CN117252662B - Digital mall shopping system based on VR technology - Google Patents
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Abstract
The invention discloses a digital mall shopping system based on VR technology, which particularly relates to the technical field of virtual reality and is used for solving the problem that the prior digital mall cannot reasonably utilize user behavior pattern data to improve the utilization degree of preferential measures.
Description
Technical Field
The invention relates to the technical field of virtual reality, in particular to a digital mall shopping system based on VR technology.
Background
The VR technology is a computer technology, and by simulating the environment and the scene of a three-dimensional space, a user interacts with a virtual world, and the VR technology is applied to a digital mall, so that the user can comprehensively and specifically enjoy shopping services of the mall without going out.
The VR technology is used for tracking eyes of users, so that the effect of simulating cursor movement in an optical display is achieved, the places where the eyes of the users reach are the components of user behavior pattern information, the existing digital mall is insufficient in the integrated utilization efficiency of the user behavior pattern information and commodity information, the users are difficult to finish consumption closed loops in a targeted and effective manner, the trend range of a consumption guiding policy cannot be determined, and a reasonable user consumption tendency judging method is lacked.
In order to solve the defects, a technical scheme is provided.
Disclosure of Invention
The invention aims to provide a digital mall shopping system based on VR technology, which aims to solve the defects in the background technology.
In order to achieve the above purpose, the present invention provides the following technical solutions: a digital mall shopping system based on VR technology comprises a data acquisition module, a feature extraction module, an analysis processing module and a decision generation module;
the data acquisition module is used for collecting customer behavior mode data and commodity information data in the digital mall;
the feature extraction module is used for extracting customer behavior mode data and commodity information data and preprocessing the data;
the analysis processing module is used for calculating the attention index of a customer entering the digital mall, establishing a personal transaction facilitating model, calculating a personal transaction facilitating coefficient, and grading the potential heat of the commodity according to the comparison result of the personal transaction facilitating coefficient and the personal transaction facilitating threshold;
the decision generation module calculates commodity directional preferential proportion according to potential heat level data of all customers on commodities, sorts personal transaction promoting coefficients of the commodities, and determines commodity directional preferential scope according to the arrangement of the personal transaction promoting coefficients from large to small.
In a preferred embodiment, the method of calculating the attention index;
the attention index Ai has the expression ofWherein T is the concentrated time of the commodity observed by the customer, V is the number of times of the commodity inspection by the customer, sa is the attention degree of the similar commodity of the observed commodity by the customer, na is the total number of times of the digital mall access by the customer, and alpha 1 、α 2 、α 3 、α 4 The proportion coefficients of the concentration time T, the number of times of inspection V, the attention degree Sa of the similar commodity and the total entrance number Na are respectively, and alpha 1 、α 2 、α 3 、α 4 Are all greater than 0;
the centralized time T starts to count when the sight-line end of the customer points to the commodity, and stops counting when the sight-line end leaves the commodity;
the number of times of checking is the number of times of checking the commodity selected by the customer and carrying out detail checking;
the attention degree of the similar commodity is the attention degree of the same class commodity except the commodity pointed by the sight line end, if the sight line end points to other commodities similar to the commodity except the commodity pointed by the sight line end point are not detected, the attention degree of the similar commodity is taken as 1, and if the sight line end points to the similar commodity are detected, the type number of the similar commodity is taken as A i Wherein i= {1,2,3 … n }, n is a positive integer, and the number of times of customer's inspection of the commodity is B j Wherein j= {1,2,3 … m }, m is a positive integer, and the similar commodity interest expression isWhere i=j.
In a preferred embodiment, the personal transaction facilitation factor is calculated;
the expression of the personal transaction facilitation coefficient Dm isWherein Pm is commodity profit margin, bc is commodity backlog coefficient, beta 1 、β 2 、β 3 The attention index Ai, the commodity profit margin Pm and the backlog coefficient Bc are respectively proportional coefficients and beta 1 、β 2 、β 3 Are all greater than 0;
the commodity profit margin is the ratio of the difference between the commodity selling price and the commodity cost to the commodity selling price, namely commodity profit margin= (commodity selling price-commodity cost)/commodity selling price;
the commodity backlog coefficient is the digestion period change state of commodity inventory, the commodity monomer volume is calibrated to be Vo, and the commodity inventory change in the period isCommodity backlog coefficient-> In the formula, [ t ]1,t2]And I (t) is the commodity inventory at the moment t for the cycle time.
