CN115659832A - Enterprise operation analysis and early warning method and system based on big data analysis - Google Patents
Enterprise operation analysis and early warning 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 used for collecting analysis data required by enterprise operation analysis and early warning monitoring, converting the collected analysis data into a standard format, extracting the analysis data by combining manual selection and big data clustering to determine a dynamic evaluation index system, obtaining an enterprise operation analysis result by adopting single index analysis and multi-index comprehensive evaluation, forming a new early warning network by adopting four independent neural networks of historical data, industrial data, abnormal value data, policy and market data after fusion, and then performing risk early warning monitoring. 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 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 integrated evaluation of the development index and the project health index of the enterprise. With the rapid development of society, enterprise operation data risks also appear, and a method for enterprise operation analysis and risk early warning monitoring needs to be formed, so that the good guarantee of enterprise operation is realized. Reliable information basis can be provided for relevant managers and supervisors in time, so that the relevant managers can conveniently carry out subsequent management work or supervision work.
The existing enterprise operation and risk monitoring method has the problems of low operation analysis reliability, poor risk monitoring effect and low accuracy of risk analysis results.
Disclosure of Invention
The invention aims to solve the problems in the prior art, and provides an enterprise operation analysis and early warning 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 purpose, the invention provides an enterprise operation analysis and early warning method based on big data analysis, which comprises the following steps of 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; and step S3: uniformly managing the stored analysis data; and step S4: extracting the analysis data by combining manual selection and big data clustering to determine a dynamic evaluation index system; step S5: obtaining an enterprise operation analysis result by adopting single index analysis and multi-index comprehensive evaluation; step S6: and forming a new early warning network by adopting four independent neural networks of historical data, industry data, abnormal value data, policy and market data after fusion, and then carrying out risk early warning monitoring.
Preferably, step S5 specifically includes the following step S5.1: determining the enterprise operation analysis input index, extracting through big data clustering, fusing expert experience index data, and jointly forming the input index of the enterprise operation analysis, wherein an index system formed by m indexes is I = { I = (the index system is characterized by comprising m indexes) 1 ,I 1 ,L,I m W = [ W ] weight of each index 1 ,w 2 ,L,w m ]Let each index value in the time t index system be X t ＝[X t1 ,X t2 ,L,X tm ](ii) a Obtaining index I of normal operation range according to historical statistical conditions i (i =1,2,L, m) minimum, recommended, maximum, and is given as
Preferably, after the step S5.1, a step S5.2 is further included to evaluate the single index, so as to obtain a single index evaluation value, which specifically includes: if it is not
The index is within the normal range, and the score value is [0.6]Calculated according to the following formula
Wherein the parameter a =0.4,E (X) ti ) A single index evaluation value of the index i at the time t,
are respectively an index I i (i＝1,2, L, m) minimum, recommended, maximum value of the normal range of operation.
If it is not
Or
The index is not within the normal range, the score value [0, 0.6),
in the formula, E (X) ti ) A single index evaluation value of the index i at the time t,
are respectively an index I i (i =1,2,l, m) runs the minimum, recommended, maximum of the normal range, parameter b =0.6.
Preferably, after step S5.2, step S5.3 is further included to obtain an operation analysis evaluation result by using a multi-index comprehensive method, which specifically includes: the ideal values of the evaluation values of the indexes are:
negative ideal value of
The operation analysis evaluation result is
Wherein D () is a distance metric function and is calculated by an absolute value method
According to the operation analysis evaluation result P t And measuring the operation condition of the enterprise.
Preferably, the step S6 specifically includes:
s6.1, determining input data of a neural network, wherein the comprehensive data of the big data processing center and the operation analysis evaluation result is input of the neural network, and before the comprehensive data enters a network model, performing data standardization processing;
step S6.2, a network architecture model is established, the network architecture model is divided into two layers of neural networks, the first layer of neural network analyzes the historical data, the data of the same industry, the data of abnormal values, the policies and the market data in four dimensions, and four independent prediction networks are constructed; the second layer of neural network takes the output data of the first layer of neural network as input to carry out fusion prediction;
s6.3, outputting risk early warning data, wherein the first layer of neural network and the second layer of neural network are both in three-output design, and the output value is a Boolean variable [ O ] 1 ,O 2 ,O 3 ]In which O is i And the output result is 0 or 1, and a basis is provided for risk early warning and alarming.
