User deep interaction system and method based on multi-source data platform

Abstract

The invention discloses a user deep interaction system and method based on a multi-source data platform, and relates to the technical field of user deep interaction, and the method comprises the steps: obtaining a historical online interaction record and a historical offline interaction record of a user in a shopping mall, evaluating the interaction tendency of the user to commodities in the shopping mall, and obtaining interaction tendency data; obtaining interaction tendency data of each user in the shopping mall, and analyzing the overall interaction tendency of each user to commodities in the shopping mall to obtain target interaction tendency data of the shopping mall; acquiring historical consumption records of the user in the shopping mall, and analyzing consumption collocation of commodities of the user in the shopping mall to obtain consumption collocation data of the user; and acquiring consumption matching data of each user in the shopping mall, analyzing consumption matching degrees among different commodities in the shopping mall, and optimizing and adjusting an interaction strategy of the shopping mall in combination with the target interaction tendency data.

Description

Technical Field

[0001] The present invention relates to the technical field of user in-depth interaction, and specifically to a user in-depth interaction system and method based on a multi-source data platform. Background Art

[0002] A multi-source data platform integrates data from different channels and of different specifications. With the continuous development of technology, the sources and forms of users' information acquisition are various. Therefore, it has become increasingly common to use a multi-source data platform for user in-depth interaction. The specific advantages of using a multi-source data platform include but are not limited to the following: 1. Provide precise services. By integrating multi-source data such as social media and transaction records, data silos can be broken, and a full-dimensional view of user behavior and needs can be formed; 2. Optimize operation efficiency. Based on the multi-source data platform, various data can be obtained, and a prediction model can be used to predict the user churn situation, and intervention strategies can be made in advance to optimize the operation; 3. Improve cross-channel collaboration. The interaction data of users on different platforms can be coordinated with each other to ensure the coherence of services.

[0003] There are numerous merchants in a large shopping mall. Every day, many users carry out various activities online and offline. Each time a user conducts an activity, an interaction record is generated. In reality, in most cases, these users' interaction records are isolated from each other. The large shopping mall cannot effectively analyze the interaction records of each user, lacks the understanding of users' consumption preferences, and cannot tap the consumption potential of users. This will not only reduce the revenue of the merchants in the mall, but also cause problems in the supply chains of various merchants in the mall, bringing huge losses to the mall. Summary of the Invention

[0004] The purpose of the present invention is to provide a user in-depth interaction system and method based on a multi-source data platform to solve the problems raised in the prior art.

[0005] To achieve the above purpose, the present invention provides the following technical solution: A user in-depth interaction method based on a multi-source data platform, the method includes: Step S100: Obtain the historical online interaction records and historical offline interaction records of users in the shopping mall, evaluate the interaction tendency of users towards the goods in the shopping mall, and obtain interaction tendency data; Step S200: Obtain the interaction tendency data of each user in the shopping mall, analyze the overall interaction tendency of each user towards the goods in the shopping mall, and obtain the target interaction tendency data of the shopping mall; Step S300: Obtain the historical consumption records of users in the shopping mall, analyze the consumption compatibility of users with the goods in the shopping mall, and obtain the consumption compatibility data of users; Step S400: Obtain the consumption matching data of each user in the mall, analyze the consumption matching degree between different commodities in the mall, and optimize and adjust the interaction strategy of the mall in combination with the target interaction tendency data.

[0006] Further, step S100 includes: Step S101: Obtain the historical online interaction records and historical offline interaction records of the user in the mall respectively, and obtain the total number S´ of the consumption commodities of the user on the online platform in the mall sum , and obtain the total number S of the consumption commodities of the user offline in the mall sum ; Step S102: Calculate the characteristic proportion coefficient B = S sum / (S´ sum + S sum ), calculate the characteristic proportion coefficient B´ = S´ sum / (S´ sum + S sum ); Step S103: Obtain the commodity types of the commodities with which the user interacts from the historical online interaction records and historical offline interaction records respectively, and calculate the interaction tendency values of the user for each commodity type. Among them, the interaction tendency value F of the user for the c-th commodity type c : , where A´ c represents the total number of the historical online interaction records of the user when the commodity type of the interaction commodity is the c-th commodity type; A c represents the total number of the historical offline interaction records of the user when the commodity type of the interaction commodity is the c-th commodity type; A´ sum represents the total number of the historical online interaction records of the user; A sum represents the total number of the historical offline interaction records of the user; When the interaction tendency value F c is greater than the preset interaction tendency threshold, it is determined that the user has an interaction tendency for the commodities of the c-th commodity type, and the c-th commodity type is recorded as the tendency commodity type of the user; Step S104: Obtain and collect the various tendency commodity types of the user to obtain the interaction tendency data of the user.