In a preferred embodiment, logic to rank the potential heat of the commodity by comparing the personal transaction facilitation factor to a personal transaction facilitation threshold;
personal transaction facilitation model there is a personal transaction facilitation first threshold D 1 And personal transaction facilitating second threshold D 2 Comparing the personal transaction facilitation factor with a first personal transaction facilitation threshold and a second personal transaction facilitation threshold when the personal transaction facilitation factor Dm>Personal transactions facilitating a first threshold D 1 Defining the potential heat level of the customer to the commodity as S1 level;
when personal transactions contribute to a first threshold D 1 Personal transaction facilitation coefficient Dm is less than or equal to personal transaction facilitation second threshold D 2 Defining the potential heat level of the customer to the commodity as S2;
when personal transaction facilitating coefficient Dm<Personal transactions facilitating a second threshold D 2 When the potential heat level of the customer to the commodity is defined as level S3.
In a preferred embodiment, a method of calculating a directional offer ratio;
the expression of the directional preferential proportion Pr isWhere SumS1 is the number of customers with a potential heat level S1, sumS2 is the number of customers with a potential heat level S2, sumS3 is the number of customers with a potential heat level S3, gamma 1 And gamma 2 A proportionality coefficient of the number of customers with a potential heat level S2 and the number of customers with a potential heat level S3, respectively, and gamma 1 、γ 2 Are all greater than 0.
In a preferred embodiment, logic for determining a range of directional offers based on the proportion of directional offers;
for commodities in a preferential activity state, taking personal transaction promoting coefficients of all customers, arranging the personal transaction promoting coefficients from large to small, generating a personal transaction promoting coefficient sorting table, dividing the personal transaction promoting coefficient sorting table in proportion according to the calculated directional preferential proportion, and framing the directional preferential range according to the sorting value from large to small.
In the technical scheme, the invention has the technical effects and advantages that:
according to the invention, through integrating and analyzing the behavior mode data of the customer and the commodity information data, the VR technology detects the calculated attention index, generates the personal transaction promoting model, calculates the personal transaction promoting coefficient, compares the personal transaction promoting coefficient with the threshold value of the personal transaction promoting coefficient, so that the potential heat level of the customer is classified and judged, the potential heat level distribution of the same commodity is calculated to orient preferential proportion, and the head directory of the personal transaction promoting coefficient ranking table is framed, so that the preferential measure issuing range can be determined.
The invention can effectively utilize the behavior pattern data of the customers, can reasonably reduce the implementation cost of preferential treatment, improve the completion of consumption guiding measures and promote the consumption behavior of potential customers.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments described in the present invention, and other drawings may be obtained according to these drawings for a person having ordinary skill in the art.
FIG. 1 is a block diagram of a system according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1: referring to fig. 1, the present embodiment is a digital mall shopping system based on VR technology, where the system includes a data acquisition module, a feature extraction module, an analysis processing module, and a decision generation module;
the data acquisition module is used for collecting customer behavior mode data and commodity information data in the digital mall;
the feature extraction module is used for extracting customer behavior mode data and commodity information data and preprocessing the data;
the analysis processing module is used for calculating the attention index of a customer entering the digital mall, establishing a personal transaction facilitating model, calculating a personal transaction facilitating coefficient, and grading the potential heat of the commodity according to the comparison result of the personal transaction facilitating coefficient and the personal transaction facilitating threshold;
the decision generation module calculates commodity directional preferential proportion according to potential heat level data of all customers on commodities, sorts personal transaction promoting coefficients of the commodities, and determines commodity directional preferential scope according to the arrangement of the personal transaction promoting coefficients from large to small.
The data collected by the data collection module in this embodiment includes customer behavior pattern data and commodity information data, the customer behavior pattern data tracks eyes of customers through VR technology, tracking calculation is performed according to commodities pointed by the end point of the line of sight of the customers, and the commodity information data is obtained from commodity sales data and inventory data;
the characteristic extraction module receives the data transmitted by the data acquisition module, performs normalization preprocessing on the data, maintains the data quality and transmits the data to the analysis processing module, the characteristic extraction module comprises an extraction unit and a preprocessing unit, the customer behavior pattern data extracted by the extraction unit comprises the attention concentrating time, the number of times of inspection, the attention degree of similar commodities and the total entrance number of the customers, and the commodity information data comprises commodity profit rate and backlog coefficient; the preprocessing unit is used for preprocessing the extracted data, and the preprocessing method comprises the technical means of screening out repeated items, inserting deletion values, deleting error items and the like;
the analysis processing module calculates the attention index of the customer according to the received data, builds a personal transaction promotion model of the individual customer for the individual commodity according to the attention index, calculates the personal transaction promotion index through the model, compares the personal transaction promotion coefficient with a threshold value of the personal transaction promotion index, and evaluates the potential heat level of the commodity according to the comparison result of the personal transaction promotion coefficient with the first threshold value of the personal transaction promotion coefficient and the second threshold value of the personal transaction promotion coefficient;
the decision generation module calculates directional preferential proportion according to potential heat level data of all customers on single commodity, generates a personal transaction promoting coefficient sorting table through the personal transaction promoting coefficients, sorts the personal transaction promoting coefficients from big to small, calculates the number of customers at the head of the sorting table according to the directional preferential proportion, and directionally issues preferential conditions for the customers at the head of the sorting table meeting the proportion requirements so as to promote the transaction.
