AI-based adaptive desktop cloud transmission protocol optimization method and system

Abstract

The invention discloses an AI-based adaptive desktop cloud transport protocol optimization method and system, belongs to the technical field of cloud computing and remote desktops, and aims to solve the technical problem of how to realize adaptive desktop cloud transport protocol optimization. Comprising the following steps: a user terminal and a cloud desktop server carry out a session based on a desktop cloud transmission protocol provided by a protocol adaptation layer; network state monitoring, user behavior analysis and image content identification are carried out through an AI optimization engine, protocol parameters of a desktop cloud transmission protocol are dynamically optimized based on an analysis result, and an adjustment instruction is issued to a protocol adaptation layer; dynamically switching a desktop cloud transmission protocol or adjusting protocol parameters of the desktop cloud transmission protocol through a protocol adaptation layer; and the user terminal receives the optimized desktop image and operation feedback in real time.

Description

Technical Field

[0001] The present invention relates to the technical fields of cloud computing and remote desktop, and more specifically to an optimization method and system for an AI-based adaptive desktop cloud transmission protocol. Background Art

[0002] Desktop cloud transmission protocols (such as RDP, PCoIP, SPICE, etc.) are widely used in remote desktops and virtual desktop infrastructures (VDIs). However, traditional protocols cannot dynamically adjust transmission strategies during network fluctuations, resulting in lags or delays. Fixed compression ratios and bandwidth allocations cannot meet the requirements of different application scenarios. In low-bandwidth environments, image quality significantly deteriorates, affecting the user experience. Existing protocols lack the ability to intelligently analyze user behavior and network environments.

[0003] How to achieve optimization of the adaptive desktop cloud transmission protocol to improve the efficiency, image quality, and user experience of desktop cloud transmission is a technical problem that needs to be solved. Summary of the Invention

[0004] The technical task of the present invention is to address the above deficiencies by providing an optimization method and system for an AI-based adaptive desktop cloud transmission protocol to solve the technical problem of how to achieve optimization of the adaptive desktop cloud transmission protocol.

[0005] In a first aspect, an optimization method for an AI-based adaptive desktop cloud transmission protocol according to the present invention is applied to a system including a user terminal, a cloud desktop server, an AI optimization engine, and a protocol adaptation layer, and includes the following steps:

[0006] The user terminal connects to the cloud desktop server through the protocol adaptation layer and starts a session. The user terminal and the cloud desktop server conduct a session based on the desktop cloud transmission protocol provided by the protocol adaptation layer.

[0007] For the session initiated between the user terminal and the cloud desktop server, the AI optimization engine monitors the network state, analyzes user behavior, and identifies image content, and dynamically optimizes the protocol parameters of the desktop cloud transmission protocol based on the analysis results, and issues adjustment instructions to the protocol adaptation layer.

[0008] Based on the adjustment instructions, the protocol adaptation layer dynamically switches the desktop cloud transmission protocol or adjusts the protocol parameters of the desktop cloud transmission protocol, and the user terminal and the cloud desktop server conduct a session based on the optimized desktop cloud transmission protocol.

[0009] The user terminal receives the optimized desktop image and operation feedback in real time.

[0010] Preferably, the user terminal is a device that supports users to access the cloud desktop, including a personal computer (PC), a mobile phone, and a tablet.

[0011] Preferably, the cloud desktop server is a server running a virtual desktop environment.

[0012] Preferably, the AI optimization engine is deployed on edge nodes and is used to perform the following operations:

[0013] Network status monitoring: Real-time monitoring of the network status, where the network status includes network bandwidth, latency, and packet loss rate;

[0014] User behavior analysis: Real-time monitoring of user behavior, and analysis of user operations and prediction of user needs based on a pre-configured AI model. Among them, user operations include mouse movement, keyboard input, and application switching;

[0015] Image content recognition: Based on computer vision technology, recognize the image content and dynamically adjust the compression ratio and transmission priority. Among them, the image content includes text, graphics, and video;

[0016] Dynamic optimization: Dynamically adjust the protocol parameters of the desktop cloud transmission protocol according to the analysis results of network status monitoring, user behavior analysis, and image content recognition. The protocol parameters include compression ratio, frame rate, and resolution.

