CN115965456A - Data change analysis method and device - Google Patents
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- CN115965456A CN115965456A CN202310025213.9A CN202310025213A CN115965456A CN 115965456 A CN115965456 A CN 115965456A CN 202310025213 A CN202310025213 A CN 202310025213A CN 115965456 A CN115965456 A CN 115965456A
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Abstract
The application provides a data change analysis method and a device, which can be applied to the financial field and other fields, wherein the method comprises the following steps: acquiring identity information of a target to be analyzed, and calling corresponding historical data from a preset database according to the identity information; comparing the data volume of the historical data with a preset threshold value, and obtaining a data volume grade according to a comparison result; acquiring a preset analysis strategy according to the data volume grade, and constructing a data change analysis model according to a corresponding machine learning algorithm in the analysis strategy and the historical data; and obtaining a data change result of the target to be analyzed according to the data change analysis model and the real-time data of the target to be analyzed.
Description
Technical Field
The application relates to the field of big data analysis, which can be applied to the financial field and other fields, in particular to a data change analysis method and device.
Background
Along with the development of computer science, various user contact points such as online and offline are generated by banks, so that users can be better known and satisfied in sequence. However, facing a large number of customers, the method cannot be seen by banks, and more comprehensive and accurate insight on customer requirements is needed. Therefore, when the business is carried out, the conditions of user fund loss and the like are analyzed in advance, and the fund change condition of the customer is mastered, so that the corresponding service is provided for the corresponding customer more predictably, and the operation cost is reduced.
The technical scheme in the prior art is as follows: the method is characterized in that a user portrait is always a hot topic based on data, and the method is used in banks, wherein a business scene for judging the fund flow condition of a client is included. However, most of the users are in the traditional data dimension, that is, based on big data, based on the basic characteristics and behavior characteristics of the users, the flow condition of the user funds is judged by the obtained model by combining the experience of the service with the basic regression or classification method, which is a commonly used method for describing the user drawings.
Although the method reduces the operation cost of the bank to a certain extent, the prediction accuracy of the liquidity of the customer funds is low because the adopted algorithm is simple in foundation and insufficient in information mining contained in the data.
Disclosure of Invention
The application aims to provide a data change analysis method and device, and the data change analysis method and device are used for training data by adopting an integrated algorithm of machine learning, so that the problems of insufficient data information mining, overfitting and the like in the prior art are solved, and the computing power of a computer is more fully utilized, so that the accuracy rate of predicting the fund flow condition of a client is improved.
To achieve the above object, the present invention provides a data change analyzing method, which comprises: acquiring identity information of a target to be analyzed, and calling corresponding historical data from a preset database according to the identity information; comparing the data volume of the historical data with a preset threshold value, and obtaining a data volume grade according to a comparison result; acquiring a preset analysis strategy according to the data volume grade, and constructing a data change analysis model according to a corresponding machine learning algorithm in the analysis strategy and the historical data; and obtaining a data change result of the target to be analyzed according to the data change analysis model and the real-time data of the target to be analyzed.
In the above data change analysis method, optionally, the machine learning algorithm in the analysis strategy includes a bagging algorithm and a lifting algorithm.
In the above data change analysis method, optionally, the constructing a data change analysis model according to the corresponding machine learning algorithm in the analysis policy and the historical data includes: and when the analysis strategy is a bagging algorithm, constructing the change analysis model by adopting a random forest algorithm according to the historical data.
In the above data change analysis method, optionally, the constructing the change analysis model by using a random forest algorithm according to the historical data includes: extracting and obtaining characteristic attributes according to the historical data, and generating sample data according to the characteristic attributes; the method comprises the steps of generating a plurality of sub-sample sets by extracting sample data repeatedly with samples replaced, respectively training a plurality of decision trees according to the sub-sample sets, and constructing a random forest model through the decision trees; and obtaining a data change analysis model through the random forest model.
In the above data change analysis method, optionally, the constructing a data change analysis model according to the corresponding machine learning algorithm in the analysis policy and the historical data includes: and when the analysis strategy is a lifting algorithm, constructing the change analysis model by adopting a gradient lifting algorithm according to the historical data.