According to the invention, the attention index of a customer to the commodity is calculated by collecting the customer behavior mode data and the commodity information data, the personal transaction promotion model is constructed by the attention index, the commodity potential heat is graded by the comparison result of the personal transaction promotion coefficient and the personal transaction promotion threshold value, the directional preferential proportion is calculated by the commodity potential heat grade distribution, the directional preferential distribution range is determined according to the ranking table generated by the personal transaction promotion coefficient of the commodity, and the transaction promotion possibility can be reasonably improved.
Example 2: before the trade is reached, the tendency of customer to commodity is difficult to demonstrate through customer behavior pattern data, and under the intervention of VR technique, customer's tendency to commodity can be calculated through the target that customer's sight pointed, and the core of VR technique is VOG eye video analysis, and its basic principle is: the eyes are irradiated by near infrared light, interactive recording is carried out by using a camera, the sight line direction of people is estimated through light and back-end algorithm analysis, the current VR technology is developed to be mature, the market of consumer products is gradually expanded, the specific technical means are not described in detail herein, the embodiment takes an eye link 1000Plus type eye tracker produced by SR Research as an example, the eye link 1000Plus type eye tracker has a binocular mode sampling rate reaching 2000Hz, the end-to-end delay is lower than 3ms, and the precision and aging requirements of data acquisition can be met.
The eye movement instrument is used for positioning the sight line end point, calculating the attention focusing time of the commodity pointed by the sight line end point, when a customer selects to know the detail of the commodity and inspect the commodity, recording the inspection times of the customer, carrying out centralized analysis on the commodity data of the same type of the commodity observed by the customer, recording the total entrance times of the customer to the digital commodity, calculating the attention index according to the data, calibrating the focusing time of the customer to the commodity to be observed as T, the inspection times of the customer to the commodity to be observed as V, the attention degree of the customer to the similar commodity of the observed commodity to be Sa and the total entrance times of the commodity to be observed to be Na, and the expression of the attention index Ai is as followsWherein alpha is 1 、α 2 、α 3 、α 4 The proportion coefficients of the concentration time T, the number of times of inspection V, the attention degree Sa of the similar commodity and the total entrance number Na are respectively, and alpha 1 、α 2 、α 3 、α 4 Are all greater than 0.
The centralized time T starts to count when the sight-line end of the customer points to the commodity, and stops counting when the sight-line end leaves the commodity;
the number of times of checking is the number of times of checking the commodity selected by the customer and carrying out detail checking;
the attention degree of the similar commodity is the attention degree of the same class commodity except the commodity pointed by the sight line end, if the sight line end points to other commodities similar to the commodity except the commodity pointed by the sight line end point are not detected, the attention degree of the similar commodity is taken as 1, and if the sight line end points to the similar commodity are detected, the type number of the similar commodity is taken as A i Wherein i= {1,2,3 … n }, n is a positive integer, and the number of times of customer's inspection of the commodity is B j Wherein j= {1,2,3 … m }, m is a positive integer, and the similar commodity interest expression isWhere i=j.
The attention index is used for collecting data through the eye tracker, so that the tendency state of each commodity in the digital market, which is shown by the line of sight transfer of a customer, can be effectively analyzed.
The attention index and commodity information data are combined and analyzed, a personal transaction promoting model is established, a personal transaction promoting coefficient is calculated, the commodity profit margin is calibrated to be Pm, the commodity backlog coefficient is calibrated to be Bc, and the expression of the personal transaction promoting coefficient Dm is Wherein beta is 1 、β 2 、β 3 The attention index Ai, the commodity profit margin Pm and the backlog coefficient Bc are respectively proportional coefficients and beta 1 、β 2 、β 3 Are all greater than 0.
