CN116562923B - Big data analysis method, system and medium based on electronic commerce behaviors - Google Patents
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Abstract
The embodiment of the application provides a big data analysis method, a big data analysis system and a big data analysis medium based on electronic commerce behaviors, wherein the method comprises the following steps: capturing user behavior data through a web crawler, and preprocessing the user behavior data to obtain result information; extracting result information characteristic values, and comparing the result information characteristic values with a preset characteristic threshold value to obtain a deviation rate; judging whether the deviation rate is larger than or equal to a preset deviation rate threshold value; if the user behavior data is greater than or equal to the user behavior data, generating correction information, and carrying out feedback correction on the user behavior data through the correction information; if the user behavior data is smaller than the preset threshold, performing cluster analysis on the user behavior data to obtain a behavior classification result; the user behavior data is analyzed and processed by judging the deviation rate of the user behavior data, so that the user behavior data can be analyzed more accurately, and the analysis result is closer to the actual value.
Description
Technical Field
The application relates to the field of big data analysis, in particular to a big data analysis method, a big data analysis system and a big data analysis medium based on electronic commerce behaviors.
Background
Electronic commerce refers to commerce activities involving the exchange of goods by using information network technology as a means. It can also be understood that electronic transactions and related service activities on the internet, intranet and value added network are all links of the traditional business activities. Commercial activities mediated by the internet all fall within the scope of electronic commerce.
The existing electronic commerce behavior big data analysis precision is poor, deviation judgment can not be carried out on the user behavior data by capturing the user behavior data through a web crawler, so that correction is carried out according to deviation results, and an effective technical solution is needed at present.
Disclosure of Invention
The embodiment of the application aims to provide a big data analysis method, a big data analysis system and a big data analysis medium based on electronic commerce behaviors, which can analyze and process user behavior data by judging the deviation rate of the user behavior data, so that the user behavior data can be more accurately analyzed, and an analysis result is closer to an actual value.
The embodiment of the application also provides a big data analysis method based on electronic commerce behaviors, which comprises the following steps:
capturing user behavior data through a web crawler, and preprocessing the user behavior data to obtain result information;
extracting result information characteristic values, and comparing the result information characteristic values with a preset characteristic threshold value to obtain a deviation rate;
judging whether the deviation rate is larger than or equal to a preset deviation rate threshold value;
if the user behavior data is greater than or equal to the user behavior data, generating correction information, and carrying out feedback correction on the user behavior data through the correction information;
if the user behavior data is smaller than the preset threshold, performing cluster analysis on the user behavior data to obtain a behavior classification result.
Optionally, in the big data analysis method based on electronic commerce behavior according to the embodiment of the present application, capturing user behavior data by a web crawler, and preprocessing the user behavior data to obtain result information, where the method includes:
capturing a webpage, setting a traversing rule, traversing network nodes, and extracting data of the network nodes;
calculating an evaluation value of the data of the network node through an analysis algorithm;
comparing the evaluation value of the network node with a preset threshold value to obtain an evaluation difference value;
judging whether the evaluation difference value is larger than or equal to a preset evaluation difference value;
if the rule is larger than or equal to the first rule, generating correction information, and adjusting the traversal rule through the correction information;
if the data is smaller than the storage data, the data of the network node is stored in the storage node, and the storage data is generated.
Optionally, in the big data analysis method based on electronic commerce behavior according to the embodiment of the present application, the capturing a web page, setting a traversal rule, traversing a network node, and extracting data of the network node includes:
acquiring the number of network nodes;
performing difference calculation on the number of network nodes and the preset number to obtain a number difference;
judging whether the quantity difference value is positive or not;
if yes, judging that the number of the network nodes is large, establishing a superposition rule, and fusing the network nodes through the superposition rule;
if the number of the network nodes is negative, the number of the network nodes is judged to be small, a segmentation rule is established, and the network nodes are segmented through the segmentation rule.
Optionally, in the big data analysis method based on electronic commerce behavior according to the embodiment of the present application, if the result is positive, the number of network nodes is determined to be large, a stacking rule is established, and the network nodes are fused through the stacking rule, including:
acquiring data of a network node and generating node information;
randomly sequencing node information;
comparing the adjacent two node information according to the sequence to obtain a first similarity;
judging whether the first similarity is larger than or equal to a first similarity threshold value;
if the number of the network nodes is greater than or equal to the number of the network nodes, fusing two adjacent network nodes, and generating the number of the network nodes;
if the number of the network nodes is equal to the preset number of the network nodes, stopping network node fusion.
