Advertisement putting method and system based on big data

Abstract

The invention provides an advertisement putting method and system based on big data, and relates to the technical field of advertisement putting, and the method comprises the steps: obtaining the text description information of a plurality of to-be-put advertisements; determining a K value in a K-means clustering algorithm by using a word processing model based on the word description information of the plurality of advertisements to be delivered; clustering the character description information of the plurality of advertisements to be put based on a K-means clustering algorithm and a K value in the K-means clustering algorithm to obtain K clusters; determining a plurality of target advertising advertisements based on the K clusters; and putting the user on the basis of the target put advertisement. The method can accurately screen out the advertisement which the user is interested in.

Description

Technical Field

[0001] The present invention relates to the technical field of advertising delivery, and in particular to a method and system for advertising delivery based on big data. Background Art

[0002] With the rapid development of internet technology, advertising has become a crucial tool for businesses to promote their products and services. Traditional advertising typically relies on crude user categorization and placement based on simple attributes (such as age, gender, and location). This fails to accurately capture user interests and preferences, resulting in poor advertising effectiveness. When ad content doesn't align with users' actual needs and interests, users may feel interrupted or even annoyed, negatively impacting their user experience. Ineffective advertising not only wastes companies' advertising budgets but also consumes network bandwidth and user time, reducing the overall return on investment (ROI). Because ad content doesn't align with users' actual interests, users often feel interrupted or even annoyed, negatively impacting their user experience.

[0003] Therefore, how to accurately filter out advertisements that users are interested in is an urgent problem that needs to be solved. Summary of the Invention

[0004] The main technical problem solved by the present invention is how to accurately screen out advertisements that are of interest to users.

[0005] According to a first aspect, the present invention provides an advertising delivery method based on big data, comprising: obtaining text description information of multiple advertisements to be delivered; determining the K value in the K-means clustering algorithm based on the text description information of the multiple advertisements to be delivered using a text processing model; clustering the text description information of the multiple advertisements to be delivered based on the K-means clustering algorithm and the K value in the K-means clustering algorithm to obtain K clusters; determining multiple target delivery advertisements based on the K clusters; and delivering the target delivery advertisements to users based on the target delivery advertisements.

[0006] In a possible implementation, determining the target advertisement delivery based on the K clusters includes: Inputting the K clusters into a screening model to screen out a preliminary advertisement from each cluster; generating a promotional image for the preliminary advertisement in each cluster using a diffusion model based on the textual description information of the preliminary advertisement in each cluster; displaying the promotional image for the preliminary advertisement in each cluster, and obtaining a selected image selected by a user from the promotional images for the preliminary advertisement in each cluster and the cluster where the selected image is located; constructing an advertisement graph structure based on the cluster where the selected image is located, the advertisement graph structure comprising a plurality of nodes and a plurality of edges between the plurality of nodes, the plurality of nodes comprising a selected image node and a plurality of nodes to be placed advertisements, the selected image node being a central node, and the plurality of nodes to be placed advertisements respectively establishing edges with a selected image node; processing the advertisement graph structure based on a graph attention network to determine a plurality of target placement advertisements; In one possible implementation, the node features of the selected image node include text description information of the advertisement corresponding to the selected image, the node features of each node for advertisement to be placed include text description information of the advertisement to be placed, and the features of each edge established between the node for advertisement to be placed and a selected image node include the Euclidean distance between the selected image node and the node for advertisement to be placed.

[0007] In a possible implementation, the input of the diffusion model is text description information of the preliminary selected advertisements in each cluster, and the output of the diffusion model is a promotional image of the preliminary selected advertisements in each cluster.

[0008] According to a second aspect, the present invention provides an advertising delivery system based on big data, comprising: A first acquisition module is used to acquire text description information of multiple advertisements to be delivered; A text processing module, configured to determine a K value in a K-means clustering algorithm using a text processing model based on text description information of the plurality of advertisements to be delivered; A second acquisition module is configured to cluster the text description information of the plurality of advertisements to be delivered to obtain K clusters based on a K-means clustering algorithm and a K value in the K-means clustering algorithm; a determination module, configured to determine a plurality of target advertisements based on the K clusters; The delivery module is used to deliver advertisements to users based on the target delivery.

[0009] In a possible implementation, the determining module is further configured to: Inputting the K clusters into the screening model to screen out a preliminary advertisement from each cluster; Generate promotional images for the primary ads in each cluster using a diffusion model based on the text descriptions of the primary ads in each cluster; Display the promotional images of the preliminary selected ads in each cluster, and obtain the selected image selected by the user from the promotional images of the preliminary selected ads in each cluster and the cluster to which the selected image belongs; Constructing an advertising graph structure based on the cluster where the selected image is located, the advertising graph structure including multiple nodes and multiple edges between the multiple nodes, the multiple nodes including a selected image node and multiple nodes to be placed advertisements, the selected image node being the central node, and the multiple nodes to be placed advertisements each establishing an edge with one selected image node; The advertisement graph structure is processed based on a graph attention network to determine multiple target advertisements.

