Telecommunications business promotion and push method based on artificial intelligence

Abstract

The present invention relates to the technical field of service promotion and push technology, and in particular to a telecommunications service promotion and push method based on artificial intelligence; the method comprises the following steps: S1: collecting user behavior data, preference data and telecommunications service data, and sending the collected data to a database for storage; S2: obtaining the push value of telecommunications service products through multi-dimensional analysis of user information and telecommunications service product information; S3: realizing accurate push of related telecommunications services through comprehensive analysis of user active time, login frequency and response behavior; S4: obtaining real-time feedback from users on pushed content by providing a user feedback portal; pushing during user active time by analyzing time periods and activity levels, thereby improving the open rate and click rate of push, and adjusting the push frequency and interval to avoid excessive interference to users; reducing the number of invalid pushes, avoiding pushes in low-response-rate channels and inactive time periods, and optimizing resource utilization.

Description

Technical Field

[0001] The present invention relates to the technical field of service promotion and push technology, and in particular to a telecommunications service promotion and push method based on artificial intelligence. Background Art

[0002] Telecom service promotion and marketing plays a strategic role in the modern communications industry. It is not only an engine for business growth but also a crucial tool for optimizing user experience and enhancing market competitiveness. The importance of telecom service promotion and marketing lies not only in the direct growth of business revenue, but also in its long-term impact on maintaining user relationships, enhancing brand value, and strengthening market competitiveness.

[0003] At present, telecommunication business promotion is mainly pushed through telephone and text messages. Although this method has the advantage of wide coverage, it usually adopts fixed telecommunication business push, and has problems such as single content and high repetition rate. At the same time, due to the lack of real-time analysis and dynamic adjustment of user behavior, the pushed content often does not match user needs, resulting in a decline in promotional effects and even invalid push information. Summary of the Invention

[0004] To this end, the present invention provides an artificial intelligence-based telecommunications service promotion and push method to overcome the problems mentioned in the above background technology.

[0005] To achieve the above-mentioned object, the present invention provides a method for promoting and pushing telecommunication services based on artificial intelligence, comprising the following steps:

[0006] S1: Collect user behavior data, preference data, and telecommunications service data, and send the collected data to the database for storage;

[0007] S2: derive the push value of telecom service products through multi-dimensional analysis of user information and telecom service product information, and recommend telecom services to users based on the push value. The specific analysis steps are as follows:

[0008] Construct a cone with the values ​​of browsing evaluation value Tβ, price evaluation value Qij and function-related value Gij. Use the browsing evaluation value as the diameter of the cone, the function-related value as the height of the cone, and the price evaluation value as the tangent. Cut a small cone vertically downward from the vertex of the cone. The height of the cut cone is the value of the price evaluation value. Substitute the values ​​of browsing evaluation value Tβ, price evaluation value Qij and function evaluation value Gij into the formula The volume of the cut cone is calculated, which is the comprehensive matching value Pij between the user and the recommended telecommunication service; That is, the tangent length Qij does not exceed the cone height Gij; the values ​​of the comprehensive matching value Pij and the feedback coefficient λ are substituted into the formula Vij = Pij × (1 + λ) to calculate the push value of the telecommunications service product;

[0009] A product recommendation threshold is set, and the product push value of each telecommunications service is compared and analyzed with the set product recommendation threshold. When the product push value is greater than or equal to the set product recommendation threshold, the telecommunications service is marked as a related telecommunications service, and the recommendation frequency is increased or it is given priority in the recommendation list. When the product push value is less than the set product recommendation threshold, the recommendation frequency of the telecommunications service is reduced. For irrelevant telecommunications service products, the recommendation frequency is reduced or they are temporarily removed from the recommendation list.

[0010] S3: Comprehensive analysis of user activity time, login frequency, and response behavior enables accurate push of relevant telecommunications services;

[0011] S4: By providing a user feedback portal, we can obtain real-time feedback from users on pushed content and ensure that the pushed content matches user interests and needs.

[0012] As a preferred embodiment of the present invention, user behavior data collection includes: user basic information, browsing service time, user's current telecommunications service price and user's current telecommunications service function; user preference data collection includes: user login frequency, interaction records and active time; telecommunications service data includes the number of times the user likes or dislikes.

