Artificial intelligence-based advertising push method, device, and medium

Abstract

The present invention discloses an artificial intelligence-based advertising push method, device, and medium, relating to the field of advertising push technology. The method includes collecting user data information and performing preprocessing operations to divide the data into a scene data set and a user interest data set; constructing a scene recognition model and training the scene recognition model using the scene data set; constructing a user interest model and training the user interest model using the user interest data set; analyzing the advertising content and extracting advertising keywords, defining scene keywords for the scene based on the user's current scene, matching and pushing the keywords; and evaluating and optimizing the effects of the pushed advertisements. The method of the present invention can push advertisements related to the current scene to users through scene-aware advertising push, thereby improving the click-through rate and conversion rate of the advertisements, increasing user satisfaction, and enhancing user loyalty to the brand.

Description

Technical Field

[0001] The present invention relates to the field of advertising push technology, and in particular to an artificial intelligence-based advertising push method, device, and medium. Background Art

[0002] Traditional advertising methods primarily rely on users' historical behavior and interests. However, with the development of mobile devices and the Internet of Things (IoT), context-aware advertising is gaining traction. This approach captures real-time contextual information about users (such as location, time, and device status) to deliver relevant ads, thereby increasing click-through and conversion rates.

[0003] In the existing technology, how to accurately identify the user's current scene and push relevant advertisements to the user based on the scene information has become a major problem. Summary of the Invention

[0004] In view of the problems existing in the above-mentioned existing artificial intelligence-based advertising push methods, the present invention is proposed.

[0005] Therefore, the problem to be solved by the present invention is how to provide an advertisement pushing method based on artificial intelligence.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: an artificial intelligence-based advertising push method, which includes collecting user data information and performing preprocessing operations to divide the data into a scene data set and a user interest data set; constructing a scene recognition model and training the scene recognition model through the scene data set; constructing a user interest model and training the user interest model through the user interest data set; analyzing the advertising content, extracting advertising keywords, defining scene keywords for the scene according to the user's current scene, and matching and pushing them; and evaluating and optimizing the effects of the pushed advertisements.

[0007] As a preferred solution of the artificial intelligence-based advertising push method described in the present invention, the user's data information includes location information, time information, and current application information, the preprocessing operation includes denoising, normalization, and classification processing, and the advertisements include text advertisements, video advertisements, and voice advertisements.

[0008] As a preferred solution of the artificial intelligence-based advertising push method described in the present invention, the following steps are included: dividing the scene data set includes the following steps: applying the DBSCAN algorithm to cluster the pre-processed location information data to form several candidate areas, each candidate area represents an area where the user often appears; statistics are collected on the records in each candidate area to divide them into different time periods; the application usage data characteristics in each time period are analyzed; according to the user's behavior pattern in the time period, the records in the candidate area are marked with clear scene labels; when there is a conflict in the behavior patterns of different time periods in the same area, a time feature weight judgment mechanism is used to mark the records as "double records"; all records that have been clustered by location and subdivided by time and confirmed by behavior pattern comparison are classified into the scene data set; dividing the user interest data set includes the following steps: normalizing the application usage data in each record; establishing a pre-defined application category label library; matching each behavior data in the record with the label library to determine the user's interest intensity in each category.

[0009] As a preferred solution of the artificial intelligence-based advertising push method of the present invention, wherein: constructing a scene recognition model and training the scene recognition model using a scene data set includes the following steps:

[0010] Design the network structure, including input layer, convolution layer, pooling layer, fully connected layer and output layer;

[0011] Use the Softmax function as the activation function of the output layer to obtain the probability distribution of each scene;

[0012] The scene recognition model is trained using the scene dataset and cross entropy is used as the loss function. The formula is as follows:

[0013]

[0014] Where L is the cross entropy loss, N is the number of data, and y i is the true label, is the predicted value of the model;

[0015] Use the Adam optimizer to update the model parameters, input the real-time collected data into the trained scene recognition model, obtain the scene probability distribution output by the model, and select the scene with the highest probability as the current scene.

