CN111581499A - Data normalization method, device and equipment and readable storage medium - Google Patents
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Abstract
The invention provides a data normalization method, a device, equipment and a readable storage medium, wherein the method comprises the following steps: acquiring user behavior data; classifying the user behavior data to obtain a plurality of categories of user behavior subdata; performing characteristic analysis on the user behavior subdata of each category, and matching a corresponding data normalization mode for the user behavior subdata of each category; and carrying out data normalization processing on the user behavior sub-data of the corresponding category by adopting a corresponding data normalization mode to obtain a user behavior data set for carrying out user preference analysis. According to the invention, through carrying out feature analysis according to the acquired user data and matching with a corresponding data normalization mode for normalization processing, different types of user data can be converted into comparable data with the same dimension and the same order of magnitude, which can be subjected to mathematical operation mutually, so that the user preference analysis can be carried out, and the accuracy and reliability of a user preference analysis result can be effectively improved.
Description
Technical Field
The present invention relates to the field of data processing technologies, and in particular, to a data normalization method, apparatus, device, and readable storage medium.
Background
With the gradual popularization of the internet and the increasing abundance of network information resources, people gradually move from the information-deficient era to the information-overloaded era, and the continuous increase of the information quantity causes great difficulties and challenges to be met by both information producers and information consumers: it has become increasingly difficult to find information that is needed from a vast amount of information. Meanwhile, it is increasingly difficult to get the attention of consumers to make products stand out of a lot of information.
In order to efficiently, quickly and accurately push information to a user, information producers often need to mine information required by the user from mass data, wherein one important ring is to perform corresponding user behavior preference analysis according to user behavior data. In order to analyze user's preferences more accurately, user behavior data needs to be acquired from multiple channels and aspects for analysis. However, there are large differences between the data obtained from various aspects, usually with different attributes and dimensions, which affect the results of the data analysis. Therefore, in order to ensure the reliability of the result, it is necessary to perform normalization processing on the acquired raw data.
The existing method for analyzing the preference based on the user behavior data does not perform normalization processing on the data, and only simply performs weighted average calculation analysis on the obtained data, so that the difference between the preference analysis result and the actual situation is large, and the analysis accuracy and reliability are low. Therefore, a method for normalizing data of a plurality of user sample data is needed.
Disclosure of Invention
The technical problem to be solved by the embodiments of the present invention is to provide a data normalization method, apparatus, device and readable storage medium, which can perform data normalization processing on different types of acquired data, thereby improving accuracy and reliability of a user preference analysis result.
In order to solve the above technical problem, an embodiment of the present invention provides a data normalization method, including:
acquiring user behavior data from a plurality of data channels;
classifying the user behavior data according to a preset classification rule to obtain a plurality of categories of user behavior subdata;
performing characteristic analysis on the user behavior subdata of each category, and matching a corresponding data normalization mode for the user behavior subdata of each category according to the result of the characteristic analysis;
and performing data normalization processing on the user behavior sub-data of the corresponding category by adopting the corresponding data normalization mode to obtain a user behavior data set for user preference analysis.
Further, the classifying the user behavior data according to a preset classification rule to obtain a plurality of categories of user behavior sub-data specifically includes:
classifying the user behavior data according to a preset data type classification rule to obtain a plurality of categories of user behavior subdata; or the like, or, alternatively,
and classifying the user behavior data according to the acquisition channel of the user behavior data to obtain a plurality of categories of user behavior sub-data.
Further, the performing feature analysis on the user behavior sub-data of each category, and matching a corresponding data normalization manner for the user behavior sub-data of each category according to a result of the feature analysis specifically includes:
performing characteristic analysis on the user behavior subdata of each category, and calculating characteristic parameters of the user behavior subdata; wherein the characteristic parameters comprise mean values and standard deviations;
judging whether the user behavior subdata of the category meets a preset data normalization condition or not according to the characteristic parameters;
if so, matching a corresponding data normalization mode according to the characteristic parameters of the user behavior subdata of the category;
and if not, adding the user behavior sub-data of the category into the user behavior data set.
Further, the data normalization mode comprises a maximum and minimum normalization mode, a Z-score normalization mode and a nonlinear normalization mode.
In order to solve the same technical problem, the present invention also provides a data normalization apparatus, comprising:
the data acquisition module is used for acquiring user behavior data from a plurality of data channels;
the data classification module is used for classifying the user behavior data according to a preset classification rule to obtain a plurality of categories of user behavior subdata;
the mode matching module is used for carrying out characteristic analysis on the user behavior subdata of each category and matching a corresponding data normalization mode for the user behavior subdata of each category according to the result of the characteristic analysis;
and the normalization module is used for carrying out data normalization processing on the user behavior subdata of the corresponding category by adopting the corresponding data normalization mode so as to obtain a user behavior data set for carrying out user preference analysis.