[0010] In one possible implementation, the node features of the selected image node include text description information of the advertisement corresponding to the selected image, the node features of each node for advertisement to be placed include text description information of the advertisement to be placed, and the features of each edge established between the node for advertisement to be placed and a selected image node include the Euclidean distance between the selected image node and the node for advertisement to be placed.

[0011] In a possible implementation, the input of the diffusion model is text description information of the preliminary selected advertisements in each cluster, and the output of the diffusion model is a promotional image of the preliminary selected advertisements in each cluster.

[0012] According to a third aspect, an embodiment of the present invention provides an electronic device, comprising: a processor; a memory; and a computer program; wherein the computer program is stored in the memory and configured to be executed by the processor to implement the method as described above, the method comprising: obtaining text description information of multiple advertisements to be delivered; determining the K value in the K-means clustering algorithm based on the text description information of the multiple advertisements to be delivered using a text processing model; clustering the text description information of the multiple advertisements to be delivered based on the K-means clustering algorithm and the K value in the K-means clustering algorithm to obtain K clusters; determining multiple target delivery advertisements based on the K clusters; and delivering the target delivery advertisements to users based on the target delivery advertisements.

[0013] According to the fourth aspect, this embodiment provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the aforementioned big data-based advertising delivery method, the method comprising: obtaining text description information of multiple advertisements to be delivered; determining the K value in the K-means clustering algorithm based on the text description information of the multiple advertisements to be delivered using a text processing model; clustering the text description information of the multiple advertisements to be delivered based on the K-means clustering algorithm and the K value in the K-means clustering algorithm to obtain K clusters; determining multiple target delivery advertisements based on the K clusters; and delivering the target delivery advertisements to users based on the target delivery advertisements.

[0014] The present invention provides an advertising delivery method and system based on big data, which includes obtaining text description information of multiple advertisements to be delivered; determining the K value in the K-means clustering algorithm based on the text description information of the multiple advertisements to be delivered using a text processing model; clustering the text description information of the multiple advertisements to be delivered based on the K-means clustering algorithm and the K value in the K-means clustering algorithm to obtain K clusters; determining multiple target delivery advertisements based on the K clusters; and delivering the target delivery advertisements to users. The method can accurately screen out advertisements that users are interested in. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A schematic diagram of an application scenario of a big data-based advertising delivery method provided by an embodiment of the present invention; Figure 2 A flowchart of a method for delivering advertisements based on big data provided by an embodiment of the present invention; Figure 3 A schematic diagram of a process for determining multiple target advertisements provided by an embodiment of the present invention; Figure 4 A schematic diagram of an advertising delivery system based on big data provided by an embodiment of the present invention; Figure 5 A schematic diagram of an electronic device provided by an embodiment of the present invention; Figure 6 A schematic structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0016] The present invention will be further described in detail below by means of specific embodiments in conjunction with the accompanying drawings. Similar elements in different embodiments are numbered with associated similar elements. In the following embodiments, many detailed descriptions are provided to enable the present invention to be better understood. However, those skilled in the art will readily appreciate that some of the features may be omitted under different circumstances, or may be replaced by other elements, materials, or methods. In some cases, some operations related to the present invention are not shown or described in the specification. This is to avoid the core of the present invention being overwhelmed by excessive descriptions, and for those skilled in the art, it is not necessary to describe these related operations in detail. They can fully understand the related operations based on the description in the specification and the general technical knowledge in the art.

[0017] Figure 1 A schematic diagram of an application scenario of a big data-based advertising delivery method provided by an embodiment of the present invention. Figure 1 The application scenario of the big data-based advertising delivery method may include a server 11, a network 12, a terminal 13 and a storage device 14.

[0018] In some embodiments, the server 11 may be a single server or a server group. The server 11 may access information and / or data stored in the terminal 13 or the storage device 14 via the network 12. In some embodiments, the server 11 may be used to execute Figure 2 The advertising delivery method based on big data is shown in .

[0019] The network 12 can facilitate the exchange of information and / or data. In some embodiments, the network 12 can be any form of wired or wireless network, or any combination thereof.

[0020] Terminal 13 may refer to one or more terminal devices used by a user. In some embodiments, terminal 13 may include one or more combinations of a mobile device, a tablet computer, a laptop computer, and the like.

[0021] The storage device 14 may store data and / or instructions. For example, the storage device 14 may store data instructions for an advertisement delivery method based on big data.

[0022] In an embodiment of the present invention, there is provided Figure 2 An advertisement delivery method based on big data is shown, and the advertisement delivery method based on big data includes steps S1 to S5: Step S1: Obtain text description information of multiple advertisements to be delivered.

[0023] The text description information includes the title, body and other text content of each advertisement, which is used to describe the main information of the advertisement, such as product features, promotional activities, etc. In some embodiments, the text description information of the advertisement can be directly read from the database of the advertisement platform.

[0024] Step S2: Determine the K value in the K-means clustering algorithm using a text processing model based on the text description information of the multiple advertisements to be delivered.