[0007] Further, step S200 includes: Step S201: Obtain the interaction tendency data of each user in the mall, and obtain several tendency commodity types of the user from the interaction tendency data; Step S202: Analyze the overall interaction tendency of each user towards the goods of various commodity types in the mall. Among them, when analyzing the overall interaction tendency of each user towards the goods of the h-th commodity type in the mall, the specific process is as follows: Obtain the maximum value W of the total number of consumer goods of each user in the mall max , calculate the overall interaction tendency value L of each user towards the goods of the h-th commodity type in the mall h : , where z represents the total number of users who mark the h-th commodity type as the preferred commodity type; W e i represents the total number of consumer goods of the i-th user who marks the h-th commodity type as the preferred commodity type in the mall; Step S203: When the overall interaction tendency value L h is greater than the preset overall interaction tendency threshold, it is determined that each user has an overall interaction tendency towards the goods of the h-th commodity type, mark the h-th commodity type as the target tendency commodity type of the mall, obtain several target tendency commodity types in the mall and gather them to obtain the target interaction tendency data.

[0008] Furthermore, step S300 includes: Step S301: Record the online and offline consumption behaviors of users in the mall to obtain the historical consumption records of users, and obtain the commodity types of several commodities consumed by users from the historical consumption records; Step S302: Analyze the consumption compatibility between various commodity types of goods during the consumption process of users in the mall. Among them, when analyzing the consumption compatibility between the goods of the e-th commodity type and the goods of the g-th commodity type during the consumption process of users in the mall, the specific process is as follows: Obtain the total number Q of historical consumption records of users that only contain the goods of the e-th commodity type e sum , obtain the total number Q of historical consumption records of users that only contain the goods of the g-th commodity type g sum , obtain the total number Q of historical consumption records of users that contain both the goods of the e-th commodity type and the goods of the g-th commodity type e,g sum , obtain the total number Q of historical consumption records of users that do not contain the goods of the e-th commodity type and the goods of the g-th commodity type sum ; Step S303: Respectively set the random variable X of the e-th commodity type and the random variable Y of the g-th commodity type, and set the eigenvalue k, where k > 0, X = {k, 0}, Y = {k, 0}, and calculate the joint probability p(k, 0) = Q of the commodity of the e-th commodity type and the commodity of the g-th commodity type e sum / n, p(0, k) = Q g sum / n, p(k, k) = Q e,g sum / n, p(0, 0) = Q sum / n; Calculate the marginal probability p(k) = (Q e sum + Q e,g sum ) / n, p(0) = (Q g sum + Q sum ) / n; Calculate the marginal probability p(k) = (Q g sum + Q e,g sum ) / n, p(0) = (Q e sum + Q sum ) / n; Step S304: Obtain the total number n of each historical consumption record of the user, and calculate the consumption matching value R between the commodity of the e-th commodity type and the commodity of the g-th commodity type e,g : , where p(x, y) is the joint probability of the commodity of the e-th commodity type and the commodity of the g-th commodity type; p(x) is the marginal probability of the commodity of the e-th commodity type; p(y) is the marginal probability of the commodity of the g-th commodity type; Step S305: When the consumption matching value R e,g is greater than the preset consumption matching threshold, it is determined that there is a consumption match between the commodity of the e-th commodity type and the commodity of the g-th commodity type during the user's consumption in the mall. Record the e-th commodity type and the g-th commodity type as a consumption matching group of the user, obtain each consumption matching group of the user and gather them to obtain consumption matching data.