[0016] As a preferred solution of the artificial intelligence-based advertising push method of the present invention, the steps of constructing a user interest model and training the user interest model using a user interest dataset include the following:

[0017] Use word segmentation tools to segment user interest datasets;

[0018] Use the TF-IDF algorithm to calculate the weight of the user interest dataset after word segmentation. The formula is as follows:

[0019]

[0020] Where TF(t,d) is the frequency of word t in document d, and IDF(t) is the inverse document frequency of word t;

[0021] The BERT model is used as the basic model for user interest modeling and optimized. The optimized model is as follows: , where w i is the i-th word in the sequence,

[0022] Token embedding layer: ;

[0023] Position Embedding Layer: ;

[0024] BERT input layer: ;

[0025] Transformer layer: ;

[0026] Output layer: ;

[0027] Where, I (w i ) is the vocabulary w i One-hot encoding, W token is the token embedding matrix, I(i) is the one-hot encoding of position i, and W position is the position embedding matrix, b is the number of Transformer layers, is the output of layer b, W O and B O are the weights and biases of the output layer, is the output of the last Transformer layer;

[0028] Use labeled user interest datasets for model training and use cross entropy as the loss function;

[0029] Use the Adam optimizer to update the model parameters, input the real-time collected data into the trained BERT model, obtain the user interest distribution output by the model, and select the interest with the highest probability as the user's current interest.

[0030] As a preferred solution of the artificial intelligence-based advertising push method of the present invention, the following steps are included: analyzing the advertising content, extracting advertising keywords, defining scene keywords for the scene according to the user's current scene, and matching and pushing.

[0031] Conduct text analysis on each advertisement content to extract keywords and themes;

[0032] Use the TF-IDF algorithm to calculate the weight of keywords in the advertising content;

[0033] Based on the user's current scenario, define a set of keywords and weights for the scenario;

[0034] Use cosine similarity to calculate the similarity between scene keywords and advertising content keywords, as well as the similarity between user interests and advertising content keywords. The formula is as follows:

[0035]

[0036]

[0037] In the formula, A is the scene keyword vector, B is the keyword vector of the advertising content, and C is the user interest keyword vector. is the similarity between the scene keywords and the advertising content keywords, The similarity between user interests and keywords in advertising content;

[0038] Combine the similarity between the scene and the advertisement and the similarity between the user's interest and the advertisement to calculate the comprehensive similarity. The formula is as follows:

[0039]

[0040] Where α is a weight parameter used to adjust the importance of scene similarity and interest similarity;

[0041] Advertisements are sorted based on comprehensive similarity, and the ads with the highest similarity are selected for push. The time, content, and user feedback of the pushed ads are recorded for subsequent model optimization.

[0042] As a preferred solution of the artificial intelligence-based advertising push method of the present invention, when pushing advertisements, the following steps are also included:

[0043] Collect the user's daily active time through the user's application usage records;

[0044] For each user, record their activity level for each hour of the day to form a 24-dimensional vector;

[0045] Use the survival analysis model to predict the end time of the user's scenario. The formula is as follows:

[0046]

[0047] Where S(t) is the probability that the event has not occurred after time t, and T is the time when the event occurs;

[0048] The Cox proportional hazard model is used to consider the effects of multiple covariates on the time to event. The formula is as follows:

[0049]

[0050] Where h(t, X) is the hazard function at time t given the covariate X, is the baseline hazard function and β is the coefficient of the covariate.

[0051] As a preferred solution of the artificial intelligence-based advertising push method of the present invention, wherein: evaluating and optimizing the effect of pushed advertisements includes the following steps:

[0052] Set a unique identifier for each ad push, collect user behavior data related to the ad, and associate the collected data with the ad's unique identifier;

[0053] Define the evaluation indicators of advertising effectiveness. The formula is as follows:

[0054]

[0055]

[0056] Where, clicks is the total number of times users clicked on the ad, impressions is the total number of times the ad was shown to users, and target action completion is the number of users who completed the specified target action.