Optionally, in the big data analysis method based on electronic commerce behavior according to the embodiment of the present application, if the result is negative, it is determined that the number of network nodes is small, and a segmentation rule is established, where the network nodes are segmented by the segmentation rule, including:
if the number of the network nodes is smaller than the preset number of the network nodes;
acquiring data information in the same network node;
comparing the data information in the network node with preset data information to obtain a second similarity;
judging whether the second similarity is greater than or equal to a second similarity threshold;
if the data is greater than or equal to the data, dividing the data in the network node into the same type of data;
if the data is smaller than the preset value, judging the corresponding data is of different types, and dividing the network nodes by the network data of different types.
Optionally, in the big data analysis method based on electronic commerce behavior according to the embodiment of the present application, if the big data analysis method is greater than or equal to the big data analysis method, correction information is generated, and feedback correction is performed on user behavior data through the correction information, including:
extracting result information characteristic values, and comparing the result information characteristic values with a preset characteristic threshold value to obtain a deviation rate;
judging whether the deviation rate is larger than or equal to a first deviation rate threshold value and smaller than a second deviation rate threshold value;
if the first deviation rate threshold value is greater than or equal to the first deviation rate threshold value and smaller than the second deviation rate threshold value, generating first correction information, generating first feedback data through the first correction information, and adjusting the user behavior data according to the first feedback data;
and if the deviation rate is larger than the second deviation rate threshold value, generating second correction information, generating second feedback data through the second correction information, and adjusting the user behavior data according to the second feedback data.
In a second aspect, embodiments of the present application provide a big data analysis system based on electronic commerce behavior, the system including: the system comprises a memory and a processor, wherein the memory comprises a program of a big data analysis method based on electronic commerce, and the program of the big data analysis method based on the electronic commerce realizes the following steps when being executed by the processor:
capturing user behavior data through a web crawler, and preprocessing the user behavior data to obtain result information;
extracting result information characteristic values, and comparing the result information characteristic values with a preset characteristic threshold value to obtain a deviation rate;
judging whether the deviation rate is larger than or equal to a preset deviation rate threshold value;
if the user behavior data is greater than or equal to the user behavior data, generating correction information, and carrying out feedback correction on the user behavior data through the correction information;
if the user behavior data is smaller than the preset threshold, performing cluster analysis on the user behavior data to obtain a behavior classification result.
Optionally, in the big data analysis system based on electronic commerce behavior according to the embodiment of the present application, the capturing, by a web crawler, user behavior data, and preprocessing the user behavior data to obtain result information includes:
capturing a webpage, setting a traversing rule, traversing network nodes, and extracting data of the network nodes;
calculating an evaluation value of the data of the network node through an analysis algorithm;
comparing the evaluation value of the network node with a preset threshold value to obtain an evaluation difference value;
judging whether the evaluation difference value is larger than or equal to a preset evaluation difference value;
if the rule is larger than or equal to the first rule, generating correction information, and adjusting the traversal rule through the correction information;
if the data is smaller than the storage data, the data of the network node is stored in the storage node, and the storage data is generated.
Optionally, in the big data analysis system based on electronic commerce behavior according to the embodiment of the present application, the capturing a web page, setting a traversal rule, traversing a network node, and extracting data of the network node includes:
acquiring the number of network nodes;
performing difference calculation on the number of network nodes and the preset number to obtain a number difference;
judging whether the quantity difference value is positive or not;
if yes, judging that the number of the network nodes is large, establishing a superposition rule, and fusing the network nodes through the superposition rule;
if the number of the network nodes is negative, the number of the network nodes is judged to be small, a segmentation rule is established, and the network nodes are segmented through the segmentation rule.
In a third aspect, an embodiment of the present application further provides a computer readable storage medium, where the computer readable storage medium includes an electronic commerce-based big data analysis method program, where the electronic commerce-based big data analysis method program, when executed by a processor, implements the steps of the electronic commerce-based big data analysis method according to any one of the above embodiments.
