User consumption incentive platform and method

Abstract

The invention discloses a user consumption incentive platform and method, and relates to the technical field of user consumption incentive, and the platform comprises a consumption incentive mechanism evaluation module, an interactive function perfection evaluation module, a consumption incentive mechanism optimization module, a visual output terminal and a database. According to the invention, rationality of a formulating strategy, a formulating process and visual display of the consumption incentive mechanism of the user consumption platform is guaranteed, user experience is improved, participation and activeness of the user are improved, and flow and transaction volume of the platform are guaranteed, so that efficiency of the consumption incentive mechanism of the user consumption platform is improved; according to the invention, the development direction of the interaction function of the shopping platform is judged, the defects in the prior art are overcome, a foundation is laid for the investment of manpower and material resources of the subsequent shopping platform, the communication convenience and experience of the user are further improved, and the long-term development of the shopping platform is facilitated.

Description

Technical Field

[0001] The present invention relates to the technical field of user consumption incentives, and in particular to a user consumption incentive platform and method. Background Art

[0002] User consumption incentive platforms play a vital role in the contemporary economy. They can not only stimulate consumers' desire to buy, but also promote user loyalty and brand stickiness through various incentive mechanisms. Analyzing the consumption mechanism of such platforms is of great importance for enterprises to formulate market strategies, optimize user experience and improve efficiency.

[0003] Prior art, such as the invention application patent with announcement number: CN112232888A, discloses a consumer behavior intelligent analysis system and method, which collects consumer consumption records on online platforms and tracks the total consumption process of each consumer consumption record on the online platform. According to the number of consumption records of each consumer, consumers are divided into demand groups with different consumption amounts, and the hidden consumption types of consumers on the online platform are inferred. A consumer behavior analysis system is established, and the consumption psychological state of different demand groups is decomposed according to the content of each consumer's consumption record. The consumption records of different demand groups are compared through reverse incentives, and different marketing strategies are independently formulated based on the comprehensive comparison results and the current consumption psychological state of different demand groups. Recommend suitable products to consumers based on each consumer's effective consumption information and nepotism consumption type. The present invention actively guides new users, improves the initial impression of consumption on the online platform, and continuously consolidates old users.

[0004] The prior art, such as the invention application patent with announcement number: CN109636484B, discloses an incentive method for a crowd perception system based on the preference theory of behavioral economics, which abstracts the workflow of the crowd perception system into 6 steps: 1) The platform releases tasks. 2) Users select tasks. 3) Decide on participation plans. 4) The platform selects participating users. 5) Users report perception data. 6) Remuneration payment. For the platform side, the situational effect in behavioral economics is introduced into the task release mechanism, and a function of the degree of attractiveness of tasks to users in crowd perception is established, thereby improving the enthusiasm of users. For the user side, by designing a new reward payment mechanism, the utility function based on traditional economics is corrected to improve the utility of users. According to the theories and experiments on situational effects and fairness preferences in behavioral economics, the parameters of the new model are debugged. This invention reduces the platform cost to a certain extent, improves the benefits of the platform and user sides, and thus further improves the social benefits.

[0005] Combined with the above scheme, it can be found that the user consumption platform in the existing technology still has certain deficiencies, which are specifically reflected in the following aspects: In the existing technology, the confidence, process rationality and integration accuracy of the consumption incentive mechanism of the user consumption platform are rarely analyzed. The confidence, process rationality and integration accuracy of the consumption incentive mechanism reflect to a certain extent the user's trust in the incentive mechanism of the consumption platform, the rationality of the process of the consumption incentive mechanism and the display quality of the consumption incentive mechanism on various terminals. The neglect of this aspect in the existing technology makes it difficult to ensure the rationality of the formulation strategy, formulation process and visual display of the consumption incentive mechanism of the user consumption platform, reduce the user's participation and activity, and then affect the platform's traffic and transaction volume, thereby reducing the efficiency of the consumption incentive mechanism of the user consumption platform. At the same time, little attention is paid to the development direction of the interactive functions of the shopping platform. Since there are many communication tools in the existing technology, and the shopping platform does not carry all communication tools, the development of communication tools with a high degree of correlation with the shopping platform provides users with communication convenience. The neglect of this aspect in the existing technology reduces the user's communication convenience and experience, which is not conducive to the long-term development of the shopping platform. Summary of the invention

[0006] The purpose of the present invention is to provide a user consumption incentive platform and method, which solves the problems existing in the background technology.