[0017] Preferably, when dynamically adjusting the protocol parameters of the desktop cloud transmission protocol according to the analysis results of network status monitoring, user behavior analysis, and image content recognition, the following adjustment strategy is followed: In a low-bandwidth environment, give priority to transmitting text and graphics and reduce the video frame rate; in a high-latency environment, enable predictive rendering technology to reduce the latency perceived by users.

[0018] In a second aspect, an AI-based adaptive desktop cloud transmission protocol optimization system of the present invention includes a user terminal, a cloud desktop server, an AI optimization engine, and a protocol adaptation layer, and is used to adjust protocol parameters through an AI-based adaptive desktop cloud transmission protocol optimization method according to any item in the first aspect;

[0019] The user terminal connects to the cloud desktop server through the protocol adaptation layer to start a session. A session is carried out between the user terminal and the cloud desktop server based on the desktop cloud transmission protocol provided by the protocol adaptation layer;

[0020] The AI optimization engine is used to perform the following: For the session initiated between the user terminal and the cloud desktop server, perform network status monitoring, user behavior analysis, and image content recognition, dynamically optimize the protocol parameters of the desktop cloud transmission protocol based on the analysis results, and send an adjustment instruction to the protocol adaptation layer;

[0021] Correspondingly, the protocol adaptation layer is used to dynamically switch the desktop cloud transmission protocol or adjust the protocol parameters of the desktop cloud transmission protocol based on the adjustment instruction. A session is carried out between the user terminal and the cloud desktop server based on the optimized desktop cloud transmission protocol;

[0022] Correspondingly, the user terminal is used to receive the optimized desktop image and operation feedback in real time.

[0023] Preferably, the user terminal is a device that supports the user to access the cloud desktop, including a personal computer (PC), a mobile phone, and a tablet.

[0024] Preferably, the cloud desktop server is a server running a virtual desktop environment.

[0025] Preferably, the AI optimization engine is deployed on an edge node and is used to perform the following operations:

[0026] Network status monitoring: Real-time monitoring of the network status, where the network status includes network bandwidth, latency, and packet loss rate;

[0027] User behavior analysis: Real-time monitoring of user behavior, and based on a pre-configured AI model, analyzing user operations and predicting user needs, where user operations include mouse movement, keyboard input, and application switching;

[0028] Image content recognition: Based on computer vision technology, identifying the image content and dynamically adjusting the compression ratio and transmission priority, where the image content includes text, graphics, and video;

[0029] Dynamic optimization: Dynamically adjusting the protocol parameters of the desktop cloud transmission protocol according to the analysis results of network status monitoring, user behavior analysis, and image content recognition, where the protocol parameters include compression ratio, frame rate, and resolution.

[0030] Preferably, when dynamically adjusting the protocol parameters of the desktop cloud transmission protocol according to the analysis results of network status monitoring, user behavior analysis, and image content recognition, the AI optimization engine is used to follow the following adjustment strategy: In a low-bandwidth environment, give priority to transmitting text and graphics and reduce the video frame rate; in a high-latency environment, enable predictive rendering technology to reduce the latency perceived by the user.

[0031] The AI-based adaptive desktop cloud transmission protocol optimization method and system of the present invention have the following advantages: Dynamically optimizing the transmission strategy through AI to reduce bandwidth occupancy and latency. Utilizing intelligent compression and content-aware technology to ensure image quality under low bandwidth. Enhancing the user experience: Providing a smooth operation experience through predictive resource allocation and dynamic optimization. Supporting multi-protocol adaptation: Compatible with multiple desktop cloud transmission protocols to adapt to different scenario requirements. Description of the Drawings

[0032] To more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the accompanying drawings required for use in the embodiments or the description of the prior art. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0033] The present invention will be further described below with reference to the accompanying drawings.