[0013] As a preferred embodiment of the present invention, the specific analysis steps of the browsing evaluation value are:

[0014] Get the time when the user last browsed telecommunication services and mark it as T 浏览 ; Mark the current browsing time as Tij, where i = 1, 2, 3 ... I, I is a positive integer, I represents the total number of users, i represents the number of any one of them; where j = 1, 2, 3 ... J, J is a positive integer, J represents the total number of telecommunications service product types, j represents the serial number of any one of them; the current browsing time Tij and the time when the user last browsed the telecommunications service T 浏览 Substitute the numerical value into the formula The browsing evaluation value Tβ is calculated, where e is a natural constant and β1 is a set weight factor;

[0015] Set a browsing evaluation value threshold, compare and analyze the user's browsing evaluation value with the set browsing evaluation threshold. When the browsing evaluation value is greater than or equal to the set browsing evaluation threshold, the user is marked as an active user and precise push is performed; when the browsing evaluation value is less than the set browsing evaluation value, the user is marked as a normal user and no additional telecommunications services are pushed.

[0016] As a preferred embodiment of the present invention, the specific analysis steps of the price evaluation value are as follows:

[0017] Get the price of the telecom service product currently used by the active user and mark it as Q 当前 ; The price of the telecom service product recommended to active users is marked as Q 推荐 ; Set the current price of telecom service products for active users to Q 当前 and recommended price of telecom business products Q 推荐 Substitute the numerical value into the formula The user's price evaluation value Qij is calculated, where β2 is the set weight factor.

[0018] As a preferred embodiment of the present invention, the specific analysis steps of the function-related value are:

[0019] Get the total number of active users' current telecom business functions as G 总 ; Set the number of parts of the telecom service functions recommended to active users that have the same or similar functional characteristics as the functions provided by the telecom service to G 相 Similar; the total number of telecommunication service functions required by the user G 总 and the number of similar functions G 相 Substitute similar values ​​into the formula The function-related value Gij of the telecommunication service is calculated.

[0020] As a preferred embodiment of the present invention, the specific analysis steps of the feedback coefficient are as follows:

[0021] Get the user's feedback on the recommended product, where the number of likes for the recommended product is recorded as D 赞 ; The number of clicks and dislikes of the recommended product is recorded as D 踩 ; Record the time point of the user's like and record it as Cn, where n = 1, 2, 3...N, N is a positive integer, N represents the total number of time points of all the user's like behaviors, and n represents the time point of any one of the likes; Record the time point of the user's dislike and record it as Cm, where m = 1, 2, 3...M, M is a positive integer, M represents the total number of time points of the user's dislike behaviors, and m represents the time point of any one of the dislikes; Get the current time point and record it as C1; The number of times the user likes the recommended product D 赞, number of clicks D 踩 , the values ​​of the like time point Cn, the dislike time point Cm and the current time point C1 are substituted into the set formula group The feedback value λn is calculated, where α1 and α2 are the set weight factors, α1>0 and α2<0, α3 is the set time attenuation coefficient and α3>0; the like feedback value λ 赞 and the tap feedback value λ 踩 Substitute the numerical value into the formula Calculate the total feedback value λ 总 ; Set the total feedback value λ 总 Substitute into the feedback coefficient formula The feedback coefficient λ is calculated.

[0022] As a preferred embodiment of the present invention, the specific steps for analyzing the user's active time are as follows:

[0023] Divide a day into multiple time periods, namely early morning, morning, afternoon and evening; obtain the cumulative number of logins of the user corresponding to each time period and mark them as K 凌晨 , K 上午 , K 下午 and K 晚上 The browsing time corresponding to the cumulative number of logins in each time period is summed up to get the cumulative time of each time period F 凌晨 、F 上午 、F 下午 and F 晚上 ; The cumulative number of logins in each time period K 凌晨 , K 上午 , K 下午 , K 晚上 and the cumulative duration of each time period F 凌晨 、F 上午 、F 下午 、F 晚上 Substitute into the formula group Calculate the user's activity H in each time period, where a1 and a2 are the set weight factors; compare the user's activity H in all time periods 凌晨 、H 上午 、H 下午 、H 晚上 , sort by activity from high to low, select the time period with the highest activity as the user's recommended time period, and mark it as H 推荐 After obtaining the user's recommended time period, push telecommunications services based on the time point when the user logs in most frequently during the recommended time period.