[0009] Further, step S400 includes: Step S401: Obtain the consumption matching data of each user in the mall, and analyze the consumption matching degree among the commodities of various commodity types in the mall. Among them, when analyzing the commodities of the v-th commodity type in the mall and the commodities of the u-th commodity type, the specific process is as follows: When the v-th commodity type and the u-th commodity type are in the same consumption matching group in the consumption matching data of a certain user, mark the certain user, and obtain several marked users; Step S402: Calculate the characteristic consumption matching value T between the commodities of the v-th commodity type in the mall and the commodities of the u-th commodity type v,u : , where β is the total number of several marked users; R α v,u is the consumption matching value between the commodity of the e-th commodity type and the commodity of the u-th commodity type among the α-th marked user; Step S403: When the characteristic consumption matching value T v,u is greater than the preset characteristic consumption matching threshold, it is determined that there is consumption matching between the commodities of the v-th commodity type in the mall and the commodities of the u-th commodity type, and the v-th commodity type is recorded as the matching commodity type of the u-th commodity type; Step S404: Obtain several commodity types with consumption matching in the mall, obtain the target interaction tendency data of the mall in the current period, and obtain several target tendency commodity types of the mall from the target interaction tendency data; Step S405: Record the commodities of several target tendency commodity types as hot commodities, and record the commodities of the matching commodity types of several target tendency commodity types as hot commodities at the same time. Adjust the commodity procurement in the mall in the current period, increase the procurement volume of each hot commodity in the current period, and reduce the procurement volume of other commodities; Step S406: Increase the recommendation times of the hot commodities in the current period on the online platform and offline platform of the mall respectively, and increase the frequency of the hot commodities participating in activities, and adjust the interaction strategy of the mall; In the above steps, by analyzing the consumption matching degree among the commodities of various commodity types in the mall, it is possible to understand which two commodity types of commodities will be purchased by users at the same time in the actual consumption process. Therefore, when carrying out activity promotion, the promotion of commodities can be more targeted, so as to increase the probability and frequency of in-depth interaction of users, and further increase the sales volume of commodities.

[0010] In order to better implement the above method, a user in-depth interaction system based on a multi-source data platform is also proposed. The system includes an interaction tendency data module, a target interaction tendency data module, a consumption matching analysis module, and an interaction strategy optimization module; The interaction tendency data module is used to evaluate the interaction tendency of users towards the goods in the mall and obtain interaction tendency data; The target interaction tendency data module is used to analyze the overall interaction tendency of each user towards the goods in the mall and obtain the target interaction tendency data of the mall; The consumption matching analysis module is used to analyze the consumption matching of the goods purchased by users in the mall and obtain the consumption matching data of the users; The interaction strategy optimization module is used to analyze the consumption matching degree between different goods in the mall and, in combination with the target interaction tendency data, optimize and adjust the interaction strategy of the mall.

[0011] Furthermore, the interaction tendency data module includes an interaction tendency value unit and an interaction tendency data unit; The interaction tendency value unit is used to calculate the interaction tendency value of users towards various types of goods; The interaction tendency data unit is used to evaluate the interaction tendency of users towards the goods in the mall based on the interaction tendency value and obtain the interaction tendency data.

[0012] Furthermore, the target interaction tendency data module includes an overall interaction tendency value unit and a target interaction tendency data unit; The overall interaction tendency value unit is used to calculate the overall interaction tendency value of each user towards the goods of various types in the mall; The target interaction tendency data unit is used to analyze the overall interaction tendency of each user towards the goods of various types in the mall based on the overall interaction tendency value and obtain the target interaction tendency data.

[0013] Furthermore, the consumption matching analysis module includes a consumption matching value unit and a consumption matching analysis unit; The consumption matching value unit is used to calculate the consumption matching value between the goods of various types; The consumption matching analysis unit is used to analyze the consumption matching between the goods of various types during the consumption process of users in the mall based on the consumption matching value and obtain the consumption matching data.