[0025] The text processing model is a deep neural network model. Its input is the text description information of the multiple ads to be delivered, and its output is the K value in the K-means clustering algorithm. Deep neural network models include deep neural networks (DNNs). A DNN can include multiple processing layers, each composed of multiple neurons, and each neuron performs a matrix transformation on the data. The K-means clustering algorithm is an unsupervised learning algorithm that partitions a dataset into K clusters, with data points within each cluster having similar features. The K value is the number of clusters in the K-means clustering algorithm. Using a deep neural network model to extract features from the ad text descriptions and automatically determine the K value in the K-means clustering algorithm can improve clustering accuracy and effectiveness. Traditional methods for determining the K value (such as the elbow method and the silhouette coefficient method) rely on manual settings or simple statistical methods. Using a deep neural network model allows for a more scientific determination of the K value, avoiding the subjectivity and uncertainty of manual settings and improving clustering accuracy and effectiveness.

[0026] Step S3 : clustering the text description information of the multiple advertisements to be placed based on a K-means clustering algorithm and a K value in the K-means clustering algorithm to obtain K clusters.

[0027] Each of the K clusters contains a type of ad to be delivered. For example, if we cluster Ad A, Ad B, Ad C, and Ad D, and assume that the K value is 3, we will get three clusters: Cluster 1 [Ad A, Ad B], Cluster 2 [Ad C], and Cluster 3 [Ad D].

[0028] In some embodiments, the text description information of the advertisement can be converted into a vector representation using a BERT model, and then clustered using a K-means clustering algorithm to obtain K clusters.

[0029] Step S4: determining a plurality of target advertisements based on the K clusters.

[0030] In some embodiments, Figure 3 A schematic diagram of a process for determining multiple target advertisements provided by an embodiment of the present invention, wherein the process for determining multiple target advertisements includes steps S21 to S25: Step S21: Input the K clusters into the screening model to screen out a preliminary advertisement from each cluster.

[0031] The screening model is a deep neural network model. The input to the screening model is the K clusters, and the output of the screening model is a preliminary advertisement selected from each cluster. For example, assuming K is 3 and there are three clusters, the screening model selects one preliminary advertisement from each cluster, resulting in three preliminary advertisements.

[0032] The screening model selects the most representative and attractive ads from each cluster, providing a basis for subsequent generation of promotional images and user selection.

[0033] Step S22 : generating a promotional image of the preliminary selected advertisement in each cluster using a diffusion model based on the text description information of the preliminary selected advertisement in each cluster.

[0034] The input of the diffusion model is the text description information of the primary selected advertisements in each cluster, and the output of the diffusion model is the promotional image of the primary selected advertisements in each cluster.

[0035] The diffusion model is a generative model that is used to generate a corresponding image based on a given text. In some embodiments, the diffusion model includes DALL-E, Stable Diffusion, etc. For example, the text description information of advertisement A, "the latest smartphone, super long battery life, high-definition camera", is input into the diffusion model, and the diffusion model generates a high-definition picture showing the latest smartphone. The diffusion model can generate personalized promotional pictures based on the text description information of the advertisement, ensuring that the picture of each advertisement is highly relevant to the advertisement content, thereby improving the relevance and attractiveness of the advertisement. The diffusion model is able to generate new samples from a given data distribution. The diffusion model generates high-quality images by gradually adding noise and gradually removing noise.

[0036] Step S23 : Display the promotional images of the pre-selected advertisements in each cluster, and obtain the selected image selected by the user from the promotional images of the pre-selected advertisements in each cluster and the cluster where the selected image is located.

[0037] In some embodiments, the promotional images of the initially selected advertisements in each cluster may be displayed on the user's screen, and the image selected by the user from the promotional images may be obtained.

[0038] The cluster to which the selected image belongs is the cluster to which the user-selected image belongs.

[0039] Step S23 understands the user's interests and preferences through the user's actual choices, providing a basis for subsequent advertising delivery.

[0040] Compared to pure text descriptions, images are more intuitive and easy to understand. Users can quickly understand the content and characteristics of the ad, improving browsing efficiency. Users can click on the image to select the ad they are interested in, which enhances user interactivity and participation, and improves the user experience.

[0041] Step S24: construct an advertising graph structure based on the cluster where the selected image is located. The advertising graph structure includes multiple nodes and multiple edges between the multiple nodes. The multiple nodes include a selected image node and multiple nodes for advertisements to be placed. The selected image node is the central node, and the multiple nodes for advertisements to be placed respectively establish edges with one selected image node.

[0042] The node features of a selected image node include text description information of the advertisement corresponding to the selected image, the node features of each node for advertisement to be placed include text description information of the advertisement to be placed, and the features of each edge established between a node for advertisement to be placed and a selected image node include the Euclidean distance between the selected image node and the node for advertisement to be placed.

[0043] The ad graph is a graph structure used to represent relationships between ads, where nodes represent ads and edges represent relationships between ads. For example, let's construct an ad graph structure by selecting the image node as Ad 1, the ad nodes to be delivered as Ad 2 and Ad 3, and the Euclidean distance as the edge characteristic between the image node and the ad nodes to be delivered.