[0007] In order to solve the above technical problems, the present invention adopts the following technical solutions: The first aspect of the present invention provides a user consumption incentive platform, including: a consumption incentive mechanism evaluation module, which is used to obtain consumption incentive issuance layer data and consumption incentive user layer data from the shopping platform, and evaluate the confidence, process rationality and integration accuracy of the consumption incentive mechanism of the shopping platform.

[0008] The interactive function completeness evaluation module is used to obtain consumer incentive interaction data from the shopping platform, and then evaluate the completeness of the consumer incentive interactive function of the shopping platform, and determine the development direction of the interactive function of the shopping platform.

[0009] The consumption incentive mechanism optimization module is used to determine the optimization data of the consumption incentive mechanism of the shopping platform based on the confidence, process rationality and integration accuracy of the consumption incentive mechanism of the shopping platform.

[0010] The visual output terminal is used to visually output the development direction of the interactive functions of the shopping platform and the optimization data of the consumption incentive mechanism.

[0011] The second aspect of the present invention provides a method for executing the user consumption incentive platform described in the present invention, comprising: A1. obtaining consumption incentive issuance layer data and consumption incentive user layer data from the shopping platform, and evaluating the confidence, process rationality and integration accuracy of the consumption incentive mechanism of the shopping platform.

[0012] A2. Obtain consumer incentive interaction data from the shopping platform, and then evaluate the completeness of the consumer incentive interaction function of the shopping platform, and determine the development direction of the interactive function of the shopping platform.

[0013] A3. Based on the confidence, process rationality and integration accuracy of the consumption incentive mechanism of the shopping platform, determine the optimization data of the consumption incentive mechanism of the shopping platform.

[0014] A4. Visually output the development direction of the interactive functions of the shopping platform and the optimization data of the consumer incentive mechanism.

[0015] The beneficial effects of the present invention are: (1) the present invention obtains background data from the shopping platform in the consumption incentive mechanism evaluation module, and evaluates the confidence, process rationality and integration accuracy of the consumption incentive mechanism of the shopping platform, which makes up for the deficiencies in the prior art and ensures the rationality of the formulation strategy, formulation process and visual display of the consumption incentive mechanism of the user consumption platform. On the one hand, it avoids the unreasonable strategy and inappropriate frequency of the consumption incentive mechanism, which reduces the user trust and stickiness. On the other hand, it avoids the loss of participants due to the overly cumbersome or difficult process of the consumption incentive mechanism. On the other hand, it ensures the compatibility of the consumption incentive mechanism with the display terminal, improves the user experience, improves the user's participation and activity, and ensures the platform's traffic and transaction volume, thereby improving the efficiency of the consumption incentive mechanism of the user consumption platform.

[0016] (2) The present invention obtains background data from the shopping platform in the interactive function perfection evaluation module, evaluates the perfection of the consumer incentive interactive function of the shopping platform, and thus determines the development direction of the interactive function of the shopping platform. This overcomes the defects in the prior art, lays a foundation for the subsequent investment of manpower and material resources in the shopping platform, further improves the user's communication convenience and experience, and is conducive to the long-term development of the shopping platform. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 It is a schematic diagram of the system structure connection of the present invention.

[0019] Figure 2 Figure 2 is a schematic diagram of the air quality prediction module structure. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] Reference Figure 1 As shown, the present invention provides a user consumption incentive platform, including: a consumption incentive mechanism evaluation module, an interactive function perfection evaluation module, a consumption incentive mechanism optimization module, and a visual output terminal.

[0022] It should be noted that the present invention also includes a database for storing appropriate discount frequencies for each discount intensity range, appropriate redemption ratios corresponding to unit distribution quantities, allowed durations corresponding to each defect type, and a set of excellent keywords.

[0023] It should also be noted that the consumption incentive mechanism evaluation module is connected to the consumption incentive mechanism optimization module, the interactive function perfection evaluation module and the consumption incentive mechanism optimization module are both connected to the visual output terminal, and the database is respectively connected to the consumption incentive mechanism evaluation module and the interactive function perfection evaluation module.