[0014] Furthermore, the interaction strategy optimization module includes a consumption matching degree analysis unit and an interaction strategy optimization unit; The consumption matching degree analysis unit is used to obtain the consumption matching data of each user in the mall and analyze the consumption matching degree between different goods in the mall; An interaction strategy optimization unit is used to optimize and adjust the interaction strategy of the mall according to the consumption matching degree between different commodities in the mall and in combination with the target interaction tendency data.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention realizes in-depth interaction with users in the mall. Considering the different interaction tendency degrees of different users for different commodity types of commodities, starting from reality, according to the user's historical online interaction records and historical offline interaction records in the mall, the interaction tendency of the user for the commodities in the mall is evaluated, so as to obtain the commodity types of the commodities that the user is more inclined to interact with. And considering the actual situation that for commodities of different commodity types, they will be purchased in a matching manner during the user's purchase process, the consumption matching degree between commodity types is analyzed, so as to optimize and adjust the interaction strategy of the mall, and tap the consumption potential of users. This will not only increase the income of the merchants in the mall, but also make the supply chains of each merchant in the mall more reasonable, and avoid the shortage or backlog of commodities. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is the flowchart of the method for user in-depth interaction based on the multi-source data platform of the present invention; Figure 2 is the schematic diagram of the modules of the user in-depth interaction system based on the multi-source data platform of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0018] Embodiment: As Figure 1 - Figure 2 shown, the present invention provides a technical solution, a method for user in-depth interaction based on a multi-source data platform, and the method includes: Step S100: Obtain the user's historical online interaction records and historical offline interaction records in the mall, evaluate the interaction tendency of the user for the commodities in the mall, and obtain interaction tendency data; Among them, step S100 includes: Step S101: Respectively obtain the user's historical online interaction records and historical offline interaction records in the mall, and obtain the total number S´ of the consumer commodities of the user in the online platform of the mall sum , and obtain the total number S of the consumer commodities of the user offline in the mall sum ; Step S102: Calculate the characteristic proportion coefficient B of the user's historical offline interaction records: B = S sum / (S´ sum +S sum ), and calculate the characteristic proportion coefficient B´ of the user's historical online interaction records: B´ = S´ sum / (S´ sum +S sum ); Step S103: Obtain the commodity types of the commodities with which the user interacts from the historical online interaction records and the historical offline interaction records respectively, and calculate the interaction tendency values of the user for each commodity type. Among them, the interaction tendency value F c of the user for the c-th commodity type is: , where A´ c represents the total number of the user's historical online interaction records when the commodity type of the interaction commodity is the c-th commodity type; A c represents the total number of the user's historical offline interaction records when the commodity type of the interaction commodity is the c-th commodity type; A´ sum represents the total number of the user's historical online interaction records; A sum represents the total number of the user's historical offline interaction records; For example, A´ sum is 100; A sum is 100; B is 0.6; B´ is 0.4; A´ 3 is 30; A 3 is 40; Calculate the interaction tendency value F 2 of the user for the 2nd commodity type: , When the interaction tendency value F c is greater than the preset interaction tendency threshold, it is determined that the user has an interaction tendency for the commodities of the c-th commodity type, and the c-th commodity type is recorded as the user's tendency commodity type; For example, each commodity type includes washing powder, sanitary napkins, toothbrushes, etc.; Step S104: Obtain each of the user's tendency commodity types and pool them to obtain the user's interaction tendency data; Step S200: Obtain the interaction tendency data of each user in the mall, analyze the overall interaction tendency of each user for the commodities in the mall, and obtain the target interaction tendency data of the mall; Among them, Step S200 includes: Step S201: Obtain the interaction tendency data of each user in the mall, and obtain several tendency commodity types of the user from the interaction tendency data; Step S202: Analyze the overall interaction tendency of each user towards the commodities of various commodity types in the mall. Among them, when analyzing the overall interaction tendency of each user towards the commodities of the h-th commodity type in the mall, the specific process is as follows: Obtain the maximum value W of the total number of commodities consumed by each user in the mall max , and calculate the overall interaction tendency value L of each user towards the commodities of the h-th commodity type in the mall h : , where z represents the total number of users who mark the h-th commodity type as the tendency commodity type; W e i represents the total number of commodities consumed by the i-th user who marks the h-th commodity type as the tendency commodity type in the mall; For example, z is 4; W 2 1 is 30; W 2 2 is 40; W 2 3 is 60; W 2 4 is 50; W max is 100; Calculate the overall interaction tendency value L of each user towards the commodities of the 2nd commodity type in the mall 2 : , Step S203: When the overall interaction tendency value L h is greater than the preset overall interaction tendency threshold, it is determined that each user has an overall interaction tendency towards the commodities of the h-th commodity type, mark the h-th commodity type as the target tendency commodity type of the mall, obtain several target tendency commodity types in the mall and gather them to obtain the target interaction tendency data; Step S300: Obtain the historical consumption records of users in the mall, analyze the consumption collocation of commodities by users in the mall, and obtain the consumption collocation data of users; Among them, Step S300 includes: Step S301: Record the online and offline consumption behaviors of users in the mall to obtain the historical consumption records of users, and obtain the commodity types of several commodities consumed by users from the historical consumption records; Step S302: Analyze the consumption collocation between the commodities of various commodity types during the consumption process of users in the mall. Among them, when analyzing the consumption collocation between the commodities of the e-th commodity type and the commodities of the g-th commodity type during the consumption process of users in the mall, the specific process is as follows: Obtain the total number Q of the historical consumption records of the user's commodities that only contain the e-th commodity type e sum ,Obtain the total number Q of the historical consumption records of the user's commodities that only contain the g-th commodity type g sum ,Obtain the total number Q of the historical consumption records of