Further, the data classification module is specifically configured to:
classifying the user behavior data according to a preset data type classification rule to obtain a plurality of categories of user behavior subdata; or the like, or, alternatively,
and classifying the user behavior data according to the acquisition channel of the user behavior data to obtain a plurality of categories of user behavior sub-data.
Further, the mode matching module specifically includes:
the characteristic analysis unit is used for carrying out characteristic analysis on the user behavior subdata of each category and calculating characteristic parameters of the user behavior subdata; wherein the characteristic parameters comprise mean values and standard deviations;
the data judgment unit is used for judging whether the user behavior subdata of the category meets a preset data normalization condition or not according to the characteristic parameters;
the mode matching unit is used for matching a corresponding data normalization mode according to the characteristic parameters of the user behavior subdata of the category;
and the data adding unit is used for adding the user behavior sub-data of the category into the user behavior data set.
Further, the data normalization mode comprises a maximum and minimum normalization mode, a Z-score normalization mode and a nonlinear normalization mode.
In order to solve the same technical problem, the present invention further provides a data normalization terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the memory is coupled to the processor, and when the processor executes the computer program, any one of the data normalization methods is implemented.
In order to solve the same technical problem, the present invention further provides a computer-readable storage medium, where a computer program is stored, where the computer program, when running, controls a device in which the computer-readable storage medium is located to execute any one of the data normalization methods.
Compared with the prior art, the invention has the following beneficial effects:
the embodiment of the invention provides a data normalization method, a device, equipment and a readable storage medium, wherein the method comprises the following steps: acquiring user behavior data from a plurality of data channels; classifying the user behavior data according to a preset classification rule to obtain a plurality of categories of user behavior subdata; performing characteristic analysis on the user behavior subdata of each category, and matching a corresponding data normalization mode for the user behavior subdata of each category according to the result of the characteristic analysis; and performing data normalization processing on the user behavior sub-data of the corresponding category by adopting the corresponding data normalization mode to obtain a user behavior data set for user preference analysis. According to the invention, through carrying out feature analysis according to the acquired user data and matching with a corresponding data normalization mode for normalization processing, different types of user data can be converted into comparable data with the same dimension and the same order of magnitude, which can be subjected to mathematical operation mutually, so that the user preference analysis can be carried out, and the accuracy and reliability of a user preference analysis result can be effectively improved.
Drawings
FIG. 1 is a schematic flow chart of a data normalization method according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a data normalization apparatus according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be described clearly and completely with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, an embodiment of the present invention provides a data normalization method, including:
and S1, acquiring user behavior data from a plurality of data channels.
This step is used to obtain user behavior data from several data channels. By way of example, user behavior data of a user on different software platforms can be obtained, and the user behavior data includes but is not limited to commodity browsing amount, purchase amount, comment information, advertisement click rate, page dwell time and the like; the channels for acquiring the user behavior data can be various large transaction websites, social platforms, entertainment applications and the like under the same registered user.
S2, classifying the user behavior data according to a preset classification rule to obtain a plurality of categories of user behavior sub-data.
In the embodiment of the present invention, further, step S2 specifically includes:
classifying the user behavior data according to a preset data type classification rule to obtain a plurality of categories of user behavior subdata; or the like, or, alternatively,
and classifying the user behavior data according to the acquisition channel of the user behavior data to obtain a plurality of categories of user behavior sub-data.
In the embodiment of the present invention, it should be noted that, in step S2, the user behavior data is classified according to a preset classification rule, which may be classified according to a data type, or may be classified according to a channel of data acquisition. For example, it may be set that the acquisition channels of the user behavior data are divided into several categories, and the user behavior data acquired from the same category of acquisition channels are collectively classified into the sub-data of the category. It may also be set to classify the data according to the type of the data, for example, the data is divided into a certain threshold range, and the data falling within a certain range is classified into a class data.
S3, performing characteristic analysis on the user behavior subdata of each category, and matching corresponding data normalization modes for the user behavior subdata of each category according to the result of the characteristic analysis. Further, the data normalization mode comprises a maximum and minimum normalization mode, a Z-score normalization mode and a nonlinear normalization mode.
In the embodiment of the present invention, further, step S3 specifically includes:
performing characteristic analysis on the user behavior subdata of each category, and calculating characteristic parameters of the user behavior subdata; wherein the characteristic parameters comprise mean values and standard deviations;
judging whether the user behavior subdata of the category meets a preset data normalization condition or not according to the characteristic parameters;
if so, matching a corresponding data normalization mode according to the characteristic parameters of the user behavior subdata of the category;
and if not, adding the user behavior sub-data of the category into the user behavior data set.