[0024] The consumption incentive mechanism evaluation module is used to obtain consumption incentive issuance layer data and consumption incentive user layer data from the shopping platform, and evaluate the confidence, process rationality and integration accuracy of the consumption incentive mechanism of the shopping platform.

[0025] In a specific embodiment of the present invention, the consumption incentive issuance layer data includes the time point and preferential strength of each flow historical incentive, the issuance amount, redemption ratio, allowed redemption item parameters and time point of each regular historical incentive.

[0026] It should be noted that the above-mentioned conventional historical incentives include points redemption, card and coupon redemption, etc.

[0027] The consumption incentive user layer data includes the number of users who were accurately delivered to each flow historical incentive, the number of actual consuming users and the number of actual participating users in each stage, and the redemption failure data of each conventional historical incentive.

[0028] The consumption incentive interaction data includes the number of displays, evaluation text, label text, and forwarding times of the shopping platform consumption incentives in other software.

[0029] In a specific embodiment of the present invention, the confidence of the consumption incentive mechanism of the shopping platform is specifically evaluated by the following method: obtaining the time point and discount strength of each flow historical incentive from the consumption incentive issuance layer data of the shopping platform, screening the time point of each flow historical incentive in each discount strength interval, and then counting the number and duration of the flow historical incentives in each discount strength interval, and dividing the number of flow historical incentives in each discount strength interval by the duration to obtain the discount frequency of each discount strength interval of the shopping platform.

[0030] It should be noted that the various flow history incentives in each preferential intensity interval are arranged in chronological order, and the spanning time between the first flow history incentive and the last flow history incentive in each preferential intensity interval is used as the duration of the flow history incentive in each preferential intensity interval.

[0031] From the consumer incentive user layer data of the shopping platform, we can obtain the precise number of users who received each historical incentive, the number of actual consumer users, and the number of actual participating users at each stage, and then evaluate the activity participation of the consumer incentive of the shopping platform based on this.

[0032] In the formula, A m , A′ m , A′ mp , A′ m(p-1) are respectively the number of users precisely delivered to the mth flow history incentive, the number of users actually consuming, the number of users actually participating in the pth stage, and the number of users actually participating in the p+1th stage; m is the number of each flow history incentive, m=1,2,...,l, l is any integer greater than 2, p is the number of each stage, p=1,2,...,q, q is any integer greater than 2, l and q are the number of flow history incentives and the number of stages, respectively.

[0033] Discount frequency α based on the discount intensity range of the shopping platform i , the activity participation rate of consumption incentives on shopping platforms, combined with the first confidence evaluation model of consumption incentive mechanisms Output the first confidence ε of the consumption incentive mechanism of the shopping platform _0 , where α i ′ is the appropriate discount frequency of the i-th discount intensity interval stored in the database, i is the number of each discount intensity interval, i=1,2,...,n, n is any integer greater than 2, and n is the number of discount intensity intervals.

[0034] It should be noted that the appropriate discount frequency for each discount intensity interval is specifically as follows: the sales of each flow historical incentive in the previous period are obtained from the shopping platform, and the average is processed to obtain the reference sales of the shopping platform; if the sales of a certain flow historical incentive is less than the reference sales, the flow historical incentive is eliminated to obtain the processed flow historical incentives of the shopping platform, and the flow historical incentives of each discount intensity interval are screened to obtain the time point of each flow historical incentive in each discount intensity interval. Similarly, the frequency of each flow historical incentive in each discount intensity interval is obtained as the appropriate discount frequency for each discount intensity interval and stored in the database.

[0035] Based on the consumption incentive issuance layer data of the shopping platform, the second confidence level ε of the consumption incentive mechanism of the shopping platform is evaluated _1 .

[0036] Import the first confidence and second confidence of the consumption incentive mechanism of the shopping platform into the confidence evaluation model of the consumption incentive mechanism , output the confidence η of the consumption incentive mechanism of the shopping platform, where ε _0 , ε _1 They represent the preset first confidence threshold and second confidence threshold respectively, and ∧ and ∨ are logical symbols and and or.

[0037] It should be noted that the preset first confidence threshold and second confidence threshold are specifically set by the shopping platform staff. For example, in order to improve the consumption incentive quality of the shopping platform, the first confidence threshold and the second confidence threshold are increased.