the user's commodities that contain both the e-th commodity type and the g-th commodity type e,g sum ,Obtain the total number Q of the historical consumption records of the user's commodities that do not contain the e-th commodity type and the g-th commodity type sum ; Step S303: Respectively set the random variable X of the e-th commodity type and the random variable Y of the g-th commodity type, set the eigenvalue k, where k > 0, X = {k, 0), Y = {k, 0}, calculate the joint probability p(k, 0) = Q of the commodity of the e-th commodity type and the commodity of the g-th commodity type e sum / n, p(0, k) = Q g sum / n, p(k, k) = Q e,g sum / n, p(0, 0) = Q sum / n; Calculate the marginal probability p(k) = (Q e sum +Q e,g sum ) / n, p(0) = (Q g sum +Q sum ) / n; Calculate the marginal probability p(k) = (Q g sum +Q e,g sum ) / n, p(0) = (Q e sum +Q sum ) / n; Step S304: Obtain the total number n of each historical consumption record of the user, and calculate the consumption matching value R between the commodity of the e-th commodity type and the commodity of the g-th commodity type e,g : , where p(x, y) is the joint probability of the commodity of the e-th commodity type and the commodity of the g-th commodity type; p(x) is the marginal probability of the commodity of the e-th commodity type; p(y) is the marginal probability of the commodity of the g-th commodity type; Step S305: When the consumption matching value Re,g If it is greater than a preset consumption matching threshold, it is determined that there is a consumption matching between the item of the e-th commodity type and the item of the g-th commodity type during the user's shopping in the mall. The e-th commodity type and the g-th commodity type are recorded as a consumption matching group of the user. Each consumption matching group of the user is obtained and aggregated to obtain consumption matching data; Step S400: Obtain the consumption matching data of each user in the mall, analyze the consumption matching degree between different commodities in the mall, and optimize and adjust the interaction strategy of the mall in combination with the target interaction tendency data; Among them, step S400 includes: Step S401: Obtain the consumption matching data of each user in the mall, and analyze the consumption matching degree between the commodities of each commodity type in the mall. Among them, the process of analyzing the consumption matching degree between the commodity of the v-th commodity type and the commodity of the u-th commodity type in the mall is as follows: When the v-th commodity type and the u-th commodity type are in the same consumption matching group in the consumption matching data of a certain user, mark the certain user, and obtain several marked users; Step S402: Calculate the characteristic consumption matching value T between the commodity of the v-th commodity type and the commodity of the u-th commodity type in the mall v,u : , where β is the total number of several marked users; R α v,u is the consumption matching value between the commodity of the e-th commodity type and the commodity of the u-th commodity type in the α-th marked user; Step S403: When the characteristic consumption matching value T v,u is greater than the preset characteristic consumption matching threshold, it is determined that there is a consumption matching between the commodity of the v-th commodity type and the commodity of the u-th commodity type in the mall, and the v-th commodity type is recorded as the matching commodity type of the u-th commodity type; Step S404: Obtain several commodity types with consumption matching in the mall, obtain the target interaction tendency data of the mall in the current period, and obtain several target tendency commodity types of the mall from the target interaction tendency data; Step S405: Record the commodities of several target tendency commodity types as popular commodities, and also record the commodities of the matching commodity types of several target tendency commodity types as popular commodities. Adjust the commodity procurement in the mall in the current period, increase the procurement volume of each popular commodity in the current period, and reduce the procurement volume of other commodities; Step S406: On the online platform and offline platform of the mall, increase the recommendation times of popular products within the current period respectively, increase the frequency of popular products participating in activities, and adjust the interaction strategy of the mall; To better implement the above method, a user in-depth interaction system based on a multi-source data platform is also proposed. The system includes an interaction tendency data module, a target interaction tendency data module, a consumption matching analysis module, and an interaction strategy optimization module; The interaction tendency data module is used to evaluate the interaction tendency of users towards products in the mall and obtain interaction tendency data; The target interaction tendency data module is used to analyze the overall interaction tendency of each user towards products in the mall and obtain the target interaction tendency data of the mall; The consumption matching analysis module is used to analyze the consumption matching of products that users purchase in the mall and obtain the consumption matching data of users; The interaction strategy optimization module is used to analyze the consumption matching degree between different products in the mall and, in combination with the target interaction tendency data, optimize and adjust the interaction strategy of the mall; Among them, the interaction tendency data module includes an interaction tendency value unit and an interaction tendency data unit; The interaction tendency value unit is used to calculate the interaction tendency value of users towards various product types; The interaction tendency data unit is used to evaluate the interaction tendency of users towards products in the mall based on the interaction tendency value and obtain interaction tendency data; Among them, the target interaction tendency data module includes an overall interaction tendency value unit and a target interaction tendency data unit; The overall interaction tendency value unit is used to calculate the overall interaction tendency value of each user towards products of various product types in the mall; The target interaction tendency data unit is used to analyze the overall interaction tendency of each user towards products of various product types in the mall based on the overall interaction tendency value and obtain target interaction tendency data; Among them, the consumption matching analysis module includes a consumption matching value unit and a consumption matching analysis unit; The consumption matching value unit is used to calculate the consumption matching value between products of various product types; The consumption matching analysis unit is used to analyze the consumption matching between various product types of products during the consumption process of users in the mall based on the consumption matching value and obtain consumption matching data; Among them, the interaction strategy optimization module includes a consumption matching degree analysis unit and an interaction strategy optimization unit; The consumption matching degree analysis unit is used to obtain the consumption matching data of each user in the mall and analyze the consumption matching degree between different products in the mall; An interaction strategy optimization unit, configured to optimize and adjust the interaction strategy of the shopping mall according to the consumption matching degree between different commodities in the shopping mall and in combination with the target interaction tendency data.