Preferably, the analysis data includes operation data, domestic market data, international market data, policy and regulation data, and industry development data.
In another aspect, the present invention provides an enterprise operation analysis and early warning system based on big data analysis, including:
the data collection module is used for collecting analysis data required by enterprise operation analysis and early warning monitoring;
the data conversion module is used for converting the collected analysis data into a standard format and transmitting 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 extracting the analysis data by combining manual selection and big data clustering 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;
and the risk early warning module adopts four independent neural networks of historical data, industry data, abnormal value data, policy 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 disposal and alarm elimination module.
Compared with the prior art, the invention has the beneficial effects that:
1) By the method of single index analysis and evaluation and multi-index fusion evaluation, the relationship between the index value and the minimum value, the recommender and the maximum value of the normal operation range is fully considered, the quantification is reasonably carried out, the evaluation result is easy to understand, and the operability is strong.
2) The invention adopts two layers of fusion networks, which can reduce the overall parameters of the model and is convenient for training and optimization; secondly, each model is relatively independent and can be optimized independently, so that the robustness of the system is improved;
and thirdly, early warning monitoring is carried out on the operation condition of the enterprise from different dimensions, and higher accuracy is achieved through data fusion.
Drawings
Fig. 1 is a method for enterprise operation analysis and early warning monitoring based on big data analysis according to a first embodiment of the present invention;
fig. 2 is a specific flowchart of an enterprise operation analysis and monitoring method based on big data analysis according to an embodiment of the present invention;
fig. 3 is a structural 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 accompanying drawings:
for a better understanding of the present invention, embodiments thereof are explained in detail below with reference to the accompanying drawings.
An enterprise operation analysis and early warning method based on big data analysis provided by an embodiment of the present invention is shown in fig. 1, and a specific flow is shown in fig. 2, and specifically includes the following steps:
step S1: the method comprises the steps of collecting data required by enterprise operation analysis and early warning monitoring, wherein the data sources comprise network public data, enterprise report data and enterprise internal networks, and the 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 standard format to a big data center for storage.
And step S3: and uniformly managing the stored data. The big data processing center processes the acquired data, specifically comprises data mining, data cleaning, data association and data clustering, and provides a data basis for enterprise operation analysis and early warning monitoring.
And step S4: and determining a dynamic evaluation index system by combining manual selection and big data clustering extraction data. When enterprise operation analysis is carried out, due to the fact that the data size is large and the data relevance is different, relevant indexes need to be automatically clustered and generated according to a big data clustering method. Meanwhile, in order to fuse expert experience, different analysis emphasis is added to different enterprises, partial indexes are added in a manual mode, and the two indexes form a final evaluation index system together.
Step S5: and obtaining an enterprise operation analysis result by adopting single index analysis and multi-index comprehensive evaluation.
Step S5.1: and determining the operation analysis input index of the enterprise.
According to the index system determination method, the big data clustering is adopted for automatic extraction, and the expert experience index data is fused, so that the input index of enterprise operation analysis is formed together. The index system formed by m indexes is I = { I = { (I) 1 ,I 1 ,L,I m W = [ W ] weight of each index 1 ,w 2 ,L,w m ]Let each index value in the time t index system be X t ＝[X t1 ,X t2 ,L,X tm ]。
According to historical statistical conditions, an index I in the normal operation range can be obtained i (i =1,2,L,m) minimum, recommended, maximum, and is reported as
And national resource committee industry evaluation standards such as excellent values, average values and reference values can also be cited.
Step S5.2: and evaluating the single index to obtain a single index evaluation value.
If it is not
The index is in 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, calculated according to the following formula:
preferably, a =0.4;
if it is not
Or
The indicator is not within the normal range and the score value is 0, 0.6.
In the formula, X ti The value of the ith index at time t,
are respectively an index I i (i =1,2,l, m) minimum, recommended, maximum value of the normal range of operation.
Preferably, b =0.6.
Step S5.3: and calculating by adopting a TOPSIS (approximate ideal solution ordering method) method to obtain the final evaluation condition.