[0038] In a specific embodiment of the present invention, the second confidence level of the consumption incentive mechanism of the shopping platform is evaluated by the following specific evaluation method: the issuance amount, redemption ratio, and time point of each regular historical incentive are obtained from the consumption incentive issuance layer data of the shopping platform, the number and elapsed time of regular historical incentives are counted, and the number of regular historical incentives is divided by the elapsed time to obtain the frequency of regular historical incentives of the shopping platform, and the issuance amount of each regular historical incentive is multiplied by the appropriate redemption ratio corresponding to the unit issuance amount stored in the database to obtain the appropriate redemption ratio of each regular historical incentive.

[0039] It should be noted that the exchange ratio specifically refers to the ratio of the amount issued to the exchanged items.

[0040] Obtain the corresponding redemption amount B for each item from the allowed redemption item parameters of each regular historical incentive of the shopping platform jh and quantity B j ' h , evaluate the appropriateness of the amount of regular historical incentives issued by the shopping platform j is the number of each regular historical incentive, j=1,2,...,k, k is any integer greater than 2, h is the number of each item, h=1,2,...,g, g is any integer greater than 2.

[0041] The frequency P of the regular historical incentives of the shopping platform and the redemption ratio C of each regular historical incentive are j 、Suitable exchange ratio C j ′、The evaluation model of the second confidence level of the consumption incentive mechanism is introduced into the appropriateness of the issuance amount , output the second confidence of the shopping platform, where P′ is the appropriate frequency of regular historical incentives and k is the number of regular historical incentives.

[0042] It should be noted that the exchange ratio involves the value of the amount issued. If the amount issued is too large and the number of items allowed for exchange is fixed, the value of the amount issued will be reduced, affecting the redemption user's attention to the amount issued, thereby reducing the quality of conventional incentives.

[0043] It should also be noted that the appropriate frequency of the conventional historical incentives is consistent with the analysis method of the appropriate preferential frequency of each preferential intensity range.

[0044] In a specific embodiment of the present invention, the process rationality of the consumption incentive mechanism of the shopping platform is specifically evaluated by: comparing the actual number of participating users in each stage of each flow history incentive of the shopping platform; if the difference between the actual number of participating users in a certain stage and the previous stage is greater than the preset allowed user attenuation number, then the flow history incentive to this stage is recorded as an abnormal flow history incentive, and the abnormal flow history incentives of the shopping platform are obtained, and the number of abnormal flow history incentives and the number of flow history incentives of the shopping platform are counted.

[0045] The redemption failure data of each regular historical incentive is obtained from the consumption incentive user layer data of the shopping platform, where the redemption failure data includes the number of active failures in each redemption stage, and compared with the preset number of active failures allowed; if the number of active failures in a certain redemption stage is greater than the number of active failures allowed, the regular historical incentive to which the redemption stage belongs is recorded as an abnormal regular historical incentive, and the abnormal regular historical incentives of the shopping platform are obtained, and the number of abnormal regular historical incentives and the number of regular historical incentives of the shopping platform are counted.

[0046] It should be noted that the number of people who actively failed in each redemption stage specifically refers to: the total number of people who gave up the redemption despite having sufficient issuance volume.

[0047] The abnormal flow historical incentive times L, flow historical incentive times L′, abnormal regular historical incentive times G, regular historical incentive times G′ of the shopping platform are introduced into the process rationality evaluation model of the consumption incentive mechanism. Output the rationality of the process of consumer incentive mechanism of shopping platform

[0048] Where R and R′ are the preset appropriate abnormal flow historical incentive ratio and the appropriate abnormal conventional historical incentive ratio, respectively.

[0049] It should be noted that the preset appropriate abnormal flow historical incentive ratio and the appropriate abnormal regular historical incentive ratio are specifically set by the staff of the shopping platform. For example, in order to ensure the rationality of the flow incentive and regular incentive processes, the appropriate abnormal flow historical incentive ratio and the appropriate abnormal regular historical incentive ratio are both set to 0.01.