The commodity profit margin is the ratio of the difference between the commodity selling price and the commodity cost to the commodity selling price, namely commodity profit margin= (commodity selling price-commodity cost)/commodity selling price;
the commodity backlog coefficient is the digestion period change state of commodity inventory, the commodity monomer volume is calibrated to be Vo, and the commodity inventory change in the period isCommodity backlog coefficient-> Wherein [ t1, t2 ]]And I (t) is the commodity inventory at the moment t for the cycle time.
Personal transaction facilitation model there is a personal transaction facilitation first threshold D 1 And personal transaction facilitating second threshold D 2 Comparing the personal transaction facilitation factor with a first personal transaction facilitation threshold and a second personal transaction facilitation threshold when the personal transaction facilitation factor Dm>Personal transactions facilitating a first threshold D 1 Defining the potential heat level of the customer to the commodity as S1 level;
when personal transactions contribute to a first threshold D 1 Personal transaction facilitation coefficient Dm is less than or equal to personal transaction facilitation second threshold D 2 Defining the potential heat level of the customer to the commodity as S2;
when personal transaction facilitating coefficient Dm<Personal transactions facilitating a second threshold D 2 When the potential heat level of the customer to the commodity is defined as level S3.
According to the embodiment, the behavior mode data of the customer in the digital market is analyzed through the VR technology by means of the eye tracker, meanwhile, commodity information data are introduced, a personal transaction promotion model is built, a personal transaction promotion coefficient is calculated, the shopping tendency of the customer is analyzed through the comparison result of the personal transaction promotion coefficient, the personal transaction promotion first threshold value and the personal transaction promotion second threshold value, and the consumption tendency degree of the customer can be estimated in a classified mode through potential heat grading.
Example 3: when the digitalized market opens the preferential activity for the commodity, the preferential area of the commodity is directionally issued to the potential consumption objects, the issuing range of the preferential area is judged according to the potential heat level in the embodiment, the higher the potential heat level is, the stronger the consumer tendency of the commodity is, and the specific calculating method is as follows:
for commodities in a preferential activity state, taking personal transaction promoting coefficients of all customers, arranging the personal transaction promoting coefficients from large to small to generate a personal transaction promoting coefficient sorting table, calculating directional preferential proportion according to the number of customers at each potential heat level, wherein the expression of the directional preferential proportion Pr is as followsWhere SumS1 is the number of customers with a potential heat level S1, sumS2 is the number of customers with a potential heat level S2, sumS3 is the number of customers with a potential heat level S3, gamma 1 And gamma 2 A proportionality coefficient of the number of customers with a potential heat level S2 and the number of customers with a potential heat level S3, respectively, and gamma 1 、γ 2 Are all greater than 0.
And according to the calculated directional preferential proportion, carrying out proportion division on the personal transaction promoting coefficient sorting table, framing a directional preferential range from big to small according to the sorting value, and issuing coupons to customers in the directional preferential range, thereby carrying out directional preferential issuing processing of the digital mall preferential promotion activities.
According to the invention, the personal transaction promoting model is established by combining the behavior mode data and commodity information data of the customers in the digital mall, the consumption tendency of the customers is evaluated by calculating the personal transaction promoting coefficient, and the directional preferential proportion is calculated according to different potential heat levels of the customers on the commodities, so that the consumption behavior of the customers can be effectively promoted, and the consumption guiding cost is reduced.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas with a large amount of data collected for software simulation to obtain the latest real situation, and preset parameters in the formulas are set by those skilled in the art according to the actual situation.
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 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 described in accordance with the embodiments of the present application are all or partially produced. 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 a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center by wired or wireless means (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains one or more sets 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.
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.
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 application.
It will be clear to those skilled in the art that, for convenience and brevity of description, the specific working procedures of the above-described system and unit may refer to the corresponding procedures in the foregoing method embodiments, which are not repeated here.
In the several embodiments provided in this application, it should be understood that the disclosed system or unit may be implemented in other ways. For example, the embodiments described above are merely illustrative, e.g., the division of the elements is merely a logical functional division, and there may be additional divisions in actual implementation, e.g., multiple elements 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 each embodiment of the present application 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.