The ideal values of the index evaluation values are:
negative ideal value of
The final operation situation evaluation value is:
wherein D () is a distance metric function and is calculated by an absolute value method
E(X ti ) Single index evaluation value, w, for the ith index at time t i Is the weight of the index i.
According to the final evaluation value P t Measuring Enterprise operation conditions, P t The larger the value is, the better the business operation condition is, and the smaller the value is, the worse the operation condition is. By P t Whether the enterprise operation is normal or not can be judged, and the actual operation is convenient. A certain threshold P may typically be selected 0 As a reference to measure whether enterprise operations are within a reasonable state, P t >P 0 The enterprise operates normally, otherwise, the operation is abnormal. The early warning condition of a single index can be judged according to corresponding threshold values. And multi-dimensional deviation analysis is adopted, early warning and deviation correction are timely carried out, risks are controlled, and enterprise operation conditions are timely reminded.
Step S6: the method comprises the steps of adopting four independent neural networks of historical data, industry data, abnormal value data, policies and market data to form a new early warning network after fusion, 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. And the data of the operation evaluation results of the big data processing center and the enterprise are input into the neural network, and the data needs to be subjected to standardized processing before entering the network model.
Step S6.2: and establishing a network architecture model. Because the operation condition of an enterprise is related to various factors, the false alarm rate is high and the difference from the actual false alarm rate is large only from one aspect, and therefore a new early warning monitoring network architecture is adopted. The whole network is divided into two layers, wherein the first layer analyzes the historical data, the data of the same industry, the data of abnormal values, the policies and the market data respectively to construct four independent prediction networks; the second layer takes the output data of the first layer as input and performs fusion prediction.
Step S6.3: and outputting risk early warning data. The first layer and the second layer of the network are both designed with three outputs, and the output value is a Boolean variable [ O ] 1 ,O 2 ,O 3 ]In which O is i Is 0 or 1. The output is four results [1,0 ]]、[0,1,0]、[0,0,1]、[0,0,0]Respectively showing four lights of red, orange, yellow and green. And risk early warning and alarming are carried out in three states of red, orange and yellow, and alarming is not needed in a green state.
The two-layer fusion network has the advantages that the overall parameters of the model are reduced, and training and optimization are facilitated. And secondly, each model is relatively independent and can be optimized independently, so that the robustness of the system is improved. And thirdly, early warning and monitoring are carried out on the operation condition of the enterprise from different dimensions, and higher accuracy is achieved through data fusion.
The invention provides an enterprise operation analysis and early warning monitoring system based on big data analysis, which comprises a data collection module, a data analysis module and a warning module, wherein the data collection module is used for collecting analysis data required by enterprise operation analysis and early warning monitoring;
the data conversion module is used for converting the collected analysis data into a standard format and transmitting the standard format to the 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 index is generally of a data aggregation type, such as dimensions of completion progress, peer-to-peer bidding, peer-to-peer growth and the like, and generally comprises net asset profitability, economic added value, new sign contract amount completion rate, net profit completion rate, business income completion rate and the like; the manual selection indexes are as follows: first, key indexes, such as national resource committee assessment indexes like whole worker labor productivity; and the second is special indexes, such as leader speaking requirement in annual working meeting, increase of concordance increase of the spot exchange project, and proportion of management cost to earning revenue.
The enterprise operation analysis module is used for obtaining an enterprise operation analysis result by adopting single index analysis and multi-index comprehensive evaluation; corresponding analysis standards of different types of businesses of enterprises are different, and analysis can be performed according to businesses such as external menstruation, capital construction, design and consultation scientific and technological services, investment and operation, real estate, equipment manufacturing, financial services, business services and the like in specific application.
The risk early warning module adopts four independent neural networks of historical data, industry data, abnormal value data, policy and market data to form a new early warning network after fusion, and then carries out risk early warning monitoring, the early warning process mainly comprises 5 flows of early warning setting, early warning monitoring, early warning notification, early warning disposal and alarm elimination, the early warning setting flow mainly carries out index definition and rule setting, the early warning monitoring flow mainly carries out information acquisition and index processing and index research and judgment, the early warning notification flow mainly carries out early warning grade comprehensive evaluation and issues early warning information to relevant units, the early warning disposal flow mainly comprises a group, a second-level unit, a third-level unit, a fourth-level unit and a project group to process early warning conditions, and the alarm elimination flow mainly comprises that the relevant units achieve expected target alarm removal after finishing rectification.