[0019] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above-described exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention. Therefore, in any aspect, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be embraced within the present invention. Any reference signs in the claims should not be construed as limiting the claims involved.

Claims

1. A user deep interaction method based on a multi-source data platform, characterized in that: The method comprises: Step S100: obtaining the user's historical online interaction records and historical offline interaction records in the mall, evaluating the user's interaction tendency towards the commodities in the mall, and obtaining interaction tendency data; Step S200: Acquire the interaction tendency data of each user in the mall, analyze the overall interaction tendency of each user to the commodities in the mall, and obtain the target interaction tendency data of the mall; Step S300: Obtain the historical consumption records of the user in the mall, analyze the consumption matching of the goods of the user in the mall, and obtain the consumption matching data of the user; Step S400: Acquire the consumption matching data of each user in the mall, analyze the consumption matching degree between different commodities in the mall, and optimize and adjust the interaction strategy of the mall in combination with the target interaction tendency data.

2. The user deep interaction method based on a multi-source data platform according to claim 1 is characterized in that: The step S100 includes: Step S101: Obtain the historical online interaction records and offline interaction records of the user in the mall, and obtain the total number S of the goods consumed by the user in the online platform of the mall. sum , obtain the total number of goods consumed by the user offline in the mall S sum ; Step S102: Calculate the characteristic ratio coefficient B=S of the user's historical offline interaction records sum / (S´ sum +S sum ), calculate the characteristic ratio coefficient B´=S´ of the user's historical online interaction records sum / (S´ sum +S sum ); Step S103: Obtain the commodity types of the commodities interacted by the user from the historical online interaction records and the historical offline interaction records, and calculate the interaction tendency value of the user for each commodity type, wherein the interaction tendency value F of the user for the cth commodity type is c : ， Among them, A´ c A represents the total number of historical online interaction records of the user when the product type of the interactive product is the product type of item c; c Indicates the total number of historical offline interaction records of the user when the product type of the interactive product is the product type of item c; A´ sum A represents the total number of historical online interaction records of the user; sum Indicates the total number of historical offline interaction records of the user; When the interaction tendency value F c If the interaction tendency is greater than a preset interaction tendency threshold, it is determined that the user has an interaction tendency for the product of the c-th product type, and the c-th product type is recorded as the user's preferred product type; Step S104: Acquire and aggregate various preferred commodity types of the user to obtain the user's interaction preference data.

