CN117196640B - Full-flow visual management system and method based on service experience - Google Patents
Full-flow visual management system and method based on service experience Download PDFInfo
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Abstract
The invention discloses a full-flow visual management system and method based on service experience, which belong to the field of data processing systems specially suitable for management purposes.
Description
Technical Field
The invention belongs to the technical field of data processing systems specially suitable for management purposes, and particularly relates to a full-flow visual management system and method based on service experience.
Background
In the process of selling the existing commodity, a consumer usually observes the service experience good rating and sales volume to evaluate the commodity in the whole process, but the functions and effects of the different types of the same commodity in each life cycle are different, the functions and effects of the different types of the same commodity in each life cycle cannot be counted and compared rapidly in the existing calculation, so that the accuracy of commodity pushing is reduced, meanwhile, the condition that a large number of malicious good comments exist on the existing network merchant platform seriously influences the correct evaluation of the commodity by the consumer, and the problems exist in the prior art;
for example, in chinese patent with application publication number CN116823304a, an electronic commerce transaction data classification integration processing system is disclosed, which relates to the technical field of electronic commerce transaction data processing application, including that when a user login module is based on the purchasing requirement of a user, the user inputs personal information to enter a commodity interface of the system; the goose down jacket region dividing module is used for obtaining different collecting regions of the goose down jacket; the goose down quality control acquisition module performs quality control and quality monitoring on the goose down output by the outside of the day to obtain the transaction amount of the goose down acquired by the day; the after-sales order data module of the goose down jacket is used for acquiring after-sales service evaluation data to obtain an after-sales service evaluation coefficient of the goose down collected on the same day; the e-commerce sales module obtains an e-commerce sales index based on the transaction success index and the after-sales service index; the cloud database module is used for collecting customer information and merchant information and maintaining system data; the background feedback module pushes the proper commodity to a user conforming to the information label;
Meanwhile, for example, in chinese patent with application publication number CN114444934a, an enterprise sales periodic algorithm and a tool application thereof are disclosed, and the enterprise sales situation is periodically evaluated according to the following steps: s1: acquiring data and enterprise sales data; s2: data arrangement, namely carrying out statistics and summarization on scattered sales data according to months to obtain month sales data; s3: data grouping, namely dividing monthly sales data into two groups according to time; s4: data correction, namely calculating absolute deviation of two groups of monthly sales data, and eliminating extremum in the two groups of data; s5: calculating data correlation, namely calculating pearson correlation coefficients of two groups of monthly sales data; s6: and (3) evaluating data, namely respectively evaluating the pearson correlation coefficients in different threshold intervals. The invention quantitatively analyzes the periodicity of enterprise operation through a statistical algorithm, provides a scientific evaluation method, has more scientific basis when monitoring the enterprise operation risk, can effectively help decision and improve the efficiency.
The problems proposed in the background art exist in the above patents: consumers usually observe the service experience good evaluation rate and sales volume to evaluate the whole commodity process, but the functions and effects of different kinds of the same commodity are different, the functions and effects of different kinds of the same commodity are not counted and compared rapidly, so that the accuracy of commodity pushing is reduced, meanwhile, the condition that a large number of malicious good evaluation forms exist on the existing network merchant platform, the correct evaluation of the commodity by the consumers is seriously influenced, and in order to solve the problems, the application designs the whole process visual management system and method based on the service experience.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a full-flow visual management system and method based on service experience, wherein the commodity in a price section to be purchased is divided into a plurality of stages according to the life cycle, service experience evaluation data of each stage of the commodity in the price section to be purchased after a consumer purchases the commodity is extracted, an evaluation bill recognition model is constructed, the service experience evaluation data of each stage of the commodity after the consumer purchases the commodity is imported into the evaluation bill recognition model to calculate the abnormal value of each service experience data, the maximum abnormal value of each service experience data obtained by calculation is compared with a set abnormal threshold, and if the abnormal value of the service experience data is greater than or equal to the set abnormal threshold, the service experience data is judged to be bill data, and the service experience data is removed from statistics; if the abnormal value of the service experience data is smaller than the set abnormal threshold value, judging that the service experience data is normal service experience data, extracting normal service experience data of each commodity stage, importing the normal service experience data into a commodity evaluation value calculation strategy to calculate the evaluation value of each commodity stage, importing the evaluation value of each commodity stage into a commodity evaluation coefficient judgment strategy to judge commodity evaluation coefficients, arranging the commodity evaluation coefficients in descending order to obtain a plurality of commodities with commodity evaluation coefficients in the front, importing the commodity evaluation coefficients of the optimal plurality of commodities and the evaluation values of each commodity stage into a selection strategy according to the needs of purchasers to select the most suitable commodity in the similar commodities, pushing the most suitable commodity information in the similar commodities to a display for the purchasers to select, optimizing a commodity pushing mechanism, improving the commodity pushing accuracy, and avoiding the obstruction of a malicious good bill to the selection of the commodity.