In the description of the present invention, it should be noted that, unless explicitly stated or limited otherwise, the terms "connected" and "connected" are to be interpreted broadly, e.g., as being fixed or detachable or integrally connected; can be mechanically or electrically connected; may be directly connected or indirectly connected through an intermediate. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
In the description of the present invention, unless otherwise specified, the terms "upper", "lower", "left", "right", "inner", "outer", and the like indicate orientations or positional relationships based on those shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the referred device or element must have a specific orientation, be constructed in a specific orientation, and be operated, 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 it will be apparent to those skilled in the art that various modifications and variations can be easily made based on the application method and principle of the present invention disclosed herein, and the method is not limited to the method described in the above-mentioned embodiment of the present invention, so that the above-mentioned embodiment is only preferred and not restrictive.
Claims (7)
1. An enterprise operation analysis and early warning 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;
and step S3: uniformly managing the stored analysis data;
and step S4: extracting the analysis data by combining manual selection and big data clustering to determine a dynamic evaluation index system;
step S5: obtaining an enterprise operation analysis result by adopting single index analysis and multi-index comprehensive evaluation;
step S6: and forming a new early warning network by adopting four independent neural networks of historical data, industry data, abnormal value data, policy and market data after fusion, and then carrying out risk early warning monitoring.
2. The method according to claim 1, characterized in that said step S5 comprises in particular the following step S5.1:
determining the enterprise operation analysis input index, extracting through big data clustering, fusing expert experience index data, and jointly forming the input index of the enterprise operation analysis, wherein an index system formed by m indexes is I = { I = (the index system is characterized by comprising m indexes) 1 ,I 1 ,L,I m W = weight of each index[w 1 ,w 2 ,L,w m ]Let each index value in the time t index system be X t ＝[X t1 ,X t2 ,L,X tm ](ii) a Obtaining index I of normal operation range according to historical statistical conditions i (i =1,2,L,m) minimum, recommended, maximum, and is reported as
3. The method according to claim 2, wherein 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 is used
The index is within the normal range, and the score value is [0.6]Calculated according to the following formula
Wherein the parameter a =0.4,E (X) ti ) A single index evaluation value of the index i at the time t,
are respectively an index I i (i =1,2,l,m) minimum, recommended, maximum value of the normal range of operation;
if it is not
Or
The index is not within the normal range, the score value [0, 0.6),
in the formula, E (X) ti ) A single index evaluation value of the index i at the time t,
are respectively an index I i (i =1,2,l, m) runs the minimum, recommended, maximum of the normal range, parameter b =0.6.
4. The method of claim 3, wherein after the step S5.2, a step S5.3 of obtaining an operation analysis evaluation result by using a multi-index integration method specifically comprises:
the ideal values of the index evaluation values are:
negative ideal value of
The operation analysis evaluation result is
In the formula, D () is a distance measurement function and is calculated by adopting an absolute value method
According to the operation analysis evaluation result P t And measuring the operation condition of the enterprise.
5. The method according to claim 4, wherein the step S6 specifically comprises:
s6.1, determining input data of a neural network, wherein the comprehensive data of the big data processing center and the operation analysis evaluation result is input of the neural network, and before the comprehensive data enters a network model, performing data standardization processing;
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 the historical data, the data of the same industry, the abnormal value data, the policy and the market data to construct four independent prediction networks; the second layer of neural network takes the output data of the first layer of neural network as input to carry out fusion prediction;
s6.3, outputting risk early warning data, wherein the first layer neural network and the second layer neural network are both in three-output design, and the output value is a Boolean variable [ O ] 1 ,O 2 ,O 3 ]In which O is i And the output result is 0 or 1, and a basis is provided for risk early warning and alarming.
6. The method of claim 5, the analytical data comprising operational data, domestic market data, international market data, policy and regulation data, industry development data.
7. The utility model provides an enterprise operation analysis and early warning system based on big data analysis which 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 is used for converting the collected analysis data into a standard format and transmitting 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 extracting the analysis data by combining manual selection and big data clustering 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;
and the risk early warning module adopts four independent neural networks of historical data, industry data, abnormal value 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 disposal and alarm elimination.
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