[0050] In a specific embodiment of the present invention, the integration accuracy of the consumption incentive mechanism of the shopping platform is evaluated by a specific method as follows: obtaining integrated test data from the shopping platform, wherein the integrated test data includes flow history incentive test data and conventional history incentive test data, and the flow history incentive test data includes each defect type of each display terminal and its corresponding duration T rf , conventional historical stimulus test data includes each defect type of each display terminal and its corresponding duration, r is the number of each display terminal, r = 1, 2, ..., w, w is any integer greater than 2, f is the number of each defect type, f = 1, 2, ..., t, t is any integer greater than 2.

[0051] It should be noted that when a shopping platform releases a consumption incentive mechanism, it tests the adaptability of the consumption incentive mechanism on mobile devices of different sizes, so as to provide decision support for the subsequent optimization of the consumption incentive mechanism of the shopping platform.

[0052] It should be noted that the defect types include incomplete display and chaotic layout, etc. The duration corresponding to the defect type is specifically the duration for the defect type to return to normal.

[0053] Evaluate the integration accuracy of mobile consumption incentive mechanism based on the mobile historical incentive test data of the shopping platform Where T f ′ is the allowed duration corresponding to the f-th defect type stored in the database, Test risk thresholds for pre-set liquidity incentives and similarly analyze the integration accuracy of conventional consumer incentive mechanisms.

[0054] The integration accuracy of the mobile consumption incentive mechanism and the integration accuracy of the conventional consumption incentive mechanism are averaged to obtain the integration accuracy of the consumption incentive mechanism of the shopping platform.

[0055] The present invention obtains background data from the shopping platform in a consumption incentive mechanism evaluation module, and evaluates the confidence, process rationality and integration accuracy of the consumption incentive mechanism of the shopping platform, which makes up for the deficiencies in the prior art and ensures the rationality of the formulation strategy, formulation process and visual display of the consumption incentive mechanism of the user consumption platform. On the one hand, it avoids the reduction of user trust and stickiness due to unreasonable strategy and inappropriate frequency of the consumption incentive mechanism, and on the other hand, it avoids the loss of participants due to the overly cumbersome or difficult process of the consumption incentive mechanism. On the other hand, it ensures the compatibility of the consumption incentive mechanism with the display terminal, improves the user experience, improves the user's participation and activity, ensures the platform's traffic and transaction volume, and thus improves the efficiency of the consumption incentive mechanism of the user consumption platform.

[0056] The interactive function perfection evaluation module is used to obtain consumption incentive interaction data from the shopping platform, and then evaluate the perfection of the consumption incentive interactive function of the shopping platform, and determine the development direction of the interactive function of the shopping platform.

[0057] In a specific embodiment of the present invention, the shopping platform's consumption incentive interactive function perfection is analyzed by: obtaining the number of times the shopping platform consumption incentive is displayed in each other software S _0b , evaluation text, label text, forwarding times S _1b , and then screen the target software for consumption incentives on the shopping platform, b is the serial number of each other software, b=1,2,...,d, d is any integer greater than 2.

[0058] It should be noted that the specific screening method for screening the target software of shopping platform consumption incentives is: based on the evaluation texts and label texts of the shopping platform consumption incentives in other software, a number of evaluation keywords and label keywords are extracted through keyword extraction technology, and compared with a number of excellent keywords in the excellent keyword set stored in the database, to screen out a number of excellent evaluation keywords in the evaluation texts and a number of excellent label keywords in the label texts.

[0059] Count the number of excellent evaluation keywords Y of shopping platform consumption incentives in other software _0b 、Number of evaluation keywords Y _ ' 0b 、Number of excellent tag keywords Y _1b 、Number of tag keywords Y _ ' 1b , and then evaluate the development recommendation value of each other software

[0060] Obtain various related software from the shopping platform and construct a shopping platform related software keyword set E _0, and based on the target software of shopping platform consumption incentives, build the shopping platform target software keyword set E _1 , and then evaluate the completeness of the consumer incentive interaction function of the shopping platform Where U is the overlap ratio between the preset associated software and the target software.

