converting the collected analysis data into a standard format, and transmitting the standard format to a big data center for storage;
step S3: uniformly managing the stored analysis data;
step S4: combining manual selection and big data clustering to extract the analysis data to determine a dynamic evaluation index system;
step S5: obtaining enterprise operation analysis results by adopting single index analysis and multi-index comprehensive evaluation;
the step S5 specifically includes a step S5.1:
determining the input index of the enterprise operation analysis, merging expert experience index data through big data cluster extraction to jointly form the input index of the enterprise operation analysis, and recording an index system formed by m indexes as I= { I 1 ,I 2 ,…,I m Each index has a weight of w= [ W ] 1 ,w 2 ,…,w m ]Let each index value in the index system at time t be X t ＝[X t1 ,X t2 ,…,X tm ]The method comprises the steps of carrying out a first treatment on the surface of the Obtaining index I of normal operation range according to historical statistics i Minimum, recommended, maximum, noted asWherein i=1, 2, …, m;
step S5.2, evaluating the single index to obtain a single index evaluation value, which specifically comprises the following steps:
if it isThe index is within the normal range, and the score value is [0.6,1 ]]The calculation is performed according to the following formula
Where parameter a=0.4, e (X ti ) A single index evaluation value for the index i at time t,respectively index I i Minimum, recommended, maximum of the operating normal range, where i=1, 2, …, m;
if it isOr->The index is not within the normal range, the scoring value 0,0.6,
wherein E (X) ti ) A single index evaluation value for the index i at time t,respectively index I i Minimum, recommended, maximum of the operating normal range, parameter b=0.6, where i=1, 2, …, m;
step S5.3, obtaining operation analysis and evaluation results by adopting a multi-index comprehensive method, wherein the method specifically comprises the following steps:
the ideal values of the index evaluation values are set as follows:negative ideal value of
The operation analysis and evaluation result is that
Wherein D () is a distance measurement function, and is calculated by absolute value methodAccording to the operation analysis and evaluation result P t Measuring the operation condition of an enterprise;
step S6: the method comprises the steps that four independent neural networks of historical data, industry data, outlier data, policies and market data are integrated to form a new early warning network, and risk early warning monitoring is conducted;
the step S6 specifically includes:
s6.1, determining the input data of the neural network, wherein the comprehensive data of the big data processing center and the operation analysis evaluation result is the input of the neural network, and performing data standardization processing before the comprehensive data enters a network model;
s6.2, establishing a network architecture model, wherein the network architecture model is divided into two layers of neural networks, and the first layer of neural network analyzes four dimensions of historical data, same industry data, outlier data, policies and market data to construct four independent prediction networks; the second layer neural network takes output data of the first layer neural network as input to carry out fusion prediction;
step S6.3, outputting risk early warning data, wherein the first layer neural network and the second layer neural network are both of three-output design, and the output value is Boolean variable [ O ] 1 ,O 2 ,O 3 ]Wherein O is i And 0 or 1, and outputting a result to provide basis for risk early warning and alarming.
2. The method of claim 1, the analysis data comprising operational data, domestic market data, international market data, policy regulation data, industry development data.
3. An enterprise operation analysis and early warning monitoring system based on big data analysis, for executing the method of any one of claims 1-2, characterized in that:
the data collection module is used for collecting analysis data required by enterprise operation analysis and early warning monitoring;
the data conversion module converts the collected analysis data into a standard format and transmits the standard format to a big data center for storage;
the data management module is used for uniformly managing the stored analysis data;
the dynamic evaluation module is used for combining manual selection and big data clustering to extract the analysis data to determine a dynamic evaluation index system;
the enterprise operation analysis module is used for obtaining an enterprise operation analysis result by adopting single index analysis and multi-index comprehensive evaluation;
the risk early warning module adopts four independent neural networks of historical data, industry data, outlier data, policies and market data to form a new early warning network after fusion, and then carries out risk early warning monitoring, wherein the early warning process comprises early warning setting, early warning monitoring, early warning notification, early warning treatment and alarm elimination.
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