[0044] As an example, assuming that the user selects the promotional image of Advertisement A from the promotional images of Advertisement A, Advertisement C, and Advertisement D, the cluster where the image is located is selected as Cluster 1, and the other advertisements in Cluster 1 are respectively used as the nodes for advertisements to be delivered. For example, if Cluster 1 also includes Advertisement 2 and Advertisement 3, then Advertisement 2 and Advertisement 3 are respectively used as the nodes for advertisements to be delivered.

[0045] The selected image node represents the actual ad selected by the user and provides direct feedback on their interests. Using the user's selected ad as the central node ensures that recommended ads are highly relevant to the user's interests. By centering the selected image node, ads relevant to the user's interests can be more accurately recommended, improving click-through and conversion rates. Since both the selected image node and the ad node to be served are points in the cluster, the Euclidean distance between the selected image node and the ad node to be served can be calculated to obtain the edge characteristics between the selected image node and the ad node to be served.

[0046] Step S25: Process the advertisement graph structure based on the graph attention network to determine multiple target advertisements.

[0047] The Graph Attention Network is a deep learning model used to process graph-structured data. It uses an attention mechanism to capture relationships between nodes. The input of the Graph Attention Network is the ad graph structure, and the output is multiple targeted ads.

[0048] The images a user selects are a direct reflection of their interests. By building a graph structure based only on the clusters containing the selected images, we ensure that recommended ads are highly relevant to the user's interests. Given the large number of ads, building a graph structure that considers all ads would be computationally expensive. By considering only the clusters containing the selected images, we can significantly reduce the number of ads that the graph attention network must process, lowering computational complexity.

[0049] Graph Attention Networks (GANs) generate personalized recommendations based on the relationship between user-selected images (selected image nodes) and other ads (pending ad nodes). Using an attention mechanism, GANs can more accurately identify which ads are most likely to pique user interest. GANs can efficiently process large-scale graph-structured data, making them suitable for the massive amounts of ad data used in ad recommendation scenarios. By limiting consideration to the cluster containing the selected image, the number of nodes required for processing is further reduced, improving computational efficiency.

[0050] Step S5: delivering advertisements to users based on the target delivery advertisements.

[0051] When the target delivery advertisement is determined, the target delivery advertisement is delivered to the user based on the target delivery advertisement. In some embodiments, the target delivery advertisement can be recommended to the user through a pop-up window.

[0052] Based on the same inventive concept, Figure 4 A schematic diagram of a big data-based advertising delivery system provided in an embodiment of the present invention, wherein the big data-based advertising delivery system includes: A first acquisition module 41 is used to acquire text description information of multiple advertisements to be delivered; A text processing module 42 is configured to determine a K value in a K-means clustering algorithm based on the text description information of the plurality of advertisements to be delivered using a text processing model; A second acquisition module 43 is configured to cluster the text description information of the plurality of advertisements to be delivered to obtain K clusters based on a K-means clustering algorithm and a K value in the K-means clustering algorithm; A determination module 44 is configured to determine a plurality of target advertisements based on the K clusters; The delivery module 45 is configured to deliver advertisements to users based on the target delivery.

[0053] Based on the same inventive concept, an embodiment of the present invention provides an electronic device, such as Figure 5 Shown, including: The invention comprises: a processor 51; a memory 52; and a computer program; wherein the computer program is stored in the memory 52 and is configured to be executed by the processor 51 to implement the big data-based advertising delivery method provided above, the method comprising: obtaining text description information of multiple advertisements to be delivered; determining the K value in the K-means clustering algorithm based on the text description information of the multiple advertisements to be delivered using a text processing model; clustering the text description information of the multiple advertisements to be delivered based on the K-means clustering algorithm and the K value in the K-means clustering algorithm to obtain K clusters; determining multiple target delivery advertisements based on the K clusters; and delivering the target delivery advertisements to users based on the target delivery advertisements.

[0054] Based on the same inventive concept, this embodiment provides a computer-readable storage medium having a computer program stored thereon, which, when executed by the processor 51, implements the aforementioned big data-based advertising delivery method, the method comprising: obtaining text description information of multiple advertisements to be delivered; determining the K value in the K-means clustering algorithm based on the text description information of the multiple advertisements to be delivered using a text processing model; clustering the text description information of the multiple advertisements to be delivered based on the K-means clustering algorithm and the K value in the K-means clustering algorithm to obtain K clusters; determining multiple target delivery advertisements based on the K clusters; and delivering the target delivery advertisements to users based on the target delivery advertisements.

[0055] The big data-based advertising delivery method provided in the embodiments of the present application can be applied to terminal devices (such as mobile phones), tablet computers, laptops, ultra-mobile personal computers (UMPCs), handheld computers, netbooks, personal digital assistants (PDAs), wearable devices (such as smart watches, smart glasses, or smart helmets), augmented reality (AR) and virtual reality (VR) devices, smart home devices, car computers, and other electronic devices. The embodiments of the present application do not impose any restrictions on this.

[0056] Taking the mobile phone 100 as an example of the electronic device, Figure 6 FIG. 1 shows a schematic structural diagram of the mobile phone 100 .