In order to achieve the above purpose, the present invention provides the following technical solutions:
a full-flow visual management method based on service experience comprises the following specific steps:
s1, dividing a commodity in a price section to be purchased into a plurality of stages according to a life cycle, and extracting service experience evaluation data of each stage of the commodity in the price section to be purchased after a consumer purchases the commodity;
s2, constructing an evaluation form recognition model, importing service experience evaluation data of the consumer at each stage of the commodity after the consumer purchases the commodity into the evaluation form recognition model, and calculating abnormal values of the service experience data;
s3, comparing the calculated maximum abnormal value of each service experience data with a set abnormal threshold, and judging that the service experience data is the bill refreshing data and removing the bill refreshing data from statistics if the abnormal value of the service experience data is greater than or equal to the set abnormal threshold; if the abnormal value of the service experience data is smaller than the set abnormal threshold value, judging that the service experience data is normal service experience data, removing commodities of which the normal service experience data is smaller than the set service experience threshold value from statistics, and executing the rest S4;
s4, extracting normal service experience data of each stage of the rest commodity, and importing the normal service experience data into a commodity evaluation value calculation strategy to calculate the evaluation value of each stage of the commodity;
S5, leading the evaluation values of all stages of the commodities into a commodity evaluation coefficient judgment strategy to judge commodity evaluation coefficients, and arranging the commodity evaluation coefficients in a descending order to obtain a plurality of commodities with the commodity evaluation coefficients in the front;
s6, importing the obtained optimal commodity evaluation coefficients of the multiple commodities and the evaluation values of the commodity at each stage into a selection strategy according to the needs of purchasers so as to select the most suitable commodity in the similar commodities;
and S7, pushing the most suitable commodity information in the similar commodities to a display for the buyer to select.
Specifically, the step S1 includes the following specific steps:
s11, selecting a commodity of a price section to be selected according to the provided commodity price section, dividing the selected commodity of the price section to be selected into a plurality of stages according to the life cycle, wherein the dividing into a plurality of stages according to the life cycle is particularly dividing the commodity of a function into a plurality of stages according to the function cycle, such as air conditioning into stages of refrigeration, heating, dehumidification and the like, and the dividing into a plurality of stages according to the life cycle, such as toothpaste dividing into just-used effect, effect of using a certain period, effect of using a plurality of periods and the like;
S12, extracting service experience evaluation data of the consumer at each stage of the price section commodity to be purchased after the consumer purchases the commodity, wherein the service experience evaluation data is evaluation data of the consumer and comprises evaluation scores, evaluation characters and evaluation pictures, the evaluation scores are scoring data of the consumer for the consumer product, the online commerce platform is taken as an example, the scoring data corresponds to a few stars of good scoring, the evaluation characters are the character evaluation data of the consumer for the consumer product, and the comment pictures are photo data attached when the consumer reviews.
Specifically, the evaluation form recognition model in S2 includes the following specific contents:
s21, extracting any two pieces of service experience evaluation data in each stage, and respectively extracting evaluation scores, evaluation characters and evaluation picture data in the service experience evaluation data;
s22, extracting evaluation text data in the service experience evaluation data, importing the evaluation text data into a text similarity calculation formula, and calculating the text similarity of any two service experience evaluation data, wherein the text similarity calculation formula is as follows:wherein->Literal set of rating data for one of the service experiences, +.>A text set of evaluation data is experienced for another service, and m is the number of elements in the set;
S23, extracting evaluation picture data, character similarity data obtained through calculation and evaluation score data in service experience evaluation data, and substituting the evaluation picture data, the calculated character similarity data and the evaluation score data into an outlier calculation formula to calculate outlier of the service experience data, wherein the outlier calculation formula is as follows:where exp is the power of e,is->Evaluation score of corresponding service experience evaluation data, +.>Is->Evaluation score of corresponding service experience evaluation data, +.>Is->Pixel value of i-th pixel of evaluation picture of corresponding service experience evaluation data,/>Is thatPixel value of ith pixel point of evaluation picture of corresponding service experience evaluation data, n is number of pixel points of the evaluation picture, < ->For the first duty cycle, +.>For a second duty cycle->。
Here, it is to be noted that, here、/>And the value of the abnormal threshold is obtained by manually finding out the evaluation data belonging to the evaluation list from 500 groups of service experience evaluation data and fitting the data to obtain +.>、/>And an optimal value of the anomaly threshold;
specifically, the specific steps of the commodity evaluation value calculation strategy in S4 are as follows:
s41, extracting an evaluation score data set in the normal service experience data of each commodity stage, and calculating an average value of the evaluation score data in the normal service experience data of each commodity stage;
S42, counting the proportion of the number of the normal service experience data in each commodity stage to the total normal service experience data as an important coefficient of each commodity stage;
s43, substituting an average value of evaluation score data in normal service experience data of each commodity stage and an important coefficient of each commodity stage into a commodity evaluation value calculation formula to calculate an evaluation value of the commodity of each stage, wherein the commodity evaluation value calculation formula of the j stage is as follows:wherein->Experience data amount for normal service of jth stage, < >>Experiencing data quantity for total normal service, +.>For the number of j-stage evaluation scores, +.>The c-th evaluation score value of the j stage.
