CN111581499B - Data normalization method, device, 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 user behavior sub-data of a plurality of categories; performing feature analysis on the user behavior sub-data of each category, and matching corresponding data normalization modes for the user behavior sub-data 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 so as 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 carrying out normalization processing by matching with a corresponding data normalization mode, different types of user data can be converted into data which can be mutually subjected to mathematical operation and have the same dimension and the same order of magnitude and have comparability so as to be used for carrying out user preference analysis, thereby effectively improving the accuracy and reliability of the user preference analysis result.
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 increasing popularity of the internet and the increasing abundance of network information resources, people gradually move from the information starvation age to the information overload age, and the increasing amount of information has made it difficult and challenging for both information producers and information consumers to: finding the information that is needed by itself from a huge amount of information has become increasingly difficult. At the same time, it is becoming increasingly difficult to get consumer attention to products that are prominent in a wide variety of information.
In order to efficiently, quickly and accurately push information to users, information producers often need to mine massive data to information needed by users, wherein a particularly important ring is to perform corresponding user behavior preference analysis according to user behavior data. In order to analyze the user's preferences more accurately, it is necessary to obtain user behavior data from multiple channels and aspects for analysis. However, there are large differences between the data obtained from the various aspects, often with different properties and dimensions, which can 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.
In the existing method for carrying out preference analysis based on user behavior data, data normalization is not carried out, but weighted average calculation analysis is simply carried out on the acquired data, so that the preference analysis result has a larger difference from the actual situation, and the analysis accuracy and reliability are lower. Therefore, there is a need for a method of data normalization of a variety of user sample data.
Disclosure of Invention
The technical problem to be solved by the embodiment of the invention is to provide a data normalization method, a device, equipment and a readable storage medium, which can perform data normalization processing on different types of acquired data, thereby improving the accuracy and reliability of user preference analysis results.
In order to solve the above technical problems, 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 user behavior sub-data of a plurality of categories;
performing feature analysis on the user behavior sub-data of each category, and matching corresponding data normalization modes for the user behavior sub-data of each category according to the result of the feature analysis;
and carrying out data normalization processing on the user behavior sub-data 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 classifying the user behavior data according to a preset classifying rule to obtain a plurality of categories of user behavior sub-data, specifically:
classifying the user behavior data according to a preset data type classification rule to obtain user behavior sub-data of a plurality of categories; or alternatively, the first and second heat exchangers may be,
classifying the user behavior data according to the acquisition channels of the user behavior data to obtain user behavior sub-data of a plurality of categories.
Further, the feature analysis is performed on the user behavior sub-data of each category, and the corresponding data normalization mode is matched for the user behavior sub-data of each category according to the result of the feature analysis, which specifically includes:
performing feature analysis on the user behavior sub-data of each category, and calculating feature parameters of the user behavior sub-data; wherein the characteristic parameters comprise an average value and a standard deviation;
judging whether the user behavior sub-data of the category accords with a preset data normalization condition according to the characteristic parameters;
if yes, matching the corresponding data normalization mode according to the characteristic parameters of the user behavior sub-data of the category;
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 invention also provides a data normalization device, which comprises:
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 user behavior sub-data of a plurality of categories;
the pattern matching module is used for carrying out feature analysis on the user behavior sub-data of each category and matching corresponding data normalization patterns for the user behavior sub-data of each category according to the result of the feature analysis;
and the normalization module is used for carrying out data normalization processing on the user behavior sub-data 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 user behavior sub-data of a plurality of categories; or alternatively, the first and second heat exchangers may be,
classifying the user behavior data according to the acquisition channels of the user behavior data to obtain user behavior sub-data of a plurality of categories.
Further, the mode matching module specifically includes:
the characteristic analysis unit is used for carrying out characteristic analysis on the user behavior sub-data of each category and calculating characteristic parameters of the user behavior sub-data; wherein the characteristic parameters comprise an average value and a standard deviation;
the data judging unit is used for judging whether the user behavior sub-data of the category accords with a preset data normalization condition according to the characteristic parameters;
the mode matching unit is used for matching corresponding data normalization modes according to the characteristic parameters of the user behavior sub-data 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.
To solve the same technical problem, the present invention also provides a data normalization terminal device, comprising 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 the processor implements any one of the data normalization methods when executing the computer program.
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 controls a device where the computer readable storage medium is located to execute any one of the data normalization methods when the computer program runs.
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 user behavior sub-data of a plurality of categories; performing feature analysis on the user behavior sub-data of each category, and matching corresponding data normalization modes for the user behavior sub-data of each category according to the result of the feature analysis; and carrying out data normalization processing on the user behavior sub-data 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. According to the invention, through carrying out feature analysis according to the acquired user data and carrying out normalization processing by matching with a corresponding data normalization mode, different types of user data can be converted into data which can be mutually subjected to mathematical operation and have the same dimension and the same order of magnitude and have comparability so as to be used for carrying out user preference analysis, thereby effectively improving the accuracy and reliability of the user preference analysis result.