[0057] like Figure 6 As shown, the mobile phone 100 may include a processing module 110, an external memory interface 120, an internal memory 121, a universal serial bus (USB) interface 130, a charging management module 140, a power management module 141, a battery 142, an antenna 1, an antenna 2, a mobile communication module 150, a wireless communication module 160, an audio module 170, a speaker 170A, a receiver 170B, a microphone 170C, an earphone interface 170D, a sensor module 180, a button 190, a motor 191, an indicator 192, a camera 193, a display screen 194, and a subscriber identification module (SIM) card interface 195, etc.

[0058] The processing module 110 may include one or more processing units. For example, the processing module 110 may include an application processor (AP), a modem processor, a graphics processing unit (GPU), an image signal processor (ISP), a controller, a memory, a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural-network processing unit (NPU). The different processing units may be independent devices or integrated into one or more processors.

[0059] The controller can be the nerve center and command center of the mobile phone 100, and is the decision maker that directs the various components of the mobile phone 100 to coordinate operations according to instructions. The controller can generate operation control signals based on instruction opcodes and timing signals to complete the control of instruction fetching and execution.

[0060] The application processor can be installed with the mobile phone 100's operating system, which is used to manage the phone's hardware and software resources. For example, it manages and configures memory, determines the priority of system resource supply and demand, manages the file system, and manages drivers. The operating system also provides an interface for user interaction with the system. Various software can be installed within the operating system, such as drivers and application programs (apps). For example, the operating system of the mobile phone 100 can be Android, Linux, etc.

[0061] Processing module 110 may also include a memory for storing instructions and data. In some embodiments, the memory in processing module 110 is a cache memory. This memory can store instructions or data that have just been used or are being recycled by processing module 110. If processing module 110 needs to use the same instruction or data again, it can directly access the memory. This avoids duplicate accesses, reduces the waiting time of processing module 110, and thus improves system efficiency.

[0062] In some embodiments, the processing module 110 may include one or more interfaces. The interfaces may include an inter-integrated circuit (I2C) interface, an inter-integrated circuits sound (I2S) interface, a pulse code modulation (PCM) interface, a universal asynchronous receiver / transmitter (UART) interface, a mobile industry processor interface (MIPI), a general-purpose input / output (GPIO) interface, a subscriber identity module (SIM) interface, and / or a universal serial bus (USB) interface.

[0063] The processing module 110 can be used to: obtain text description information of multiple advertisements to be delivered; determine the K value in the K-means clustering algorithm based on the text description information of the multiple advertisements to be delivered using a text processing model; cluster the text description information of the multiple advertisements to be delivered based on the K-means clustering algorithm and the K value in the K-means clustering algorithm to obtain K clusters; determine multiple target delivery advertisements based on the K clusters; and deliver the target delivery advertisements to users based on the target delivery advertisements.

[0064] The charging management module 140 is configured to receive charging input from a charger. The charger can be either a wireless charger or a wired charger. In some wired charging embodiments, the charging management module 140 can receive charging input from the wired charger via the USB interface 130. In some wireless charging embodiments, the charging management module 140 can receive wireless charging input via the wireless charging coil of the mobile phone 100. While charging the battery 142, the charging management module 140 can also provide power to the electronic device through the power management module 141.

[0065] The power management module 141 is used to connect the battery 142, the charging management module 140, and the processing module 110. The power management module 141 receives input from the battery 142 and / or the charging management module 140, and provides power to the processing module 110, the internal memory 121, the external memory, the display 194, the camera 193, and the wireless communication module 160. The power management module 141 can also be used to monitor parameters such as battery capacity, battery cycle count, and battery health status (leakage, impedance). In some other embodiments, the power management module 141 can also be set in the processing module 110. In other embodiments, the power management module 141 and the charging management module 140 can also be set in the same device.

[0066] The wireless communication function of the mobile phone 100 can be implemented through the antenna 1, the antenna 2, the mobile communication module 150, the wireless communication module 160, the modem processor and the baseband processor.

[0067] Antenna 1 and Antenna 2 are used to transmit and receive electromagnetic wave signals. Each antenna in mobile phone 100 can be used to cover a single or multiple communication frequency bands. Different antennas can also be reused to improve antenna utilization. For example, antenna 1 can be reused as a diversity antenna for a wireless local area network. In other embodiments, the antennas can be used in conjunction with a tuning switch.

[0068] The mobile communication module 150 can provide solutions for wireless communications including 2G / 3G / 4G / 5G applied to the mobile phone 100. The mobile communication module 150 may include at least one filter, a switch, a power amplifier, a low noise amplifier (LNA), etc. The mobile communication module 150 can receive electromagnetic waves from the antenna 1, and filter, amplify, and process the received electromagnetic waves, and transmit them to the modulation and demodulation processor for demodulation. The mobile communication module 150 can also amplify the signal modulated by the modulation and demodulation processor, and convert it into electromagnetic waves for radiation through the antenna 1. In some embodiments, at least some of the functional modules of the mobile communication module 150 can be set in the processing module 110. In some embodiments, at least some of the functional modules of the mobile communication module 150 can be set in the same device as at least some of the modules of the processing module 110.