As can be seen from the above, according to the big data analysis method, system and medium based on electronic commerce behaviors provided by the embodiments of the present application, user behavior data is captured by a web crawler, and the user behavior data is preprocessed to obtain result information; extracting result information characteristic values, and comparing the result information characteristic values with a preset characteristic threshold value to obtain a deviation rate; judging whether the deviation rate is larger than or equal to a preset deviation rate threshold value; if the user behavior data is greater than or equal to the user behavior data, generating correction information, and carrying out feedback correction on the user behavior data through the correction information; if the user behavior data is smaller than the preset threshold, performing cluster analysis on the user behavior data to obtain a behavior classification result; the user behavior data is analyzed and processed by judging the deviation rate of the user behavior data, so that the user behavior data can be analyzed more accurately, and the analysis result is closer to the actual value.
Additional features and advantages of the application will be set forth in the description which follows, and in part will be apparent from the description, or may be learned by practice of the application embodiments. The objectives and other advantages of the application will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of a big data analysis method based on electronic commerce behavior provided in an embodiment of the present application;
FIG. 2 is a flowchart for correcting user behavior data according to the big data analysis method based on e-commerce behavior according to the embodiment of the present application;
fig. 3 is a network node processing flowchart of the big data analysis method based on e-commerce behavior provided in the embodiment of the present application;
fig. 4 is a network node fusion flowchart of a big data analysis method based on e-commerce behavior provided in an embodiment of the present application;
fig. 5 is a schematic structural diagram of a big data analysis system based on electronic commerce behavior according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. The components of the embodiments of the present application, which are generally described and illustrated in the figures herein, may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, as provided in the accompanying drawings, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present application without making any inventive effort, are intended to be within the scope of the present application.
It should be noted that like reference numerals and letters refer to like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only to distinguish the description, and are not to be construed as indicating or implying relative importance.
Referring to fig. 1, fig. 1 is a flowchart of a big data analysis method based on e-commerce behavior in some embodiments of the present application. The big data analysis method based on the electronic commerce behavior is used in the terminal equipment and comprises the following steps:
s101, capturing user behavior data through a web crawler, and preprocessing the user behavior data to obtain result information;
s102, extracting a result information characteristic value, and comparing the result information characteristic value with a preset characteristic threshold value to obtain a deviation rate;
s103, judging whether the deviation rate is larger than or equal to a preset deviation rate threshold value;
s104, if the data is greater than or equal to the data, generating correction information, and carrying out feedback correction on the user behavior data through the correction information;
and S105, if the user behavior data is smaller than the preset threshold, performing cluster analysis on the user behavior data to obtain a behavior classification result.
It should be noted that, the web crawler is a program for automatically extracting web pages, downloads web pages for a search engine, is an important component of the search engine, starts from a network node of one or several initial web pages, obtains a network node on the initial web page, and continuously extracts new network nodes from the current page and puts them into a queue in the process of capturing the web pages until a certain stop condition of the system is met.
Referring to fig. 2, fig. 2 is a flowchart of user behavior data correction according to a big data analysis method based on electronic commerce behavior in some embodiments of the present application. According to the embodiment of the invention, the user behavior data is captured by the web crawler and preprocessed to obtain the result information, which comprises the following steps:
s201, capturing a webpage, setting a traversing rule, traversing network nodes and extracting data of the network nodes;
s202, calculating an evaluation value of data of the network node through an analysis algorithm;
s203, comparing the evaluation value of the network node with a preset threshold value to obtain an evaluation difference value;
s204, judging whether the evaluation difference value is larger than or equal to a preset evaluation difference value;
s205, if the rule is larger than or equal to the rule, generating correction information, and adjusting the traversal rule through the correction information; if the data is smaller than the storage data, the data of the network node is stored in the storage node, and the storage data is generated.
It should be noted that, by evaluating the network node, when the evaluation value of the network node is greater than a preset threshold value, it is described that the data of the network node meets the requirement, and only when the data of the network node meets the requirement, the user behavior can be more accurately judged.
Referring to fig. 3, fig. 3 is a network node processing flow chart of a big data analysis method based on e-commerce behavior in some embodiments of the present application. According to the embodiment of the invention, the method comprises the steps of capturing the webpage, setting a traversing rule, traversing the network node and extracting the data of the network node, and comprises the following steps:
s301, acquiring the number of network nodes;
s302, carrying out difference calculation on the number of network nodes and the preset number to obtain a number difference;
s303, judging whether the quantity difference value is positive;
s304, if the number of the network nodes is positive, judging that the number of the network nodes is large, establishing a superposition rule, and fusing the network nodes through the superposition rule;
if the number of the network nodes is negative, the number of the network nodes is determined to be small, a segmentation rule is established, and the network nodes are segmented by the segmentation rule.
