Data processing method, apparatus, storage medium and electronic device

Abstract

Disclosed in the present application are a data processing method, an apparatus, a storage medium and an electronic device. The present application relates to the technical field of data processing. The method comprises: by means of a serverless architecture, receiving a data processing request initiated by a target; triggering a target function according to the data processing request, and, by means of the target function, loading into the serverless architecture a first artificial intelligence model and a first dependency library required by a first artificial intelligence application; and, on the basis of the first artificial intelligence model and the first dependency library and by means of the first artificial intelligence application, processing data information to be processed, so as to obtain a data processing result. The present application solves the technical problem in the prior art that when data processing is executed on the basis of AI applications, the large data volume of whole AI application code packages results in excessively long loading time and further results in low data processing efficiency.

Description

[0001] This application claims priority to Chinese patent application number 202410346127.2, filed with the China Patent Office on March 25, 2024, entitled "Data Processing Method and Apparatus, Storage Medium and Electronic Device," the entire contents of which are incorporated herein by reference. Technical Field This application relates to the field of data information processing technology, and more specifically, to a data processing method and apparatus, a storage medium, and an electronic device. Background: Serverless computing is a cloud computing execution model in which the cloud provider manages machine operations, while the client only provides the code to be run. This architecture is favored by businesses and developers for its cost-efficiency and simplified operations and maintenance. The serverless model allows users to focus on code development without worrying about underlying hardware and operations and maintenance. With the rapid development of artificial intelligence (AI) technology, AI applications have become an integral part of various industries. These applications cover a wide range of fields, from natural language processing to image recognition. The development and deployment of AI applications has become a hot topic in current technological innovation. While the Serverless architecture provides convenience for developers, when deploying large AI applications with complex dependencies, the increased code package size caused by large dependencies and models increases the cold start time of the AI ​​application, significantly impacting data processing efficiency. Regarding the aforementioned related art issues related to data processing based on AI applications, the large data volume of the entire AI application code package results in long loading times, which in turn leads to relatively low data processing efficiency. Currently, no effective solution has been proposed. SUMMARY OF THE INVENTION The present application provides a data processing method and apparatus, a storage medium, and an electronic device to at least address the technical issues related to data processing based on AI applications, which occur due to the large data volume of the entire AI application code package, resulting in long loading times and, consequently, relatively low data processing efficiency.According to one aspect of an embodiment of the present application, a data processing method is provided, comprising: receiving a data processing request initiated by a target object via a serverless architecture, wherein the data processing request is to be processed by a first artificial intelligence application and includes at least data information to be processed; a first image of the operating environment of the first artificial intelligence application is deployed in the serverless architecture; triggering a target function based on the data processing request, and loading a first artificial intelligence model and a first dependency library required by the first artificial intelligence application into the serverless architecture via the target function, wherein the target function is deployed in the serverless architecture; processing the data information to be processed by the first artificial intelligence application based on the first artificial intelligence model and the first dependency library to obtain a data processing result. Furthermore, processing the data information to be processed by the first artificial intelligence application based on the first artificial intelligence model and the first dependency library to obtain the data processing result comprises: obtaining first business logic code corresponding to the first artificial intelligence application; and processing the data information to be processed by the first artificial intelligence model, the first dependency library, and the first business logic code to obtain the data processing result. Furthermore, loading the first artificial intelligence model and the first dependent library required by the first artificial intelligence application into the serverless architecture through the objective function includes: obtaining a first configuration file corresponding to the first artificial intelligence application, wherein the first configuration file includes at least a first storage address of the first artificial intelligence model and an identifier of a first common component storing the first dependent library, wherein the first common component is a basic component in the serverless architecture; determining the first storage address of the first artificial intelligence model and the identifier of the first common component based on the first configuration file; loading the first artificial intelligence model from the first storage address into the serverless architecture through the objective function; and loading the first dependent library from the first common component into the serverless architecture through the objective function based on the identifier of the first common component. Furthermore, before receiving a data processing request initiated by a target object through the serverless architecture, the method further includes: obtaining demand information of the target object for a second artificial intelligence application; determining, based on the demand information, a second image, a second artificial intelligence model, and a second dependent library of an operating environment required by the second artificial intelligence application; constructing business logic code of the second artificial intelligence application based on the demand information; and deploying the second artificial intelligence application in the serverless architecture based on the second image, the second artificial intelligence model, the second dependent library, and the business logic code.Furthermore, deploying the second artificial intelligence application in the serverless architecture based on the second image, the second artificial intelligence model, the second dependent library, and the business logic code includes: storing the second artificial intelligence model in a target device to obtain a second storage address; creating a second common component in the serverless architecture based on the second dependent library, and storing the second dependent library in the second common component; obtaining a second configuration file of the second artificial intelligence application based on the second storage address and an identifier of the second common component; and deploying the second image, the second configuration file, and the business logic code in the serverless architecture to implement deployment of the second artificial intelligence application in the serverless architecture. Furthermore, loading the first artificial intelligence model from the first storage address into the serverless architecture via the target function includes: initiating, via the target function, an acquisition request to a first device for the first artificial intelligence model, wherein the first device is a device storing the first artificial intelligence model, the acquisition request including at least identification information of the target function; determining, via the first device, whether the identification information of the target function is within a preset list, the preset list including identification information of functions permitted to load the first artificial intelligence model; and if the identification information of the target function is within the preset list, loading the first artificial intelligence model from the first storage address into the serverless architecture via the target function. Furthermore, after processing the data information to be processed via the first artificial intelligence model and the first dependent library to obtain a data processing result, the method further includes: obtaining target data information generated by the first artificial intelligence model during processing of the data information to be processed; evaluating the first artificial intelligence model based on the target data information to obtain an evaluation result; and optimizing the first artificial intelligence model based on the evaluation result.According to another aspect of an embodiment of the present application, a data processing device is provided, comprising: a receiving unit configured to receive a data processing request initiated by a target object via a serverless architecture, wherein the data processing request is to be processed by a first artificial intelligence application and includes at least data information to be processed, and a first image of the operating environment of the first artificial intelligence application is deployed in the serverless architecture; a triggering unit configured to trigger a target function based on the data processing request and, via the target function, load a first artificial intelligence model and a first dependency library required by the first artificial intelligence application into the serverless architecture, wherein the target function is deployed in the serverless architecture; a processing unit configured to process the data information to be processed by the first artificial intelligence application based on the first artificial intelligence model and the first dependency library to obtain a data processing result. Furthermore, the processing unit comprises: a first acquisition module configured to acquire first business logic code corresponding to the first artificial intelligence application; and a processing module configured to process the data information to be processed using the first artificial intelligence model, the first dependency library, and the first business logic code to obtain the data processing result. Furthermore, the triggering unit includes: a second acquisition module, configured to acquire a first configuration file corresponding to the first artificial intelligence application, wherein the first configuration file includes at least a first storage address of the first artificial intelligence model and an identifier of a first common component storing a first dependency library, wherein the first common component is a basic component in the serverless architecture; a first determination module, configured to determine the first storage address of the first artificial intelligence model and the identifier of the first common component based on the first configuration file; a first loading module, configured to load the first artificial intelligence model into the serverless architecture from the first storage address through the target function; and a second loading module, configured to load the first dependency library into the serverless architecture from the first common component through the target function based on the identifier of the first common component.Furthermore, the device also includes: a first acquisition unit, configured to obtain the target object's demand information for the second artificial intelligence application before receiving a data processing request initiated by the target object through the serverless architecture; a determination unit, configured to determine the second image, second artificial intelligence model, and second dependency library of the operating environment required by the second artificial intelligence application based on the demand information; a construction unit, configured to construct the business logic code of the second artificial intelligence application based on the demand information; and a deployment unit, configured to deploy the second artificial intelligence application in the serverless architecture based on the second image, second artificial intelligence model, second dependency library, and the business logic code. Furthermore, the deployment unit includes: a storage module, configured to store the second artificial intelligence model in a target device to obtain a second storage address; a construction module, configured to create a second common component in the serverless architecture based on the second dependent library, and store the second dependent library in the second common component; a second determination module, configured to obtain a second configuration file of the second artificial intelligence application based on the second storage address and an identifier of the second common component; and a deployment module, configured to deploy the second image, the second configuration file, and the business logic code in the serverless architecture to implement deployment of the second artificial intelligence application in the serverless architecture. Furthermore, the first loading module includes: an acquisition submodule configured to initiate an acquisition request to a first device using the target function to acquire the first artificial intelligence model, wherein the first device is a device storing the first artificial intelligence model, the acquisition request including at least identification information of the target function, and the first device determining whether the identification information of the target function is within a preset list, wherein the preset list includes identification information of functions permitted to load the first artificial intelligence model; a recording submodule configured to load the first artificial intelligence model from the first storage address into the serverless architecture using the target function if the identification information of the target function is within the preset list. Furthermore, the apparatus further includes: a second acquisition unit configured to, after obtaining a data processing result by processing the data information to be processed using the first artificial intelligence model and the first dependency library, acquire target data information generated by the first artificial intelligence model during the processing of the data information to be processed; an evaluation unit configured to evaluate the first artificial intelligence model based on the target data information to obtain an evaluation result; and an optimization unit configured to optimize the first