Step S3 is to perform feature analysis on the user behavior sub-data and match the corresponding data normalization mode. In the embodiment of the invention, firstly, the characteristic analysis is carried out on the user behavior subdata, and the relevant parameters of the user behavior subdata, including but not limited to the average value, standard deviation and the like of the data, are calculated; and then, by means of a set strategy, data normalization can be selected not to be performed on the sub-data of the category, and if data normalization is required, corresponding data normalization modes, including but not limited to a maximum and minimum normalization mode, a Z-score normalization mode, a nonlinear normalization mode and the like, are matched according to the characteristic parameters of the data and a preset strategy.
And S4, performing data normalization processing on the user behavior sub-data of the corresponding category by adopting the corresponding data normalization mode to obtain a user behavior data set for user preference analysis.
Step S4 is to perform data normalization on the corresponding user behavior sub-data by using the matched data normalization method, and use the normalized data as data for performing user preference analysis.
According to the invention, through carrying out feature analysis according to the acquired user data and matching with a corresponding data normalization mode for normalization processing, different types of user data can be converted into comparable data with the same dimension and the same order of magnitude, which can be subjected to mathematical operation mutually, so that the user preference analysis can be carried out, and the accuracy and reliability of a user preference analysis result can be effectively improved.
In multi-data hybrid computation, the data are different in size and magnitude due to different properties. In order to ensure the reliability of the calculation result, the raw data needs to be standardized. The data standardization (normalization) process is a basic work of data mining, different types of data often have different standards and different dimensions, which affect the result of data analysis, and in order to eliminate the dimension influence between data, the data standardization process is required to solve the comparability between data.
The data normalization is to transform the data of samples with different dimensions to make each index in the same order of magnitude, so that the data normalization is suitable for comprehensive comparison calculation. Normalization is to limit the processed data (by some algorithm) to a certain range required by you so that the absolute value of the value becomes some relative value relationship. Firstly, normalization is for the convenience of later data processing, and secondly, normalization of the data is ensured, so that analysis is carried out on the same standard. The data dimensionless processing mainly solves the comparability of user behavior data, removes the unit limitation of the data, converts the unit limitation of the data into a dimensionless pure numerical value, and facilitates comparison and weighting of data of different units or magnitude. Normalization of the data is to scale the data to fall within a small specified interval.
It should be noted that the above method or flow embodiment is described as a series of acts or combinations for simplicity, but those skilled in the art should understand that the present invention is not limited by the described acts or sequences, as some steps may be performed in other sequences or simultaneously according to the present invention. Further, those skilled in the art will appreciate that the embodiments described in the specification are exemplary embodiments and that no single embodiment is necessarily required by the inventive embodiments.
Referring to fig. 2, in order to solve the same technical problem, the present invention further provides a data normalization apparatus, including:
the data acquisition module 1 is used for acquiring user behavior data from a plurality of data channels;
the data classification module 2 is used for classifying the user behavior data according to a preset classification rule to obtain a plurality of categories of user behavior subdata;
the mode matching module 3 is used for carrying out characteristic analysis on the user behavior subdata of each category and matching a corresponding data normalization mode for the user behavior subdata of each category according to the result of the characteristic analysis;
and the normalization module 4 is used for performing data normalization processing on the user behavior sub-data of the corresponding category by adopting the corresponding data normalization mode to obtain a user behavior data set for performing user preference analysis.
Further, the data classification module 2 is specifically configured to:
classifying the user behavior data according to a preset data type classification rule to obtain a plurality of categories of user behavior subdata; or the like, or, alternatively,
and classifying the user behavior data according to the acquisition channel of the user behavior data to obtain a plurality of categories of user behavior sub-data.
Further, the mode matching module 3 specifically includes:
the characteristic analysis unit is used for carrying out characteristic analysis on the user behavior subdata of each category and calculating characteristic parameters of the user behavior subdata; wherein the characteristic parameters comprise mean values and standard deviations;
the data judgment unit is used for judging whether the user behavior subdata of the category meets a preset data normalization condition or not according to the characteristic parameters;
the mode matching unit is used for matching a corresponding data normalization mode according to the characteristic parameters of the user behavior subdata of the category;
and the data adding unit is used for adding the user behavior sub-data of the category into the user behavior data set.
Further, the data normalization mode comprises a maximum and minimum normalization mode, a Z-score normalization mode and a nonlinear normalization mode.
It is to be understood that the foregoing device item embodiments correspond to the method item embodiments of the present invention, and the data normalization device provided in the embodiments of the present invention can implement the data normalization method provided in any method item embodiment of the present invention.
In order to solve the same technical problem, the present invention further provides a data normalization terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the memory is coupled to the processor, and when the processor executes the computer program, any one of the data normalization methods is implemented.