Drawings
FIG. 1 is a 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 device according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, an embodiment of the present invention provides a data normalization method, including the steps of:
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 may be obtained, where the user behavior data includes, but is not limited to, merchandise browsing volume, purchase volume, comment information, advertisement click rate, page residence time, etc.; the channels for obtaining these user behavior data may be large transaction websites, social platforms, entertainment applications, etc. under the same registered user.
S2, classifying the user behavior data according to a preset classification rule to obtain user behavior sub-data of a plurality of categories.
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 user behavior sub-data of a plurality of categories; or alternatively, the first and second heat exchangers may be,
classifying the user behavior data according to the acquisition channels of the user behavior data to obtain user behavior sub-data of a plurality of categories.
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 a data acquisition channel. For example, it may be set that the acquisition channels of the user behavior data are classified into several categories, and the user behavior data acquired from the same category of acquisition channels are collectively classified into sub-data of the category. The data may be classified according to the type of the data, for example, the data is classified into a certain threshold range, and the data falling within a certain range is classified into one type of sub data.
And S3, carrying out feature analysis on the user behavior sub-data of each category, and matching corresponding data normalization modes for the user behavior sub-data of each category according to the result of the feature 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 feature analysis on the user behavior sub-data of each category, and calculating feature parameters of the user behavior sub-data; wherein the characteristic parameters comprise an average value and a standard deviation;
judging whether the user behavior sub-data of the category accords with a preset data normalization condition according to the characteristic parameters;
if yes, matching the corresponding data normalization mode according to the characteristic parameters of the user behavior sub-data of the category;
if not, adding the user behavior sub-data of the category into the user behavior data set.
And S3, carrying out feature analysis on the user behavior sub-data and matching the corresponding data normalization mode. In the embodiment of the invention, firstly, characteristic analysis is carried out on the user behavior sub-data, and relevant parameters of the user behavior sub-data are calculated, including but not limited to average value, standard deviation and the like of the data; and then, according to a set strategy, selecting not to normalize the sub-data of the category, and if the data is required to be normalized, matching corresponding data normalization modes according to the characteristic parameters of the data according to a preset strategy, wherein the data normalization modes comprise, but are not limited to, a maximum and minimum normalization mode, a Z-score normalization mode, a nonlinear normalization mode and the like.
And S4, carrying out data normalization processing on the user behavior sub-data 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.
Step S4 is to normalize the data of the corresponding user behavior sub-data by using the matched data normalization mode, and use the normalized data as the data for user preference analysis.
According to the invention, through carrying out feature analysis according to the acquired user data and carrying out normalization processing by matching with a corresponding data normalization mode, different types of user data can be converted into data which can be mutually subjected to mathematical operation and have the same dimension and the same order of magnitude and have comparability so as to be used for carrying out user preference analysis, thereby effectively improving the accuracy and reliability of the user preference analysis result.
In multiple data mix calculations, there are often different dimensions and magnitude due to the nature of the individual data. In order to ensure the reliability of the calculation result, the original data needs to be standardized. The data normalization (normalization) process is a basic work of data mining, different types of data often have different standards and different dimensions, and the situation can affect the result of data analysis, so that in order to eliminate the dimension effect among the data, the data normalization process needs to be performed to solve the comparability among the data.
The data normalization is to transform the data of the samples with different dimensions to ensure that all indexes are in the same order of magnitude, and is suitable for comprehensive comparison calculation. Normalization is to limit the data to be processed (by some algorithm) to a certain range that you need, so that the absolute value of the value becomes some relative value relation. Firstly, normalization is for convenience of later data processing, and secondly, normalization of data is ensured so that the data are in the same standard for analysis. The dimensionless data processing mainly solves the comparability of user behavior data, removes the unit limitation of the data, converts the data into dimensionless pure numerical values, and is convenient for comparing and weighting the data with different units or orders. Normalization of data is to scale the data to fall within a small specified interval.
It should be noted that, for simplicity of description, the above method or flow embodiments are all described as a series of combinations of acts, but it should be understood by those skilled in the art that the embodiments of the present invention are not limited by the order of acts described, as some steps may occur in other orders or concurrently in accordance with the embodiments of the present invention. Further, those skilled in the art will appreciate that the embodiments described in the specification are all alternative embodiments and that the actions involved are not necessarily required for the embodiments of the present invention.
Referring to fig. 2, in order to solve the same technical problem, the present invention further provides a data normalization device, 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 user behavior sub-data of a plurality of categories;
the mode matching module 3 is used for carrying out feature analysis on the user behavior sub-data of each category and matching corresponding data normalization modes for the user behavior sub-data of each category according to the result of the feature analysis;
and the normalization module 4 is used for carrying out data normalization processing on the user behavior sub-data 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 2 is specifically configured to:
classifying the user behavior data according to a preset data type classification rule to obtain user behavior sub-data of a plurality of categories; or alternatively, the first and second heat exchangers may be,
classifying the user behavior data according to the acquisition channels of the user behavior data to obtain user behavior sub-data of a plurality of categories.