artificial intelligence model based on the evaluation result.According to another aspect of an embodiment of the present application, a computer-readable storage medium is provided. The storage medium stores a program. When the program is executed, the device containing the storage medium is controlled to execute any of the aforementioned data processing methods. According to another aspect of an embodiment of the present application, an electronic device is provided. The electronic device includes a memory storing an executable program and a processor configured to execute the program. When the program is executed, the data processing method described above is executed. According to another aspect of an embodiment of the present application, a computer program product is provided. The computer program product includes a stored computer program. When the computer program is executed by the processor, the data processing method described above is implemented. In an embodiment of the present application, the following steps are adopted: receiving a data processing request initiated by a target object through a serverless architecture, wherein the data processing request needs to be processed by a first artificial intelligence application, and the data processing request includes at least data information to be processed, and a first image of the operating environment of the first artificial intelligence application is deployed in the serverless architecture; triggering a target function based on the data processing request, and loading a first artificial intelligence model and a first dependency library required by the first artificial intelligence application into the serverless architecture through the target function, wherein the target function is deployed in the serverless architecture; processing the data information to be processed based on the first artificial intelligence model and the first dependency library by the first artificial intelligence application to obtain a data processing result, thereby solving the technical problem in the related art that when performing data processing based on an AI application, due to the large amount of data in the entire AI application code package, the loading time is too long, thereby resulting in relatively low data processing efficiency. In the present application, a first image of the operating environment of a first artificial intelligence application is directly deployed in a serverless architecture. Then, after the serverless architecture receives a data processing request initiated by a target object, a target function is triggered due to the data processing request. After the target function is triggered, the target function simultaneously loads the first artificial intelligence model and the first dependent library required by the first artificial intelligence application into the serverless architecture. Finally, the first artificial intelligence application uses the first artificial intelligence model and the first dependent library to process the data information to be processed. By splitting the code package of the artificial intelligence application into an image of the operating environment, the required artificial intelligence model, and the dependent library required for runtime, and then deploying the image of the operating environment in the serverless architecture, the artificial intelligence model and the dependent library are simultaneously loaded into the serverless architecture through a function when a data processing request is received. This avoids the problem of loading the entire AI application code package in the prior art, which causes a loading time process, thereby achieving the effect of improving data processing efficiency.BRIEF DESCRIPTION OF THE DRAWINGS The accompanying drawings described herein are intended to provide a further understanding of the present application and constitute a part of the present application. The exemplary embodiments of the present application and their descriptions are intended to explain the present application and do not constitute undue limitations thereon. In the accompanying drawings: FIG1 is a hardware block diagram of a computer terminal according to Embodiment 1 of the present application; FIG2 is a flowchart of a data processing method according to Embodiment 1 of the present application; FIG3 is a flowchart of a data processing method according to Embodiment 1 of the present application; FIG4 is a schematic diagram of a data processing device according to Embodiment 2 of the present application; FIG5 is a schematic diagram of a data processing system according to Embodiment 3 of the present application; and FIG6 is a block diagram of a computer terminal according to Embodiment 4 of the present application. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS To help those skilled in the art better understand the present invention, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings. It should be understood that the described embodiments represent only a portion of the present application, not all of the embodiments. Based on the embodiments of the present application, all other embodiments derived by persons of ordinary skill in the art without inventive effort shall fall within the scope of protection of the present application. It should be noted that the terms "first," "second," and so on, in the specification and claims of this application, and in the accompanying drawings, are used to distinguish similar objects and are not necessarily used to describe a specific order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. Furthermore, the terms "including," "having," and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or elements is not necessarily limited to those steps or elements explicitly listed, but may include other steps or elements not explicitly listed or inherent to such process, method, product, or apparatus. First, some nouns or terms appearing in the description of the embodiments of this application are subject to the following interpretations: Serverless: This is a model of cloud computing based on Platform as a Service (PaaS). Serverless computing provides a micro-architecture where end users do not need to deploy, configure, or manage server services. The server services required for code execution are all provided by the cloud platform. A dependency library is a file containing program code and data that can be used by other programs or components. The code in a dependency library can be shared between multiple programs, thus avoiding the problem of duplication of code.It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, storage, and display) involved in this application are all information and data authorized by the user or fully authorized by all parties. The collection, use, and processing of the relevant data must comply with the relevant laws, regulations, and standards of the relevant region, and corresponding operation portals are provided for the user to choose to authorize or refuse. Example 1 According to an embodiment of the present application, a data processing method is also provided. It should be noted that the steps shown in the flowchart of the accompanying figure can be executed in a computer system such as a set of computer-executable instructions. Although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than shown. The method embodiment provided in Example 1 of the present application can be executed in a mobile terminal, computer terminal, or similar computing device. Figure 1 shows a hardware structure block diagram of a computer terminal (or mobile device) for implementing the data processing method. As shown in FIG1 , a computer terminal (or mobile device) 10 may include a processor assembly 102 (the processor assembly 102 may include, but is not limited to, a processing device such as a microprocessor (MCU) or a field programmer gate array (FPGA), and the processor assembly 102 may include a processor assembly, as shown in FIG1 by 102 a, 102 b, ..., 102 n), a memory 104 for storing data, and a transmission module 106 for communication functions. In addition, the computer terminal (or mobile device) 10 may include a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of a BUS), a network interface, a power supply, and / or a camera. Those skilled in the art will appreciate that the structure shown in FIG1 is merely illustrative and does not limit the structure of the electronic device. For example, the computer terminal 10 may include more or fewer components than shown in FIG1 , or have a configuration different from that shown in FIG1 . It should be noted that the one or more processors 102 and / or other data processing circuits described above may generally be referred to herein as "data processing circuitry." The data processing circuitry may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof.Furthermore, the data processing circuit can be a single independent processing module, or fully or partially integrated into any other component of the computer terminal 10 (or mobile device). As described in the embodiments of the present application, the data processing circuit acts as a processor control (e.g., selecting a variable resistor terminal path connected to an interface). Memory 104 can be used to store application software programs and modules, such as the program instructions / data storage device corresponding to the data processing method in the embodiments of the present application. Processor 102 executes the software programs and modules stored in memory 104 to perform various functional applications and data processing, thereby implementing the aforementioned data processing method. Memory 104 can include high-speed random access memory (RAM) and non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, memory 104 can further include memory remotely located from processor 102, and such remote memory can be connected to computer terminal 10 via a network. Examples of such networks include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof. Transmission device 106 is used to receive or transmit data via a network. A specific example of the aforementioned network may include a wireless network provided by the communications provider of the computer terminal 10. In one example, the transmission device 106 includes a network adapter (NIC), which can be connected to other network devices via a base station to enable communication with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module for wireless communication with the Internet. The display may be a touch-screen liquid crystal display that enables a user to interact with the user interface of the computer terminal 10 (or mobile device). In the aforementioned operating environment, the present application provides a data processing method as shown in FIG2 . FIG2 is a flowchart of a data processing method according to Embodiment 1 of the present application. The data processing method includes: Step S201: Receive a data processing request initiated by a target object via a serverless architecture, wherein the data processing request is to be processed by a first artificial intelligence application and includes at least data information to be processed. A first image of the operating environment of the first artificial intelligence application is deployed in the serverless architecture. Optionally, a first image of the operating environment of the first artificial intelligence application is deployed in the serverless architecture, and the first image is used to provide a stable operating environment for the first artificial intelligence application.A data processing request initiated by a user (i.e., the target object described above) is received via the serverless architecture. In step S202, a target function is triggered based on the data processing request. The target function then loads the first artificial intelligence model and first dependency library required by the first artificial intelligence application into the serverless architecture, where the target function is deployed within the serverless architecture. Optionally, after receiving the data processing request, a trigger can be used to trigger the target function for processing the data processing request. Once triggered, the target function loads the first artificial intelligence model required by the first artificial intelligence application and the first dependency library required to execute the data processing request into the serverless architecture. In step S203, the first artificial intelligence application processes the data to be processed based on the first artificial intelligence model and the first dependency library to obtain a data processing result. Optionally, the data to be processed is processed using the first artificial intelligence model and the first dependency library in a first image of the first artificial intelligence application's runtime environment to obtain a final data processing result. In summary, a first image of the runtime environment of a first artificial intelligence application is directly deployed in a serverless architecture. Then, after the serverless architecture receives a data processing request initiated by a target object, a target function is triggered due to the data processing request. After the target function is triggered, the target function simultaneously loads the first artificial intelligence model and the first dependent library required by the first artificial intelligence application into the serverless architecture. Finally, the first artificial intelligence application uses the first artificial intelligence model and the first dependent library to process the data information to be processed. By splitting the code package of the artificial intelligence application into an image of the runtime environment, the required artificial intelligence model, and the dependent library required for runtime, and then deploying the image of the runtime environment in the serverless architecture, the artificial intelligence model and the dependent library are simultaneously loaded into the serverless architecture through a function when a data processing request is received. This avoids the problem of loading the entire AI application code package in the prior art, which causes a long loading process, thereby achieving the effect of improving data processing efficiency. To improve data processing efficiency, in the data processing method provided in Example 1 of the present application, processing the data information to be processed by a first artificial intelligence application based on a first artificial intelligence model and a first dependent library to obtain a data processing result includes: obtaining a first business logic code corresponding to the first artificial intelligence application; and processing the data information to be processed by using the first artificial intelligence model, the first dependent library, and the first business logic code to obtain a data processing result.Optionally, processing the data information to be processed by the first artificial intelligence model and the first dependent library includes the following steps: After the first artificial intelligence model and the first dependent library are loaded into the serverless architecture, obtaining the first business logic code