[0024] As a preferred embodiment of the present invention, the specific steps of analyzing the user response behavior are as follows:

[0025] Get the user's push response data and record the total number of pushes as R 总 ; Get the number of times the user opens the push after receiving it as R 打开 ; Get the number of times the user clicks a link or button after opening the push notification, and record it as R 点击 ; Get the number of times a user opens a push notification, clicks to purchase a product, registers an account, or fills out a form, and record it as R 转化 ; The total number of pushes to the user R 总 , Number of opens after push R 打开 , the number of times push notifications are opened and clicked R 点击 And the number of times the push notification was opened and clicked to purchase R 转化 Substitute the numerical value into the formula Calculate the user's push interval value R 间隔 , where a3, a4 and a5 are the set weight factors respectively.

[0026] As a preferred embodiment of the present invention, the specific analysis steps of the push strategy are:

[0027] Count the user response rates on different channels, setting the current channels to SMS, APP notifications, emails, and social media; get the total number of users who received the notification and record it as P 总 ; Get the number of users who click on the push content to interact (click, view, reply), and record it as P 响应 , using the formula Calculate the user's response rate P in each channel 响应率 ; Get the number of users who clicked on the push content link or button and record it as P 点击 , using the formula Calculate the user's click rate P in each channel 点击率 ; Get the number of users who purchase goods, register accounts, or fill out forms after clicking the push content link or button, and record it as P 转化 , using the formula Calculate the user conversion rate P in each channel 转化率 ; The user's response rate P in each channel 响应率 , the click rate of users in various channels P 点击率 and the user conversion rate P in each channel 转化率 Substitute the values ​​into the formula group Calculate the priority value P of each channel 短信 、P App 、P 邮件 and P 社交媒体 , where a6, a7 and a8 are the set weight factors respectively; the priority value P of each channel is obtained 短信 、P App、P 邮件 and P 社交媒体 Perform comparative analysis and select the channel with the largest priority value as the preferred channel. When the user does not respond on the preferred channel, select the channel with the second largest priority value as the suboptimal channel to continue pushing.

[0028] As a preferred embodiment of the present invention, the specific steps for analyzing the real-time feedback of the pushed content are as follows:

[0029] Set a feedback button under each push content, allowing users to like, dislike or choose "no longer receive this content";

[0030] When the user gives feedback and clicks "like" or "dislike", the user's likes or dislikes and the corresponding telecommunications services will be updated to the database in real time; when the user chooses "no longer receive this content", the telecommunications service will be immediately marked as "no longer recommended" and removed from the user's subsequent push list.

[0031] Beneficial effects of the present invention:

[0032] 1. By analyzing users' browsing behavior, price matching and functional relevance, it can accurately match user needs, effectively prevent users from receiving irrelevant or low-matching push notifications, and ensure that recommended telecommunications services are highly consistent with user needs; through dynamic evaluation of time decay and price ratio, it ensures that recommendation results are optimized in real time as time changes or user behavior updates; by effectively screening highly compatible products, it improves users' trust and satisfaction with recommended content, and promotes users' choice and conversion to telecommunications services.

[0033] 2. By analyzing time periods and calculating activity levels, push notifications are concentrated during user active hours, significantly increasing the open and click-through rates of push notifications. The push frequency and intervals are dynamically adjusted to avoid excessive interference with users. The number of invalid push notifications is reduced, and push notifications are avoided in low-response channels and inactive time periods, optimizing resource utilization. Multiple channels such as SMS, app notifications, emails, and social media are linked to achieve comprehensive coverage and increase user conversion rates. Overall, this significantly improves the publicity effectiveness of telecommunications services, reduces costs, optimizes user experience, brings more traffic and higher conversion revenue to the business, and enhances user recognition and trust in push content.