[0069] The modem processor may include a modulator and a demodulator. The modulator is used to modulate the low-frequency baseband signal to be transmitted into a medium-high frequency signal. The demodulator is used to demodulate the received electromagnetic wave signal into a low-frequency baseband signal. The demodulator then transmits the demodulated low-frequency baseband signal to the baseband processor for processing. After being processed by the baseband processor, the low-frequency baseband signal is passed to the application processor. The application processor outputs a sound signal through an audio device (not limited to the speaker 170A, the receiver 170B, etc.) or displays an image or video through the display screen 194. In some embodiments, the modem processor may be an independent device. In other embodiments, the modem processor may be independent of the processing module 110 and be set in the same device as the mobile communication module 150 or other functional modules.

[0070] The wireless communication module 160 can provide wireless communication solutions including wireless local area networks (WLAN) (such as wireless fidelity (Wi-Fi) networks), Bluetooth (BT), global navigation satellite system (GNSS), frequency modulation (FM), near field communication (NFC), infrared (IR), etc., which are applied to the mobile phone 100. The wireless communication module 160 can be one or more devices that integrate at least one communication processing module. The wireless communication module 160 receives electromagnetic waves via the antenna 2, frequency modulates and filters the electromagnetic wave signals, and sends the processed signals to the processing module 110. The wireless communication module 160 can also receive the signal to be sent from the processing module 110, frequency modulate it, amplify it, and convert it into electromagnetic waves for radiation through the antenna 2.

[0071] In some embodiments, the antenna 1 of the mobile phone 100 is coupled to the mobile communication module 150, and the antenna 2 is coupled to the wireless communication module 160, so that the mobile phone 100 can communicate with the network and other devices through wireless communication technologies. The wireless communication technologies may include global system for mobile communications (GSM), general packet radio service (GPRS), code division multiple access (CDMA), wideband code division multiple access (WCDMA), time-division code division multiple access (TD-SCDMA), long term evolution (LTE), BT, GNSS, WLAN, NFC, FM, and / or IR technology. The GNSS may include a global positioning system (GPS), a global navigation satellite system (GLONASS), a Beidou navigation satellite system (BDS), a quasi-zenith satellite system (QZSS) and / or a satellite based augmentation system (SBAS).

[0072] Mobile phone 100 implements display functionality through a GPU, display screen 194, and an application processor. The GPU is a microprocessor for image processing that connects display screen 194 and the application processor. The GPU is used to perform mathematical and geometric calculations for graphics rendering. Processing module 110 may include one or more GPUs that execute program instructions to generate or modify display information.

[0073] Display screen 194 is used to display images, videos, etc. Display screen 194 includes a display panel. The display panel can be a liquid crystal display (LCD), an organic light-emitting diode (OLED), an active-matrix organic light-emitting diode (AMOLED), a flexible light-emitting diode (FLED), a MiniLED, a MicroLED, a Micro-oLED, or a quantum dot light-emitting diode (QLED). In some embodiments, mobile phone 100 may include one or N display screens 194, where N is a positive integer greater than 1.

[0074] The mobile phone 100 can implement a shooting function through an ISP, a camera 193, a video codec, a GPU, a display screen 194, and an application processor. In some embodiments, the mobile phone 100 can implement a video communication function through an ISP, a camera 193, a video codec, a GPU, and an application processor.

[0075] The ISP processes data fed back by camera 193. For example, when taking a photo, the shutter is opened, and light is transmitted through the lens to the camera's photosensitive element. The light signal is converted into an electrical signal, which is then passed to the ISP for processing and transformed into a visible image. The ISP can also perform algorithmic optimization for image noise, brightness, and skin tone. It can also optimize parameters such as exposure and color temperature of the captured scene. In some embodiments, the ISP can be located within camera 193.

[0076] The camera 193 is used to capture still images or videos. The object generates an optical image through the lens and projects it onto the photosensitive element. The photosensitive element can be a charge coupled device (CCD) or a complementary metal-oxide-semiconductor (CMOS) phototransistor. The photosensitive element converts the light signal into an electrical signal, and then passes the electrical signal to the ISP for conversion into a digital image signal. The ISP outputs the digital image signal to the DSP for processing. The DSP converts the digital image signal into an image signal in a standard RGB, YUV or other format. In some embodiments, the mobile phone 100 may include 1 or N cameras 193, where N is a positive integer greater than 1.

[0077] The digital signal processor is used to process digital signals. In addition to processing digital image signals, it can also process other digital signals. For example, when the mobile phone 100 is selecting a frequency point, the digital signal processor is used to perform Fourier transform on the frequency point energy.

[0078] Video codecs are used to compress or decompress digital video. Mobile phone 100 may support one or more video codecs. This allows mobile phone 100 to play or record videos in various encoding formats, such as Moving Picture Experts Group (MPEG) 1, MPEG2, MPEG3, and MPEG4.

[0079] The NPU is a neural network (NN) computing processor. Drawing on the structure of biological neural networks, such as the transmission patterns between neurons in the human brain, it rapidly processes input information and can continuously self-learn. The NPU enables intelligent cognitive applications in the phone 100, such as image recognition, face recognition, voice recognition, and text comprehension.