[0057] Calculate the effectiveness of each ad push based on defined evaluation metrics, compare the effects of different ad content, push timing, and push strategies, and find the best combination.

[0058] Update the user's interest model based on the user's feedback on the advertisement.

[0059] A computer device includes a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, it implements the steps of an artificial intelligence-based advertising push method.

[0060] A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the computer program implements the steps of an artificial intelligence-based advertising push method.

[0061] The beneficial effects of the present invention are as follows: through scene-aware advertising push, advertisements related to the current scene can be pushed to users, thereby improving the click-through rate and conversion rate of advertisements; since the advertising content and push timing are matched with the user's actual needs and scenes, user satisfaction can be improved and user loyalty to the brand can be enhanced; by collecting user feedback information on pushed advertisements, the user's interest model and scene recognition model can be updated in real time, thereby optimizing the advertising push strategy to make it more accurate and efficient. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort. Among them:

[0063] Figure 1 This is a flowchart of the artificial intelligence-based advertising push method in Example 1. DETAILED DESCRIPTION

[0064] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0065] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0066] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0067] Example 1

[0068] Reference Figure 1 , which is the first embodiment of the present invention, provides an artificial intelligence-based advertising push method, which includes the following steps:

[0069] S1. Collect user data and perform preprocessing operations to divide the data into a scene data set and a user interest data set. The user data includes location information, time information, and current application information. The preprocessing operations include denoising, normalization, and classification. The advertisements include text ads, video ads, and voice ads.

[0070] S2. Build a scene recognition model and train it using the scene dataset.

[0071] S3. Build a user interest model and train the user interest model using the user interest dataset;

[0072] S4. Analyze the advertisement content, extract advertisement keywords, define scenario keywords for the scenario based on the user's current scenario, and perform matching and push;

[0073] S5. Evaluate and optimize the effectiveness of pushed advertisements.

[0074] Specifically, dividing the scene data set includes the following steps:

[0075] S11. Clustering the pre-processed location information data to form candidate regions.

[0076] (1) Using the denoised, normalized and time-synchronized location information data completed in the preprocessing stage, the DBSCAN algorithm is used to cluster the data;

[0077] (2) During the implementation process, the key parameters of the DBSCAN algorithm, namely the radius threshold ε and the minimum number of sample points MinPts, are set to ensure that the clustering results can accurately reflect the areas where users frequently appear;

[0078] (3) After clustering, each cluster constitutes a candidate area, which represents the places that users often visit in their daily lives, such as home areas, office areas, or business district areas.

[0079] S12: Count and divide the data records in each candidate area into time periods.

[0080] (1) For each candidate area, based on the time information included in the records, the data records in the area are divided into several time periods according to the user's activity time (for example, by hourly period, morning, morning, afternoon, evening and night);

[0081] (2) The time period division adopts a method that combines preset fixed time periods with adaptive adjustment: first, a preliminary division is made based on the regular time period, and then the boundaries are automatically fine-tuned based on the time distribution characteristics of user behavior data in the candidate area to more accurately reflect the actual activity peak;

[0082] (3) All records are retained in each time period and provide time context information for subsequent behavior pattern analysis.

[0083] S13. Analyze application usage data characteristics in each time period.

[0084] (1) For each time period divided within each candidate area, statistical analysis is performed on the application usage data (e.g., clicks, browsing, and dwell time of various applications) contained in the data records;

[0085] (2) Based on a pre-defined application category tag library (such as "office", "entertainment", "social", "shopping", etc.), calculate the frequency of occurrence and usage intensity of each category in each time period;

[0086] (3) The statistical results serve as an important basis for measuring user behavior patterns during the time period, reflecting the types of activities that users mainly engage in during different time periods.

[0087] S14. Add clear scene labels to candidate area records based on the user's behavior pattern within the time period.