It should be noted that, the network nodes are integrated by judging the number of the network nodes, and the principle of integration is to judge and identify according to the similarity of the network node information, so as to ensure that the integrated network node data still maintains the original data information category.
Referring to fig. 4, fig. 4 is a network node fusion flowchart of a big data analysis method based on e-commerce behavior in some embodiments of the present application. According to the embodiment of the invention, if the number of the network nodes is positive, the number of the network nodes is judged to be large, a superposition rule is established, and the network nodes are fused through the superposition rule, which comprises the following steps:
s401, acquiring data of network nodes, generating node information, and randomly sequencing the node information;
s402, comparing two adjacent node information according to the sequence to obtain a first similarity;
s403, judging whether the first similarity is larger than or equal to a first similarity threshold value;
s404, if the number of the network nodes is greater than or equal to the number of the network nodes, fusing two adjacent network nodes, and generating the number of the network nodes;
and S405, if the number of the network nodes is equal to the preset number of the network nodes, stopping the network node fusion.
It should be noted that, the similarity comparison is performed on the node information after the sorting, the comparison principle is that the first network node is compared with the second network node, the third network node is compared with the fourth network node, and so on.
According to the embodiment of the invention, if the number of the network nodes is negative, the number of the network nodes is judged to be small, a segmentation rule is established, and the network nodes are segmented through the segmentation rule, which comprises the following steps:
if the number of the network nodes is smaller than the preset number of the network nodes;
acquiring data information in the same network node;
comparing the data information in the network node with preset data information to obtain a second similarity;
judging whether the second similarity is larger than or equal to a second similarity threshold value;
if the data is greater than or equal to the data, dividing the data in the network node into the same type of data;
if the data is smaller than the preset value, judging the corresponding data is of different types, and dividing the network nodes by the network data of different types.
According to the embodiment of the invention, if the user behavior data is greater than or equal to the user behavior data, the method generates correction information, and carries out feedback correction on the user behavior data through the correction information, and comprises the following steps:
extracting result information characteristic values, and comparing the result information characteristic values with a preset characteristic threshold value to obtain a deviation rate;
judging whether the deviation rate is larger than or equal to a first deviation rate threshold value and smaller than a second deviation rate threshold value;
if the first deviation rate threshold value is greater than or equal to the first deviation rate threshold value and smaller than the second deviation rate threshold value, generating first correction information, generating first feedback data through the first correction information, and adjusting the user behavior data according to the first feedback data;
and if the deviation rate is larger than the second deviation rate threshold value, generating second correction information, generating second feedback data through the second correction information, and adjusting the user behavior data according to the second feedback data.
It should be noted that, user behavior data is determined, different ways of adjustment are performed on the user behavior data according to the deviation rate of the user behavior data, and the adjusted user behavior data is more accurate.
According to an embodiment of the present invention, after performing cluster analysis on the user behavior data to obtain the behavior classification result, the method further includes:
comparing and analyzing the classification result of the user behavior data with a preset classification result to obtain a third phase value;
judging whether the third similar value is larger than a preset third similar threshold value, if so, marking corresponding user behavior data, and triggering warning information;
and sending the marked user behavior data to a preset management end for reminding.
It should be noted that, the preset classification result is a classification result of the bad action behavior data, where when the third similarity value is greater than a preset third similarity threshold value, it is indicated that the corresponding user behavior data belongs to the bad action behavior, the corresponding user behavior data is marked and sent to the preset management end for reminding, and the third similarity threshold value is set by those skilled in the art according to actual requirements.
According to an embodiment of the present invention, further comprising:
acquiring a time value of user behavior data based on a preset time sensor;
marking the corresponding user behavior data according to the time value of the user behavior data to obtain the user behavior data at different time points;
and storing the user behavior data at different time points according to the time sequence.
It should be noted that, the user behaviors at different time points are time-identified through a preset time sensor, and stored according to time sequence.
According to an embodiment of the present invention, further comprising:
based on a preset time period, comparing and analyzing the user behavior data at different time points in the preset time period to obtain a fourth similar value;
judging whether the fourth similar value is larger than a preset fourth similar threshold value, if so, triggering whether the corresponding user behavior action is repeated;
and sending the repeated information of the user behavior action to the user for confirmation.