[0080] The external memory interface 120 can be used to connect an external memory card, such as a Micro SD card, to expand the storage capacity of the mobile phone 100. The external memory card communicates with the processing module 110 via the external memory interface 120 to implement data storage functions. For example, files such as music and videos can be stored on the external memory card.

[0081] The internal memory 121 can be used to store computer executable program codes, which include instructions. The processing module 110 executes various functional applications and data processing of the mobile phone 100 by running the instructions stored in the internal memory 121. The internal memory 121 may include a program storage area and a data storage area. Among them, the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc. The data storage area can store data created during the use of the mobile phone 100 (such as audio data, a phone book, etc.), etc. In addition, the internal memory 121 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, a universal flash storage (UFS), etc.

[0082] The mobile phone 100 can implement audio functions such as music playback and recording through the audio module 170, the speaker 170A, the receiver 170B, the microphone 170C, the headphone jack 170D, and the application processor.

[0083] The audio module 170 is used to convert digital audio information into analog audio signal output, and is also used to convert analog audio input into digital audio signals. The audio module 170 can also be used to encode and decode audio signals. In some embodiments, the audio module 170 can be provided in the processing module 110, or some functional modules of the audio module 170 can be provided in the processing module 110.

[0084] The speaker 170A, also called a "horn," is used to convert audio electrical signals into sound signals. The mobile phone 100 can listen to music or make hands-free calls through the speaker 170A.

[0085] The receiver 170B, also called the "earpiece", is used to convert audio electrical signals into sound signals. When the mobile phone 100 receives a call or a voice message, the voice can be heard by placing the receiver 170B close to the ear.

[0086] Microphone 170C, also known as "microphone" or "microphone", is used to convert sound signals into electrical signals. When making a call or sending a voice message, the user can speak by putting their mouth close to the microphone 170C to input the sound signal into the microphone 170C. The mobile phone 100 can be provided with at least one microphone 170C. In other embodiments, the mobile phone 100 can be provided with two microphones 170C, which can not only collect sound signals but also realize noise reduction function. In other embodiments, the mobile phone 100 can also be provided with three, four or more microphones 170C to realize sound signal collection, noise reduction, and identification of sound sources, and realize directional recording function, etc.

[0087] The headphone jack 170D is used to connect a wired headphone and can be the USB interface 130 or a 3.5mm open mobile terminal platform (OMTP) standard interface or a cellular telecommunications industry association of the USA (CTIA) standard interface.

[0088] Keys 190 include a power button, a volume button, etc. Keys 190 may be mechanical keys or touch keys. Mobile phone 100 may receive key inputs and generate key signal inputs related to user settings and function control of mobile phone 100.

[0089] Motor 191 can generate vibration prompts. Motor 191 can be used for incoming call vibration prompts, and can also be used for touch vibration feedback. For example, touch operations acting on different applications (such as taking pictures, audio playback, etc.) can correspond to different vibration feedback effects. For touch operations acting on different areas of the display screen 194, motor 191 can also correspond to different vibration feedback effects. Different application scenarios (for example: time reminders, receiving messages, alarm clocks, games, etc.) can also correspond to different vibration feedback effects. The touch vibration feedback effect can also support customization.

[0090] The indicator 192 may be an indicator light, which may be used to indicate the charging status, power level changes, messages, missed calls, notifications, etc.

[0091] The SIM card interface 195 is used to connect a SIM card. The SIM card can be connected to and disconnected from the mobile phone 100 by inserting it into or removing it from the SIM card interface 195. The mobile phone 100 can support 1 or N SIM card interfaces, where N is a positive integer greater than 1. The SIM card interface 195 can support Nano SIM cards, Micro SIM cards, SIM cards, and the like. Multiple cards can be inserted into the same SIM card interface 195 at the same time. The types of the multiple cards can be the same or different. The SIM card interface 195 can also be compatible with different types of SIM cards. The SIM card interface 195 can also be compatible with external memory cards. The mobile phone 100 interacts with the network through the SIM card to implement functions such as calls and data communications. In some embodiments, the mobile phone 100 uses an eSIM, i.e., an embedded SIM card. The eSIM card can be embedded in the mobile phone 100 and cannot be separated from the mobile phone 100.

[0092] While the basic concepts have been described above, it will be apparent to those skilled in the art that the detailed disclosure is merely illustrative and does not limit this specification. Although not explicitly stated herein, various modifications, improvements, and revisions to this specification may be made by those skilled in the art. Such modifications, improvements, and revisions are suggested in this specification and remain within the spirit and scope of the exemplary embodiments of this specification.

[0093] This specification also uses specific terms to describe the embodiments of this specification. For example, "one embodiment," "an embodiment," and / or "some embodiments" refer to a feature, structure, or characteristic associated with at least one embodiment of this specification. Therefore, it should be emphasized and noted that references to "one embodiment," "an embodiment," or "an alternative embodiment" two or more times in different locations in this specification do not necessarily refer to the same embodiment. Furthermore, certain features, structures, or characteristics of one or more embodiments of this specification may be appropriately combined.