of the first artificial intelligence application. It should be noted that business logic code refers to the code portion that implements specific business requirements. It is responsible for tasks such as processing data, performing calculations, validating input, and making decisions to ensure that the application operates as expected. It should be noted that when deploying the first artificial intelligence application, the corresponding business logic code is deployed to the Serverless platform. After obtaining the business logic code, the target function executes the business logic code. The business logic code processes the data information to be processed by calling the first artificial intelligence model and the first dependent library, and obtains the corresponding processing results. It should be noted that the business logic code defines interfaces for loading and using the artificial intelligence model so that it can be seamlessly called and executed after the artificial intelligence model is mounted. Processing the data information to be processed through the business logic code improves the data processing efficiency. To improve the loading efficiency of the objective function, in the data processing method provided in the first embodiment of the present application, loading the first artificial intelligence model and first dependency library required by the first artificial intelligence application into the serverless architecture via the objective function includes: obtaining a first configuration file corresponding to the first artificial intelligence application, wherein the first configuration file includes at least the first storage address of the first artificial intelligence model and the identifier of the first common component storing the first dependency library, wherein the first common component is a basic component in the serverless architecture; determining the first storage address and the identifier of the first common component of the first artificial intelligence model based on the first configuration file; loading the first artificial intelligence model into the serverless architecture from the first storage address via the objective function; and loading the first dependency library from the first common component into the serverless architecture based on the identifier of the first common component via the objective function. Optionally, when the objective function loads the first artificial intelligence model and the first dependency library, the following steps are included: obtaining a first configuration file corresponding to the first artificial intelligence application from the serverless architecture, wherein the first configuration file includes at least the first storage address of the first artificial intelligence model and the identifier of the first common component storing the first dependency library. The first configuration file may also include specific parameters for running the artificial intelligence application, such as an API key and a database connection. After obtaining the first configuration file, the objective function loads the first artificial intelligence model into the serverless architecture from the first storage address, and loads the first dependent library into the serverless architecture from the first common component.It should be noted that the common component is the Layer component, a foundational component in the serverless architecture. This component allows for quick and convenient creation, configuration, and management of layer resources for AI applications. It should be noted that AI models are deployed in a mounted manner. For example, dynamic loading: During function execution, the AI ​​model is dynamically loaded from the cloud storage service based on the first storage address. Mount point configuration: In the configuration file, the AI ​​model's storage location (such as a URL or path) and mount point are specified, allowing the AI ​​model to be loaded locally. Layer technology significantly improves the deployment efficiency and operational performance of AI applications in the Serverless architecture. It not only reduces repetitive dependency management tasks but also accelerates application startup and execution, effectively improving data processing efficiency. To improve data processing efficiency, the data processing method provided in Example 1 of the present application, before receiving a data processing request initiated by a target object via a serverless architecture, further includes: obtaining the target object's requirement information for a second artificial intelligence application; determining, based on the requirement information, a second image, a second artificial intelligence model, and a second dependency library of the operating environment required by the second artificial intelligence application; constructing the business logic code of the second artificial intelligence application based on the requirement information; and deploying the second artificial intelligence application in the serverless architecture based on the second image, the second artificial intelligence model, the second dependency library, and the business logic code. Optionally, the resources required by the AI ​​application are classified by type to optimize deployment and operational efficiency. The resources required by the AI ​​application primarily include the following: Dependent Libraries: These include all third-party libraries and frameworks required for the AI ​​application to run, such as TensorFlow. Runtime Environment: This includes the basic operating system, programming language interpreters (such as Python, Node.js), and necessary system libraries. One or more common base images can be set up based on commonly used runtime environments, allowing users to select and load appropriate resources based on their needs. AI Models: This includes machine learning or deep learning models, which are typically large and take a long time to load. Business Logic Code: This includes application-specific code, including data processing and request handling. Configuration Files: This includes application configuration files, environment variables, and necessary small data files.Based on the aforementioned resource partitioning, deploying the second AI model involves the following steps: Obtaining the user's (i.e., the target object) defined requirements for the second AI application; then, based on this information, selecting the second image, second AI model, and second dependency library required for the second AI application's runtime environment. Business logic code for the second AI application, including data processing and request handling, is written based on the requirements. Finally, the second AI application is deployed based on the second image, second AI model, second dependency library, and business logic code. This detailed resource partitioning allows for more efficient management of the various components of an AI application, optimizing resource loading and execution efficiency. This partitioning approach can significantly mitigate cold start issues in Serverless environments, particularly for applications requiring large dependency libraries and AI models, while maintaining application flexibility and scalability. Optionally, deploying the second artificial intelligence application in the serverless architecture based on the second image, the second artificial intelligence model, the second dependency library, and the business logic code includes: storing the second artificial intelligence model on a target device to obtain a second storage address; creating a second common component in the serverless architecture based on the second dependency library and storing the second dependency library in the second common component; obtaining a second configuration file for the second artificial intelligence application based on the second storage address and the identifier of the second common component; and deploying the second image, the second configuration file, and the business logic code in the serverless architecture to deploy the second artificial intelligence application in the serverless architecture. Optionally, deploying the second artificial intelligence application based on the second image, the second artificial intelligence model, the second dependency library, and the business logic code includes the following steps: First, storing the second artificial intelligence model on the target device. It should be noted that the target device can be a cloud storage service or local storage. After storing the second artificial intelligence model, mounting the second artificial intelligence application, for example, dynamically mounting the AI ​​model from the cloud storage service during function execution. Mounting a model to a mount point: Specify the model's storage location (e.g., URL or path) and mount point in the Serverless function configuration. The AI ​​model is then stored in the mount point. Note that to improve performance, frequently used models can be cached in the Serverless environment to reduce subsequent loading time.Then, based on the second dependency library, a second common component, also known as a Layer component, is created in the serverless architecture. In the Serverless architecture, a Layer is a reusable code and / or data package that stores common components of a function, such as libraries, custom runtimes, and other dependencies. Layers make sharing and managing common resources more efficient. By using Layers, different Serverless functions can share the same dependencies without having to include them repeatedly in every deployment package. A Layer containing a specific dependency library can be created and uploaded to the Serverless platform. Layers support version control, ensuring consistency and compatibility by specifying a specific version of the Layer. For AI applications, dedicated Layers can be created to store large machine learning libraries and commonly used AI toolkits. AI applications can dynamically reference different Layers based on demand, for example, switching between different machine learning frameworks or library versions. Layers are isolated from function code at runtime, ensuring security and stability. Access to Layers can also be controlled to ensure that only authorized functions can use specific Layers. oBy placing common dependencies in a layer, the size of the function's deployment package can be reduced, accelerating the deployment process. Sharing the layer reduces resource duplication and optimizes memory and storage usage. After storing the second dependency library, the second configuration file for the second AI application is configured based on the second storage address and the identifier of the second common component. Finally, the second image, second configuration file, and business logic code are deployed in the serverless architecture to implement the second AI application. This deployment process significantly reduces cold start time. Layer technology and dynamic model mounting improve resource efficiency, thereby increasing data processing efficiency. To improve data security, in the data processing method provided in the first embodiment of the present application, loading a first artificial intelligence model from a first storage address into a serverless architecture via a target function includes: initiating, via the target function, a request to obtain the first artificial intelligence model to a first device, wherein the first device is a device storing the first artificial intelligence model, the request including at least identification information of the target function; determining, by the first device, whether the identification information of the target function is within a preset list, the preset list including identification information of functions permitted to load the first artificial intelligence model; and if the identification information of the target function is within the preset list, loading the first artificial intelligence model from the first storage address into the serverless architecture via the target function. Optionally, loading the first artificial intelligence model from the first storage address into the serverless architecture via the target function includes the following steps: initiating, via the target function, a request to obtain the first artificial intelligence model to the first device, the request including at least identification information of the target function and identification information of the target function. After receiving the request, the first device determines whether the identification information of the target function is within the preset list. If the identification information of the target function is within the preset list, the first device returns the first artificial intelligence model to the serverless architecture. Optionally, to further enhance data security, the artificial intelligence model is encrypted during transmission and storage to further ensure data security. To enhance the accuracy of data processing by the artificial intelligence model, in the data processing method provided in the first embodiment of the present application, after the first artificial intelligence model and the first dependency library process the data information to be processed to obtain a data processing result, the method further includes: obtaining target data information generated by the first artificial intelligence model during processing of the data information to be processed; evaluating the first artificial intelligence model based on the target data information to obtain an evaluation result; and optimizing the first artificial intelligence model based on the evaluation result.Optionally, after processing the data to be processed, target data generated by the first artificial intelligence model during the processing of the data is collected, such as execution efficiency and memory usage data. The first artificial intelligence model is then evaluated based on the target data to determine whether optimization of the first artificial intelligence model is necessary, i.e., optimizing the first artificial intelligence model based on the evaluation results. Optionally, in AI applications based on the Serverless architecture, continuous optimization and adjustment are key to ensuring application performance and cost-efficiency. The following is a detailed description of the optimization and adjustment process: Performance testing: Testing includes key performance indicators such as startup time, execution time, memory usage, and error rate. Runtime data is collected using the testing tools provided by the Serverless platform. Resource configuration optimization: Memory and CPU allocation: Adjust the memory and CPU resources allocated to Serverless functions based on application performance requirements and actual usage. Concurrent execution management: Adjust concurrent execution settings, such as increasing or decreasing the number of concurrent function instances, to balance performance and cost. Code and dependency optimization: Code refactoring: Regularly refactor code to improve efficiency and reduce unnecessary resource consumption. Dependency updates: Update the dependency libraries in Layer to the latest versions to ensure good performance.