It should be noted that, in the preset time period, if the similarity value of the user behavior data at different time points is greater than the fourth similarity threshold, the user behavior data at different time points is the repeated data, the corresponding user terminal may have the phenomenon of hand sliding, and whether the user behavior action is repeated is sent to the user terminal for confirmation, if so, the repeated user behavior action is deleted, and the user behavior action data before the time point is reserved; if not, storing the corresponding user behavior data respectively.
Referring to fig. 5, fig. 5 is a schematic structural diagram of a big data analysis system based on e-commerce behavior according to some embodiments of the present application. In a second aspect, embodiments of the present application provide a big data analysis system 5 based on electronic commerce behavior, the system comprising: the memory 51 and the processor 52, the memory 51 includes a program of the big data analysis method based on the electronic commerce, and the following steps are implemented when the program of the big data analysis method based on the electronic commerce is executed by the processor:
capturing user behavior data through a web crawler, and preprocessing the user behavior data to obtain result information;
extracting result information characteristic values, and comparing the result information characteristic values with a preset characteristic threshold value to obtain a deviation rate;
judging whether the deviation rate is larger than or equal to a preset deviation rate threshold value;
if the user behavior data is greater than or equal to the user behavior data, generating correction information, and carrying out feedback correction on the user behavior data through the correction information;
if the user behavior data is smaller than the preset threshold, performing cluster analysis on the user behavior data to obtain a behavior classification result.
It should be noted that, the web crawler is a program for automatically extracting web pages, downloads web pages for a search engine, is an important component of the search engine, starts from a network node of one or several initial web pages, obtains a network node on the initial web page, and continuously extracts new network nodes from the current page and puts them into a queue in the process of capturing the web pages until a certain stop condition of the system is met.
According to the embodiment of the invention, the user behavior data is captured by the web crawler and preprocessed to obtain the result information, which comprises the following steps:
capturing a webpage, setting a traversing rule, traversing network nodes, and extracting data of the network nodes;
calculating an evaluation value of the data of the network node through an analysis algorithm;
comparing the evaluation value of the network node with a preset threshold value to obtain an evaluation difference value;
judging whether the evaluation difference value is larger than or equal to a preset evaluation difference value;
if the rule is larger than or equal to the first rule, generating correction information, and adjusting the traversal rule through the correction information;
if the data is smaller than the storage data, the data of the network node is stored in the storage node, and the storage data is generated.
It should be noted that, by evaluating the network node, when the evaluation value of the network node is greater than a preset threshold value, it is described that the data of the network node meets the requirement, and only when the data of the network node meets the requirement, the user behavior can be more accurately judged.
According to the embodiment of the invention, the method comprises the steps of capturing the webpage, setting a traversing rule, traversing the network node and extracting the data of the network node, and comprises the following steps:
acquiring the number of network nodes;
performing difference calculation on the number of network nodes and the preset number to obtain a number difference;
judging whether the quantity difference value is positive or not;
if yes, judging that the number of the network nodes is large, establishing a superposition rule, and fusing the network nodes through the superposition rule;
if the number of the network nodes is negative, the number of the network nodes is judged to be small, a segmentation rule is established, and the network nodes are segmented through the segmentation rule.
It should be noted that, the network nodes are integrated by judging the number of the network nodes, and the principle of integration is to judge and identify according to the similarity of the network node information, so as to ensure that the integrated network node data still maintains the original data information category.
According to the embodiment of the invention, if the number of the network nodes is positive, the number of the network nodes is judged to be large, a superposition rule is established, and the network nodes are fused through the superposition rule, which comprises the following steps:
acquiring data of a network node and generating node information;
randomly sequencing node information;
comparing the adjacent two node information according to the sequence to obtain a first similarity;
judging whether the first similarity is larger than or equal to a first similarity threshold value;
if the number of the network nodes is greater than or equal to the number of the network nodes, fusing two adjacent network nodes, and generating the number of the network nodes;
if the number of the network nodes is equal to the preset number of the network nodes, stopping network node fusion.
It should be noted that, the similarity comparison is performed on the node information after the sorting, the comparison principle is that the first network node is compared with the second network node, the third network node is compared with the fourth network node, and so on.