Specifically, the specific content of the commodity evaluation coefficient judgment policy in S5 is: substituting the calculated commodity evaluation values in each stage into a commodity evaluation coefficient calculation formula to calculate the commodity evaluation coefficient, wherein the commodity evaluation coefficient calculation formula is as follows:where t is the number of stages.
Specifically, the selection strategy comprises the following specific steps:
s61, selecting a required stage preference by a purchaser, and extracting an evaluation value of a stage corresponding to the obtained stage preference and a commodity evaluation coefficient obtained by calculation; for example, the purchaser needs an air conditioner with good refrigeration effect, the stage preference is refrigeration, and the evaluation value of the stage corresponding to the stage preference is the evaluation value of the refrigeration stage;
S62, importing the evaluation value of the corresponding stage of the extracted stage preference and the calculated commodity evaluation coefficient into an optimal value calculation formula to calculate an optimal value, wherein the optimal value calculation formula is as follows:wherein z is the evaluation value of the phase corresponding to the phase preference,/->The evaluation value of the corresponding stage of the stage preference is the ratio of +.>For the calculated commodity evaluation coefficient duty factor, < ->Wherein->The standard value of the quantity of the service experience data is set;
and S63, arranging the calculated optimal values in a descending order, and obtaining the commodity corresponding to the maximum optimal value, namely the most suitable commodity in the similar commodities.
Here, it is to be noted that, hereAnd->The values of (2) are obtained by selecting the evaluation value of the corresponding stage of 500 groups of stage preference items and the calculated commodity evaluation coefficient data, simultaneously, artificially selecting the most suitable commodity, importing fitting software, and performing continuous iteration to obtain +.>And->Is a solution to the optimization of (3).
The full-flow visual management system based on service experience is realized based on the full-flow visual management method based on service experience, and comprises a data extraction module, an evaluation bill identification model construction module, an outlier calculation module, a data comparison module, an evaluation value calculation module, an evaluation coefficient judgment module, a commodity selection module, a commodity pushing module and a control module, wherein the data extraction module is used for dividing commodities of a price section to be purchased into a plurality of stages according to a life cycle, extracting service experience evaluation data of each stage of the commodities of the price section to be purchased by a consumer after the consumer purchases the commodities, the evaluation bill identification model construction module is used for constructing an evaluation bill identification model, and the outlier calculation module is used for importing the service experience evaluation data of each stage of the commodities into the evaluation bill identification model after the consumer purchases the commodities to calculate the outlier of each service experience data.
Specifically, the data comparison module is used for comparing the maximum abnormal value of each service experience data obtained through calculation with a set abnormal threshold value, the evaluation value calculation module is used for extracting normal service experience data of each commodity stage and guiding the normal service experience data of each commodity stage into a commodity evaluation value calculation strategy to calculate the evaluation value of each commodity stage, the evaluation coefficient judgment module is used for guiding the evaluation value of each commodity stage into a commodity evaluation coefficient judgment strategy to judge commodity evaluation coefficients, commodity evaluation coefficients are arranged in descending order to obtain a plurality of commodities with commodity evaluation coefficients in the front, the commodity selection module is used for guiding the obtained commodity evaluation coefficients of the optimal plurality of commodities and the evaluation value of each commodity stage into a selection strategy according to the needs of purchasers to select the most suitable commodity in the similar commodity, the commodity pushing module is used for pushing the most suitable commodity information in the similar commodity to a display for the purchasers to select, and the control module is used for controlling the operation of the data extraction module, the commodity refreshing identification module, the abnormal value calculation module, the data comparison module, the evaluation value calculation module, the evaluation coefficient judgment module, the commodity selection module and the commodity pushing module.
An electronic device, comprising: a processor and a memory, wherein the memory stores a computer program for the processor to call;
the processor executes the full-flow visual management method based on the service experience by calling the computer program stored in the memory.
A computer readable storage medium storing instructions that when executed on a computer cause the computer to perform a full-flow visualization management method based on a service experience as described above.