[0034] Figure 1 It is a flowchart of an AI-based adaptive desktop cloud transmission protocol optimization method for Embodiment 1. Specific embodiments

[0035] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, so that those skilled in the art can better understand the present invention and be able to implement it. However, the embodiments cited are not intended to limit the present invention. Without conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0036] The embodiments of the present invention provide an AI-based adaptive desktop cloud transmission protocol optimization method and system, which are used to solve the technical problem of how to implement the optimization of the adaptive desktop cloud transmission protocol.

[0037] Embodiment 1:

[0038] An AI-based adaptive desktop cloud transmission protocol optimization method of the present invention is applied to a system including a user terminal, a cloud desktop server, an AI optimization engine, and a protocol adaptation layer, and includes the following steps:

[0039] Step S100: The user terminal connects to the cloud desktop server through the protocol adaptation layer and starts a session. The user terminal and the cloud desktop server conduct a session based on the desktop cloud transmission protocol provided by the protocol adaptation layer.

[0040] Step S200: For the session initiated between the user terminal and the cloud desktop server, the AI optimization engine performs network status monitoring, user behavior analysis, and image content recognition, and dynamically optimizes the protocol parameters of the desktop cloud transmission protocol based on the analysis results, and issues an adjustment instruction to the protocol adaptation layer.

[0041] Step S300: Based on the adjustment instruction, the protocol adaptation layer dynamically switches the desktop cloud transmission protocol or adjusts the protocol parameters of the desktop cloud transmission protocol. The user terminal and the cloud desktop server conduct a session based on the optimized desktop cloud transmission protocol.

[0042] Step S400: The user terminal receives the optimized desktop image and operation feedback in real time.

[0043] In this embodiment, the user terminal is a device that supports users to access the cloud desktop, including a personal computer (PC), a mobile phone, and a tablet.

[0044] The cloud desktop server is a server that runs a virtual desktop environment.

[0045] The protocol adaptation layer supports multiple desktop cloud transmission protocols (such as RDP, PCoIP, SPICE, etc.), and dynamically switches or adjusts protocol parameters according to the instructions of the AI optimization engine.

[0046] The AI optimization engine is deployed at the edge node and is used to perform the following operations:

[0047] (1) Network status monitoring: Real-time monitoring of the network status, where the network status includes network bandwidth, latency, and packet loss rate;

[0048] (2) User behavior analysis: Real-time monitoring of user behavior, and analysis of user operations and prediction of user needs based on a pre-configured AI model. Among them, user operations include mouse movement, keyboard input, and application switching;

[0049] (3) Image content recognition: Based on computer vision technology, identify the image content and dynamically adjust the compression ratio and transmission priority. Among them, the image content includes text, graphics, and video;

[0050] (4) Dynamic optimization: Dynamically adjust the protocol parameters of the desktop cloud transmission protocol according to the analysis results of network status monitoring, user behavior analysis, and image content recognition. The protocol parameters include compression ratio, frame rate, and resolution.

[0051] Among them, when dynamically adjusting the protocol parameters of the desktop cloud transmission protocol according to the analysis results of network status monitoring, user behavior analysis, and image content recognition, the following adjustment strategy is followed: In a low-bandwidth environment, text and graphics are preferentially transmitted, and the video frame rate is reduced; in a high-latency environment, predictive rendering technology is enabled to reduce the latency perceived by the user.

[0052] The method of this embodiment dynamically optimizes the transmission protocol through AI technology, improving the transmission efficiency, image quality, and user experience.

[0053] Embodiment 2:

[0054] An AI-based adaptive desktop cloud transmission protocol optimization system of the present invention includes a user terminal, a cloud desktop server, an AI optimization engine, and a protocol adaptation layer.

[0055] The user terminal connects to the cloud desktop server through the protocol adaptation layer to start a session. The session between the user terminal and the cloud desktop server is based on the desktop cloud transmission protocol provided by the protocol adaptation layer.

[0056] Among them, the user terminal is a device that supports users to access the cloud desktop, including personal computers (PCs), mobile phones, and tablets. The cloud desktop server is a server that runs a virtual desktop environment.