Further, the mode matching module 3 specifically includes:
the characteristic analysis unit is used for carrying out characteristic analysis on the user behavior sub-data of each category and calculating characteristic parameters of the user behavior sub-data; wherein the characteristic parameters comprise an average value and a standard deviation;
the data judging unit is used for judging whether the user behavior sub-data of the category accords with a preset data normalization condition according to the characteristic parameters;
the mode matching unit is used for matching corresponding data normalization modes according to the characteristic parameters of the user behavior sub-data 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.
The embodiment of the device item is corresponding to the embodiment of the method item of the present invention, and the data normalization device provided by the embodiment of the present invention may implement the data normalization method provided by any one of the embodiment of the method item of the present invention.
To solve the same technical problem, the present invention also provides a data normalization terminal device, comprising 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 the processor implements any one of the data normalization methods when executing the computer program.
The data normalization terminal equipment can be computing equipment such as a desktop computer, a notebook computer, a palm computer, a cloud server and the like. The processor may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like, which is a control center of the data normalization terminal device, and connects the respective parts of the entire data normalization terminal device 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 cellular phone, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart Media Card (SMC), secure Digital (SD) Card, flash Card (Flash Card), at least one disk storage device, flash memory device, or other volatile solid-state storage device.
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 controls a device where the computer readable storage medium is located to execute any one of the data normalization methods when the computer program runs.
The computer program may be stored in a computer readable storage medium, which computer program, when being executed by a processor, may carry out the steps of the various method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. It should be noted that the computer readable medium contains content that can be appropriately scaled according to the requirements of jurisdictions in which such content is subject to legislation and patent practice, such as in certain jurisdictions in which such content is subject to legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunication signals.
It should be noted that the above-described apparatus embodiments are merely illustrative, and the units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. In addition, in the drawings of the embodiment of the device provided by the invention, the connection relation between the modules represents that the modules have communication connection, and can be specifically implemented as one or more communication buses or signal lines. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
While the foregoing is directed to the preferred embodiments of the present invention, it will be appreciated by those skilled in the art that changes and modifications may be made without departing from the principles of the invention, such changes and modifications are also intended to be within the scope of the invention.
Claims (6)
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 user behavior sub-data of a plurality of categories;
classifying the user behavior data according to a preset data type classification rule to obtain user behavior sub-data of a plurality of categories; or classifying the user behavior data according to the acquisition channels of the user behavior data to obtain user behavior sub-data of a plurality of categories;
performing feature analysis on the user behavior sub-data of each category, and matching corresponding data normalization modes for the user behavior sub-data of each category according to the result of the feature analysis;
performing feature analysis on the user behavior sub-data of each category, and calculating feature parameters of the user behavior sub-data; wherein the characteristic parameters comprise an average value and a standard deviation;
judging whether the user behavior sub-data of the category accords with a preset data normalization condition according to the characteristic parameters;
if yes, matching the corresponding data normalization mode according to the characteristic parameters of the user behavior sub-data of the category;
if not, adding the user behavior sub-data of the category into the user behavior data set;
and carrying out data normalization processing on the user behavior sub-data of the corresponding category by adopting the corresponding data normalization mode so as to obtain the user behavior data set for carrying out user preference analysis.
2. The method of claim 1, wherein the data normalization mode comprises a maximum-minimum normalization mode, a Z-score normalization mode, or a nonlinear normalization mode.
3. A data normalization device, 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 user behavior sub-data of a plurality of categories;
the data classification module is specifically configured to: classifying the user behavior data according to a preset data type classification rule to obtain user behavior sub-data of a plurality of categories; or classifying the user behavior data according to the acquisition channels of the user behavior data to obtain user behavior sub-data of a plurality of categories;
the pattern matching module is used for carrying out feature analysis on the user behavior sub-data of each category and matching corresponding data normalization patterns for the user behavior sub-data of each category according to the result of the feature analysis;
the mode matching module specifically comprises: the characteristic analysis unit is used for carrying out characteristic analysis on the user behavior sub-data of each category and calculating characteristic parameters of the user behavior sub-data; wherein the characteristic parameters comprise an average value and a standard deviation; the data judging unit is used for judging whether the user behavior sub-data of the category accords with a preset data normalization condition according to the characteristic parameters; the mode matching unit is used for matching corresponding data normalization modes according to the characteristic parameters of the user behavior sub-data of the category; a data adding unit, configured to add the user behavior sub-data of the category to the user behavior data set;
and the normalization module is used for carrying out data normalization processing on the user behavior sub-data 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.
4. A data normalization device according to claim 3, in which the data normalization means comprises a maximum minimum normalization means, a Z-score normalization means, a non-linear normalization means.
5. A data normalization terminal device, comprising a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, the memory being coupled to the processor, and the processor implementing the data normalization method according to any one of claims 1 to 2 when executing the computer program.
6. A computer readable storage medium, characterized in that the computer readable storage medium stores a computer program, wherein the computer program, when run, controls a device in which the computer readable storage medium is located to perform the data normalization method according to any one of claims 1 to 2.
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