Compared with the prior art, the invention has the beneficial effects that:
dividing a commodity of a price section to be purchased into a plurality of stages according to a life cycle, extracting service experience evaluation data of each stage of the commodity of the price section to be purchased by a consumer after the commodity is purchased, constructing an evaluation form recognition model, importing the service experience evaluation data of each stage of the commodity into the evaluation form recognition model after the commodity is purchased by the consumer, calculating abnormal values of each service experience data, comparing the maximum abnormal value of each calculated service experience data with a set abnormal threshold value, judging that the service experience data is the form data if the abnormal value of the service experience data is greater than or equal to the set abnormal threshold value, and removing the service experience data from statistics; if the abnormal value of the service experience data is smaller than the set abnormal threshold value, judging that the service experience data is normal service experience data, extracting normal service experience data of each commodity stage, importing the normal service experience data into a commodity evaluation value calculation strategy to calculate the evaluation value of each commodity stage, importing the evaluation value of each commodity stage into a commodity evaluation coefficient judgment strategy to judge commodity evaluation coefficients, arranging the commodity evaluation coefficients in descending order to obtain a plurality of commodities with commodity evaluation coefficients in the front, importing the commodity evaluation coefficients of the optimal plurality of commodities and the evaluation values of each commodity stage into a selection strategy according to the needs of purchasers to select the most suitable commodity in the similar commodities, pushing the most suitable commodity information in the similar commodities to a display for the purchasers to select, optimizing a commodity pushing mechanism, improving the commodity pushing accuracy, and avoiding the obstruction of a malicious good bill to the selection of the commodity.
Drawings
FIG. 1 is a flow diagram of a full-flow visual management method based on service experience;
fig. 2 is a schematic diagram of an overall framework of a full-flow visual management system based on service experience.
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.
Example 1
Referring to fig. 1, an embodiment of the present invention is provided: a full-flow visual management method based on service experience comprises the following specific steps:
s1, dividing a commodity in a price section to be purchased into a plurality of stages according to a life cycle, and extracting service experience evaluation data of each stage of the commodity in the price section to be purchased after a consumer purchases the commodity;
it should be noted that, S1 includes the following specific steps:
s11, selecting a commodity of a price section to be selected according to the provided commodity price section, dividing the selected commodity of the price section to be selected into a plurality of stages according to the life cycle, wherein the dividing into a plurality of stages according to the life cycle is particularly dividing the commodity of a function into a plurality of stages according to the function cycle, such as air conditioning into stages of refrigeration, heating, dehumidification and the like, and the dividing into a plurality of stages according to the life cycle, such as toothpaste dividing into just-used effect, effect of using a certain period, effect of using a plurality of periods and the like;
S12, extracting service experience evaluation data of the consumer at each stage of the price section commodity to be purchased after the consumer purchases the commodity, wherein the service experience evaluation data is evaluation data of the consumer and comprises evaluation scores, evaluation characters and evaluation pictures, the evaluation scores are scoring data of the consumer for the consumer product, the online commerce platform is taken as an example, the scoring data corresponds to a few stars of good scoring, the evaluation characters are character evaluation data of the consumer for the consumer product, and the comment pictures are photo data attached when the consumer reviews;
the method is characterized in that service experience evaluation data of the consumer at each stage of the price section commodity to be purchased after the consumer purchases the commodity is extracted through codes, and in the C language, the service experience evaluation data of the consumer can be represented by using a structural body, wherein the service experience evaluation data comprises relevant information of evaluation scores, evaluation characters and evaluation pictures. The following is a simple example code for creating a data structure containing such information:
#include<stdio.h>
structure definition: representing service experience assessment data
struct ServiceExperience {
int score;// evaluation score
char comment [100 ]// evaluate literal assuming a maximum of 100 characters
The file path of the character/rating picture, assuming a maximum of 50 characters
};
int main() {
Instance of/creating a service experience assessment data
struct ServiceExperience experience;
Data for evaluation of// filling
Science. Score=4;// evaluation score
sncrintf (science. Com), sizeof (science. Com), "good quality of merchandise, reasonable price"),// evaluation words
snrintf (experientience. Image, sizeof (experientence. Image), "image001. Jpg");// evaluating file path of picture
Data of evaluation of printing
printf ("evaluation score:% d)
", experience.score);
printf ("evaluation word:% s)
", experience.comment);
printf ("evaluation picture:% s)
", experience.image);
Where data may be saved to a file or otherwise manipulated
return 0;
}
In this example, a structure named 'ServiceExperience' is defined, which contains information of evaluation scores, evaluation characters, and evaluation pictures. You can create multiple such structure instances as needed to store multiple consumers ' evaluation data, this example also includes a simple ' main ' function to demonstrate how to populate and print these evaluation data, which in actual practice can be saved to files or databases as needed for subsequent analysis and processing;
s2, constructing an evaluation form recognition model, importing service experience evaluation data of the consumer at each stage of the commodity after the consumer purchases the commodity into the evaluation form recognition model, and calculating abnormal values of the service experience data;
It should be noted that, the evaluation form recognition model in S2 includes the following specific contents:
s21, extracting any two pieces of service experience evaluation data in each stage, and respectively extracting evaluation scores, evaluation characters and evaluation picture data in the service experience evaluation data;
s22, extracting evaluation text data in the service experience evaluation data, importing the evaluation text data into a text similarity calculation formula, and calculating the text similarity of any two service experience evaluation data, wherein the text similarity calculation formula is as follows:wherein->Literal set of rating data for one of the service experiences, +.>A text set of evaluation data is experienced for another service, and m is the number of elements in the set;
S23. extracting evaluation picture data, calculated text similarity data and evaluation score data in service experience evaluation data, and substituting the evaluation picture data, the calculated text similarity data and the calculated evaluation score data into an outlier calculation formula to calculate outliers of the service experience data, wherein the outlier calculation formula is as follows:where exp is the power of e,is->Evaluation score of corresponding service experience evaluation data, +.>Is->Evaluation score of corresponding service experience evaluation data, +.>Is->Pixel value of i-th pixel of evaluation picture of corresponding service experience evaluation data,/ >Is thatPixel value of ith pixel point of evaluation picture of corresponding service experience evaluation data, n is number of pixel points of the evaluation picture, < ->For the first duty cycle, +.>For a second duty cycle->。
Here, it is to be noted that, here、/>And the value of the abnormal threshold is obtained by manually finding out the evaluation data belonging to the evaluation list from 500 groups of service experience evaluation data and fitting the data to obtain +.>、/>And an optimal value of the anomaly threshold;
s3, comparing the calculated maximum abnormal value of each service experience data with a set abnormal threshold, and judging that the service experience data is the bill refreshing data and removing the bill refreshing data from statistics if the abnormal value of the service experience data is greater than or equal to the set abnormal threshold; if the abnormal value of the service experience data is smaller than the set abnormal threshold value, judging that the service experience data is normal service experience data, removing commodities of which the normal service experience data is smaller than the set service experience threshold value from statistics, and executing the rest S4;
s4, extracting normal service experience data of each stage of the rest commodity, and importing the normal service experience data into a commodity evaluation value calculation strategy to calculate the evaluation value of each stage of the commodity;
the specific steps of the commodity evaluation value calculation strategy in S4 are as follows:
S41, extracting an evaluation score data set in the normal service experience data of each commodity stage, and calculating an average value of the evaluation score data in the normal service experience data of each commodity stage;
s42, counting the proportion of the number of the normal service experience data in each commodity stage to the total normal service experience data as an important coefficient of each commodity stage;
s43, averaging evaluation score data in normal service experience data of each commodity stageSubstituting the value and the important coefficient of each commodity stage into a commodity evaluation value calculation formula to calculate the commodity evaluation value of each stage, wherein the commodity evaluation value calculation formula of the j stage is as follows:wherein->Experience data amount for normal service of jth stage, < >>Experiencing data quantity for total normal service, +.>For the number of j-stage evaluation scores, +.>The c-th evaluation score value of the j stage;
s5, leading the evaluation values of all stages of the commodities into a commodity evaluation coefficient judgment strategy to judge commodity evaluation coefficients, and arranging the commodity evaluation coefficients in a descending order to obtain a plurality of commodities with the commodity evaluation coefficients in the front;
the specific content of the commodity evaluation coefficient determination policy in S5 is as follows: substituting the calculated commodity evaluation values in each stage into a commodity evaluation coefficient calculation formula to calculate the commodity evaluation coefficient, wherein the commodity evaluation coefficient calculation formula is as follows: Wherein t is the number of stages;
s6, importing the obtained optimal commodity evaluation coefficients of the multiple commodities and the evaluation values of the commodity at each stage into a selection strategy according to the needs of purchasers so as to select the most suitable commodity in the similar commodities;
it should be noted that the selection strategy includes the following specific steps:
s61, selecting a required stage preference by a purchaser, and extracting an evaluation value of a stage corresponding to the obtained stage preference and a commodity evaluation coefficient obtained by calculation; for example, the purchaser needs an air conditioner with good refrigeration effect, the stage preference is refrigeration, and the evaluation value of the stage corresponding to the stage preference is the evaluation value of the refrigeration stage;
s62, importing the evaluation value of the corresponding stage of the extracted stage preference and the calculated commodity evaluation coefficient into an optimal value calculation formula to calculate an optimal value, wherein the optimal value calculation formula is as follows:wherein z is the evaluation value of the phase corresponding to the phase preference,/->The evaluation value of the corresponding stage of the stage preference is the ratio of +.>For the calculated commodity evaluation coefficient duty factor, < ->Wherein->The standard value of the quantity of the service experience data is set;
s63, arranging the calculated optimal values in a descending order, and obtaining the commodity corresponding to the maximum optimal value, namely the most suitable commodity in the similar commodities;
Here, it is to be noted that, hereAnd->The values of (2) are obtained by selecting the evaluation value of the corresponding stage of 500 groups of stage preference items and the calculated commodity evaluation coefficient data, simultaneously, artificially selecting the most suitable commodity, importing fitting software, and performing continuous iteration to obtain +.>And->Is the optimal solution of (a);
and S7, pushing the most suitable commodity information in the similar commodities to a display for the buyer to select.