According to the embodiment of the invention, if the number of the network nodes is negative, the number of the network nodes is judged to be small, a segmentation rule is established, and the network nodes are segmented through the segmentation rule, which comprises the following steps:
if the number of the network nodes is smaller than the preset number of the network nodes;
acquiring data information in the same network node;
comparing the data information in the network node with preset data information to obtain a second similarity;
judging whether the second similarity is larger than or equal to a second similarity threshold value;
if the data is greater than or equal to the data, dividing the data in the network node into the same type of data;
if the data is smaller than the preset value, judging the corresponding data is of different types, and dividing the network nodes by the network data of different types.
According to the embodiment of the invention, if the user behavior data is greater than or equal to the user behavior data, the method generates correction information, and carries out feedback correction on the user behavior data through the correction information, and comprises the following steps:
extracting result information characteristic values, and comparing the result information characteristic values with a preset characteristic threshold value to obtain a deviation rate;
judging whether the deviation rate is larger than or equal to a first deviation rate threshold value and smaller than a second deviation rate threshold value;
if the first deviation rate threshold value is greater than or equal to the first deviation rate threshold value and smaller than the second deviation rate threshold value, generating first correction information, generating first feedback data through the first correction information, and adjusting the user behavior data according to the first feedback data;
and if the deviation rate is larger than the second deviation rate threshold value, generating second correction information, generating second feedback data through the second correction information, and adjusting the user behavior data according to the second feedback data.
It should be noted that, user behavior data is determined, different ways of adjustment are performed on the user behavior data according to the deviation rate of the user behavior data, and the adjusted user behavior data is more accurate.
According to an embodiment of the present invention, after performing cluster analysis on the user behavior data to obtain the behavior classification result, the method further includes:
comparing and analyzing the classification result of the user behavior data with a preset classification result to obtain a third phase value;
judging whether the third similar value is larger than a preset third similar threshold value, if so, marking corresponding user behavior data, and triggering warning information;
and sending the marked user behavior data to a preset management end for reminding.
It should be noted that, the preset classification result is a classification result of the bad action behavior data, where when the third similarity value is greater than a preset third similarity threshold value, it is indicated that the corresponding user behavior data belongs to the bad action behavior, the corresponding user behavior data is marked and sent to the preset management end for reminding, and the third similarity threshold value is set by those skilled in the art according to actual requirements.
According to an embodiment of the present invention, further comprising:
acquiring a time value of user behavior data based on a preset time sensor;
marking the corresponding user behavior data according to the time value of the user behavior data to obtain the user behavior data at different time points;
and storing the user behavior data at different time points according to the time sequence.
It should be noted that, the user behaviors at different time points are time-identified through a preset time sensor, and stored according to time sequence.
According to an embodiment of the present invention, further comprising:
based on a preset time period, comparing and analyzing the user behavior data at different time points in the preset time period to obtain a fourth similar value;
judging whether the fourth similar value is larger than a preset fourth similar threshold value, if so, triggering whether the corresponding user behavior action is repeated;
and sending the repeated information of the user behavior action to the user for confirmation.
It should be noted that, in the preset time period, if the similarity value of the user behavior data at different time points is greater than the fourth similarity threshold, the user behavior data at different time points is the repeated data, the corresponding user terminal may have the phenomenon of hand sliding, and whether the user behavior action is repeated is sent to the user terminal for confirmation, if so, the repeated user behavior action is deleted, and the user behavior action data before the time point is reserved; if not, storing the corresponding user behavior data respectively.
A third aspect of the present invention provides a computer-readable storage medium having embodied therein an electronic commerce-based big data analysis method program which, when executed by a processor, implements the steps of the electronic commerce-based big data analysis method as in any one of the above.
According to the big data analysis method, the big data analysis system and the big data analysis medium based on the electronic commerce behavior, which are disclosed by the invention, user behavior data are captured through a web crawler, and the user behavior data are preprocessed to obtain result information; extracting result information characteristic values, and comparing the result information characteristic values with a preset characteristic threshold value to obtain a deviation rate; judging whether the deviation rate is larger than or equal to a preset deviation rate threshold value; if the user behavior data is greater than or equal to the user behavior data, generating correction information, and carrying out feedback correction on the user behavior data through the correction information; if the user behavior data is smaller than the preset threshold, performing cluster analysis on the user behavior data to obtain a behavior classification result; the user behavior data is analyzed and processed by judging the deviation rate of the user behavior data, so that the user behavior data can be analyzed more accurately, and the analysis result is closer to the actual value.