[0057] The AI optimization engine is used to perform the following: for the session initiated between the user terminal and the cloud desktop server, perform network status monitoring, user behavior analysis, and image content recognition, and dynamically optimize the protocol parameters of the desktop cloud transmission protocol based on the analysis results, and send adjustment instructions to the protocol adaptation layer.

[0058] In this embodiment, the AI optimization engine is deployed at the edge node and is used to perform the following operations:

[0059] (1) Network status monitoring: Real-time monitor the network status, where the network status includes network bandwidth, latency, and packet loss rate;

[0060] (2) User behavior analysis: Real-time monitor user behavior, and analyze user operations and predict user needs based on a pre-configured AI model, where user operations include mouse movement, keyboard input, and application switching;

[0061] (3) Image content recognition: Based on computer vision technology, identify the image content and dynamically adjust the compression ratio and transmission priority, where the image content includes text, graphics, and video;

[0062] (4) Dynamic optimization: Dynamically adjust the protocol parameters of the desktop cloud transmission protocol according to the analysis results of network status monitoring, user behavior analysis, and image content recognition. The protocol parameters include compression ratio, frame rate, and resolution.

[0063] Among them, when dynamically adjusting the protocol parameters of the desktop cloud transmission protocol according to the analysis results of network status monitoring, user behavior analysis, and image content recognition, the following adjustment strategy is followed: in a low-bandwidth environment, give priority to transmitting text and graphics and reduce the video frame rate; in a high-latency environment, enable predictive rendering technology to reduce the latency perceived by users.

[0064] Correspondingly, the protocol adaptation layer is used to dynamically switch the desktop cloud transmission protocol or adjust the protocol parameters of the desktop cloud transmission protocol based on the adjustment instructions, and the session between the user terminal and the cloud desktop server is based on the optimized desktop cloud transmission protocol.

[0065] Correspondingly, the user terminal is used to receive the optimized desktop image and operation feedback in real time.

[0066] The system of this embodiment can execute the method disclosed in Embodiment 1 to achieve parameter optimization and dynamic switching of the adaptive desktop cloud transmission protocol.

[0067] The above has introduced in detail the optimization method and system of the AI-based adaptive desktop cloud transmission protocol provided by the present invention. Specific examples are used in this article to elaborate on the principle and implementation manner of the present invention. The description of the above embodiments is only used to help understand the method and its core idea of the present invention; at the same time, for those of ordinary skill in the art, according to the idea of the present invention, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation to the present invention.

Claims

1. An AI-based adaptive desktop cloud transmission protocol optimization method, characterized in that: Applied to a system including a user terminal, a cloud desktop server, an AI optimization engine, and a protocol adaptation layer, including the following steps: The user terminal connects to the cloud desktop server through the protocol adaptation layer and starts a session. The user terminal and the cloud desktop server conduct a session based on the desktop cloud transmission protocol provided by the protocol adaptation layer. For sessions initiated between user terminals and cloud desktop servers, the AI ​​optimization engine performs network status monitoring, user behavior analysis, and image content recognition. Based on the analysis results, the protocol parameters of the desktop cloud transmission protocol are dynamically optimized, and adjustment instructions are sent to the protocol adaptation layer. Based on the adjustment instruction, the desktop cloud transmission protocol is dynamically switched or the protocol parameters of the desktop cloud transmission protocol are adjusted through the protocol adaptation layer, and a conversation is conducted between the user terminal and the cloud desktop server based on the optimized desktop cloud transmission protocol; The user terminal receives the optimized desktop image and operation feedback in real time.

2. The AI-based adaptive desktop cloud transmission protocol optimization method according to claim 1 is characterized in that: The user terminal is a device that supports users to access the cloud desktop, including personal computers PCs, mobile phones and tablets.

3. The AI-based adaptive desktop cloud transmission protocol optimization method according to claim 1 is characterized in that: The cloud desktop server is a server running a virtual desktop environment.