Dividing a commodity of a price section to be purchased into a plurality of stages according to a life cycle, extracting service experience evaluation data of each stage of the commodity of the price section to be purchased by a consumer after the commodity is purchased, constructing an evaluation form recognition model, importing the service experience evaluation data of each stage of the commodity into the evaluation form recognition model after the commodity is purchased by the consumer, calculating abnormal values of each service experience data, comparing the maximum abnormal value of each calculated service experience data with a set abnormal threshold value, judging that the service experience data is the form data if the abnormal value of the service experience data is greater than or equal to the set abnormal threshold value, and removing the service experience data from statistics; if the abnormal value of the service experience data is smaller than the set abnormal threshold value, judging that the service experience data is normal service experience data, extracting normal service experience data of each commodity stage, importing the normal service experience data into a commodity evaluation value calculation strategy to calculate the evaluation value of each commodity stage, importing the evaluation value of each commodity stage into a commodity evaluation coefficient judgment strategy to judge commodity evaluation coefficients, arranging the commodity evaluation coefficients in descending order to obtain a plurality of commodities with commodity evaluation coefficients in the front, importing the commodity evaluation coefficients of the optimal plurality of commodities and the evaluation values of each commodity stage into a selection strategy according to the needs of purchasers to select the most suitable commodity in the similar commodities, pushing the most suitable commodity information in the similar commodities to a display for the purchasers to select, optimizing a commodity pushing mechanism, improving the commodity pushing accuracy, and avoiding the obstruction of a malicious good bill to the selection of the commodity.
Example 2
The full-process visualized management system based on service experience is realized based on the full-process visualized management method based on service experience, and comprises a data extraction module, an evaluation bill identification model construction module, an outlier calculation module, a data comparison module, an evaluation value calculation module, an evaluation coefficient judgment module, a commodity selection module, a commodity pushing module and a control module, wherein the data extraction module is used for dividing a commodity of a price section to be purchased into a plurality of stages according to a life cycle, extracting service experience evaluation data of each stage of the commodity of the price section to be purchased by a consumer after the commodity is purchased, the evaluation bill identification model construction module is used for constructing an evaluation bill identification model, and the outlier calculation module is used for importing the service experience evaluation data of each stage of the commodity into the evaluation bill identification model after the consumer purchases the commodity to calculate the outlier of each service experience data.
In this embodiment, the data comparison module is configured to compare the maximum abnormal value of each calculated service experience data with a set abnormal threshold, the evaluation value calculation module is configured to extract normal service experience data of each stage of a commodity, import the normal service experience data of each stage of the commodity into the commodity evaluation value calculation policy, calculate the evaluation value of each stage of the commodity, the evaluation coefficient determination module is configured to import the evaluation value of each stage of the commodity into the commodity evaluation coefficient determination policy, determine the commodity evaluation coefficients, and arrange the commodity evaluation coefficients in descending order to obtain a plurality of commodities with commodity evaluation coefficients in the front, the commodity selection module is configured to import the obtained commodity evaluation coefficients of the optimal plurality of commodities and the evaluation value of each stage of the commodity into the selection policy according to the needs of the purchaser, so as to select the most suitable commodity in the same class, and the commodity pushing module is configured to push the most suitable commodity information in the same class to the display for the purchaser to select, and the control module is configured to control the operation of the data extraction module, the evaluation bill identification model construction module, the abnormal value calculation module, the data comparison module, the evaluation value calculation module, the evaluation coefficient determination module, the commodity selection module, and the commodity selection module.
Example 3
The present embodiment provides an electronic device including: a processor and a memory, wherein the memory stores a computer program for the processor to call;
the processor executes the full-flow visual management method based on the service experience by calling the computer program stored in the memory.