[0088] (1) Based on the data obtained in step S13, determine the main activity type of the user in each time period, and determine one or more scene labels that best describe the behavior in that time period, such as "office place", "leisure and entertainment place" or "shopping center", etc.;

[0089] (2) Associate each data record with the scene label of its time period to form a preliminary scene description;

[0090] (3) At this time, the records in each candidate area have a scene identifier of the corresponding time period, which serves as an important basis for subsequent advertising matching.

[0091] S15. Deal with conflicts in behavior patterns at different time periods in the same area.

[0092] (1) If data records from different time periods within the same candidate area show obvious behavioral pattern conflicts (for example, both office behavior and entertainment behavior occur in the same geographical location), the preset “time feature weight judgment mechanism” is used to evaluate the records in the area;

[0093] (2) Based on the weight values ​​of the application usage data in each time period, different scene labels are compared. When the weights of two or more scene labels are similar or difficult to distinguish, the record is marked as a "double record"; this mechanism ensures that when the behavior pattern is unclear, the record will not be simply classified as a single scene, but will retain the possibility of further correction based on subsequent advertising feedback.

[0094] S16. All records that have been confirmed by location clustering, time division, and behavior pattern comparison are classified into a scene data set.

[0095] (1) For all data records in the candidate area, based on the scene tags and "dual record" flags obtained in steps S14 and S15, if a record has only a single scene tag, the scene attribute of the record is directly set to the tag and marked as "single scene" in the data structure. If a record contains two or more scene tags at the same time, the system saves all candidate scene tags in the data record. For "single scene" records, they are directly stored in the standard record area of ​​the scene data set. For "dual record" records, the system stores them in a dedicated record format in the scene data set. In addition to the conventional location information, time information and behavior data, the record also has a "multi-scene tag" field.

[0096] (2) The records included in the scene dataset contain not only candidate area and time period information, but also scene labels determined after behavioral pattern comparison, providing complete and accurate training data for the subsequent construction of the scene recognition model.

[0097] Dividing the user interest dataset includes the following steps:

[0098] S17. normalizing the application usage data contained in each preprocessed data record to eliminate differences between different data sources and scales;

[0099] S18. Establish a predefined application category tag library, which includes but is not limited to categories such as "office", "entertainment", "social", and "shopping", and each category can be further divided into subcategories;

[0100] S19. Match the normalized behavioral data in each data record with the application category label library. By comparing the numerical values ​​of each category, determine the intensity of the user's interest in each category reflected by the record. Based on the matching results, classify each data record into a user interest dataset to provide input data for the subsequent construction of a user interest model.

[0101] Furthermore, building a scene recognition model and training the scene recognition model using a scene dataset includes the following steps:

[0102] Design the network structure, including input layer, convolution layer, pooling layer, fully connected layer and output layer. The formula is as follows:

[0103]

[0104] Where y is the output of the network, f is the activation function, x is the input data, w is the weight, and b is the bias;

[0105] Use the Softmax function as the activation function of the output layer to obtain the probability distribution of each scene. The formula is as follows:

[0106]

[0107] Where, P (y i ) is the probability of category i, y i is the original output value of category i, j is the length of vector y, which is also the total number of categories, and M is the total length of vector y;

[0108] The scene recognition model is trained using the scene dataset and cross entropy is used as the loss function. The formula is as follows:

[0109]

[0110] Where L is the cross entropy loss, N is the number of data, and y i is the true label, is the predicted value of the model;

[0111] Use the Adam optimizer to update the model parameters, input the real-time collected data into the trained scene recognition model, obtain the scene probability distribution output by the model, and select the scene with the highest probability as the current scene.