[0002] AI Model Optimization: Model Efficiency: Analyze and adjust AI models to improve their operational efficiency, such as reducing model size through techniques like model pruning and quantization. Loading Strategy Adjustment: Adjust model loading strategies, such as caching frequently used models to reduce loading time. Cost Efficiency Analysis: Cost Detection: Detect and analyze the operating costs of Serverless functions, including computing resource usage and storage costs. Cost Optimization Strategy: Adjust resource usage based on cost analysis results, such as optimizing function execution frequency and duration, to reduce costs. Automated Optimization Tools: Use automated tools and scripts to conduct performance and cost analysis and automatically adjust resource allocation. User Feedback and Adjustment: Regularly collect user feedback to identify performance and functionality issues. Based on this feedback, adjust the application to improve the user experience. Through these optimization and adjustment steps, AI applications in the Serverless architecture can continuously improve their performance while maintaining cost efficiency. This continuous optimization process is critical to adapting to the ever-changing technical environment and business needs. Alternatively, the data processing process can be implemented according to the flowchart shown in Figure 3: Code Preparation: Business logic code is written specifically for the AI ​​application, including functions such as data processing and user request response. Environment configuration involves setting environment variables and configuration files. These configurations specify specific application parameters, such as API keys and database connections. Code Packaging and Uploading: Packaging involves packaging the business logic code and necessary configuration files. Typically, this packaging process requires excluding large dependencies and AI models, which are handled by the Layer and Model Mounting services, respectively. The packaged code is uploaded to the Serverless platform.