The electronic device may be configured or configured differently to generate a larger difference, and may include one or more processors (Central Processing Units, CPU) and one or more memories, where at least one computer program is stored in the memories, and the computer program is loaded and executed by the processors to implement a full-flow visualization management method based on service experience provided by the above method embodiments. The electronic device can also include other components for implementing the functions of the device, for example, the electronic device can also have wired or wireless network interfaces, input-output interfaces, and the like, for inputting and outputting data. The present embodiment is not described herein.
Example 4
The present embodiment proposes a computer-readable storage medium having stored thereon an erasable computer program;
The computer program, when executed on the computer device, causes the computer device to perform a full-flow visualization management method based on service experience as described above.
For example, the computer readable storage medium can be Read-Only Memory (ROM), random access Memory (Random Access Memory, RAM), compact disk Read-Only Memory (Compact Disc Read-Only Memory, CD-ROM), magnetic tape, floppy disk, optical data storage device, etc.
It should be understood that, in various embodiments of the present application, the sequence numbers of the foregoing processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic thereof, and should not constitute any limitation on the implementation process of the embodiments of the present application.
It should be understood that determining B from a does not mean determining B from a alone, but can also determine B from a and/or other information.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions in accordance with embodiments of the present invention are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in or transmitted from one computer-readable storage medium to another, for example, by way of wired or/and wireless networks from one website site, computer, server, or data center to another. Computer readable storage media can be any available media that can be accessed by a computer or data storage devices, such as servers, data centers, etc. that contain one or more collections of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the partitioning of units is merely one way of partitioning, and there may be additional ways of partitioning in actual implementation, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
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 units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (5)
1. The full-flow visual management method based on service experience is characterized by comprising the following specific steps of:
s1, dividing a commodity in a price section to be purchased into a plurality of stages according to a life cycle, and extracting service experience evaluation data of each stage of the commodity in the price section to be purchased after a consumer purchases the commodity;
s2, constructing an evaluation form recognition model, importing service experience evaluation data of the consumer at each stage of the commodity after the consumer purchases the commodity into the evaluation form recognition model, and calculating abnormal values of the service experience data;
s3, comparing the calculated maximum abnormal value of each service experience data with a set abnormal threshold, and judging that the service experience data is the bill refreshing data and removing the bill refreshing data from statistics if the abnormal value of the service experience data is greater than or equal to the set abnormal threshold; if the abnormal value of the service experience data is smaller than the set abnormal threshold value, judging that the service experience data is normal service experience data, removing commodities of which the normal service experience data is smaller than the set service experience threshold value from statistics, and executing the rest S4;
S4, extracting normal service experience data of each stage of the rest commodity, and importing the normal service experience data into a commodity evaluation value calculation strategy to calculate the evaluation value of each stage of the commodity;
s5, leading the evaluation values of all stages of the commodities into a commodity evaluation coefficient judgment strategy to judge commodity evaluation coefficients, and arranging the commodity evaluation coefficients in a descending order to obtain a plurality of commodities with the commodity evaluation coefficients in the front;
s6, importing the obtained optimal commodity evaluation coefficients of the multiple commodities and the evaluation values of the commodity at each stage into a selection strategy according to the needs of purchasers so as to select the most suitable commodity in the similar commodities;
s7, pushing the most proper commodity information in the similar commodities to a display for the buyer to select; the S1 comprises the following specific steps:
s11, selecting a commodity of a price section to be selected according to the provided commodity price section, and dividing the selected commodity of the price section to be selected into a plurality of stages according to the life cycle;
s12, extracting service experience evaluation data of each stage of the price section commodity to be purchased by the consumer after the consumer purchases the commodity, wherein the service experience evaluation data is the evaluation data of the consumer and comprises evaluation scores, evaluation characters and evaluation pictures; the evaluation form recognition model in the S2 comprises the following specific contents:
S21, extracting any two pieces of service experience evaluation data in each stage, and respectively extracting evaluation scores, evaluation characters and evaluation picture data in the service experience evaluation data;
s22, extracting evaluation text data in the service experience evaluation data, importing the evaluation text data into a text similarity calculation formula, and calculating the text similarity of any two service experience evaluation data, wherein the text similarity calculation formula is as follows:wherein->Literal set of rating data for one of the service experiences, +.>A text set of evaluation data is experienced for another service, and m is the number of elements in the set;