The functions, if implemented in the form of software functional units and sold or used as stand-alone goods, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of software goods 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 perform all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a random access memory (random access memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing is merely specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope of the present application, and the changes and substitutions are intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (4)
1. A digital mall shopping system based on VR technology is characterized in that: the system comprises a data acquisition module, a feature extraction module, an analysis processing module and a decision generation module;
the data acquisition module is used for collecting customer behavior mode data and commodity information data in the digital mall;
the feature extraction module is used for extracting customer behavior mode data and commodity information data and preprocessing the data;
the analysis processing module is used for calculating the attention index of a customer entering the digital mall, establishing a personal transaction facilitating model, calculating a personal transaction facilitating coefficient, and grading the potential heat of the commodity according to the comparison result of the personal transaction facilitating coefficient and the personal transaction facilitating threshold;
the decision generation module calculates commodity directional preferential proportion according to potential heat level data of all customers on commodities, sorts personal transaction promoting coefficients of the commodities, and determines commodity directional preferential scope according to the arrangement of the personal transaction promoting coefficients from large to small;
a method of calculating an attention index;
the attention index Ai has the expression ofWherein T is the concentration time of a customer for observing the commodity, V is the number of times of the customer for observing the commodity, sa is the attention degree of the customer for the similar commodity of the observed commodity, na is the total entrance times of the customer for visiting the digital mall, and alpha 1, alpha 2, alpha 3 and alpha 4 are the proportion coefficients of the concentration time T, the number of times of the inspection V, the attention degree of the similar commodity Sa and the total entrance times Na respectively, and alpha 1, alpha 2, alpha 3 and alpha 4 are all larger than 0;
the centralized time T starts to count when the sight-line end of the customer points to the commodity, and stops counting when the sight-line end leaves the commodity;
the number of times of checking is the number of times of checking the commodity selected by the customer and carrying out detail checking;
the attention degree of the similar commodity is the attention degree of the same class commodity except the commodity pointed by the sight line end, if the sight line end points to other commodities similar to the commodity except the commodity pointed by the sight line end point are not detected, the attention degree of the similar commodity is taken as 1, and if the sight line end points to the similar commodity are detected, the type number of the similar commodity is taken asAi, wherein i= {1,2,3 … n }, n is a positive integer, the number of times of the customer's inspection on the commodity is Bj, wherein j= {1,2,3 … m }, m is a positive integer, and the similar commodity attention expression isWherein i=j;
a method of calculating a personal transaction facilitation factor;
the expression of the personal transaction facilitation coefficient Dm isWherein Pm is commodity profit margin, bc is backlog coefficient of commodity, β1, β2, β3 are proportional coefficients of attention index Ai, commodity profit margin Pm, backlog coefficient Bc, respectively, and β1, β2, β3 are all greater than 0;
the commodity profit margin is the ratio of the difference between the commodity selling price and the commodity cost to the commodity selling price, namely commodity profit margin= (commodity selling price-commodity cost)/commodity selling price;
the commodity backlog coefficient is the digestion period change state of commodity inventory, the commodity monomer volume is calibrated to be Vo, and the commodity inventory change in the period isCommodity backlog coefficient-> Wherein [ t1, t2 ]]And I (t) is the commodity inventory at the moment t for the cycle time.
2. The VR technology based digital mall shopping system of claim 1, wherein: logic for ranking the commodity for potential heat by comparing the personal transaction facilitation factor to a personal transaction facilitation threshold;
the personal transaction facilitating model has a personal transaction facilitating first threshold D1 and a personal transaction facilitating second threshold D2, the personal transaction facilitating coefficient is compared with the personal transaction facilitating first threshold and the personal transaction facilitating second threshold, and when the personal transaction facilitating coefficient Dm > the personal transaction facilitating first threshold D1, the potential heat level of the commodity by the customer is defined as grade S1;
when the personal transaction contribution first threshold D1 is less than or equal to the personal transaction contribution coefficient Dm is less than or equal to the personal transaction contribution second threshold D2, defining the potential heat level of the commodity by the customer as S2 level;
when the personal transaction facilitation coefficient Dm < the personal transaction facilitation second threshold value D2, the potential heat level of the customer to the commodity is defined as level S3.
3. The VR technology based digital mall shopping system of claim 1, wherein: a calculation method of directional preferential proportion;
the expression of the directional preferential proportion Pr isWhere SumS1 is the number of customers with a potential heat level S1, sumS2 is the number of customers with a potential heat level S2, sumS3 is the number of customers with a potential heat level S3, gamma 1 and gamma 2 are the scaling factors of the number of customers with a potential heat level S2 and the number of customers with a potential heat level S3, respectively, and gamma 1 and gamma 2 are both greater than 0.
4. A digital mall shopping system based on VR technology as set forth in claim 3, wherein: logic for determining a directional offer issuing range according to the directional offer proportion;
for commodities in a preferential activity state, taking personal transaction promoting coefficients of all customers, arranging the personal transaction promoting coefficients from large to small, generating a personal transaction promoting coefficient sorting table, dividing the personal transaction promoting coefficient sorting table in proportion according to the calculated directional preferential proportion, and framing the directional preferential range according to the sorting value from large to small.
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