In the above data change analysis method, optionally, the constructing the change analysis model by using a gradient lifting algorithm according to the historical data includes: extracting and obtaining characteristic attributes according to the historical data, and generating sample data according to the characteristic attributes; and constructing a plurality of classifiers according to different fitting targets and the sample data, and constructing a data change analysis model through the plurality of classifiers.
The application also provides a data change analysis device, which comprises an acquisition module, a comparison module, a construction module and an analysis module; the acquisition module is used for acquiring the identity information of a target to be analyzed and calling corresponding historical data from a preset database according to the identity information; the comparison module is used for comparing the data volume of the historical data with a preset threshold value and obtaining a data volume grade according to a comparison result; the construction module is used for acquiring a preset analysis strategy according to the data volume grade and constructing a data change analysis model according to a corresponding machine learning algorithm in the analysis strategy and the historical data; the analysis module is used for obtaining a data change result of the target to be analyzed according to the data change analysis model and the real-time data of the target to be analyzed.
The application also provides an electronic device, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the computer program to realize the method.
The present application also provides a computer-readable storage medium storing a computer program for executing the above method.
The present application also provides a computer program product comprising computer programs/instructions which, when executed by a processor, implement the steps of the above-described method.
The beneficial technical effect of this application lies in: training data by adopting various integration algorithms of machine learning, and calculating by adopting different integration algorithms according to different requirements of data characteristics for flexible adoption; for example, a bag bagging method (bagging) in an integrated algorithm represents a random forest, and the problem of model overfitting is solved; a representative gradient lifting method (GBDT) of a lifting method (boosting) pays more attention to abnormal values, reduces model deviation and improves performance, an extreme gradient lifting method (XGboost) makes full use of computing capacity of a computer, and the prediction accuracy of the capital flow situation of a client can be improved in the face of corresponding business scenes.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application, are incorporated in and constitute a part of this application, and are not intended to limit the application. In the drawings:
fig. 1 is a schematic flow chart illustrating a data change analysis method according to an embodiment of the present application;
FIG. 2 is a schematic diagram illustrating a process for constructing a variation analysis model according to an embodiment of the present application;
FIG. 3 is a schematic diagram of a logic for constructing a random forest algorithm according to an embodiment of the present disclosure;
FIG. 4 is a schematic diagram illustrating a process for constructing a variation analysis model according to an embodiment of the present application;
FIG. 5 is a schematic diagram illustrating a logic diagram of a gradient boosting method according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of a data change analysis apparatus according to an embodiment of the present application;
fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the application.
Detailed Description
The following detailed description will be provided with reference to the drawings and examples to explain how to apply the technical means to solve the technical problems and to achieve the technical effects. It should be noted that, as long as there is no conflict, the embodiments and the features of the embodiments in the present application may be combined with each other, and the technical solutions formed are all within the scope of the present application.
Additionally, the steps illustrated in the flowcharts of the figures may be performed in a computer system such as a set of computer-executable instructions, and while a logical order is illustrated in the flowcharts, in some cases, the steps illustrated or described may be performed in an order different than here.
Referring to fig. 1, a data change analysis method provided in the present application specifically includes:
s101, acquiring identity information of a target to be analyzed, and calling corresponding historical data from a preset database according to the identity information;
s102, comparing the data volume of the historical data with a preset threshold value, and obtaining a data volume grade according to a comparison result;
s103, acquiring a preset analysis strategy according to the data volume grade, and constructing a data change analysis model according to a corresponding machine learning algorithm in the analysis strategy and the historical data;
s104, obtaining a data change result of the target to be analyzed according to the data change analysis model and the real-time data of the target to be analyzed.
Wherein, the machine learning algorithm in the analysis strategy comprises a bagging algorithm and a lifting algorithm. Specifically, two modes of an integrated algorithm can be adopted in actual work, and the prediction accuracy rate of the conditions that the fund change condition of a client is ascending, descending, stable maintenance and the like is improved aiming at two service scenes; certainly, the user may also use three or more algorithms to target more service scenarios due to different actual requirements, which is not further limited in the present application.