[0061] In a specific embodiment of the present invention, the specific judgment method for determining the development reverse direction of the interactive function of the shopping platform is as follows: if the perfection degree of the consumer incentive interactive function of the shopping platform is -1, then each associated software of the shopping platform is compared with each target software one by one. If a certain target software is inconsistent with all associated software, then the target software is recorded as the interactive software to be developed, and the interactive software to be developed of the shopping platform is screened, the names of the interactive software to be developed of the shopping platform are obtained, and a set of names of the interactive software to be developed of the interactive function of the shopping platform is generated, which is used as the development reverse direction of the interactive function of the shopping platform.

[0062] The present invention obtains background data from the shopping platform in the interactive function perfection evaluation module, evaluates the perfection of the consumer incentive interactive function of the shopping platform, and thus determines the development direction of the interactive function of the shopping platform. It overcomes the defects in the prior art, lays a foundation for the subsequent investment of manpower and material resources in the shopping platform, further improves the user's communication convenience and experience, and is conducive to the long-term development of the shopping platform.

[0063] The consumption incentive mechanism optimization module is used to determine the optimization data of the consumption incentive mechanism of the shopping platform based on the confidence, process rationality and integration accuracy of the consumption incentive mechanism of the shopping platform.

[0064] In a specific embodiment of the present invention, the optimization data for determining the consumption incentive mechanism of the shopping platform is specifically determined by the following method: if the confidence level of the consumption incentive mechanism of the shopping platform is -1, it is determined that the formulation strategy of the consumption incentive mechanism of the shopping platform needs to be optimized.

[0065] If the process rationality of the consumption incentive mechanism of the shopping platform is -1, it is judged that the formulation process of the consumption incentive mechanism of the shopping platform needs to be optimized.

[0066] If the integration accuracy of the consumption incentive mechanism of the shopping platform is -1, it is determined that the terminal display of the consumption incentive mechanism of the shopping platform needs to be optimized.

[0067] To sum up, the optimization data of the consumption incentive mechanism of the shopping platform is determined.

[0068] The visual output terminal is used to visually output the development direction of the interactive functions of the shopping platform and the optimization data of the consumption incentive mechanism.

[0069] Reference Figure 2As shown, the second aspect of the present invention provides a method for executing the user consumption incentive platform described in the present invention, comprising: A1. Acquiring consumption incentive issuance layer data and consumption incentive user layer data from the shopping platform, and evaluating the confidence, process rationality and integration accuracy of the consumption incentive mechanism of the shopping platform.

[0070] A2. Obtain consumer incentive interaction data from the shopping platform, and then evaluate the completeness of the consumer incentive interaction function of the shopping platform, and determine the development direction of the interactive function of the shopping platform.

[0071] A3. Based on the confidence, process rationality and integration accuracy of the consumption incentive mechanism of the shopping platform, determine the optimization data of the consumption incentive mechanism of the shopping platform.

[0072] A4. Visually output the development direction of the interactive functions of the shopping platform and the optimization data of the consumer incentive mechanism.

[0073] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they shall all fall within the protection scope of the present invention.

Claims

1. A user consumption incentive platform, characterized in that: include: The consumption incentive mechanism evaluation module is used to obtain consumption incentive issuance layer data and consumption incentive user layer data from the shopping platform, and evaluate the confidence, process rationality and integration accuracy of the consumption incentive mechanism of the shopping platform; An interactive function perfection evaluation module is used to obtain consumer incentive interaction data from the shopping platform, and then evaluate the perfection of the consumer incentive interactive function of the shopping platform, and determine the development direction of the interactive function of the shopping platform; The consumption incentive mechanism optimization module is used to determine the optimization data of the consumption incentive mechanism of the shopping platform based on the confidence, process rationality and integration accuracy of the consumption incentive mechanism of the shopping platform; The visual output terminal is used to visually output the development direction of the interactive functions of the shopping platform and the optimization data of the consumption incentive mechanism.

2. A user consumption incentive platform according to claim 1, characterized in that: The consumption incentive issuance layer data includes the time point and preferential strength of each flow historical incentive, the issuance amount, exchange ratio, allowed exchange item parameters and time point of each conventional historical incentive; The consumption incentive user layer data includes the number of users who were accurately delivered to each flow historical incentive, the number of actual consuming users, the number of actual participating users at each stage, and the redemption failure data of each conventional historical incentive; The consumption incentive interaction data includes the number of displays, evaluation text, label text, and forwarding times of the shopping platform consumption incentives in other software.