[0034] 3. By enabling users to actively click “Like” or “Thumbs Up”, users’ participation and interaction frequency in pushed content can be increased; allowing users to directly participate in the selection of pushed content can stimulate their enthusiasm and improve the overall push response rate; users can control the content they are not interested in, reduce interference, and improve their satisfaction with the push system; by no longer pushing unpopular content and providing customized options, users can be reduced from being lost due to too many irrelevant pushes. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0036] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are merely used to explain the present invention and are not intended to limit the present invention.

[0037] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0038] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside", and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0039] Furthermore, it should be noted that, in the description of the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; and internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0040] See also Figure 1 As shown, the present invention is a method for promoting and pushing telecommunication services based on artificial intelligence, comprising the following steps:

[0041] S1: Collect user behavior data, preference data, and telecom service data, and send the collected data to the database for storage; user behavior data collection includes: user basic information, browsing service time, user's current telecom service price, and user's current telecom service function; user preference data collection includes: user login frequency, interaction records, and active time; telecom service data includes the number of users' likes or dislikes;

[0042] S2: derive the push value of telecom service products through multi-dimensional analysis of user information and telecom service product information, and recommend telecom services to users based on the push value. The specific analysis steps are as follows:

[0043] Get the time when the user last browsed telecommunication services and mark it as T 浏 Browse; mark the current browsing time as Tij, where i = 1, 2, 3 ... I, I is a positive integer, I represents the total number of users, i represents the number of any one of them; where j = 1, 2, 3 ... J, J is a positive integer, J represents the total number of telecommunications service product types, j represents the serial number of any one of them; the current browsing time Tij and the time when the user last browsed the telecommunications service T 浏览 Substitute the value of into the set formula The browsing evaluation value Tβ is calculated, where e is a natural constant and β1 is a set weight factor. As can be seen from the formula, the longer the interval between the user's last browsing time and the current browsing time, the smaller the browsing evaluation value, that is, (Tij-T 浏览 ) is larger, the smaller the browsing evaluation value is; the smaller the time interval between the user's most recent browsing time and the current browsing time is, that is, the closer the current browsing time is to the most recent browsing time, the larger the browsing evaluation value is; a browsing evaluation value threshold is set, and the user's browsing evaluation value is compared and analyzed with the set browsing evaluation threshold. When the browsing evaluation value is greater than or equal to the set browsing evaluation threshold, it means that the user is relatively active, and the user is marked as an active user, and precise push is performed; when the browsing evaluation value is less than the set browsing evaluation value, the user is marked as a normal user, and no additional telecommunications services are pushed;

[0044] Get the price of the telecom service product currently used by the active user and mark it as Q 当前 ; Mark the price of the telecommunications service product recommended to the user as Q 推荐 ; Set the current price of telecom service products for active users to Q 当前 and recommended price of telecom business products Q 推荐 Substitute the value of into the set formula The user's price evaluation value Qij is calculated, where β2 is the set weight factor. The formula shows that when the price ratio of the active user's current telecommunications service product is closer to the price of the recommended telecommunications service product, the larger the price evaluation value, the higher the recommendation priority.

[0045] Get the total number of active users' current telecom business functions as G 总 ; Set the number of parts of the telecom service functions recommended to active users that have the same or similar functional characteristics as the functions provided by the telecom service to G 相似 ; The total number of telecommunication service functions required by the user G 总 and the number of similar functions G 相似 Substitute the value of into the set formula The function relevance value Gij of the telecommunication service is calculated. As can be seen from the formula, when the number of similar functions of the telecommunication service is greater than the total number of functions of the user's current telecommunication services, the function relevance value of the telecommunication service is greater; when the difference between the number of similar functions of the telecommunication service and the total number of functions of the user's current telecommunication services is greater, the function relevance value of the telecommunication service is smaller.