In an embodiment of the present application, constructing a data change analysis model according to the corresponding machine learning algorithm in the analysis policy and the historical data includes: and when the analysis strategy is a bagging algorithm, constructing the change analysis model by adopting a random forest algorithm according to the historical data. Specifically, referring to fig. 2, the constructing the change analysis model by using a random forest algorithm according to the historical data includes:
s201, extracting and obtaining a characteristic attribute according to the historical data, and generating sample data according to the characteristic attribute;
s202, generating a plurality of sub-sample sets by extracting the sample data repeatedly, training a plurality of decision trees according to the sub-sample sets, and constructing a random forest model through the decision trees;
s203, obtaining a data change analysis model through the random forest model.
In actual work, aiming at predicting future fund change conditions of customers when the data volume is relatively small, a bagging idea in an integrated algorithm, namely a random forest, can be adopted to avoid the problem of model overfitting caused by the fact that the data volume is small and information is relatively missing. The flow is shown in fig. 3, and specifically includes the following steps:
(1) Acquiring source data, and then performing data cleaning, data processing and feature engineering construction on feature attributes by technical personnel and business personnel to obtain a sample set (provided with N samples and M attributes);
(2) Extracting the sample set for Nn times, forming n sub-sample sets, and training a decision tree DT for each sub-sample set; each sample has M attributes, when each node of the decision tree needs to be split, M attributes are randomly selected from the M attributes, then 1 attribute is selected as the split attribute of the node by taking information gain as a strategy, the step is continuously repeated until the next attribute selected by the node is the attribute used when the father node is split, and the node is not split. The forest of the hundred trees is formed, and a large number of decision trees form a random forest RF;
the result is voted from a plurality of trees, for example, the fund change situation of the client 1 has a prediction result in each tree of the RF, that is, n prediction results, if the fund change situation is a rising majority, the prediction result of the future fund change situation of the client is a rising prediction result, and vice versa.
In another embodiment of the present application, constructing a data change analysis model according to the corresponding machine learning algorithm in the analysis strategy and the historical data includes: and when the analysis strategy is a lifting algorithm, constructing the change analysis model by adopting a gradient lifting algorithm according to the historical data. Specifically, referring to fig. 4, the constructing the change analysis model by using a gradient lifting algorithm according to the historical data includes:
s401, extracting and obtaining a characteristic attribute according to the historical data, and generating sample data according to the characteristic attribute;
s402, constructing a plurality of classifiers according to different fitting targets and the sample data, and constructing a data change analysis model through the plurality of classifiers.
In actual work, in the case of relatively large data volume, the future fund change situation of the customer is predicted, a boosting concept-GBDT in an integrated algorithm may be adopted to pay more attention to the influence of the abnormal value of the data on the model, and the prediction accuracy of the model is improved by reducing the deviation of the model, and the flow of the prediction is as shown in fig. 5, specifically as follows:
(1) Acquiring source data, and then performing data cleaning, data processing and feature engineering construction on feature attributes by technical personnel and business personnel to obtain a sample set (provided with N samples and M attributes);
(2) Here, all weak classifiers adopt the decision tree in fig. 1, but the integration idea here is not to mine information by sampling more samples continuously, but the same batch of samples, but the fitting target y of each tree is changing continuously.
The target value Y0 of the first round of training is the real value of the training sample, i.e. the capital movement of the customer is ascending or descending, and can be quantized to 0 and 1, if there are more levels, also can be quantized; obtaining a predicted value Y of a corresponding sample by using a model trained by a first tree, and then taking a residual Y1= Y0-Y as a fitting target value of a second tree; by the m tree, the residual error Ym to be fitted has become very small and even approaches or equals 0, at which point the last model is obtained; all if the result of the classifier is added to equal the predicted value, i.e., the predicted result = Y1+ Y2 +....... - + Ym, the rest of the samples are also the same.