3. The user deep interaction method based on a multi-source data platform according to claim 2 is characterized in that: The step S200 includes: Step S201: Obtaining interaction tendency data of each user in a shopping mall, and obtaining several items of preferred commodity types of the user from the interaction tendency data; Step S202: Analyze the overall interaction tendency of each user to the commodities of each commodity type in the mall, wherein the overall interaction tendency of each user to the commodities of the hth commodity type in the mall is analyzed, and the specific process is as follows: Get the maximum value W of the total number of goods consumed by each user in the mall max , calculate the overall interaction tendency value L of each user for the h-th item type of goods in the mall h : ， Wherein, z represents the total number of users who have marked the h-th item as a preferred item type; W e i represents the total number of commodities consumed by the i-th user whose h-th commodity type is marked as a preferred commodity type in the mall; Step S203: When the overall interaction tendency value L h is greater than a preset overall interaction tendency threshold, it is determined that each user has an overall interaction tendency for the h-th item of product type, the h-th item of product type is recorded as the target tendency product type of the mall, several target tendency product types in the mall are obtained and aggregated to obtain target interaction tendency data.

4. The user deep interaction method based on a multi-source data platform according to claim 3 is characterized in that: The step S300 includes: Step S301: Recording the online and offline consumption behaviors of the user in the shopping mall to obtain the historical consumption records of the user, and obtaining the commodity types of several commodities consumed by the user from the historical consumption records; Step S302: analyzing the compatibility of the consumption of various commodity types consumed by the user in the shopping mall, wherein the compatibility of the consumption of the commodity of the e-th commodity type and the commodity of the g-th commodity type consumed by the user in the shopping mall is analyzed. The specific process is as follows: Get the total number Q of historical consumption records of the user that only contain the product of the e-th product type e sum , obtain the total number Q of historical consumption records of the user that only contain the g-th item type g sum , obtain the total number Q of historical consumption records of the user that contain both the e-th item type and the g-th item type e,g sum , obtain the total number Q of historical consumption records of the user that do not contain the e-th item type and the g-th item type sum ; Step S303: Set the random variables X and Y of the e-th product type and the g-th product type respectively, set the characteristic value k, where k>0, X={k,0), Y={k,0}, and calculate the joint probability p(k,0)=Q of the product of the e-th product type and the product of the g-th product type e sum / n、p(0,k)=Q g sum / n、p(k,k)=Q e,g sum / n,p(0,0)=Q sum / n; Calculate the marginal probability p(k)=(Q e sum +Q e,g sum ) / n、p(0)=(Q g sum +Q sum ) / n; Calculate the marginal probability p(k)=(Q g sum +Q e,g sum ) / n、p(0)=(Q e sum +Q sum ) / n; Step S304: Obtain the total number n of each historical consumption record of the user, and calculate the consumption matching value R between the product of the e-th product type and the product of the g-th product type. e,g : ， Wherein, p(x,y) is the joint probability of the product of the e-th product type and the product of the g-th product type; p(x) is the marginal probability of the product of the e-th product type; p(y) is the marginal probability of the product of the g-th product type; Step S305: When the consumption matching value R e,g is greater than a preset consumption matching threshold, determining that the commodity of the e-th commodity type consumed by the user in the shopping mall is consumption compatible with the commodity of the g-th commodity type, recording the e-th commodity type and the g-th commodity type as a consumption matching group of the user, obtaining and aggregating the various consumption matching groups of the user, and obtaining consumption matching data.