[0046] Construct a cone with the values ​​of browsing evaluation value Tβ, price evaluation value Qij and function-related value Gij. Use the browsing evaluation value as the diameter of the cone, the function-related value as the height of the cone, and the price evaluation value as the tangent. Cut a small cone vertically downward from the vertex of the cone. The height of the cut cone is the price evaluation value. Substitute the values ​​of browsing evaluation value Tβ, price evaluation value Qij and function evaluation value Gij into the set formula The volume of the cut cone is calculated, which is the comprehensive matching value Pij between active users and recommended telecommunication services; That is, the tangent length Qij does not exceed the height Gij of the cone; the values ​​of the comprehensive matching value Pij and the feedback coefficient λ are substituted into the set formula Vij = Pij × (1 + λ) to calculate the push value of the telecommunications service product; it can be seen from the formula that the greater the comprehensive matching value, the greater the push value of the telecommunications service; the greater the feedback coefficient, the greater the push value of the telecommunications service; the specific analysis steps of the feedback coefficient λ are as follows:

[0047] Get the user's feedback on the recommended product, where the number of likes for the recommended product is recorded as D 赞 ; The number of clicks and dislikes of the recommended product is recorded as D 踩 ; Record the time point of the user's like and record it as Cn, where n = 1, 2, 3...N, N is a positive integer, N represents the total number of time points of all the user's like behaviors, and n represents the time point of any one of the likes; Record the time point of the user's dislike and record it as Cm, where m = 1, 2, 3...M, M is a positive integer, M represents the total number of time points of the user's dislike behaviors, and m represents the time point of any one of the dislikes; Get the current time point and record it as C1; The number of times the user likes the recommended product D 赞 , number of clicks D 踩 , the values ​​of the like time point Cn, the dislike time point Cm and the current time point C1 are substituted into the set formula group The feedback value λn is calculated, where α1 and α2 are the set weight factors, α1>0 and α2<0, α3 is the set time attenuation coefficient and α3>0; the like feedback value λ 赞 and the tap feedback value λ 踩 Substitute the value of into the set formula Calculate the total feedback value λ总 ; Set the total feedback value λ 总 Substitute the set feedback coefficient formula The feedback coefficient λ is calculated;

[0048] A product recommendation threshold is set, and the product push value of each telecommunications service is compared and analyzed with the set product recommendation threshold. When the product push value is greater than or equal to the set product recommendation threshold, it indicates that the telecommunications service has a high degree of fit with the user, and the telecommunications service is marked as a related telecommunications service. For products marked as related telecommunications services, the recommendation frequency is increased or they are given priority in the recommendation list. When the product push value is less than the set product recommendation threshold, it indicates that the telecommunications service has a low degree of fit with the user, and the recommendation frequency of the telecommunications service is reduced. For irrelevant telecommunications service products, the recommendation frequency is reduced or they are temporarily removed from the recommendation list.

[0049] By analyzing users' browsing behavior, price matching, and functional relevance, we can accurately match user needs, effectively preventing users from receiving irrelevant or poorly matched push notifications, and ensuring that recommended telecom services are highly consistent with user needs. By dynamically evaluating time decay and price ratios, we ensure that recommendation results are optimized in real time as time changes or user behavior updates. By effectively screening highly compatible products, we increase users' trust and satisfaction with recommended content, and promote their choice and conversion to telecom services.

[0050] S3: Comprehensively analyze user activity time, login frequency, and response behavior to implement different push strategies for related telecommunications services. The specific steps are as follows:

[0051] Divide a day into multiple time periods, namely early morning, morning, afternoon and evening; the early morning period is 0:00-6:00; the morning period is 6:00-12:00; the afternoon period is 12:00-18:00; and the evening period is 18:00-24:00; obtain the cumulative number of logins of the user corresponding to each time period and mark them as K 凌晨 , K 上午 , K 下午 and K 晚上 The browsing time corresponding to the cumulative number of logins in each time period is summed up to get the cumulative time of each time period F 凌晨 、F 上午 、F 下午 and F 晚上 ; The cumulative number of logins in each time period K 凌晨 , K 上午 , K 下午 , K 晚上 and the cumulative duration of each time period F 凌晨 、F 上午 、F下午 、F 晚上 Substitute the set formula group Calculate the user's activity H in each time period, where a1 and a2 are the set weight factors; compare the user's activity H in all time periods 凌晨 、H 上午 、H 下午 、H 晚上 , sort by activity from high to low, select the time period with the highest activity as the user's recommended time period, and mark it as H 推荐 After obtaining the user's recommended time period, push telecommunication services based on the time point when the user logs in most frequently during the recommended time period;