[0112] Constructing a user interest model and training the user interest model through a user interest dataset includes the following steps:

[0113] Use word segmentation tools to segment user interest datasets;

[0114] Use the TF-IDF algorithm to calculate the weight of the user interest dataset after word segmentation. The formula is as follows:

[0115]

[0116] Where TF(t,d) is the frequency of word t in document d, and IDF(t) is the inverse document frequency of word t;

[0117] The BERT model is used as the basic model for user interest modeling and optimized. The optimized model is as follows: , where w i is the i-th word in the sequence,

[0118] Token embedding layer: ;

[0119] Position Embedding Layer: ;

[0120] BERT input layer: ;

[0121] Transformer layer: ;

[0122] Output layer: ;

[0123] Where, I (w i ) is the vocabulary w i One-hot encoding, W token is the token embedding matrix, I(i) is the one-hot encoding of position i, and W position is the position embedding matrix, b is the number of Transformer layers, is the output of layer b, W O and B O are the weights and biases of the output layer, is the output of the last Transformer layer;

[0124] Use labeled user interest datasets for model training and use cross entropy as the loss function;

[0125] Use the Adam optimizer to update the model parameters, input the real-time collected data into the trained BERT model, obtain the user interest distribution output by the model, and select the interest with the highest probability as the user's current interest.

[0126] Preferably, analyzing the advertisement content, extracting advertisement keywords, defining scene keywords for the scene according to the user's current scene, and matching and pushing includes the following steps:

[0127] A preprocessing algorithm is used to segment and parse each ad content to be pushed (including text ads, video titles and descriptions, and text transcriptions of voice ads) to extract keywords and subject terms that describe the ad's theme. During this extraction process, natural language processing techniques (such as BERT- or LSTM-based models) are used to capture contextual information and ensure high accuracy in keyword extraction. For example, the segmentation results for an ad might include keywords such as "discount," "new product," and "electronic product," while also determining the ad's theme category as "shopping promotion."

[0128] Use the TF-IDF algorithm to calculate the weight of keywords in the advertising content;

[0129] For records with a single scene label, the user's current scene (e.g., "office," "shopping center," or "leisure and entertainment venue") is determined based on the scene dataset previously obtained through candidate region clustering, time division, and behavioral pattern analysis. The historical records within that scene are statistically analyzed, and keywords describing the scene and their long-term statistical weights are extracted to form a scene keyword vector. For the portions of the dual records belonging to scene A and scene B, the respective scene keyword vectors are statistically analyzed according to the processing steps for a single scene. The candidate scene keyword vectors are then fused using a weighted fusion method to obtain a comprehensive scene keyword vector.

[0130] Use cosine similarity to calculate the similarity between scene keywords and advertising content keywords, as well as the similarity between user interests and advertising content keywords. The formula is as follows:

[0131]

[0132]

[0133] In the formula, A is the scene keyword vector, B is the keyword vector of the advertising content, and C is the user interest keyword vector. is the similarity between the scene keywords and the advertising content keywords, The similarity between user interests and keywords in advertising content;

[0134] Combine the similarity between the scene and the advertisement and the similarity between the user's interest and the advertisement to calculate the comprehensive similarity. The formula is as follows:

[0135]

[0136] Where α is a weight parameter used to adjust the importance of scene similarity and interest similarity;

[0137] Advertisements are sorted based on comprehensive similarity, and the ads with the highest similarity are selected for push. The time, content, and user feedback of the pushed ads are recorded for subsequent model optimization.

[0138] Preferably, when pushing advertisements, the following steps are also included:

[0139] Collect the user's daily active time through the user's application usage records;

[0140] For each user, record their activity level for each hour of the day to form a 24-dimensional vector;

[0141] Use the survival analysis model to predict the end time of the user's scenario. The formula is as follows:

[0142]

[0143] Where S(t) is the probability that the event has not occurred after time t, and T is the time when the event occurs;

[0144] The Cox proportional hazard model is used to consider the effects of multiple covariates on the time to event. The formula is as follows:

[0145]

[0146] Where h(t, X) is the hazard function at time t given the covariate X, is the baseline hazard function and β is the coefficient of the covariate.