In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above described device embodiments are only illustrative, e.g. the division of units is only one logical function division, and there may be other divisions in actual implementation, such as: multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. In addition, the various components shown or discussed may be coupled or directly coupled or communicatively coupled to each other via some interface, whether indirectly coupled or communicatively coupled to devices or units, whether electrically, mechanically, or otherwise.
The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units; can be located in one place or distributed to a plurality of network units; some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present invention may be integrated in one processing unit, or each unit may be separately used as one unit, or two or more units may be integrated in one unit; the integrated units may be implemented in hardware or in hardware plus software functional units.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware related to program instructions, and the foregoing program may be stored in a readable storage medium, where the program, when executed, performs steps including the above method embodiments; and the aforementioned storage medium includes: a mobile storage device, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk or an optical disk, or the like, which can store program codes.
Alternatively, the above-described integrated units of the present invention may be stored in a readable storage medium if implemented in the form of software functional modules and sold or used as separate products. Based on such understanding, the technical solution of the embodiments of the present invention may be embodied in essence or a part contributing to the prior art in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, ROM, RAM, magnetic or optical disk, or other medium capable of storing program code.
Claims (5)
1. The big data analysis method based on the electronic commerce behavior is characterized by comprising the following steps of:
capturing user behavior data through a web crawler, and preprocessing the user behavior data to obtain result information;
extracting result information characteristic values, and comparing the result information characteristic values with a preset characteristic threshold value to obtain a deviation rate;
judging whether the deviation rate is larger than or equal to a preset deviation rate threshold value;
if the user behavior data is greater than or equal to the user behavior data, generating correction information, and carrying out feedback correction on the user behavior data through the correction information;
if the user behavior data is smaller than the preset threshold, performing cluster analysis on the user behavior data to obtain a behavior classification result;
capturing user behavior data through a web crawler, preprocessing the user behavior data to obtain result information, wherein the method comprises the following steps:
capturing a webpage, setting a traversing rule, traversing network nodes, and extracting data of the network nodes;
calculating an evaluation value of the data of the network node through an analysis algorithm;
comparing the evaluation value of the network node with a preset threshold value to obtain an evaluation difference value;
judging whether the evaluation difference value is larger than or equal to a preset evaluation difference value;
if the rule is larger than or equal to the first rule, generating correction information, and adjusting the traversal rule through the correction information;
if the data is smaller than the storage data, storing the data of the network node into the storage node to generate the storage data;
the capturing the web page, setting a traversing rule, traversing the network node, and extracting the data of the network node, including:
acquiring the number of network nodes;
performing difference calculation on the number of network nodes and the preset number to obtain a number difference;
judging whether the quantity difference value is positive or not;
if yes, judging that the number of the network nodes is large, establishing a superposition rule, and fusing the network nodes through the superposition rule;
if the number of the network nodes is negative, judging that the number of the network nodes is small, establishing a segmentation rule, and segmenting the network nodes through the segmentation rule;
and if the user behavior data is greater than or equal to the preset value, generating correction information, and carrying out feedback correction on the user behavior data through the correction information, wherein the method comprises the following steps:
extracting result information characteristic values, and comparing the result information characteristic values with a preset characteristic threshold value to obtain a deviation rate;
judging whether the deviation rate is larger than or equal to a first deviation rate threshold value and smaller than a second deviation rate threshold value;
if the first deviation rate threshold value is greater than or equal to the first deviation rate threshold value and smaller than the second deviation rate threshold value, generating first correction information, generating first feedback data through the first correction information, and adjusting the user behavior data according to the first feedback data;
if the deviation rate is larger than the second deviation rate threshold value, generating second correction information, generating second feedback data through the second correction information, and adjusting the user behavior data according to the second feedback data;
the step of performing cluster analysis on the user behavior data to obtain a behavior classification result further comprises the following steps:
comparing and analyzing the classification result of the user behavior data with a preset classification result to obtain a third phase value;
judging whether the third similar value is larger than a preset third similar threshold value, if so, marking corresponding user behavior data, and triggering warning information;
and sending the marked user behavior data to a preset management end for reminding.