[0052] Get the user's push response data and record the total number of pushes as R 总 ; Get the number of times the user opens the push after receiving it as R 打开 ; Get the number of times the user clicks a link or button after opening the push notification, and record it as R 点击 ; Get the number of times a user opens a push notification, clicks to purchase a product, registers an account, or fills out a form, and record it as R 转化 ; The total number of pushes to the user R 总 , Number of times opened after push R 打开 , the number of times push notifications are opened and clicked R 点击 And the number of times the push notification was opened and clicked to purchase R 转化 Substitute the value of into the set formula Calculate the user's push interval value R 间隔 , where a3, a4, and a5 are the set weight factors respectively. As can be seen from the formula, the larger the interval value, the higher the push frequency, and the smaller the interval value, the lower the push frequency. The push frequency is once a day at most during the active time period, and the minimum push frequency is once a week.

[0053] Count the user response rates on different channels, setting the current channels to SMS, APP notifications, emails, and social media; get the total number of users who received the notification and record it as P 总 ; Get the number of users who click on the push content to interact (click, view, reply), and record it as P 响应 , using the formula Calculate the user's response rate P in each channel 响应率 ; Get the number of users who clicked on the push content link or button and record it as P 点击 , using the formula Calculate the user's click rate P in each channel 点击率 ; Get the number of users who purchase goods, register accounts, or fill out forms after clicking the push content link or button, and record it as P转化 , using the formula Calculate the user conversion rate P in each channel 转化率 ; The user's response rate P in each channel 响应率 , the click rate of users in various channels P 点击率 and the user conversion rate P in each channel 转化率 Substitute the value into the set formula group Calculate the priority value P of each channel 短信 、P App 、P 邮件 and P 社交媒体 , where a6, a7 and a8 are the set weight factors respectively; the priority value P of each channel is obtained 短信 、P App 、P 邮件 and P 社交媒体 Perform comparative analysis and select the channel with the highest priority value as the preferred channel. If the user does not respond on the preferred channel, select the channel with the second highest priority value as the second-best channel for continued push.

[0054] By analyzing time periods and calculating activity levels, push notifications are concentrated during user active hours, significantly increasing push open and click-through rates. Push frequency and intervals are dynamically adjusted to avoid excessive disruption to users. Ineffective push notifications are reduced, avoiding push notifications in low-response channels and during inactive time periods, optimizing resource utilization. Multiple channels, including SMS, app notifications, email, and social media, are integrated to provide comprehensive coverage and increase user conversion rates. Overall, this significantly improves the effectiveness of telecom service promotions, reduces costs, optimizes user experience, generates more traffic and higher conversion revenue, and enhances user recognition and trust in push content.

[0055] S4: Provide a user feedback portal to obtain real-time user feedback on pushed content, ensuring that pushed content matches user interests and needs. Specific steps are as follows:

[0056] Set a feedback button under each push content, allowing users to like, dislike or choose "no longer receive this content";

[0057] When the user clicks "Like" or "Dislike" after giving feedback, the user's behavior (Like, Dislike) and the corresponding telecom service ID and push content ID are updated to the database in real time; when the user chooses "No longer receive this content", the telecom service is immediately marked as "No longer recommended" and removed from the user's subsequent push list; after the user behavior data is updated to S1, the above steps are repeated to push the telecom service to the user;

[0058] By allowing users to actively like or comment on push content, the user's participation and interaction frequency in push content can be increased; allowing users to directly participate in the selection of push content can stimulate their enthusiasm and improve the overall push response rate; users can control the content they are not interested in, reduce interference, and improve their satisfaction with the push system; by no longer pushing unpopular content and providing customized options, the user loss due to too many irrelevant pushes can be reduced.

[0059] Thus far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art may make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will fall within the scope of protection of the present invention.