3. A user consumption incentive platform according to claim 2, characterized in that: The confidence level of the consumption incentive mechanism of the shopping platform is evaluated by the following specific method: Obtain the time point and discount strength of each flow history incentive from the consumption incentive issuance layer data of the shopping platform, screen the time point of each flow history incentive in each discount strength interval, and then count the number and duration of the flow history incentives in each discount strength interval, and divide the number of flow history incentives in each discount strength interval by the duration to obtain the discount frequency of each discount strength interval of the shopping platform; From the consumer incentive user layer data of the shopping platform, we can obtain the precise number of users who received each historical incentive, the number of actual consumer users, and the number of actual participating users at each stage, and then evaluate the activity participation of the consumer incentive of the shopping platform based on this. In the formula, A m , A′ m , A′ mp , A′ m(p-1) are the number of users who received precise delivery of the mth flow history incentive, the number of users who actually consumed, the number of users who actually participated in the pth stage, and the number of users who actually participated in the p+1th stage, respectively; m is the number of each flow history incentive, m=1,2,...,l, l is any integer greater than 2, p is the number of each stage, p=1,2,...,q, q is any integer greater than 2, l and q are the number of flow history incentives and the number of stages, respectively; Discount frequency α based on each discount intensity range of the shopping platform i , the activity participation rate of consumption incentives on shopping platforms, combined with the first confidence evaluation model of consumption incentive mechanisms Output the first confidence ε of the consumption incentive mechanism of the shopping platform _0 , where α i ′ is the appropriate discount frequency of the ith discount intensity interval stored in the database, i is the number of each discount intensity interval, i=1,2,...,n, n is any integer greater than 2, and n is the number of discount intensity intervals; Based on the consumption incentive issuance layer data of the shopping platform, the second confidence level ε of the consumption incentive mechanism of the shopping platform is evaluated _1 ; Import the first confidence and second confidence of the consumption incentive mechanism of the shopping platform into the confidence evaluation model of the consumption incentive mechanism Output the confidence η of the consumption incentive mechanism of the shopping platform, where ε _0 , ε _1 They represent the preset first confidence threshold and second confidence threshold respectively, and ∧ and ∨ are logical symbols and and or.

4. A user consumption incentive platform according to claim 3, characterized in that: The specific evaluation method of the second confidence level of the consumption incentive mechanism of the shopping platform is as follows: Obtain the issuance amount, redemption ratio, and time point of each regular historical incentive from the consumption incentive issuance layer data of the shopping platform, count the number and duration of regular historical incentives, and divide the number of regular historical incentives by the duration to obtain the frequency of regular historical incentives of the shopping platform, and multiply the issuance amount of each regular historical incentive by the appropriate redemption ratio corresponding to the unit issuance amount stored in the database to obtain the appropriate redemption ratio of each regular historical incentive; Obtain the corresponding redemption amount and quantity of each item from the parameters of the allowed redemption items of each regular historical incentive of the shopping platform, and evaluate the appropriateness of the issuance amount of each regular historical incentive of the shopping platform μ j , j is the number of each conventional historical incentive, j = 1, 2, ..., k, k is an arbitrary integer greater than 2, h is the number of each item, h = 1, 2, ..., g, g is an arbitrary integer greater than 2; The frequency P of the regular historical incentives of the shopping platform and the redemption ratio C of each regular historical incentive are j 、Suitable exchange ratio C j ′、The evaluation model of the second confidence level of the consumption incentive mechanism is introduced into the appropriateness of the issuance amount , output the second confidence of the shopping platform, where P′ is the appropriate frequency of regular historical incentives and k is the number of regular historical incentives.

5. A user consumption incentive platform according to claim 2, characterized in that: The specific evaluation method for the rationality of the process of the consumption incentive mechanism of the shopping platform is as follows: Compare the actual number of participating users at each stage of each flow history incentive of the shopping platform, and count the number of abnormal flow history incentives and flow history incentives of the shopping platform; Obtain the redemption failure data of each regular historical incentive from the consumer incentive user layer data of the shopping platform, where the redemption failure data includes the number of active failures in each redemption stage, and count the number of abnormal regular historical incentives and regular historical incentives of the shopping platform; The abnormal flow historical incentive times L, flow historical incentive times L′, abnormal regular historical incentive times G, regular historical incentive times G′ of the shopping platform are introduced into the process rationality evaluation model of the consumption incentive mechanism. Output the rationality of the process of consumer incentive mechanism of shopping platform Where R and R′ are the preset appropriate abnormal flow historical incentive ratio and the appropriate abnormal conventional historical incentive ratio, respectively.