The data normalization terminal equipment can be computing equipment such as a desktop computer, a notebook computer, a palm computer and a cloud server. The Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, etc. The general-purpose processor may be a microprocessor or the processor may be any conventional processor or the like, and the processor is a control center of the data normalization terminal device and connects various parts of the whole data normalization terminal device by using various interfaces and lines.
The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created according to the use of the mobile phone, and the like. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
In order to solve the same technical problem, the present invention further provides a computer-readable storage medium, where a computer program is stored, where the computer program, when running, controls a device in which the computer-readable storage medium is located to execute any one of the data normalization methods.
The computer program may be stored in a computer readable storage medium, which when executed by a processor, may implement the steps of the various method embodiments described above. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.
It should be noted that the above-described device embodiments are merely illustrative, where the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. In addition, in the drawings of the embodiment of the apparatus provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, and may be specifically implemented as one or more communication buses or signal lines. One of ordinary skill in the art can understand and implement it without inventive effort.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention.
Claims (10)
1. A method of normalizing data, comprising:
acquiring user behavior data from a plurality of data channels;
classifying the user behavior data according to a preset classification rule to obtain a plurality of categories of user behavior subdata;
performing characteristic analysis on the user behavior subdata of each category, and matching a corresponding data normalization mode for the user behavior subdata of each category according to the result of the characteristic analysis;
and performing data normalization processing on the user behavior sub-data of the corresponding category by adopting the corresponding data normalization mode to obtain a user behavior data set for user preference analysis.
2. The data normalization method of claim 1, wherein the user behavior data is classified according to a preset classification rule to obtain user behavior sub-data of a plurality of categories, specifically:
classifying the user behavior data according to a preset data type classification rule to obtain a plurality of categories of user behavior subdata; or the like, or, alternatively,
and classifying the user behavior data according to the acquisition channel of the user behavior data to obtain a plurality of categories of user behavior sub-data.
3. The data normalization method of claim 1, wherein the performing feature analysis on the user behavior sub-data of each category and matching a corresponding data normalization manner for the user behavior sub-data of each category according to a result of the feature analysis specifically includes:
performing characteristic analysis on the user behavior subdata of each category, and calculating characteristic parameters of the user behavior subdata; wherein the characteristic parameters comprise mean values and standard deviations;
judging whether the user behavior subdata of the category meets a preset data normalization condition or not according to the characteristic parameters;
if so, matching a corresponding data normalization mode according to the characteristic parameters of the user behavior subdata of the category;
and if not, adding the user behavior sub-data of the category into the user behavior data set.
4. The method of claim 1, wherein the data normalization modes include a maximum-minimum normalization mode, a Z-score normalization mode, and a non-linear normalization mode.
5. A data normalization apparatus, comprising:
the data acquisition module is used for acquiring user behavior data from a plurality of data channels;
the data classification module is used for classifying the user behavior data according to a preset classification rule to obtain a plurality of categories of user behavior subdata;
the mode matching module is used for carrying out characteristic analysis on the user behavior subdata of each category and matching a corresponding data normalization mode for the user behavior subdata of each category according to the result of the characteristic analysis;
and the normalization module is used for carrying out data normalization processing on the user behavior subdata of the corresponding category by adopting the corresponding data normalization mode so as to obtain a user behavior data set for carrying out user preference analysis.
6. The data normalization apparatus of claim 5, wherein the data classification module is specifically configured to:
classifying the user behavior data according to a preset data type classification rule to obtain a plurality of categories of user behavior subdata; or the like, or, alternatively,
and classifying the user behavior data according to the acquisition channel of the user behavior data to obtain a plurality of categories of user behavior sub-data.
7. The data normalization device of claim 5, wherein the mode matching module specifically comprises:
the characteristic analysis unit is used for carrying out characteristic analysis on the user behavior subdata of each category and calculating characteristic parameters of the user behavior subdata; wherein the characteristic parameters comprise mean values and standard deviations;
the data judgment unit is used for judging whether the user behavior subdata of the category meets a preset data normalization condition or not according to the characteristic parameters;
the mode matching unit is used for matching a corresponding data normalization mode according to the characteristic parameters of the user behavior subdata of the category;
and the data adding unit is used for adding the user behavior sub-data of the category into the user behavior data set.
8. The data normalization apparatus of claim 5, wherein the data normalization means includes a maximum-minimum normalization means, a Z-score normalization means, and a non-linear normalization means.
9. Data normalization terminal device, characterized in that it comprises a processor, a memory and a computer program stored in said memory and configured to be executed by said processor, said memory being coupled to said processor and said processor, when executing said computer program, implementing the data normalization method according to any of claims 1 to 4.
10. A computer-readable storage medium, in which a computer program is stored, wherein the computer program, when executed, controls an apparatus in which the computer-readable storage medium is located to perform the data normalization method according to any one of claims 1 to 4.
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