[0060] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that the present invention is susceptible to various modifications and variations. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A method for promoting telecommunication services based on artificial intelligence, characterized in that: The following steps are involved: S1: Collects user behavior data, preference data, and telecommunications service data, and sends them to the database for storage; S2: Analyze user information and telecom service product information in multiple dimensions to obtain the push value of telecom service products, and recommend telecom services to users based on the push value. The specific analysis steps are as follows: Construct a cone with the values ​​of browsing evaluation value Tβ, price evaluation value Qij and function-related value Gij. Use the browsing evaluation value as the diameter of the cone, the function-related value as the height of the cone, and the price evaluation value as the tangent. Cut a small cone vertically downward from the vertex of the cone. The height of the cut cone is the value of the price evaluation value. Substitute the values ​​of browsing evaluation value Tβ, price evaluation value Qij and function evaluation value Gij into the formula The volume of the cut cone is calculated, which is the comprehensive matching value Pij between the user and the recommended telecommunication service; That is, the tangent length Qij does not exceed the cone height Gij; the values ​​of the comprehensive matching value Pij and the feedback coefficient λ are substituted into the formula Vij = Pij × (1 + λ) to calculate the push value of the telecommunications service product; Compare and analyze the product push value of each telecom service with the set product recommendation threshold: when the product push value is greater than or equal to the set product recommendation threshold, mark the telecom service as a relevant telecom service, increase the recommendation frequency, or prioritize it in the recommendation list; when the product push value is less than the set product recommendation threshold, reduce the recommendation frequency of the telecom service, and for irrelevant telecom service products, reduce the recommendation frequency or temporarily remove them from the recommendation list; S3: Comprehensive analysis of user activity time, login frequency, and response behavior enables accurate push of relevant telecommunications services; S4: Provides a user feedback portal to obtain real-time feedback on pushed content, ensuring that pushed content matches user interests and needs; The detailed analysis of the browsing evaluation value is as follows: Get the time when the user last browsed telecommunication services and mark it as T 浏览 ; Mark the current browsing time as Tij, where i = 1, 2, 3 ... I, I is a positive integer, I represents the total number of users, i represents the number of any one of them; where j = 1, 2, 3 ... J, J is a positive integer, J represents the total number of telecommunications service product types, j represents the serial number of any one of them; the current browsing time Tij and the time when the user last browsed the telecommunications service T 浏览 Substitute the value of into the set formula The browsing evaluation value Tβ is calculated, where e is a natural constant and β1 is a set weight factor; Set a browsing evaluation value threshold, compare and analyze the user's browsing evaluation value with the set browsing evaluation threshold. When the browsing evaluation value is greater than or equal to the set browsing evaluation threshold, the user is marked as an active user and precise push is performed; when the browsing evaluation value is less than the set browsing evaluation value, the user is marked as a normal user and no additional telecommunications services are pushed; The specific analysis of the price assessment is as follows: Get the price of the telecom service product currently used by the active user and mark it as Q 当前 ; Mark the price of the telecommunications service product recommended to the user as Q 推荐 ; Set the current price of telecom service products for active users to Q 当前 and recommended price of telecom business products Q 推荐 Substitute the value of into the set formula Calculate the user's price evaluation value Qij, where β2 is the set weight factor; The specific analysis of the function-related values ​​is as follows: Get the total number of active users' current telecom business functions as G 总 ; Set the number of parts of the telecom service functions recommended to active users that have the same or similar functional characteristics as the functions provided by the telecom service to G 相似 ; The total number of telecommunication service functions required by the user G 总 and the number of similar functions G 相似 Substitute the value of into the set formula Calculate and obtain the function-related value Gij of the telecommunication service; The specific analysis of the feedback coefficient is: Get the user's feedback on the recommended product, where the number of likes for the recommended product is recorded as D 赞 ; The number of clicks and dislikes of the recommended product is recorded as D 踩 ; Record the time point of the user's like and record it as Cn, where n = 1, 2, 3...N, N is a positive integer, N represents the total number of time points of all the user's like behaviors, and n represents the time point of any one of the likes; Record the time point of the user's dislike and record it as Cm, where m = 1, 2, 3...M, M is a positive integer, M represents the total number of time points of the user's dislike