2. The big data analysis method based on electronic commerce according to claim 1, wherein if the result is positive, the number of network nodes is determined to be large, a superposition rule is established, and the network nodes are fused through the superposition rule, including:
acquiring data of a network node and generating node information;
randomly sequencing node information;
comparing the adjacent two node information according to the sequence to obtain a first similarity;
judging whether the first similarity is larger than or equal to a first similarity threshold value;
if the number of the network nodes is greater than or equal to the number of the network nodes, fusing two adjacent network nodes, and generating the number of the network nodes;
if the number of the network nodes is equal to the preset number of the network nodes, stopping network node fusion.
3. The big data analysis method based on electronic commerce according to claim 2, wherein if the number of the network nodes is negative, the number of the network nodes is determined to be small, a segmentation rule is established, and the network nodes are segmented by the segmentation rule, including:
if the number of the network nodes is smaller than the preset number of the network nodes;
acquiring data information in the same network node;
comparing the data information in the network node with preset data information to obtain a second similarity;
judging whether the second similarity is greater than or equal to a second similarity threshold;
if the data is greater than or equal to the data, dividing the data in the network node into the same type of data;
if the data is smaller than the preset value, judging the corresponding data is of different types, and dividing the network nodes by the network data of different types.
4. A big data analysis system based on electronic commerce activity, the system comprising: the system comprises a memory and a processor, wherein the memory comprises a program of a big data analysis method based on electronic commerce, and the program of the big data analysis method based on the electronic commerce realizes the following steps when being executed by the processor:
capturing user behavior data through a web crawler, and preprocessing the user behavior data to obtain result information;
extracting result information characteristic values, and comparing the result information characteristic values with a preset characteristic threshold value to obtain a deviation rate;
judging whether the deviation rate is larger than or equal to a preset deviation rate threshold value;
if the user behavior data is greater than or equal to the user behavior data, generating correction information, and carrying out feedback correction on the user behavior data through the correction information;
if the user behavior data is smaller than the preset threshold, performing cluster analysis on the user behavior data to obtain a behavior classification result;
capturing user behavior data through a web crawler, preprocessing the user behavior data to obtain result information, wherein the method comprises the following steps:
capturing a webpage, setting a traversing rule, traversing network nodes, and extracting data of the network nodes;
calculating an evaluation value of the data of the network node through an analysis algorithm;
comparing the evaluation value of the network node with a preset threshold value to obtain an evaluation difference value;
judging whether the evaluation difference value is larger than or equal to a preset evaluation difference value;
if the rule is larger than or equal to the first rule, generating correction information, and adjusting the traversal rule through the correction information;
if the data is smaller than the storage data, storing the data of the network node into the storage node to generate the storage data;
the capturing the web page, setting a traversing rule, traversing the network node, and extracting the data of the network node, including:
acquiring the number of network nodes;
performing difference calculation on the number of network nodes and the preset number to obtain a number difference;
judging whether the quantity difference value is positive or not;
if yes, judging that the number of the network nodes is large, establishing a superposition rule, and fusing the network nodes through the superposition rule;
if the number of the network nodes is negative, judging that the number of the network nodes is small, establishing a segmentation rule, and segmenting the network nodes through the segmentation rule;
and if the user behavior data is greater than or equal to the preset value, generating correction information, and carrying out feedback correction on the user behavior data through the correction information, wherein the method comprises the following steps:
extracting result information characteristic values, and comparing the result information characteristic values with a preset characteristic threshold value to obtain a deviation rate;
judging whether the deviation rate is larger than or equal to a first deviation rate threshold value and smaller than a second deviation rate threshold value;
if the first deviation rate threshold value is greater than or equal to the first deviation rate threshold value and smaller than the second deviation rate threshold value, generating first correction information, generating first feedback data through the first correction information, and adjusting the user behavior data according to the first feedback data;
if the deviation rate is larger than the second deviation rate threshold value, generating second correction information, generating second feedback data through the second correction information, and adjusting the user behavior data according to the second feedback data;
the step of performing cluster analysis on the user behavior data to obtain a behavior classification result further comprises the following steps:
comparing and analyzing the classification result of the user behavior data with a preset classification result to obtain a third phase value;
judging whether the third similar value is larger than a preset third similar threshold value, if so, marking corresponding user behavior data, and triggering warning information;
and sending the marked user behavior data to a preset management end for reminding.
5. A computer-readable storage medium, characterized in that the computer-readable storage medium comprises therein an e-commerce based big data analysis method program, which when executed by a processor, implements the steps of the e-commerce based big data analysis method according to any one of claims 1 to 3.
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