[0003] Layer and model integration: In the Server ESS platform configuration, specify which layers to use. These layers contain the dependent libraries required for code execution. Model mounting configuration: Specify the storage location and mounting method for the AI ​​model to ensure dynamic loading at runtime. Function trigger setup: Define triggers based on application requirements, setting function triggers. These can be HTTP requests, database events, scheduled tasks, and so on. Trigger configuration: Configure specific trigger parameters, such as the HTTP endpoint path and the scheduled task execution frequency. Code execution process: When a trigger is triggered, the Server ESS platform launches the function instance. Dynamic resource loading: The function instance dynamically loads the required layers and AI models based on the configuration. Business logic execution: After loading the necessary resources, the business logic code begins executing, processing input data, invoking the AI ​​model for calculations, and generating responses. Performance optimization and testing: Use tools provided by the Server ESS platform to test function execution, including startup time, execution time, and memory usage. Tuning: Adjust resource configuration (such as memory size and execution time limits) and code logic based on instrumentation data to optimize performance. Security and Compliance: Code Auditing: Regularly audit code and dependencies to ensure security. Compliance: Ensure that code and data processing comply with relevant data protection and privacy regulations. Through these steps, the code deployment and execution process is optimized within the Serverless architecture, ensuring the efficient operation of AI applications. In the data processing method provided in the first embodiment of the present application, a data processing request initiated by a target object is received through a serverless architecture, wherein the data processing request needs to be processed by a first artificial intelligence application, and the data processing request includes at least data information to be processed. A first image of the operating environment of the first artificial intelligence application is deployed in the serverless architecture; a target function is triggered based on the data processing request, and a first artificial intelligence model and a first dependency library required by the first artificial intelligence application are loaded into the serverless architecture through the target function, wherein the target function is deployed in the serverless architecture; the first artificial intelligence application processes the data information to be processed based on the first artificial intelligence model and the first dependency library to obtain a data processing result, thereby solving the technical problem in the related art that when performing data processing based on an AI application, due to the large amount of data in the entire AI application code package, the loading time is too long, thereby resulting in relatively low data processing efficiency.In this application, a first image of the runtime environment of a first artificial intelligence application is directly deployed in a serverless architecture. Then, after the serverless architecture receives a data processing request initiated by a target object, a target function is triggered by the data processing request. After the target function is triggered, the target function simultaneously loads the first artificial intelligence model and first dependency library required by the first artificial intelligence application into the serverless architecture. Finally, the first artificial intelligence application uses the first artificial intelligence model and the first dependency library to process the data information to be processed. By splitting the code package of the artificial intelligence application into the image of the runtime environment, the required artificial intelligence model, and the dependency library required for runtime, and then deploying the image of the runtime environment in the serverless architecture, the function simultaneously loads the artificial intelligence model and dependency library into the serverless architecture when a data processing request is received. This avoids the problem of loading the entire AI application code package in the prior art, which leads to a long loading process, thereby achieving the effect of improving data processing efficiency. It should be noted that for the sake of simplicity, the aforementioned method embodiments are described as a series of actions. However, those skilled in the art should understand that this application is not limited by the order of the actions described, as certain steps can be performed in a different order or simultaneously according to this application. Secondly, those skilled in the art should also be aware that the embodiments described in this specification are preferred embodiments, and the actions and modules involved are not necessarily required by this application. Through the above description of the embodiments, those skilled in the art will clearly understand that the methods according to the above embodiments can be implemented using software and a necessary general-purpose hardware platform. Of course, hardware can also be used, but in many cases the former is a more preferred implementation. Based on this understanding, the technical solution of this application, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk) and includes several instructions for enabling a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods of various embodiments of this application. Example 2 According to an embodiment of this application, a data processing device for implementing the above-mentioned data processing method is also provided. As shown in FIG4 , the device includes a receiving unit 401, a triggering unit 402, and a processing unit 403.A receiving unit 401 is configured to receive a data processing request initiated by a target object through a serverless architecture, wherein the data processing request needs to be processed by a first artificial intelligence application and the data processing request includes at least data information to be processed, and a first image of the operating environment of the first artificial intelligence application is deployed in the serverless architecture; a triggering unit 402 is configured to trigger a target function based on the data processing request, and load a first artificial intelligence model and a first dependency library required by the first artificial intelligence application into the serverless architecture through the target function, wherein the target function is deployed in the serverless architecture; and a processing unit 403 is configured to process the data information to be processed based on the first artificial intelligence model and the first dependency library through the first artificial intelligence application to obtain a data processing result. In the data processing device provided in the second embodiment of the present application, a receiving unit 401 receives a data processing request initiated by a target object through a serverless architecture, wherein the data processing request needs to be processed by a first artificial intelligence application and the data processing request includes at least data information to be processed. A first image of the operating environment of the first artificial intelligence application is deployed in the serverless architecture; a triggering unit 402 triggers a target function based on the data processing request and loads a first artificial intelligence model and a first dependency library required by the first artificial intelligence application into the serverless architecture through the target function, wherein the target function is deployed in the serverless architecture; and a processing unit 403 processes the data information to be processed based on the first artificial intelligence model and the first dependency library through the first artificial intelligence application to obtain a data processing result. This solves the technical problem in the related art that when performing data processing based on an AI application, due to the large amount of data in the entire AI application code package, the loading time is too long, thereby resulting in relatively low data processing efficiency.In the present application, a first image of the operating environment of a first artificial intelligence application is directly deployed in a serverless architecture. Then, after the serverless architecture receives a data processing request initiated by a target object, a target function is triggered due to the data processing request. After the target function is triggered, the target function simultaneously loads the first artificial intelligence model and the first dependent library required by the first artificial intelligence application into the serverless architecture. Finally, the first artificial intelligence application uses the first artificial intelligence model and the first dependent library to process the data information to be processed. By splitting the code package of the artificial intelligence application into an image of the operating environment, the required artificial intelligence model, and the dependent library required for runtime, and then deploying the image of the operating environment in the serverless architecture, when a data processing request is received, the artificial intelligence model and the dependent library are simultaneously loaded into the serverless architecture through a function, thereby avoiding the problem of loading the entire AI application code package in the prior art, thereby achieving the effect of improving data processing efficiency. Optionally, in the data processing device provided in the second embodiment of the application, the processing unit 403 includes: a first acquisition module configured to acquire first business logic code corresponding to the first artificial intelligence application; a processing module configured to process the data information to be processed using the first artificial intelligence model, the first dependency library, and the first business logic code to obtain a data processing result. Optionally, in the data processing device provided in the second embodiment of the application, the triggering unit 402 includes: a second acquisition module configured to acquire a first configuration file corresponding to the first artificial intelligence application, wherein the first configuration file includes at least a first storage address of the first artificial intelligence model and an identifier of a first common component storing the first dependency library, wherein the first common component is a basic component in the serverless architecture; a first determination module configured to determine the first storage address of the first artificial intelligence model and the identifier of the first common component based on the first configuration file; a first loading module configured to load the first artificial intelligence model from the first storage address into the serverless architecture using a target function; and a second loading module configured to load the first dependency library from the first common component into the serverless architecture using a target function based on the identifier of the first common component.Optionally, in the data processing device provided in the second embodiment of the application, the device further includes: a first acquisition unit, configured to obtain the target object's demand information for the second artificial intelligence application before receiving a data processing request initiated by the target object through the serverless architecture; a determination unit, configured to determine the second image, second artificial intelligence model, and second dependency library of the operating environment required by the second artificial intelligence application based on the demand information; a construction unit, configured to construct the business logic code of the second artificial intelligence application based on the demand information; and a deployment unit, configured to deploy the second artificial intelligence application in the serverless architecture based on the second image, second artificial intelligence model, second dependency library, and business logic code. Optionally, in the data processing device provided in the second embodiment of the application, the deployment unit includes: a storage module, configured to store the second artificial intelligence model in the target device and obtain a second storage address; a construction module, configured to create a second common component in the serverless architecture based on the second dependent library, and store the second dependent library in the second common component; a second determination module, configured to obtain a second configuration file of the second artificial intelligence application based on the second storage address and the identifier of the second common component; and a deployment module, configured to deploy the second image, the second configuration file, and the business logic code in the serverless architecture to implement deployment of the second artificial intelligence application in the serverless architecture. Optionally, in the data processing device provided in the second embodiment of the application, the first loading module includes: an acquisition submodule configured to initiate an acquisition request to a first device via a target function to acquire the first artificial intelligence model, wherein the first device is a device storing the first artificial intelligence model, and the acquisition request includes at least identification information of the target function. The first device determines whether the identification information of the target function is within a preset list, wherein the preset list includes identification information of functions permitted to load the first artificial intelligence model; a recording submodule configured to load the first artificial intelligence model from a first storage address into the serverless architecture via the target function if the identification information of the target function is within the preset list. Optionally, in the data processing device provided in the second embodiment of the