behaviors, and m represents the time point of any one of the dislikes; Get the current time point and record it as C1; The number of times the user likes the recommended product D 赞 , number of clicks D 踩 , the values ​​of the like time point Cn, the dislike time point Cm and the current time point C1 are substituted into the set formula group The feedback value λn is calculated, where α1 and α2 are the set weight factors, α1>0 and α2<0, α3 is the set time attenuation coefficient and α3>0; the like feedback value λ 赞 and the tap feedback value λ 踩 Substitute the value of into the set formula Calculate the total feedback value λ 总 ; Set the total feedback value λ 总 Substitute the set feedback coefficient formula The feedback coefficient λ is calculated; The specific analysis of user active time is as follows: Divide a day into multiple time periods, namely early morning, morning, afternoon and evening; the early morning period is 0:00-6:00; the morning period is 6:00-12:00; the afternoon period is 12:00-18:00; and the evening period is 18:00-24:00; obtain the cumulative number of logins of the user corresponding to each time period and mark them as K 凌晨 , K 上午 , K 下午 and K 晚上 The browsing time corresponding to the cumulative number of logins in each time period is summed up to get the cumulative time of each time period F 凌晨 、F 上午 、F 下午 and F 晚上 ; The cumulative number of logins in each time period K 凌晨 , K 上午 , K 下午 , K 晚上 and the cumulative duration of each time period F 凌晨 、F 上午 、F 下午 、F 晚上 Substitute the set formula group Calculate the user's activity H in each time period, where a1 and a2 are the set weight factors; compare the user's activity H in all time periods 凌晨 、H 上午 、H 下午 、H 晚上 , sort by activity from high to low, select the time period with the highest activity as the user's recommended time period, and mark it as H 推荐 After obtaining the user's recommended time period, push telecommunication services based on the time point when the user logs in most frequently during the recommended time period; The specific analysis of user response behavior is as follows: Get the user's push response data and record the total number of pushes as R 总 ; Get the number of times the user opens the push after receiving it as R 打开 ; Get the number of times the user clicks a link or button after opening the push notification, and record it as R 点击 ; Get the number of times a user opens a push notification, clicks to purchase a product, registers an account, or fills out a form, and record it as R 转化 ; The total number of pushes to the user R 总 , Number of times opened after push R 打开 , the number of times push notifications are opened and clicked R 点击 And the number of times the push notification was opened and clicked to purchase R 转化 Substitute the value of into the set formula Calculate the user's push interval value R 间隔 , where a3, a4 and a5 are the set weight factors respectively; The specific analysis of the push strategy is as follows: Count the user response rates on different channels, setting the current channels to SMS, APP notifications, emails, and social media; get the total number of users who received the notification and record it as P 总 ; Get the number of users who click on the pushed content to interact, and record it as P 响应 , using the formula Calculate the user's response rate P in each channel 响应率 ; Get the number of users who clicked on the push content link or button and record it as P 点击 , using the formula Calculate the user's click rate P in each channel 点击率 ; Get the number of users who purchase goods, register accounts, or fill out forms after clicking the push content link or button, and record it as P 转化 , using the formula Calculate the user conversion rate P in each channel 转化率 ; The user's response rate P in each channel 响应率 , the click rate of users in various channels P 点击率 and the user conversion rate P in each channel 转化率 Substitute the value into the set formula group Calculate the priority value P of each channel 短信 、P App 、P 邮件 and P 社交媒体 , where a6, a7 and a8 are the set weight factors respectively; The priority value P of each channel is obtained 短信 、P App 、P 邮件 and P 社交媒体 Perform comparative analysis and select the channel with the largest priority value as the preferred channel. When the user does not respond on the preferred channel, select the channel with the second largest priority value as the suboptimal channel to continue pushing.

2. The method for promoting telecommunication services based on artificial intelligence according to claim 1, characterized in that: User behavior data collection includes: user basic information, browsing time, user's current telecommunications service price and user's current telecommunications service function; user preference data collection includes: user login frequency, interaction records and active time; telecommunications service data includes the number of times users like or dislike.

3. The method for promoting telecommunication services based on artificial intelligence according to claim 1, characterized in that: The specific analysis steps for real-time feedback of pushed content are as follows: Set a feedback button under each push content, allowing users to like, dislike or choose not to receive this content; When the user gives feedback and clicks like or dislike, the user's likes, dislikes and corresponding telecommunications services will be updated to the database in real time; when the user chooses not to receive this content anymore, the telecommunications service will be immediately marked as no longer recommended and removed from the user's subsequent push list.

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