(3) It is also contemplated to use the XGBoost algorithm, which is also effectively a BGDT, which is an Extreme (Extreme) GBDT, and is therefore referred to as XGBoost. On the basis of GBDT, the method utilizes the multithreading of CPU to accelerate the operation speed, properly improves the GBDT algorithm, adds the branch reduction and controls the complexity of the model.
Referring to fig. 6, the present application further provides a data change analyzing apparatus, which includes an acquisition module, a comparison module, a construction module, and an analysis module; the acquisition module is used for acquiring the identity information of a target to be analyzed and calling corresponding historical data from a preset database according to the identity information; the comparison module is used for comparing the data volume of the historical data with a preset threshold value and obtaining a data volume grade according to a comparison result; the construction module is used for acquiring a preset analysis strategy according to the data volume grade and constructing a data change analysis model according to a corresponding machine learning algorithm in the analysis strategy and the historical data; the analysis module is used for obtaining a data change result of the target to be analyzed according to the data change analysis model and the real-time data of the target to be analyzed.
The beneficial technical effect of this application lies in: training data by adopting various integrated algorithms of machine learning, and calculating by adopting different integrated algorithms according to different requirements of data characteristics so as to be flexibly adopted; for example, a bag bagging method (bagging) in an integrated algorithm represents a random forest, and the problem of model overfitting is solved; a representative gradient lifting method (GBDT) of a lifting method (boosting) pays more attention to abnormal values, reduces model deviation and improves performance, an extreme gradient lifting method (XGboost) makes full use of computing capacity of a computer, and the prediction accuracy of the capital flow situation of a client can be improved in the face of corresponding business scenes.
The application also provides an electronic device, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the computer program to realize the method.
The present application also provides a computer-readable storage medium storing a computer program for executing the above method.
The present application also provides a computer program product comprising computer programs/instructions which, when executed by a processor, implement the steps of the above-described method.
As shown in fig. 7, the electronic device 600 may further include: communication module 110, input unit 120, audio processing unit 130, display 160, power supply 170. It is noted that the electronic device 600 does not necessarily include all of the components shown in FIG. 7; in addition, the electronic device 600 may also include components not shown in fig. 7, which may be referred to in the prior art.
As shown in fig. 7, the central processor 100, sometimes referred to as a controller or operational control, may include a microprocessor or other processor device and/or logic device, the central processor 100 receiving input and controlling the operation of the various components of the electronic device 600.
The memory 140 may be, for example, one or more of a buffer, a flash memory, a hard drive, a removable media, a volatile memory, a non-volatile memory, or other suitable device. The information relating to the failure may be stored, and a program for executing the information may be stored. And the central processing unit 100 may execute the program stored in the memory 140 to realize information storage or processing, etc.
The input unit 120 provides input to the cpu 100. The input unit 120 is, for example, a key or a touch input device. The power supply 170 is used to provide power to the electronic device 600. The display 160 is used to display an object to be displayed, such as an image or a character. The display may be, for example, an LCD display, but is not limited thereto.
The memory 140 may be a solid state memory such as Read Only Memory (ROM), random Access Memory (RAM), a SIM card, or the like. There may also be a memory that holds information even when power is off, can be selectively erased, and is provided with more data, an example of which is sometimes called an EPROM or the like. The memory 140 may also be some other type of device. Memory 140 includes buffer memory 141 (sometimes referred to as a buffer). The memory 140 may include an application/function storage section 142, and the application/function storage section 142 is used to store application programs and function programs or a flow for executing the operation of the electronic device 600 by the central processing unit 100.
The memory 140 may also include a data store 143 for storing data, such as contacts, digital data, pictures, sounds, and/or any other data used by the electronic device. The driver storage portion 144 of the memory 140 may include various drivers of the electronic device for communication functions and/or for performing other functions of the electronic device (e.g., messaging application, address book application, etc.).
The communication module 110 is a transmitter/receiver 110 that transmits and receives signals via an antenna 111. The communication module (transmitter/receiver) 110 is coupled to the central processor 100 to provide an input signal and receive an output signal, which may be the same as in the case of a conventional mobile communication terminal.