[0147] The evaluation and optimization of the pushed ads include the following steps:

[0148] Set a unique identifier for each ad push, collect user behavior data related to the ad, such as clicks, browsing time, and purchases, and associate the collected data with the ad's unique identifier;

[0149] Define the evaluation indicators of advertising effectiveness. The formula is as follows:

[0150]

[0151]

[0152] Where, click count is the total number of times users clicked on the ad, impression count is the total number of times the ad was shown to users, and target action completion count is the number of users who completed the specified target action, where the specified target action can be purchase, click, registration, etc.

[0153] Calculate the effectiveness of each ad push based on defined evaluation metrics, compare the effects of different ad content, push timing, and push strategies, and find the best combination.

[0154] Based on user feedback on advertisements, such as clicks and purchases, the user's interest model is updated.

[0155] Example 2

[0156] The second embodiment of the present invention is different from the previous embodiment in that:

[0157] If the functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage media include various media capable of storing program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0158] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0159] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, and then editing, interpreting, or processing in another suitable manner as necessary, and then storing it in a computer memory.

[0160] It should be understood that various components of the present invention may be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods may be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof may be used: a discrete logic circuit having logic gate circuits for implementing logic functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc.

[0161] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An artificial intelligence-based advertising push method, characterized by: include, Collect user data information and perform preprocessing operations to divide the data into scenario data sets and user interest data sets; Build a scene recognition model and train it using a scene dataset; Build a user interest model and train it using a user interest dataset; Analyze the ad content and extract ad keywords. Based on the user's current scenario, define scenario keywords for the scenario. Use cosine similarity to calculate the similarity between scenario keywords and ad content keywords, as well as the similarity between user interests and ad content keywords. Combine the similarity between the scenario and the ad, and the similarity between user interests and the ad, to calculate the overall similarity, and then perform matching and push. Evaluate and optimize the effectiveness of pushed advertisements; Dividing the scene data set includes the following steps: Apply the DBSCAN algorithm to cluster the pre-processed location information data to form several candidate areas, each of which represents the area where the user often appears; For each candidate region, the data records within the region are divided into several time periods based on the user's activity time, using the time information included in the records. The time period division is based on a combination of preset fixed periods and adaptive adjustment. Initially, a preliminary division is made based on conventional time periods. Then, based on the temporal distribution characteristics of user behavior data within the candidate region, the boundaries are automatically fine-tuned to more accurately reflect actual activity peaks. All records within each time period are retained, providing temporal context for subsequent behavior pattern analysis. For each time period defined within each candidate region, statistical analysis is performed on the application usage data contained in the data records. Based on a pre-defined library of application category tags, the frequency of occurrence and intensity of use of each category within each time period are calculated. The statistical results serve as an important basis for measuring user behavior patterns within that time period, reflecting the main types of activities users engage in during different time periods. Determine the user's primary activity type within each time period and identify one or more scenario tags that best describe the behavior during that time period. Associate each data record with the scenario tag for its time period to form a preliminary scenario description. When there are conflicts in behavior patterns at different times in the same area, a time feature weight judgment mechanism is used to mark the records as "double records"; All data records within the candidate area are classified into the scene dataset based on the obtained scene tags and the "dual record" flag. If a record has only a single scene tag, the scene attribute of the record is directly set to the tag and marked as "single scene" in the data structure. If a record contains two or more scene tags at the same time, the system saves all candidate scene tags in the data record. For "single scene" records, they are directly stored in the standard record area of ​​the scene dataset. For "dual record" records, the system stores them in a dedicated record format within the scene dataset. In addition to the conventional location information, time information and behavior data, this record also has a "multi-scene tag" field. Dividing the user interest data set includes the following steps: Normalize the application usage data in each record; Establish a predefined application category tag library; Match the recorded behavioral data with the tag library to determine the user's interest intensity in each category; When pushing advertisements, the following steps are also included: Collect the user's daily active time through the user's application usage records; For each user, record their activity level for each hour of the day to form a 24-dimensional vector; Use the survival analysis model to predict the end time of the user's scenario. The formula is as follows: S(t)=P(T>t) Where S(t) is the probability that the event has not occurred after time t, and T is the time when the event occurs; The Cox proportional hazard model is used to consider the effects of multiple covariates on the time to event, as follows: h(t, X) = h0(t)exp(β T X) where h(t, X) is the hazard function at time t given the covariate X, h0(t) is the baseline hazard function, and β is the coefficient of the covariate.