[0094] In addition, unless expressly stated in the claims, the order of the processing elements and sequences, the use of alphanumeric characters, or the use of other names described in this specification are not intended to limit the order of the processes and methods of this specification. Although the above disclosure discusses some of the invention embodiments currently considered useful through various examples, it should be understood that such details are for illustrative purposes only, and the appended claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that are consistent with the spirit and scope of the embodiments of this specification. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only by software solutions, such as installing the described system on an existing server or mobile device.

[0095] Similarly, it should be noted that, in order to simplify the presentation of this specification and thus facilitate understanding of one or more embodiments of the invention, the foregoing descriptions of the embodiments of this specification sometimes combine multiple features into a single embodiment, figure, or description thereof. However, this disclosure method does not imply that the subject matter of this specification requires more features than those recited in the claims. In fact, an embodiment may have fewer features than all of the features of a single disclosed embodiment.

[0096] Finally, it should be understood that the embodiments described in this specification are intended only to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, by way of example and not limitation, alternative configurations of the embodiments of this specification may be considered consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly described and illustrated in this specification.

Claims

1. A method for advertising based on big data, characterized in that: include: Obtain text description information of multiple advertisements to be delivered; Determining a K value in a K-means clustering algorithm using a text processing model based on text description information of the plurality of advertisements to be delivered; Clustering the text description information of the plurality of advertisements to be delivered to obtain K clusters based on a K-means clustering algorithm and a K value in the K-means clustering algorithm; Determine a plurality of target advertisements based on the K clusters; Advertisements are delivered to users based on the target.

2. The method for advertising based on big data according to claim 1, wherein: Determining target advertisement delivery based on the K clusters includes: Inputting the K clusters into the screening model to screen out a preliminary advertisement from each cluster; Generate promotional images for the primary ads in each cluster using a diffusion model based on the text descriptions of the primary ads in each cluster; Display the promotional images of the preliminary selected ads in each cluster, and obtain the selected image selected by the user from the promotional images of the preliminary selected ads in each cluster and the cluster to which the selected image belongs; Constructing an advertising graph structure based on the cluster where the selected image is located, the advertising graph structure including multiple nodes and multiple edges between the multiple nodes, the multiple nodes including a selected image node and multiple nodes to be placed advertisements, the selected image node being the central node, and the multiple nodes to be placed advertisements each establishing an edge with one selected image node; The advertisement graph structure is processed based on a graph attention network to determine multiple target advertisements.

3. The method for advertising based on big data according to claim 2, characterized in that: The node features of a selected image node include text description information of the advertisement corresponding to the selected image, the node features of each node for advertisement to be placed include text description information of the advertisement to be placed, and the features of each edge established between a node for advertisement to be placed and a selected image node include the Euclidean distance between the selected image node and the node for advertisement to be placed.

4. The method for delivering advertisements based on big data according to claim 2, wherein: The input of the diffusion model is the text description information of the primary selected advertisements in each cluster, and the output of the diffusion model is the promotional image of the primary selected advertisements in each cluster.

5. An advertising delivery system based on big data, characterized in that: include: A first acquisition module is used to acquire text description information of multiple advertisements to be delivered; A text processing module, configured to determine a K value in a K-means clustering algorithm using a text processing model based on text description information of the plurality of advertisements to be delivered; A second acquisition module is configured to cluster the text description information of the plurality of advertisements to be delivered to obtain K clusters based on a K-means clustering algorithm and a K value in the K-means clustering algorithm; a determination module, configured to determine a plurality of target advertisements based on the K clusters; The delivery module is used to deliver advertisements to users based on the target delivery.

6. The big data-based advertising delivery system according to claim 5, characterized in that: The determining module is further configured to: Inputting the K clusters into the screening model to screen out a preliminary advertisement from each cluster; Generate promotional images for the primary ads in each cluster using a diffusion model based on the text descriptions of the primary ads in each cluster; Display the promotional images of the preliminary selected ads in each cluster, and obtain the selected image selected by the user from the promotional images of the preliminary selected ads in each cluster and the cluster to which the selected image belongs; Constructing an advertising graph structure based on the cluster where the selected image is located, the advertising graph structure including multiple nodes and multiple edges between the multiple nodes, the multiple nodes including a selected image node and multiple nodes to be placed advertisements, the selected image node being the central node, and the multiple nodes to be placed advertisements each establishing an edge with one selected image node; The advertisement graph structure is processed based on a graph attention network to determine multiple target advertisements.

7. The big data-based advertising delivery system according to claim 6, characterized in that: The node features of a selected image node include text description information of the advertisement corresponding to the selected image, the node features of each node for advertisement to be placed include text description information of the advertisement to be placed, and the features of each edge established between a node for advertisement to be placed and a selected image node include the Euclidean distance between the selected image node and the node for advertisement to be placed.

8. The big data-based advertising delivery system according to claim 6, characterized in that: The input of the diffusion model is the text description information of the primary selected advertisements in each cluster, and the output of the diffusion model is the promotional image of the primary selected advertisements in each cluster.

9. An electronic device, characterized in that: include: processor; Memory; And a computer program; wherein, the computer program is stored in the memory and is configured to be executed by the processor to implement the big data-based advertising delivery method as described in any one of claims 1 to 4.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the big data-based advertising delivery method as described in any one of claims 1 to 4 is implemented.

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