application, the device further includes: a second acquisition unit configured to, after processing the data information to be processed via the first artificial intelligence model and the first dependency library to obtain a data processing result, acquire target data information generated by the first artificial intelligence model during the processing of the data information to be processed; an evaluation unit configured to evaluate the first artificial intelligence model based on the target data information to obtain an evaluation result; and an optimization unit configured to optimize the first artificial intelligence model based on the evaluation result.It should be noted that the aforementioned receiving unit 401, triggering unit 402, and processing unit 403 correspond to steps S201 to S203 in Example 1. The examples and application scenarios implemented by these three units and the corresponding steps are the same, but are not limited to the content disclosed in Example 1. It should be noted that the aforementioned modules, as part of the device, can be run in the computer terminal 10 provided in Example 1. It should be noted that the preferred implementation schemes involved in the aforementioned embodiments of this application are the same as the schemes, application scenarios, and implementation processes provided in Example 1, but are not limited to the schemes provided in Example 1. Example 3 According to an embodiment of this application, a data processing system for implementing the aforementioned data processing method is also provided. As shown in FIG5 , the data processing system includes: a user layer 50, a configuration layer 51, a deployment layer 52, and an execution layer 53. User layer 50: Selects appropriate base images and Layer 1 by defining AI application requirements. Configuration layer 51: Users configure resources including dependencies, runtime environments, and models. Deployment Layer 52: Applications are deployed to the Serverless platform, including layer configuration and model mounting. Execution Layer 53: The Serverless platform runs the AI ​​application and dynamically loads required resources. The data processing system performs the following steps to process data: Select an appropriate base image based on AI application requirements; Configure required dependency layers; Configure the model mounting service and specify the model location; Deploy the business logic code to the Serverless platform. During execution, the Serverless platform dynamically loads layers and models to run the AI ​​application. Resource configuration is adjusted based on operational conditions to optimize performance. By separating dependencies from models and leveraging layer technology and hard disk mounting services, the cold start time of AI applications in the Serverless architecture is significantly reduced, while improving resource utilization efficiency and application performance. This solution provides an efficient and flexible solution for deploying and executing large-scale AI applications in a Serverless environment. It should be noted that the preferred implementation schemes involved in the above embodiments of this application are the same as the solution, application scenarios, and implementation process provided in Example 1, but are not limited to the solution provided in Example 1. Embodiment 4 An embodiment of the present application may provide a computer terminal, which may be any computer terminal device in a computer terminal group. Optionally, in this embodiment, the computer terminal may be replaced by a terminal device such as a mobile terminal.Optionally, in this embodiment, the computer terminal may be located in at least one of multiple network devices in a computer network. In this embodiment, the computer terminal may execute program code for the following steps in the data processing method: receiving a data processing request initiated by a target object via a serverless architecture, wherein the data processing request is to be processed by a first artificial intelligence application and the data processing request includes at least data information to be processed, and a first image of the operating environment of the first artificial intelligence application is deployed in the serverless architecture; triggering a target function based on the data processing request, and loading a first artificial intelligence model and a first dependency library required by the first artificial intelligence application into the serverless architecture via the target function, wherein the target function is deployed in the serverless architecture; processing the data information to be processed by the first artificial intelligence application based on the first artificial intelligence model and the first dependency library to obtain a data processing result. The computer terminal may execute program code for the following steps in the data processing method: processing the data information to be processed by the first artificial intelligence application based on the first artificial intelligence model and the first dependency library to obtain a data processing result includes: obtaining first business logic code corresponding to the first artificial intelligence application; and processing the data information to be processed using the first artificial intelligence model, the first dependency library, and the first business logic code to obtain a data processing result. The above-mentioned computer terminal can execute the program code of the following steps in the data processing method: Loading the first artificial intelligence model and the first dependent library required by the first artificial intelligence application into the serverless architecture through the objective function includes: obtaining a first configuration file corresponding to the first artificial intelligence application, wherein the first configuration file at least includes a first storage address of the first artificial intelligence model and an identifier of the first public component storing the first dependent library, wherein the first public component is a basic component in the serverless architecture; determining the first storage address of the first artificial intelligence model and the identifier of the first public component based on the first configuration file; loading the first artificial intelligence model from the first storage address into the serverless architecture through the objective function; loading the first dependent library from the first public component into the serverless architecture through the objective function based on the identifier of the first public component.The computer terminal can execute program code for the following steps in the data processing method: Before receiving a data processing request initiated by a target object via the serverless architecture, the method further includes: obtaining the target object's requirement information for a second artificial intelligence application; determining, based on the requirement information, a second image, a second artificial intelligence model, and a second dependency library of the operating environment required by the second artificial intelligence application; constructing business logic code for the second artificial intelligence application based on the requirement information; and deploying the second artificial intelligence application in the serverless architecture based on the second image, the second artificial intelligence model, the second dependency library, and the business logic code. The computer terminal can execute program code for the following steps in the data processing method: Deploying the second artificial intelligence application in the serverless architecture based on the second image, the second artificial intelligence model, the second dependency library, and the business logic code includes: storing the second artificial intelligence model in the target device to obtain a second storage address; creating a second common component in the serverless architecture based on the second dependency library and storing the second dependency library in the second common component; obtaining a second configuration file for the second artificial intelligence application based on the second storage address and the identifier of the second common component; and deploying the second image, the second configuration file, and the business logic code in the serverless architecture to implement deployment of the second artificial intelligence application in the serverless architecture. The computer terminal can execute program code for the following steps in the data processing method: Loading the first artificial intelligence model from the first storage address into the serverless architecture via the target function includes: initiating, via the target function, an acquisition request for the first artificial intelligence model to a first device, wherein the first device is a device storing the first artificial intelligence model, the acquisition request including at least identification information of the target function; determining, by the first device, whether the identification information of the target function is within a preset list, the preset list including identification information of functions permitted to load the first artificial intelligence model; and if the identification information of the target function is within the preset list, loading the first artificial intelligence model from the first storage address into the serverless architecture via the target function. The computer terminal can execute program code for the following steps in the data processing method: After processing the data information to be processed via the first artificial intelligence model and the first dependency library to obtain a data processing result, the method further includes: obtaining target data information generated by the first artificial intelligence model during the processing of the data information to be processed; evaluating the first artificial intelligence model based on the target data information to obtain an evaluation result; and optimizing the first artificial intelligence model based on the evaluation result. Optionally, Figure 6 is a block diagram of a computer terminal according to an embodiment of the present application. As shown in FIG6 , the computer terminal 10 may include: one or more (only one is shown in FIG6 ) processors 102 and a memory 104 .oThe computing terminal 10 may also include a storage controller to control and manage the memory 104. The computing terminal 10 may also include a peripheral interface to connect to a radio frequency module, an audio module, a display screen, and the like. The memory may be used to store software programs and modules, such as the program instructions / modules corresponding to the data processing methods and apparatuses described in the embodiments of the present application. The processor executes the software programs and modules stored in the memory to perform various functional applications and data processing, thereby implementing the aforementioned data processing methods. The memory may include high-speed random access memory (RAM) and non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include memory located remotely from the processor, which may be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof. The processor can access information and applications stored in the memory through a transmission device to perform the following steps: receiving a data processing request initiated by a target object through a serverless architecture, wherein the data processing request is to be processed by a first artificial intelligence application and the data processing request includes at least data information to be processed, and a first image of the operating environment of the first artificial intelligence application is deployed in the serverless architecture; triggering a target function based on the data processing request, and loading a first artificial intelligence model and a first dependency library required by the first artificial intelligence application into the serverless architecture through the target function, wherein the target function is deployed in the serverless architecture; processing the data information to be processed by the first artificial intelligence application based on the first artificial intelligence model and the first dependency library to obtain a data processing result. Optionally, the processor can also execute program code for the following steps: processing the data information to be processed by the first artificial intelligence application based on the first artificial intelligence model and the first dependency library to obtain a data processing result includes: obtaining first business logic code corresponding to the first artificial intelligence application; processing the data information to be processed by the first artificial intelligence model, the first dependency library, and the first business logic code to obtain a data processing result.Optionally, the processor may also execute the program code of the following steps: loading the first artificial intelligence model and the first dependency library required by the first artificial intelligence application into the serverless architecture through the objective function includes: obtaining a first configuration file corresponding to the first artificial intelligence application, wherein the first configuration file includes at least a first storage address of the first artificial intelligence model and an identifier of a first public component storing the first dependency library, wherein the first public component is a basic component in the serverless architecture; determining the first storage address of the first artificial intelligence model and the identifier of the first public component based on the first configuration file; loading the first artificial intelligence model from the first storage address into the serverless architecture through the objective function; and loading the first dependency library from the first public component into the serverless architecture through the objective function based on the identifier of the first public component. Optionally, the processor may further execute program code for the following steps: Before receiving a data processing request initiated by a target object via the serverless architecture, the method may further