2. The artificial intelligence-based advertising push method according to claim 1, wherein: The user data information includes location information, time information, and current application information. The preprocessing operation includes normalization processing and classification processing for denoising. The advertisement includes text advertisement, video advertisement, and voice advertisement.

3. The artificial intelligence-based advertising push method according to claim 2, wherein: Building a scene recognition model and training it with a scene dataset includes the following steps: Design the network structure, including input layer, convolution layer, pooling layer, fully connected layer and output layer; Use the Softmax function as the activation function of the output layer to obtain the probability distribution of each scene; The scene recognition model is trained using the scene dataset and cross entropy is used as the loss function; Use the Adam optimizer to update the model parameters, input the real-time collected data into the trained scene recognition model, obtain the scene probability distribution output by the model, and select the scene with the highest probability as the current scene.

4. The artificial intelligence-based advertising push method according to claim 3, wherein: Constructing a user interest model and training the user interest model through a user interest dataset includes the following steps: Use word segmentation tools to segment user interest datasets; Use the TF-IDF algorithm to calculate the weight of the user interest dataset after word segmentation; Use the BERT model as the basic model for user interest modeling and optimize it; Use labeled user interest datasets for model training and use cross entropy as the loss function; Use the Adam optimizer to update the model parameters, input the real-time collected data into the trained BERT model, obtain the user interest distribution output by the model, and select the interest with the highest probability as the user's current interest.

5. The artificial intelligence-based advertising push method according to claim 4, wherein: Analyze the ad content, extract ad keywords, define scene keywords for the scene according to the user's current scene, and match and push them, including the following steps: Conduct text analysis on each advertisement content to extract keywords and themes; Use the TF-IDF algorithm to calculate the weight of keywords in the advertising content; Based on the user's current scenario, define a set of keywords and weights for the scenario; Use cosine similarity to calculate the similarity between scene keywords and advertising content keywords, as well as the similarity between user interests and advertising content keywords. The formula is as follows: In the formula, A is the scene keyword vector, B is the keyword vector of the advertising content, C is the user interest keyword vector, S C (A, B) is the similarity between scene keywords and advertising content keywords, S i (C, B) is the similarity between user interests and keywords in advertising content; Combine the similarity between the scene and the advertisement and the similarity between the user's interest and the advertisement to calculate the comprehensive similarity. The formula is as follows: S m (A,B)=α×S C (A,B)+(1-α)S i (C,B) Where α is a weight parameter used to adjust the importance of scene similarity and interest similarity; Advertisements are sorted based on comprehensive similarity, and the ads with the highest similarity are selected for push. The time, content, and user feedback of the pushed ads are recorded for subsequent model optimization.

6. The artificial intelligence-based advertising push method according to claim 5, wherein: The evaluation and optimization of the pushed ads include the following steps: Set a unique identifier for each ad push, collect user behavior data related to the ad, and associate the collected data with the ad's unique identifier; Define the evaluation indicators of advertising effectiveness. The formula is as follows: Where, clicks is the total number of times users clicked on the ad, impressions is the total number of times the ad was shown to users, and target action completion is the number of users who completed the specified target action. Calculate the effectiveness of each ad push based on defined evaluation metrics, compare the effects of different ad content, push timing, and push strategies, and find the best combination. Update the user's interest model based on the user's feedback on the advertisement.

7. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the artificial intelligence-based advertising push method according to any one of claims 1 to 6 are implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the artificial intelligence-based advertising push method described in any one of claims 1 to 6 are implemented.

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