5. The user deep interaction method based on a multi-source data platform according to claim 4 is characterized in that: The step S400 includes: Step S401: Obtain the consumption matching data of each user in the mall, and analyze the consumption matching degree between the commodities of each commodity type in the mall, wherein the consumption matching degree between the commodity of the vth commodity type and the commodity of the uth commodity type in the mall is analyzed, and the specific process is as follows: When the v-th item of commodity type and the u-th item of commodity type in the consumption matching data of a certain user are in the same consumption matching group, the certain user is marked, and a number of marked users are obtained; Step S402: Calculate the characteristic consumption matching value T between the product of the vth product type in the mall and the product of the uth product type v,u : ， Wherein, β is the total number of the marked users; R α v,u is the consumption matching value between the product of the e-th product type and the product of the u-th product type among the α-th user marked; Step S403: When the characteristic consumption matching value T v,u is greater than a preset characteristic consumption matching threshold, determining that the product of the vth product type in the mall has consumption matching properties with the product of the uth product type, and recording the vth product type as the matching product type of the uth product type; Step S404: obtaining a plurality of commodity types with consumption compatibility in the mall, obtaining target interaction tendency data of the mall in the current period, and obtaining a plurality of target tendency commodity types of the mall from the target interaction tendency data; Step S405: Record the commodities of the plurality of items of the tendency commodity type as hot commodities, obtain the commodities of the matching commodity type of the plurality of items of the tendency commodity type, and record them as hot commodities at the same time, adjust the commodity purchase in the mall in the current cycle, increase the purchase quantity of each hot commodity in the current cycle, and reduce the purchase quantity of other commodities; Step S406: On the online platform and offline platform of the shopping mall, the number of recommendations for the popular products in the current period is increased, the frequency of the popular products participating in activities is increased, and the interactive strategy of the shopping mall is adjusted.

6. A user deep interaction system based on a multi-source data platform, used to execute the user deep interaction method based on a multi-source data platform according to any one of claims 1 to 5, characterized in that: The system includes an interaction tendency data module, a target interaction tendency data module, a consumption matching analysis module, and an interaction strategy optimization module; The interaction tendency data module is used to evaluate the user's interaction tendency towards the commodities in the mall to obtain interaction tendency data; The target interaction tendency data module is used to analyze the overall interaction tendency of each user to the commodities in the mall to obtain the target interaction tendency data of the mall; The consumption matching analysis module is used to analyze the consumption matching of the commodities of the user in the shopping mall to obtain the consumption matching data of the user; The interaction strategy optimization module is used to analyze the degree of consumer matching between different commodities in the mall, and optimize and adjust the interaction strategy of the mall in combination with the target interaction tendency data.

7. The user deep interaction system based on a multi-source data platform according to claim 6, characterized in that: The interaction tendency data module includes an interaction tendency value unit and an interaction tendency data unit; The interaction tendency value unit is used to calculate the interaction tendency value of the user for each commodity type; The interaction tendency data unit is used to evaluate the user's interaction tendency towards the commodities in the mall according to the interaction tendency value, and obtain interaction tendency data.

8. The user deep interaction system based on a multi-source data platform according to claim 6, characterized in that: The target interaction tendency data module includes an overall interaction tendency value unit and a target interaction tendency data unit; The overall interaction tendency value unit is used to calculate the overall interaction tendency value of each user for each commodity type in the mall; The target interaction tendency data unit is used to analyze the overall interaction tendency of each user to the commodities of each commodity type in the mall according to the overall interaction tendency value, so as to obtain target interaction tendency data.

9. The user deep interaction system based on a multi-source data platform according to claim 6, characterized in that: The consumption collocation analysis module includes a consumption collocation value unit and a consumption collocation analysis unit; The consumption matching value unit is used to calculate the consumption matching value between commodities of various commodity types; The consumption matching analysis unit is used to analyze the consumption matching between various types of goods consumed by the user in the shopping mall according to the consumption matching value, so as to obtain consumption matching data.

10. The user deep interaction system based on a multi-source data platform according to claim 6, characterized in that: The interaction strategy optimization module includes a consumption matching degree analysis unit and an interaction strategy optimization unit; The consumption matching degree analysis unit is used to obtain the consumption matching data of each user in the shopping mall and analyze the consumption matching degree between different commodities in the shopping mall; The interaction strategy optimization unit is used to optimize and adjust the interaction strategy of the mall according to the consumption matching degree between different commodities in the mall and in combination with the target interaction tendency data.

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