s23, extracting evaluation picture data, character similarity data obtained through calculation and evaluation score data in service experience evaluation data, and substituting the evaluation picture data, the calculated character similarity data and the evaluation score data into an outlier calculation formula to calculate outlier of the service experience data, wherein the outlier calculation formula is as follows:wherein exp is the power of e, < >>Is->Evaluation score of corresponding service experience evaluation data, +.>Is->Evaluation score of corresponding service experience evaluation data, +.>Is->Pixel value of i-th pixel of evaluation picture of corresponding service experience evaluation data,/>Is->The pixel value of the ith pixel point of the evaluation picture of the corresponding service experience evaluation data, n is the number of the pixel points of the evaluation picture, For the first duty cycle, +.>For a second duty cycle->The method comprises the steps of carrying out a first treatment on the surface of the The specific steps of the commodity evaluation value calculation strategy in the S4 are as follows:
s41, extracting an evaluation score data set in the normal service experience data of each commodity stage, and calculating an average value of the evaluation score data in the normal service experience data of each commodity stage;
s42, counting the proportion of the number of the normal service experience data in each commodity stage to the total normal service experience data as an important coefficient of each commodity stage;
s43, substituting an average value of evaluation score data in normal service experience data of each commodity stage and an important coefficient of each commodity stage into a commodity evaluation value calculation formula to calculate an evaluation value of the commodity of each stage, wherein the commodity evaluation value calculation formula of the j stage is as follows:wherein->Experience data amount for normal service of jth stage, < >>Experiencing data quantity for total normal service, +.>For the number of j-stage evaluation scores, +.>The c-th evaluation score value of the j stage; the specific content of the commodity evaluation coefficient judgment strategy in the S5 is as follows: substituting the calculated commodity evaluation values in each stage into a commodity evaluation coefficient calculation formula to calculate the commodity evaluation coefficient, wherein the commodity evaluation coefficient calculation formula is as follows: Wherein t is the number of stages; the selection strategy comprises the following specific steps:
s61, selecting a required stage preference by a purchaser, and extracting an evaluation value of a stage corresponding to the obtained stage preference and a commodity evaluation coefficient obtained by calculation; s62, importing the evaluation value of the corresponding stage of the extracted stage preference and the calculated commodity evaluation coefficient into an optimal value calculation formula to calculate an optimal value, wherein the optimal value calculation formula is as follows:wherein z is the evaluation value of the phase corresponding to the phase preference,/->The evaluation value of the corresponding stage of the stage preference is the ratio of +.>For the calculated commodity evaluation coefficient duty factor, < ->Wherein->The standard value of the quantity of the service experience data is set;
and S63, arranging the calculated optimal values in a descending order, and obtaining the commodity corresponding to the maximum optimal value, namely the most suitable commodity in the similar commodities.
2. The full-flow visual management system based on service experience is realized based on the full-flow visual management method based on service experience according to claim 1, and is characterized by comprising a data extraction module, an evaluation bill identification model construction module, an outlier calculation module, a data comparison module, an evaluation value calculation module, an evaluation coefficient judgment module, a commodity selection module, a commodity pushing module and a control module, wherein the data extraction module is used for dividing a commodity of a price section to be purchased into a plurality of stages according to a life cycle, extracting service experience evaluation data of each stage of the commodity of the price section to be purchased by a consumer after the consumer purchases the commodity, the evaluation bill identification model construction module is used for constructing an evaluation bill identification model, and the outlier calculation module is used for importing the service experience evaluation data of each stage of the commodity into the evaluation bill identification model after the consumer purchases the commodity to calculate the outlier of each service experience data.
3. The full-process visual management system based on service experience according to claim 2, wherein the data comparison module is configured to compare a maximum abnormal value of each service experience data obtained by calculation with a set abnormal threshold value, the evaluation value calculation module is configured to extract normal service experience data of each commodity stage and guide the normal service experience data of each commodity stage into a commodity evaluation value calculation policy to calculate an evaluation value of each commodity stage, the evaluation coefficient judgment module is configured to guide the evaluation value of each commodity stage into a commodity evaluation coefficient judgment policy to perform judgment of commodity evaluation coefficients, and arrange commodity evaluation coefficients in descending order to obtain a plurality of commodities with commodity evaluation coefficients in front, the commodity selection module is configured to guide the obtained commodity evaluation coefficients of the optimal plurality of commodities and the evaluation values of each commodity stage into a selection policy according to a requirement of a purchaser, the commodity pushing module is configured to push most suitable commodity information of the same class to a display for selection, and the control module is configured to control the data extraction module, the commodity pushing module, the evaluation bill identification module, the abnormal value calculation module, the data comparison module, the calculation module, the commodity evaluation coefficient judgment module, and the commodity selection module.
4. A human-machine interaction device, comprising: a processor and a memory, wherein the memory stores a computer program for the processor to call;
the method for full-process visualization management based on service experience according to claim 1 is performed by the processor by calling a computer program stored in the memory.
5. A computer readable storage medium storing instructions that when executed on a computer cause the computer to perform a full-flow visualization management method based on a service experience as recited in claim 1.
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