include: obtaining the target object's requirement information for a second artificial intelligence application; determining, based on the requirement information, a second image, a second artificial intelligence model, and a second dependency library of the operating environment required by the second artificial intelligence application; constructing business logic code for the second artificial intelligence application based on the requirement information; and deploying the second artificial intelligence application in the serverless architecture based on the second image, the second artificial intelligence model, the second dependency library, and the business logic code. Optionally, the processor may further execute program code for the following steps: deploying the second artificial intelligence application in the serverless architecture based on the second image, the second artificial intelligence model, the second dependency library, and the business logic code includes: storing the second artificial intelligence model in the target device to obtain a second storage address; creating a second common component in the serverless architecture based on the second dependency library, and storing the second dependency library in the second common component; obtaining a second configuration file for the second artificial intelligence application based on the second storage address and the identifier of the second common component; and deploying the second image, the second configuration file, and the business logic code in the serverless architecture to implement deployment of the second artificial intelligence application in the serverless architecture.Optionally, the processor may further execute program code for the following steps: loading the first artificial intelligence model from the first storage address into the serverless architecture via the target function includes: initiating, via the target function, an acquisition request to a first device for the first artificial intelligence model, wherein the first device is a device storing the first artificial intelligence model, the acquisition request including at least identification information of the target function; determining, by the first device, whether the identification information of the target function is within a preset list, the preset list including identification information of functions permitted to load the first artificial intelligence model; and if the identification information of the target function is within the preset list, loading the first artificial intelligence model from the first storage address into the serverless architecture via the target function. Optionally, the processor may further execute program code for the following steps: after processing the data information to be processed via the first artificial intelligence model and the first dependency library to obtain a data processing result, the method further includes: obtaining target data information generated by the first artificial intelligence model during the processing of the data information to be processed; evaluating the first artificial intelligence model based on the target data information to obtain an evaluation result; and optimizing the first artificial intelligence model based on the evaluation result. Those skilled in the art will appreciate that the structure shown in FIG6 is merely illustrative, and the computer terminal may also be a smartphone (such as an Android phone, an iOS phone, etc.), a tablet computer, a PDA, a mobile internet device (MID), a PAD, or other terminal device. FIG6 does not limit the structure of the aforementioned electronic devices. For example, the computer terminal 10 may include more or fewer components (such as a network interface, a display device, etc.) than those shown in FIG6 , or may have a configuration different from that shown in FIG6 . Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be completed by a program instructing the hardware associated with the terminal device. The program may be stored in a computer-readable storage medium, which may include a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk. Example 5 The embodiments of the present application also provide a computer-readable storage medium. Optionally, in this embodiment, the storage medium may be used to store program code executed by the data processing method provided in the first embodiment. Optionally, in this embodiment, the storage medium may be located in any computer terminal in a computer terminal group in a computer network, or in any mobile terminal in a mobile terminal group.Optionally, in this embodiment, the storage medium is configured to store program code for performing the following steps: receiving a data processing request initiated by a target object via a serverless architecture, wherein the data processing request is to be processed by a first artificial intelligence application and the data processing request includes at least data information to be processed, and a first image of the operating environment of the first artificial intelligence application is deployed in the serverless architecture; triggering a target function based on the data processing request, and loading a first artificial intelligence model and a first dependency library required by the first artificial intelligence application into the serverless architecture via the target function, wherein the target function is deployed in the serverless architecture; processing the data information to be processed by the first artificial intelligence application based on the first artificial intelligence model and the first dependency library to obtain a data processing result. The storage medium is configured to store program code for performing the following steps: processing the data information to be processed by the first artificial intelligence application based on the first artificial intelligence model and the first dependency library to obtain a data processing result includes: obtaining first business logic code corresponding to the first artificial intelligence application; and processing the data information to be processed using the first artificial intelligence model, the first dependency library, and the first business logic code to obtain a data processing result. The above-mentioned storage medium is configured to store program code for executing the following steps: Loading the first artificial intelligence model and the first dependent library required by the first artificial intelligence application into the serverless architecture through the objective function includes: obtaining a first configuration file corresponding to the first artificial intelligence application, wherein the first configuration file includes at least the first storage address of the first artificial intelligence model and the identifier of the first public component storing the first dependent library, wherein the first public component is a basic component in the serverless architecture; determining the first storage address of the first artificial intelligence model and the identifier of the first public component based on the first configuration file; loading the first artificial intelligence model from the first storage address into the serverless architecture through the objective function; loading the first dependent library from the first public component into the serverless architecture through the objective function based on the identifier of the first public component. The storage medium is configured to store program code for executing the following steps: Before receiving a data processing request initiated by a target object through the serverless architecture, the method further includes: obtaining the target object's demand information for a second artificial intelligence application; determining, based on the demand information, a second image, a second artificial intelligence model, and a second dependency library of the operating environment required by the second artificial intelligence application; constructing business logic code for the second artificial intelligence application based on the demand information; and deploying the second artificial intelligence application in the serverless architecture based on the second image, the second artificial intelligence model, the second dependency library, and the business logic code.The above-mentioned storage medium is configured to store program code for performing the following steps: Deploying the second artificial intelligence application in the serverless architecture based on the second image, the second artificial intelligence model, the second dependent library and the business logic code includes: storing the second artificial intelligence model in the target device to obtain a second storage address; creating a second common component in the serverless architecture based on the second dependent library, and storing the second dependent library in the second common component; obtaining a second configuration file of the second artificial intelligence application based on the second storage address and the identifier of the second common component; deploying the second image, the second configuration file and the business logic code in the serverless architecture to implement the deployment of the second artificial intelligence application in the serverless architecture. The storage medium is configured to store program code for executing the following steps: Loading the first artificial intelligence model from a first storage address into a serverless architecture via a target function includes: initiating, via the target function, an acquisition request for the first artificial intelligence model to a first device, wherein the first device is a device storing the first artificial intelligence model, the acquisition request including at least identification information of the target function; determining, by the first device, whether the identification information of the target function is within a preset list, the preset list including identification information of functions permitted to load the first artificial intelligence model; and if the identification information of the target function is within the preset list, loading the first artificial intelligence model from the first storage address into the serverless architecture via the target function. The storage medium is configured to store program code for executing the following steps: After processing the data information to be processed via the first artificial intelligence model and a first dependency library to obtain a data processing result, the method further includes: obtaining target data information generated by the first artificial intelligence model during the processing of the data information to be processed; evaluating the first artificial intelligence model based on the target data information to obtain an evaluation result; and optimizing the first artificial intelligence model based on the evaluation result. The serial numbers of the embodiments of this application are for descriptive purposes only and do not represent the merits or demerits of the embodiments. In the above-described embodiments of this application, the descriptions of each embodiment have their respective focuses. For portions not described in detail in one embodiment, reference should be made to the relevant descriptions of other embodiments. In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of the units described is merely a logical functional division. In actual implementation, other divisions may be employed. For example, multiple units or components may be combined or integrated into another system, or some features may be omitted or not implemented.Furthermore, the coupling, direct coupling, or communication connection shown or discussed may be through interfaces, indirect coupling, or communication connection between units or modules, and may be electrical or otherwise. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the objectives of the present embodiment as needed. Furthermore, the functional units in the various embodiments of the present application may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. These integrated units may be implemented in either hardware or software functional units. If these integrated units are implemented as software functional units and sold or used as independent products, they may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, or the portion that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product, stored in a storage medium, includes instructions for causing a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), a removable hard drive, a magnetic disk, or an optical disk. The above description is merely a preferred embodiment of this application. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of this application, and such improvements and modifications should also be considered within the scope of protection of this application.Industrial Applicability The data processing method provided in an embodiment of the present application directly deploys a first image of the runtime environment of a first artificial intelligence application in a serverless architecture. Then, after the serverless architecture receives a data processing request initiated by a target object, a target function is triggered due to the data processing request. After the target function is triggered, the target function simultaneously loads a first artificial intelligence model and a first dependent library required by the first artificial intelligence application into the serverless architecture. Finally, the first artificial intelligence application uses the first artificial intelligence model and the first dependent library to process the data information to be processed. By splitting the code package of the artificial intelligence application into an image of the runtime environment, the required artificial intelligence model, and the dependent libraries required for runtime, and then deploying the image of the runtime environment in the serverless architecture, the artificial intelligence model and the dependent libraries are simultaneously loaded into the serverless architecture through a function when a data processing request is received. This avoids the problem of loading the entire AI application code package in the prior art, which leads to a long loading process, thereby achieving the effect of improving data processing efficiency.