6. A user consumption incentive platform according to claim 2, characterized in that: The integration accuracy of the consumption incentive mechanism of the shopping platform is evaluated by: The integrated test data is obtained from the shopping platform, wherein the integrated test data includes flow history incentive test data and conventional history incentive test data, and the flow history incentive test data includes each defect type of each display terminal and its corresponding duration T rf , conventional historical stimulus test data includes each defect type of each display terminal and its corresponding duration, r is the number of each display terminal, r=1,2,...,w, w is any integer greater than 2, f is the number of each defect type, f=1,2,...,t, t is any integer greater than 2; Evaluate the integration accuracy of mobile consumption incentive mechanism based on the mobile historical incentive test data of the shopping platform Where T f ′ is the allowed duration corresponding to the f-th defect type stored in the database, Testing risk thresholds for pre-set liquidity incentives and similarly analyzing the integration accuracy of conventional consumer incentives; The integration accuracy of the mobile consumption incentive mechanism and the integration accuracy of the conventional consumption incentive mechanism are averaged to obtain the integration accuracy of the consumption incentive mechanism of the shopping platform.

7. A user consumption incentive platform according to claim 2, characterized in that: The specific analysis method of the completeness of the consumption incentive interaction function of the shopping platform is as follows: Obtain the display times, evaluation texts, label texts, and forwarding times of the shopping platform consumption incentives in other software, and then select the target software for the shopping platform consumption incentives; Obtain various related software from the shopping platform and construct a shopping platform related software keyword set E _0 , and based on the target software of shopping platform consumption incentives, build the shopping platform target software keyword set E _1 , and then evaluate the completeness of the consumer incentive interaction function of the shopping platform Where U is the overlap ratio between the preset associated software and the target software.

8. A user consumption incentive platform according to claim 1, characterized in that: The specific method for determining the development reverse direction of the interactive function of the shopping platform is as follows: If the degree of perfection of the consumer incentive interactive function of the shopping platform is -1, then each associated software of the shopping platform will be compared with each target software one by one. If a target software is inconsistent with all associated software, then the target software will be recorded as the interactive software to be developed, and the interactive software to be developed of the shopping platform will be screened, the names of the interactive software to be developed of the shopping platform will be obtained, and a set of names of the interactive software to be developed of the interactive functions of the shopping platform will be generated, which will serve as the reverse direction for the development of the interactive functions of the shopping platform.

9. A user consumption incentive platform according to claim 1, characterized in that: The optimization data for determining the consumption incentive mechanism of the shopping platform is specifically determined by: If the confidence of the shopping platform's consumer incentive mechanism is -1, it is judged that the formulation strategy of the shopping platform's consumer incentive mechanism needs to be optimized; If the process rationality of the consumption incentive mechanism of the shopping platform is -1, it is judged that the formulation process of the consumption incentive mechanism of the shopping platform needs to be optimized; If the integration accuracy of the consumption incentive mechanism of the shopping platform is -1, it is determined that the terminal display of the consumption incentive mechanism of the shopping platform needs to be optimized; To sum up, the optimization data of the consumption incentive mechanism of the shopping platform is determined.

10. A method for executing the user consumption incentive platform according to any one of claims 1 to 9, characterized in that: include: A1. Obtain consumption incentive issuance data and consumption incentive user data from the shopping platform, and evaluate the confidence, process rationality and integration accuracy of the shopping platform's consumption incentive mechanism; A2. Obtain consumer incentive interaction data from shopping platforms, and then evaluate the completeness of the consumer incentive interaction function of the shopping platform, and determine the development direction of the interactive function of the shopping platform; A3. Determine the optimization data of the consumption incentive mechanism of the shopping platform based on the confidence, process rationality and integration accuracy of the consumption incentive mechanism of the shopping platform; A4. Visually output the development direction of the interactive functions of the shopping platform and the optimization data of the consumer incentive mechanism.

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