4. The AI-based adaptive desktop cloud transmission protocol optimization method according to claim 1 is characterized in that: The AI ​​optimization engine is deployed on edge nodes to perform the following operations: Network status monitoring: real-time monitoring of network status, including network bandwidth, delay and packet loss rate; User behavior analysis: Real-time monitoring of user behavior, analysis of user operations based on pre-configured AI models, and prediction of user needs. User operations include mouse movement, keyboard input, and application switching. Image content recognition: Identify image content based on computer vision technology and dynamically adjust compression rate and transmission priority. Image content includes text, graphics, and video. Dynamic optimization: Dynamically adjust the protocol parameters of the desktop cloud transmission protocol based on the analysis results of network status monitoring, user behavior analysis, and image content recognition. The protocol parameters include compression rate, frame rate, and resolution.

5. The AI-based adaptive desktop cloud transmission protocol optimization method according to claim 4 is characterized in that: When dynamically adjusting the protocol parameters of the desktop cloud transmission protocol based on the analysis results of network status monitoring, user behavior analysis, and image content recognition, follow the following adjustment strategy: in a low-bandwidth environment, prioritize the transmission of text and graphics and reduce the video frame rate; in a high-latency environment, enable predictive rendering technology to reduce the delay perceived by users.

6. An AI-based adaptive desktop cloud transmission protocol optimization system, characterized in that: It includes a user terminal, a cloud desktop server, an AI optimization engine and a protocol adaptation layer, and is used to adjust protocol parameters by using an AI-based adaptive desktop cloud transmission protocol optimization method as described in any one of claims 1 to 5; The user terminal connects to the cloud desktop server through the protocol adaptation layer and starts a session. The user terminal and the cloud desktop server conduct a session based on the desktop cloud transmission protocol provided by the protocol adaptation layer. The AI ​​optimization engine is used to perform the following: for sessions initiated between user terminals and cloud desktop servers, it performs network status monitoring, user behavior analysis, and image content recognition, and dynamically optimizes the protocol parameters of the desktop cloud transmission protocol based on the analysis results, and sends adjustment instructions to the protocol adaptation layer; Correspondingly, the protocol adaptation layer is used to dynamically switch the desktop cloud transmission protocol or adjust the protocol parameters of the desktop cloud transmission protocol based on the adjustment instruction, and the user terminal and the cloud desktop server conduct a conversation based on the optimized desktop cloud transmission protocol; Correspondingly, the user terminal is used to receive the optimized desktop image and operation feedback in real time.

7. The AI-based adaptive desktop cloud transmission protocol optimization system according to claim 6 is characterized in that: The user terminal is a device that supports users to access the cloud desktop, including personal computers PCs, mobile phones and tablets.

8. The AI-based adaptive desktop cloud transmission protocol optimization system according to claim 6 is characterized in that: The cloud desktop server is a server running a virtual desktop environment.

9. The AI-based adaptive desktop cloud transmission protocol optimization system according to claim 6, characterized in that: The AI ​​optimization engine is deployed on edge nodes to perform the following operations: Network status monitoring: real-time monitoring of network status, including network bandwidth, delay and packet loss rate; User behavior analysis: Real-time monitoring of user behavior, analysis of user operations based on pre-configured AI models, and prediction of user needs. User operations include mouse movement, keyboard input, and application switching. Image content recognition: Identify image content based on computer vision technology and dynamically adjust compression rate and transmission priority. Image content includes text, graphics, and video. Dynamic optimization: Dynamically adjust the protocol parameters of the desktop cloud transmission protocol based on the analysis results of network status monitoring, user behavior analysis, and image content recognition. The protocol parameters include compression rate, frame rate, and resolution.

10. The AI-based adaptive desktop cloud transmission protocol optimization system according to claim 6, characterized in that: When dynamically adjusting the protocol parameters of the desktop cloud transmission protocol based on the analysis results of network status monitoring, user behavior analysis, and image content recognition, the AI ​​optimization engine is used to follow the following adjustment strategies: in a low-bandwidth environment, text and graphics are transmitted first and the video frame rate is reduced; in a high-latency environment, predictive rendering technology is enabled to reduce the delay perceived by users.

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