Based on different communication technologies, a plurality of communication modules 110, such as a cellular network module, a bluetooth module, and/or a wireless local area network module, may be provided in the same electronic device. The communication module (transmitter/receiver) 110 is also coupled to a speaker 131 and a microphone 132 via an audio processor 130 to provide audio output via the speaker 131 and to receive audio input from the microphone 132 to implement general telecommunication functions. Audio processor 130 may include any suitable buffers, decoders, amplifiers and so forth. In addition, an audio processor 130 is also coupled to the central processor 100, enabling recording locally through a microphone 132, and enabling locally stored sound to be played through a speaker 131.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above-mentioned embodiments are further described in detail for the purpose of illustrating the invention, and it should be understood that the above-mentioned embodiments are only illustrative of the present invention and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. A method for analyzing data changes, the method comprising:
acquiring identity information of a target to be analyzed, and calling corresponding historical data from a preset database according to the identity information;
comparing the data volume of the historical data with a preset threshold value, and obtaining a data volume grade according to a comparison result;
acquiring a preset analysis strategy according to the data volume grade, and constructing a data change analysis model according to a corresponding machine learning algorithm in the analysis strategy and the historical data;
and obtaining a data change result of the target to be analyzed according to the data change analysis model and the real-time data of the target to be analyzed.
2. The data change analysis method according to claim 1, wherein the machine learning algorithm in the analysis strategy includes a bagging algorithm and a boosting algorithm.
3. The method of claim 2, wherein constructing a data change analysis model based on the historical data and a corresponding machine learning algorithm in the analysis strategy comprises:
and when the analysis strategy is a bagging algorithm, constructing the change analysis model by adopting a random forest algorithm according to the historical data.
4. The data change analysis method of claim 3, wherein constructing the change analysis model using a random forest algorithm based on the historical data comprises:
extracting and obtaining characteristic attributes according to the historical data, and generating sample data according to the characteristic attributes;
the method comprises the steps of generating a plurality of sub-sample sets by extracting sample data repeatedly with samples replaced, respectively training a plurality of decision trees according to the sub-sample sets, and constructing a random forest model through the decision trees;
and obtaining a data change analysis model through the random forest model.
5. The data change analysis method of claim 2, wherein constructing a data change analysis model based on the corresponding machine learning algorithm in the analysis strategy and the historical data comprises:
and when the analysis strategy is a lifting algorithm, constructing the change analysis model by adopting a gradient lifting algorithm according to the historical data.
6. The data fluctuation analysis method of claim 5, wherein constructing the fluctuation analysis model using a gradient boosting algorithm based on the historical data comprises:
extracting and obtaining characteristic attributes according to the historical data, and generating sample data according to the characteristic attributes;
and constructing a plurality of classifiers according to different fitting targets and the sample data, and constructing a data change analysis model through the plurality of classifiers.
7. A data change analysis device is characterized by comprising an acquisition module, a comparison module, a construction module and an analysis module;
the acquisition module is used for acquiring the identity information of a target to be analyzed and calling corresponding historical data from a preset database according to the identity information;
the comparison module is used for comparing the data volume of the historical data with a preset threshold value and obtaining the data volume grade according to the comparison result;
the construction module is used for acquiring a preset analysis strategy according to the data volume grade and constructing a data change analysis model according to a corresponding machine learning algorithm in the analysis strategy and the historical data;
the analysis module is used for obtaining a data change result of the target to be analyzed according to the data change analysis model and the real-time data of the target to be analyzed.
8. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any of claims 1 to 6 when executing the computer program.
9. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program for executing the method of any one of claims 1 to 6 by a computer.
10. A computer program product comprising computer program/instructions, characterized in that the computer program/instructions, when executed by a processor, implement the steps of the method of any of claims 1 to 6.
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| CN117434227A (en) * | 2023-12-20 | 2024-01-23 | 河北金隅鼎鑫水泥有限公司 | Method and system for monitoring waste gas components of cement manufacturing plant |
| CN117434227B (en) * | 2023-12-20 | 2024-04-30 | 河北金隅鼎鑫水泥有限公司 | Method and system for monitoring waste gas components of cement manufacturing plant |
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