Claims

Claims 1. A data processing method, comprising: A data processing request initiated by a target object is received through a serverless architecture, wherein the data processing request needs to be processed by a first artificial intelligence application, the data processing request includes at least data information to be processed, and a first image of the operating environment of the first artificial intelligence application is deployed in the serverless architecture; a target function is triggered according to the data processing request, and a first artificial intelligence model and a first dependency library required by the first artificial intelligence application are loaded into the serverless architecture through the target function, wherein the target function is deployed in the serverless architecture; and the data information to be processed is processed by the first artificial intelligence application based on the first artificial intelligence model and the first dependency library to obtain a data processing result.

2. The method according to claim 1, wherein: Processing the data information to be processed by a first artificial intelligence application based on the first artificial intelligence model and the first dependent library to obtain a data processing result includes: obtaining a first business logic code corresponding to the first artificial intelligence application; and processing the data information to be processed by using the first artificial intelligence model, the first dependent library, and the first business logic code to obtain the data processing result.

3. The method according to claim 1, wherein: Loading the first artificial intelligence model and the first dependent library required by the first artificial intelligence application into the serverless architecture through the objective function includes: obtaining a first configuration file corresponding to the first artificial intelligence application, wherein the first configuration file at least includes a first storage address of the first artificial intelligence model and an identifier of a first common component storing the first dependent library, wherein the first common component is a basic component in the serverless architecture; determining the first storage address of the first artificial intelligence model and the identifier of the first common component based on the first configuration file; loading the first artificial intelligence model from the first storage address into the serverless architecture through the objective function; and loading the first dependent library from the first common component into the serverless architecture through the objective function based on the identifier of the first common component.

4. The method according to claim 1, wherein: When receiving data initiated by the target object through the serverless architecture 23 Before processing the request, the method further includes: obtaining demand information of the target object for the second artificial intelligence application; determining, based on the demand information, a second image, a second artificial intelligence model, and a second dependent library of the operating environment required by the second artificial intelligence application; constructing business logic code of the second artificial intelligence application based on the demand information; and deploying the second artificial intelligence application in the serverless architecture based on the second image, the second artificial intelligence model, the second dependent library, and the business logic code.

5. The method according to claim 4, wherein: Deploying the second artificial intelligence application in the serverless architecture based on the second image, the second artificial intelligence model, the second dependent library, and the business logic code includes: storing the second artificial intelligence model in a target device to obtain a second storage address; creating a second common component in the serverless architecture based on the second dependent library, and storing the second dependent library in the second common component; obtaining a second configuration file of the second artificial intelligence application based on the second storage address and an identifier of the second common component; and deploying the second image, the second configuration file, and the business logic code in the serverless architecture to implement deployment of the second artificial intelligence application in the serverless architecture.

6. The method according to claim 3, wherein: Loading the first artificial intelligence model into the serverless architecture from the first storage address through the target function includes: initiating an acquisition request for the first artificial intelligence model to a first device through the target function, wherein the first device is a device storing the first artificial intelligence model, and the acquisition request includes at least identification information of the target function; determining through the first device whether the identification information of the target function is in a preset list, and the preset list includes identification information of functions allowed to load the first artificial intelligence model; if the identification information of the target function is in the preset list, loading the first artificial intelligence model into the serverless architecture from the first storage address through the target function.

7. The method according to claim 1, wherein: After the data information to be processed is processed by the first artificial intelligence model and the first dependent library to obtain a data processing result, the method further includes: Obtain target data information generated by the first artificial intelligence model during processing of the data information to be processed; evaluate the first artificial intelligence model based on the target data information to obtain an evaluation result; and optimize the first artificial intelligence model based on the evaluation result.

8. A data processing device, comprising: A receiving unit is configured to receive a data processing request initiated by a target object through a serverless architecture, wherein the data processing request needs to be processed by a first artificial intelligence application, the data processing request includes at least data information to be processed, and a first image of the operating environment of the first artificial intelligence application is deployed in the serverless architecture; a triggering unit is configured to trigger a target function based on the data processing request, and load a first artificial intelligence model and a first dependency library required by the first artificial intelligence application into the serverless architecture through the target function, wherein the target function is deployed in the serverless architecture; and a processing unit is configured to process the data information to be processed based on the first artificial intelligence model and the first dependency library through the first artificial intelligence application to obtain a data processing result.

9. A computer-readable storage medium comprising a stored program, wherein: When the program is running, the device where the storage medium is located is controlled to execute the data processing method according to any one of claims 1 to 7.

10. An electronic device, comprising: a memory storing an executable program; A processor is configured to run the program, wherein the program executes the data processing method according to any one of claims 1 to 7 when running.

11. A computer program product, comprising a stored computer program, wherein when the computer program is executed by a processor, the computer program implements the data processing method according to any one of claims 1 to 7.

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